In a wide range of applications, large amounts of f
loating-point data are generated by Wireless Sensor
Networks (WSNs). This data is often transferred bet
ween several sensor nodes, in a multi-hop fashion,
before reaching its ultimate destination (the base
station). It is well known that data communications
is the
most energy-consuming task in sensor nodes [1]. Thi
s can be a great concern when the nodes are
constrained in energy. Therefore, the amount of dat
a to be transferred between nodes should be reduced
to
save energy. In this paper, we investigate data com
pression for resource-constraint WSNs; we introduce
MAS as a novel adaptive lossless floating-point dat
a compression algorithm for WSNs. MAS exploits the
disproportionality in energy consumption between da
ta transmission and processing. Simulation results,
obtained from OMNeT++ and Atmel Studio, show that M
AS surpasses other tested compression algorithms
in terms of compression ratio, compression speed, m
emory requirements and most importantly energy
savings
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
In wireless sensor network (WSN) there are two main problems in employing conventional compression
techniques. The compression performance depends on the organization of the routes for a larger extent.
The efficiency of an in-network data compression scheme is not solely determined by the compression
ratio, but also depends on the computational and communication overheads. In Compressive Data
Aggregation technique, data is gathered at some intermediate node where its size is reduced by applying
compression technique without losing any information of complete data. In our previous work, we have
developed an adaptive traffic aware aggregation technique in which the aggregation technique can be
changed into structured and structure-free adaptively, depending on the load status of the traffic. In this
paper, as an extension to our previous work, we provide a cost effective compressive data gathering
technique to enhance the traffic load, by using structured data aggregation scheme. We also design a
technique that effectively reduces the computation and communication costs involved in the compressive
data gathering process. The use of compressive data gathering process provides a compressed sensor
reading to reduce global data traffic and distributes energy consumption evenly to prolong the network
lifetime. By simulation results, we show that our proposed technique improves the delivery ratio while
reducing the energy and delay
The document discusses clustering algorithms for wireless sensor networks. It describes four categories of clustering algorithms: 1) identity-based, which select cluster heads based on node identifiers, 2) neighborhood-based, which select heads based on number of neighbors, 3) probabilistic, which assign selection probabilities, and 4) biologically-inspired. Example algorithms described include the Linked Cluster Algorithm, Highest Connectivity Algorithm, and Weighted Clustering Algorithm. Clustering helps optimize energy usage and extend network lifetime by reducing transmissions and aggregating data at cluster heads.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
IRJET- Load Optimization with Coverage and Connectivity for Wireless Sensor N...
The document proposes a Maximum Connected Load Balancing Cover Tree (MCLCT) algorithm to optimize coverage and connectivity in wireless sensor networks. The MCLCT consists of two strategies: 1) A Coverage Optimizing Recursive heuristic that forms maximum disjoint cover sets to ensure full coverage of points of interest. 2) A Probabilistic Load Balancing strategy that determines routing paths in a way that balances energy load evenly among nodes. Simulation results show the MCLCT achieves longer network lifetime than previous algorithms by balancing energy consumption through dynamic cover tree construction and efficient power utilization among sensor nodes.
Wireless Sensor Networks consists of sensor nodes which are scattered in the environment, gather data and transmit it to a
base station for processing. Energy conservation in the Wireless Sensor Networks (WSN) is a very important task because of their
limited battery power. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of
WSNs. In this paper, a priority based application specific congestion control clustering (PASCCC) protocol has been studied, which
often integrates the range of motion and heterogeneity of the nodes to detect congestion in a very network. Moreover a comparison of
the various clustering techniques has been done. From the survey it has been found that none of the protocol is efficient for energy
conservation. Hence the paper ends with future scope to overcome these issues.
Range-Based Data Gathering Algorithm With a Mobile Sink in Wireless Sensor Ne...
Wireless Sensor Networks (WSNs) have been emerged in many important aspects in the real world, such as
industry, agriculture, and military applications. As the main challenge that WSNs facing is the energy
consumption, it is necessary to investigate the suitability of using mobile sinks for data collection in these
networks. In this paper, therefore, a new data gathering technique with mobile elements referred to as
Intersection Point of Communication Ranges (IPCR) is proposed. The IPCR algorithm endeavours to
compute the optimal trajectory of the mobile sink for which the data collection latency is reduced.
Simulation results presented in this study showed that the IPCR algorithm has achieved the optimal Travel
Sales-Man Problem algorithm. In addition, the IPCR algorithm outperformed the Connectivity Based Data
Collection (CBDC) algorithm in terms of data gathering latency and network throughput.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
1. The document discusses techniques for reducing schedule length in tree-based wireless sensor networks, including transmission power control, using multiple frequencies, and degree-constrained routing trees.
2. It reviews related work on degree-constrained routing trees, data-centric approaches, and greedy aggregation techniques for constructing energy-efficient aggregation trees.
3. It also compares contention-based and contention-free protocols for meeting hard real-time deadlines in industrial wireless sensing applications.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORK
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
This document contains two papers. The first paper summarizes a study that designed a prototype smoke detection device for a student dormitory at Klabat University using a microcontroller, MQ-7 and UV-Tron sensors, buzzer, and SMS gateway to detect cigarette smoke and notify users. The second paper proposes a wireless sensor network design for environmental monitoring applications to measure temperature, humidity, CO2, and other factors.
The document describes localized, self-organizing approaches for constructing energy-efficient data aggregation trees in sensor networks. It proposes Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) that use localized structures like LMST and RNG to approximate a minimum spanning tree. L-PEDAP then constructs an actual routing tree over these structures using localized parent selection strategies. Simulation results show L-PEDAP can achieve close to 90% of a theoretical upper bound on network lifetime derived in the paper, outperforming centralized solutions while meeting requirements like distributed operation, scalability, and robustness to failures.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
On improvement of performance for transport protocol using sectoring scheIAEME Publication
This document proposes a sectoring scheme to improve the performance of transport protocols in wireless sensor networks (WSNs). It aims to increase reliability by logically dividing the sensing area into sectors, with each sector having a sector head node near the sink. When an event occurs, only nodes in that sector will transmit data to the sector head to reduce energy consumption. The paper simulates the proposed scheme and finds it provides better packet delivery ratio, lower routing overhead, delay and energy consumption compared to not using sectoring. In conclusion, the sectoring method improves reliability and network lifetime for WSNs.
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...ijasuc
In wireless sensor network (WSN) there are two main problems in employing conventional compression
techniques. The compression performance depends on the organization of the routes for a larger extent.
The efficiency of an in-network data compression scheme is not solely determined by the compression
ratio, but also depends on the computational and communication overheads. In Compressive Data
Aggregation technique, data is gathered at some intermediate node where its size is reduced by applying
compression technique without losing any information of complete data. In our previous work, we have
developed an adaptive traffic aware aggregation technique in which the aggregation technique can be
changed into structured and structure-free adaptively, depending on the load status of the traffic. In this
paper, as an extension to our previous work, we provide a cost effective compressive data gathering
technique to enhance the traffic load, by using structured data aggregation scheme. We also design a
technique that effectively reduces the computation and communication costs involved in the compressive
data gathering process. The use of compressive data gathering process provides a compressed sensor
reading to reduce global data traffic and distributes energy consumption evenly to prolong the network
lifetime. By simulation results, we show that our proposed technique improves the delivery ratio while
reducing the energy and delay
The document discusses clustering algorithms for wireless sensor networks. It describes four categories of clustering algorithms: 1) identity-based, which select cluster heads based on node identifiers, 2) neighborhood-based, which select heads based on number of neighbors, 3) probabilistic, which assign selection probabilities, and 4) biologically-inspired. Example algorithms described include the Linked Cluster Algorithm, Highest Connectivity Algorithm, and Weighted Clustering Algorithm. Clustering helps optimize energy usage and extend network lifetime by reducing transmissions and aggregating data at cluster heads.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
IRJET- Load Optimization with Coverage and Connectivity for Wireless Sensor N...IRJET Journal
The document proposes a Maximum Connected Load Balancing Cover Tree (MCLCT) algorithm to optimize coverage and connectivity in wireless sensor networks. The MCLCT consists of two strategies: 1) A Coverage Optimizing Recursive heuristic that forms maximum disjoint cover sets to ensure full coverage of points of interest. 2) A Probabilistic Load Balancing strategy that determines routing paths in a way that balances energy load evenly among nodes. Simulation results show the MCLCT achieves longer network lifetime than previous algorithms by balancing energy consumption through dynamic cover tree construction and efficient power utilization among sensor nodes.
Congestion Control Clustering a Review PaperEditor IJCATR
Wireless Sensor Networks consists of sensor nodes which are scattered in the environment, gather data and transmit it to a
base station for processing. Energy conservation in the Wireless Sensor Networks (WSN) is a very important task because of their
limited battery power. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of
WSNs. In this paper, a priority based application specific congestion control clustering (PASCCC) protocol has been studied, which
often integrates the range of motion and heterogeneity of the nodes to detect congestion in a very network. Moreover a comparison of
the various clustering techniques has been done. From the survey it has been found that none of the protocol is efficient for energy
conservation. Hence the paper ends with future scope to overcome these issues.
Range-Based Data Gathering Algorithm With a Mobile Sink in Wireless Sensor Ne...ijwmn
Wireless Sensor Networks (WSNs) have been emerged in many important aspects in the real world, such as
industry, agriculture, and military applications. As the main challenge that WSNs facing is the energy
consumption, it is necessary to investigate the suitability of using mobile sinks for data collection in these
networks. In this paper, therefore, a new data gathering technique with mobile elements referred to as
Intersection Point of Communication Ranges (IPCR) is proposed. The IPCR algorithm endeavours to
compute the optimal trajectory of the mobile sink for which the data collection latency is reduced.
Simulation results presented in this study showed that the IPCR algorithm has achieved the optimal Travel
Sales-Man Problem algorithm. In addition, the IPCR algorithm outperformed the Connectivity Based Data
Collection (CBDC) algorithm in terms of data gathering latency and network throughput.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
1. The document discusses techniques for reducing schedule length in tree-based wireless sensor networks, including transmission power control, using multiple frequencies, and degree-constrained routing trees.
2. It reviews related work on degree-constrained routing trees, data-centric approaches, and greedy aggregation techniques for constructing energy-efficient aggregation trees.
3. It also compares contention-based and contention-free protocols for meeting hard real-time deadlines in industrial wireless sensing applications.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORKcscpconf
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
This document contains two papers. The first paper summarizes a study that designed a prototype smoke detection device for a student dormitory at Klabat University using a microcontroller, MQ-7 and UV-Tron sensors, buzzer, and SMS gateway to detect cigarette smoke and notify users. The second paper proposes a wireless sensor network design for environmental monitoring applications to measure temperature, humidity, CO2, and other factors.
The document describes localized, self-organizing approaches for constructing energy-efficient data aggregation trees in sensor networks. It proposes Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) that use localized structures like LMST and RNG to approximate a minimum spanning tree. L-PEDAP then constructs an actual routing tree over these structures using localized parent selection strategies. Simulation results show L-PEDAP can achieve close to 90% of a theoretical upper bound on network lifetime derived in the paper, outperforming centralized solutions while meeting requirements like distributed operation, scalability, and robustness to failures.
The document discusses clustering routing protocols for wireless sensor networks. It provides an overview of clustering techniques which group sensor nodes into clusters with elected cluster heads that aggregate and transmit data to the base station. This approach provides benefits like improved scalability, reduced energy consumption and load compared to flat routing protocols. The document also outlines various objectives of clustering like data aggregation, load balancing, fault tolerance and connectivity. It reviews several popular clustering protocols and notes that no single technique performs best in all areas, leaving room for future improvements to address these issues.
QUAD TREE BASED STATIC MULTI HOP LEACH ENERGY EFFICIENT ROUTING PROTOCOL: A N...IJCNCJournal
This research work propounds a simple graph theory semblance Divide and Conquer Quad tree based Multi-hop Static Leach (DCQMS-Leach) energy efficient routing protocol for wireless sensor networks. The pivotal theme of this research work is to demonstrate how divide and conquer plays a pivotal role in a multi-hop static leach energy efficient routing protocol. This research work motivates, enforces, reckons the DCQMS-Leach energy efficient routing protocol in wireless sensor networks using Mat lab simulator.This research work also computes the performance concepts of DCQMS-Leach routing protocol using various performance metrics such as Packet Drop Rate (PDR), Throughput, and End to End Delay (EED) by comparing and contrasting alive nodes with number of nodes, number of each packets sent to the cluster heads with rounds, number of cluster heads with rounds, number of packets forwarded to the base station with rounds and finally dead nodes with number of rounds. In order to curtail energy consumption this research work proffers a routing methodology such as DCQMS-Leach in energy efficient wireless,sensor routing protocol. The recommended DCQMS-Leach overcomes the in adequacies of all other different leach protocols suggested by the previous researchers.
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
This document proposes a Clustering Based Lifetime Maximizing Aggregation Tree (CLMAT) algorithm for wireless sensor networks. The algorithm aims to reduce energy consumption by creating an aggregation tree that minimizes distance traversed, energy consumed, and cost. It considers the three factors of energy, distance, and cost simultaneously when constructing the tree, unlike previous works. The tree is structured to maximize network lifetime by selecting nodes with higher residual energy as parents where possible. Pseudocode is provided to generate the aggregation tree using clustering by calculating branch and tree energy, distance, and cost at each step to ultimately select the tree with the highest lifetime, lowest energy consumption, distance, and cost.
NODE FAILURE TIME AND COVERAGE LOSS TIME ANALYSIS FOR MAXIMUM STABILITY VS MI...IJCNCJournal
This document analyzes and compares two algorithms for data gathering in mobile sensor networks:
1) Maximum Stability Spanning Tree-based Data Gathering (Max.Stability-DG) which determines data gathering trees that exist for the longest time by assuming knowledge of future topology changes.
2) Minimum Distance Spanning Tree-based Data Gathering (MST-DG) which determines data gathering trees based on the minimum distance spanning tree at each current time instant.
An exhaustive simulation study is conducted to analyze the impact of these algorithms on node lifetime, network lifetime, and coverage loss time due to node failures in mobile sensor networks.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Ambiences on the-fly usage of available resources through personal devicesijasuc
In smart spaces such as smart homes, computation is
embedded everywhere: in toys, appliances, or the
home’s infrastructure. Most of these devices provid
e a pool of available resources which the user can
take
advantage, interacting and creating a friendly envi
ronment. The inherent composability of these system
s
and other unique characteristics such as low-cost e
nergy, simplicity in module programming, and even
their small size, make them a suitable candidate fo
r dynamic and adaptive ambient systems. This resear
ch
work focuses on what is defined as an “ambience”, a
space with a user-defined set of computational
devices. A smart-home is modeled as a collection of
ambiences, where every ambience is capable of
providing a pool of available resources to the user
. In turn, the user is supposed to carry one or sev
eral
personal devices able to interact with the ambience
s, taking advantage of his inherent mobility. In th
is way,
the whole system can benefit from resources discove
red in the spatial proximity. A software architectu
re is
designed, which is based on the implementation of l
ow-cost algorithms able to detect and update the sy
stem
when changes in an ambience occur. Ambience middlew
are implementation works in a wide range of
architectures and OSs, while showing a negligible o
verhead in the time to perform the basic output
operations.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Het concept flipping the classroom kan ook in trainingen gebruikt worden. Bekijk de slideshare wat het concept inhoudt en wat voor- en nadelen zijn. Hoe werkt het voor jou?
The document discusses how personalization and dynamic content are becoming increasingly important on websites. It notes that 52% of marketers see content personalization as critical and 75% of consumers like it when brands personalize their content. However, personalization can create issues for search engine optimization as dynamic URLs and content are more difficult for search engines to index than static pages. The document provides tips for SEOs to help address these personalization and SEO challenges, such as using static URLs when possible and submitting accurate sitemaps.
Este documento presenta información sobre las miniquest y las webquest como herramientas para la gestión de la información. Explica que las miniquest son versiones más cortas de las webquest que pueden completarse en una o dos clases. Detalla que las miniquest tienen tres partes principales: el escenario, la tarea y el producto. Luego, describe los pasos clave de una webquest más larga, incluida la introducción, la tarea, los recursos, la evaluación y la conclusión. El objetivo general es mostrar cómo las webquest y miniquest pueden usarse para ense
Este documento contiene ejercicios de conjuntos, suma de polinomios y productos notables. En la sección de conjuntos, presenta ejemplos de elementos que pertenecen y no pertenecen a conjuntos dados. Luego, muestra ejemplos resueltos de sumas de polinomios de igual y diferente grado, incluyendo casos donde hay términos nulos o no hay términos semejantes. Finalmente, proporciona ejemplos de productos notables para el binomio al cuadrado y de dos binomios.
El documento presenta los planos y descripciones preliminares de un parque ecológico en Yaguareté Corá. Se propone dividir el proyecto en dos edificios separados para el centro de interpretación y el centro de investigación, conectados por un núcleo central. Los edificios tendrán estructura de hormigón armado y madera, e implementarán iluminación y ventilación naturales.
Objetivos y propuestas lista construir cateme 2013 2014Carmina Rosario
Este documento presenta las propuestas y planificación de la lista "Construir" para ser la administración del Centro de Alumnos de la Escuela de Tecnología Médica en 2014. Incluye un manifiesto, carta a los estudiantes, principios filosóficos, objetivos y varias propuestas académicas, culturales y de bienestar para mejorar la experiencia estudiantil y celebrar el 50 aniversario de la carrera.
Advanced antenna techniques and high order sectorization with novel network t...ijwmn
Mobile operators commonly use macro cells with trad
itional wide beam antennas for wider coverage in th
e
cell, but future capacity demands cannot be achieve
d by using them only. It is required to achieve max
imum
practical capacity from macro cells by employing hi
gher order sectorization and by utilizing all possi
ble
antenna solutions including smart antennas. This pa
per presents enhanced tessellation for 6-sector sit
es
and proposes novel layout for 12-sector sites. The
main target of this paper is to compare the perform
ance
of conventional wide beam antenna, switched beam sm
art antenna, adaptive beam antenna and different
network layouts in terms of offering better receive
d signal quality and user throughput. Splitting mac
ro cell
into smaller micro or pico cells can improve the ca
pacity of network, but this paper highlights the
importance of higher order sectorization and advanc
e antenna techniques to attain high Signal to
Interference plus Noise Ratio (SINR), along with im
proved network capacity. Monte Carlo simulations a
t
system level were done for Dual Cell High Speed Dow
nlink Packet Access (DC-HSDPA) technology with
multiple (five) users per Transmission Time Interva
l (TTI) at different Intersite Distance (ISD). The
obtained results validate and estimate the gain of
using smart antennas and higher order sectorization
with
proposed network layout.
Het vraagt de nodige creativiteit om e-learning met een beperkt budget te ontwikkelen. Om je te helpen, lees je hier 5 tips om e-learning te ontwikkelen met een beperkt budget.
Deserts are defined as areas that receive less than 10 inches of rainfall per year. They cover about 1/5 of the Earth's surface and can be hot or cold, dry or icy. Deserts form due to minimal precipitation and some coastal deserts form because ocean waters are too cold to produce rain. Desert climates and ecosystems have adapted to harsh conditions with sparse but specialized plants and animals. Deserts display a variety of landforms beyond just sand dunes like mesas, inselbergs and different types of sand dunes.
Este documento presenta un ejemplo de un análisis de varianza (ANOVA) utilizando datos de 14 empleados que se sometieron a 3 programas de entrenamiento de manera aleatoria. Muestra las calificaciones promedio de los empleados en cada programa, con un promedio total de 82.14. El estudiante que realizó este análisis de varianza fue Víctor Hugo Franco García de la Universidad Tecnológica de Torreón.
Analyses and performance of techniques papr reduction for stbc mimo ofdm syst...ijwmn
An OFDM system is combined with multiple-input mult
iple-output (MIMO) in order to increase the
diversity gain and system capacity over the time va
riant frequency-selective channels. However, a maj
or
drawback of MIMO-OFDM system is that the transmitte
d signals on different antennas might exhibit high
peak-to-average power ratio (PAPR).In this paper, w
e present a PAPR analysis reduction of space-time-
block-coded (STBC) MIMO-OFDM system for 4G wireless
networks. Several techniques have been used to
reduce the PAPR of the (STBC) MIMOOFDM system: clip
ping and filtering, partial transmit sequence
(PTS) and selected mapping (SLM). Simulation result
s show that clipping and filtering provides a bette
r
PAPR reduction than the others methods and only SLM
technique conserve the PAPR reduction in
reception part of signal.
Este documento contiene ejercicios de conjuntos, suma de polinomios y productos notables. En la sección de conjuntos, presenta ejemplos de elementos que pertenecen y no pertenecen a conjuntos dados. Luego, muestra ejemplos resueltos de sumas de polinomios de igual y diferente grado, incluyendo casos donde hay términos nulos o no hay términos semejantes. Finalmente, proporciona ejemplos de productos notables para binomios al cuadrado y de dos binomios.
Design Issues and Applications of Wireless Sensor Networkijtsrd
Efficient design and implementation of wireless sensor networks has become a hot area of research in recent years, due to the vast potential of sensor networks to enable applications that connect the physical world to the virtual world. By networking large numbers of tiny sensor nodes, it is possible to obtain data about physical phenomena that was difficult or impossible to obtain in more conventional ways. In future as advances in micro-fabrication technology allow the cost of manufacturing sensor nodes to continue to drop, increasing deployments of wireless sensor networks are expected, with the networks eventually growing to large numbers of nodes.Potential applications for such large-scale wireless sensor networks exist in a variety of fields, including medical monitoring, environmental monitoring, surveillance, home security, military operations, and industrial machine monitoring etc. G. Swarnalatha | R. Srilalitha"Design Issues and Applications of Wireless Sensor Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4688.pdf http://www.ijtsrd.com/engineering/computer-engineering/4688/design-issues-and-applications-of-wireless-sensor-network/g-swarnalatha
The document proposes an error-aware data clustering technique for in-network data reduction in wireless sensor networks. It consists of three modules: 1) Histogram-based data clustering groups similar sensor data into clusters over time to reduce redundancy. 2) Recursive outlier detection and smoothing detects and replaces random outliers while maintaining a predefined error threshold. 3) Verification of RODS detects both random and frequent outliers using temporal and spatial correlations to provide more robust error-aware clustering. The technique aims to significantly reduce redundant data with minimum error for applications monitoring environmental conditions.
Performance Evaluation of Wireless Sensor Networks Communication Overhead and...ijtsrd
Powerful actors and resource constrained sensors are joined in wireless networks to form Wireless Sensor and Actor Networks WSANs . The lifespan of a sensor network may be effectively increased by clustering. The method of clustering involves breaking up sensor networks into smaller, more nimble groups of individuals with a cluster head. In hierarchically organised wireless sensor networks, clustering algorithms must choose the ideal number of clusters. In this study, we examine the effectiveness of cluster based wireless sensor networks for various wireless sensor network communication patterns WSNs . By utilising the self organizing map SOM based clustering approach, we concentrate on their performances in terms of Communication overhead and Energy consumption in WSN with varied velocities for the cluster based protocol. Mangukiya Hiteshkumar Bhupatbhai "Performance Evaluation of Wireless Sensor Networks' Communication Overhead and Energy Consumption using the Self-Organizing Map Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51936.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/51936/performance-evaluation-of-wireless-sensor-networks-communication-overhead-and-energy-consumption-using-the-selforganizing-map-method/mangukiya-hiteshkumar-bhupatbhai
ENERGY EFFICIENT MULTIHOP QUALITY PATH BASED DATA COLLECTION IN WIRELESS SENS...Editor IJMTER
In recent years there has been an increased focus on the use of sensor networks to sense and measure
the environment. This leads to a wide variety of theoretical and practical issues on appropriate protocols for data
sensing and transfer. Recent work shows sink mobility can improve the energy efficiency in wireless sensor
networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. Most of
them exploit mobility to address the problem of data collection in WSNs. The WSNs with MS (mobile Sink) and
provide a comprehensive taxonomy of their architectures, based on the role of the MS. An overview of the data
collection process in such a scenario, and identify the corresponding issues and challenges. A protocol named
weighted rendezvous planning (WRP) which is a heuristic method that finds a near-optimal traveling tour that
minimizes the energy consumption of sensor nodes. Focus on the path selection problem in delay-guaranteed
sensor networks with a path-constrained mobile sink. Concentrate an efficient data collection scheme, which
simultaneously improves the total amount of data and reduces the energy consumption. The optimal path is chosen
to meet the requirement on delay as well as minimize the energy consumption of entire network. Predictable sink
mobility is exploited to improve energy efficiency of sensor networks.
Computational Analysis of Routing Algorithm for Wireless Sensor NetworkIRJET Journal
This document discusses an energy-efficient routing algorithm for wireless sensor networks (WSNs) proposed by the authors. It begins with background on WSNs and challenges related to limited energy. Then, it discusses prior work on routing protocols like LEACH and proposes a new algorithm. The key contributions are formulating control node selection as an optimization problem considering energy and distance, and using particle swarm optimization to solve this problem. This aims to improve energy efficiency for multi-tasking in software-defined WSNs compared to traditional protocols.
A Survey of Fuzzy Logic Based Congestion Estimation Techniques in Wireless S...IOSR Journals
This document surveys fuzzy logic techniques for estimating congestion in wireless sensor networks. It begins by providing background on wireless sensor networks and issues like limited battery life. It then discusses clustering as a technique to reduce energy consumption by having cluster heads aggregate and transmit data. The document reviews applications of fuzzy logic in wireless sensor networks for clustering, data fusion, and security. It defines congestion as excessive network load and discusses how fuzzy logic techniques can help estimate congestion to reduce problems like queuing delays and packet loss compared to non-fuzzy approaches. In conclusion, fuzzy logic provides a better approach for estimating congestion in wireless sensor networks.
A survey on sensor cloud architecture, applications, and approachesNgoc Thanh Dinh
This document provides an overview of sensor-cloud infrastructure, which integrates wireless sensor networks with cloud computing. It discusses how sensor-cloud can address limitations of wireless sensor networks like limited storage, processing and scalability by leveraging cloud computing. The document outlines the definition and architecture of sensor-cloud, its applications in areas like healthcare, environment and advantages over traditional wireless sensor network approaches. It also discusses research challenges and approaches in sensor-cloud infrastructure.
This document summarizes and compares several energy-efficient routing cluster protocols for wireless sensor networks, including LEACH, LEACH-C, TL-LEACH, PEGASIS, ER-LEACH, and LEACH-SM. It first provides background on wireless sensor networks and the need for energy efficiency in routing protocols. It then reviews each of the protocols, describing their clustering approach and how they select cluster heads. The document analyzes and compares the performance of the protocols based on metrics like throughput, network lifetime, energy efficiency, and load balancing. It finds that PEGASIS and TL-LEACH generally perform best in terms of throughput and network lifetime, while LEACH-C and ER-LEACH also
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes a new protocol called MP-ECCNL to address coverage and connectivity issues in randomly deployed wireless sensor networks. The protocol aims to maximize network lifetime by optimizing routing through multi-hop transmissions while efficiently utilizing network resources. The article reviews related work on coverage and connectivity techniques, presents a problem formulation for modeling coverage and connectivity requirements, and describes how MP-ECCNL was tested against LEACH and found to better maintain coverage and connectivity over large-scale networks, extending lifetime.
Energetic Slot Allotment for Improving Interchange in Wireless Sensor NetworkIRJET Journal
This document discusses improving energy efficiency and throughput in wireless sensor networks. It begins with an introduction to wireless sensor networks and their design challenges, including limited energy capacity. It then discusses how existing medium access control protocols provide energy efficiency but at the cost of increased delay and limited throughput. The document proposes dynamic slot allocation as a way to adapt bandwidth based on traffic load, maintaining low duty cycles with light traffic but scheduling more transmission opportunities with increased traffic. This allows energy to only be used when needed to carry application traffic. The document surveys dynamic slot allocation approaches in wireless sensor networks.
Energy saving in P2P oriented Wireless Sensor Network (WSN) using the approac...IOSR Journals
This document discusses energy saving techniques in peer-to-peer wireless sensor networks through the use of data compression. It begins by introducing wireless sensor networks and their energy constraints. It then discusses how compression can reduce energy consumption by decreasing transmission time. Different compression techniques like LZO, Zlib, and Bzip2 are described. The document proposes using Adaptive Compression Environment to automatically select the best compression technique based on network conditions. It presents an algorithm for compressing data at the sender, transmitting it in chunks, and decompressing it at the receiver. The conclusion states that compression technologies can help save sensor energy in peer-to-peer networks and that more advances will optimize this solution.
Energy sink-holes avoidance method based on fuzzy system in wireless sensor ...IJECEIAES
The existence of a mobile sink for gathering data significantly extends wireless sensor networks (WSNs) lifetime. In recent years, a variety of efficient rendezvous points-based sink mobility approaches has been proposed for avoiding the energy sink-holes problem nearby the sink, diminishing buffer overflow of sensors, and reducing the data latency. Nevertheless, lots of research has been carried out to sort out the energy holes problem using controllable-based sink mobility methods. However, further developments can be demonstrated and achieved on such type of mobility management system. In this paper, a well-rounded strategy involving an energy-efficient routing protocol along with a controllablebased sink mobility method is proposed to extirpate the energy sink-holes problem. This paper fused the fuzzy A-star as a routing protocol for mitigating the energy consumption during data forwarding along with a novel sink mobility method which adopted a grid partitioning system and fuzzy system that takes account of the average residual energy, sensors density, average traffic load, and sources angles to detect the optimal next location of the mobile sink. By utilizing diverse performance metrics, the empirical analysis of our proposed work showed an outstanding result as compared with fuzzy A-star protocol in the case of a static sink.
Issues and Challenges in Distributed Sensor Networks- A ReviewIOSR Journals
1) The document discusses various design issues and challenges in distributed sensor networks, including limited resources of sensor nodes, scalability, frequent topology changes, and data aggregation.
2) Data aggregation aims to reduce redundant data by having sensor nodes combine and summarize correlated sensor readings. This helps reduce transmission costs and bandwidth usage.
3) Time synchronization is also an important challenge as many sensor network applications require correlating sensor readings with physical times, but achieving precise synchronization is difficult given the networks' constraints.
This document discusses issues and challenges in distributed sensor networks. It begins with an introduction to distributed sensor networks and their applications. It then discusses several design challenges for sensor networks, including limited resources, scalability, frequent topology changes, and energy efficiency. It also discusses specific challenges like data aggregation, time synchronization, localization, node deployment, network dynamics, and fault tolerance. Finally, it discusses security issues and challenges in distributed sensor networks, including requirements like availability, authentication, confidentiality, integrity, and data freshness. It also discusses types of security attacks on sensor networks.
This document summarizes a research paper that proposes an energy efficient clustering approach for wireless sensor networks. The approach uses a corona-based model where the area is divided into concentric circles and nodes calculate their virtual concentric circle band index to determine eligibility as a cluster head. Eligible nodes use a back-off timer approach where the node closest to the center of its band will become the cluster head. This balances energy usage and helps form stable clusters. Simulation results show the approach can prolong network lifetime by efficiently selecting cluster heads and allowing addition or removal of nodes without affecting the existing structure.
This document summarizes a research paper that proposes an energy efficient clustering approach for wireless sensor networks. The approach uses a corona-based model where the monitoring area is divided into concentric circles. Nodes calculate their virtual concentric circle band index to determine eligibility as cluster heads. Eligible nodes use a backoff timer approach where the timer is proportional to the node's distance from the center of its band. The first node's timer to expire advertises itself as the cluster head. This balances energy usage. The paper also describes adding and removing nodes from clusters to provide scalability without affecting the existing infrastructure. Simulation results show the effectiveness of the proposed clustering method.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
The document summarizes an algorithm proposed to reduce energy consumption in wireless sensor networks using duty cycling and multi-hop routing. The key aspects of the algorithm are:
1) Layering the network environment based on size and identifying the optimal number of cluster heads in each layer.
2) Selecting the first layer closest to the sink as the "gateway layer" and stopping energy usage in half of these sensors to extend the network lifespan.
3) Using multi-hop routing whereby cluster heads send data to heads in the above layer until the gateway layer, which then sends to the static or mobile sink.
4) Simulation results showed the proposed algorithm performs better than LEACH and ELEACH in terms of
A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID ME...IJCNCJournal
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
Optimized Projected Strategy for Enhancement of WSN Using Genetic AlgorithmsIJMER
This document summarizes an optimized projected strategy for enhancing wireless sensor networks using genetic algorithms. It describes a heterogeneous wireless sensor network model with normal, intermediate, and advanced sensor nodes having different initial energy levels. The proposed approach selects cluster heads based on the nodes' battery power and residual energy, giving intermediate and advanced nodes a higher probability of becoming cluster heads to balance energy consumption across the network. The strategy aims to increase the stability period when the first node dies and the overall network lifetime.
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08:30 ☕ Welcome coffee (30')
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Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
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Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
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Resource efficient floating-point data compression using mas in wsn
1. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
RESOURCE-EFFICIENT FLOATING-POINT DATA
COMPRESSION USING MAS IN WSN
Maher El Assi1, Alia Ghaddar2, Samar Tawbi3, Ghaddar Fadi4
1, 2, 3
4
Lebanese University, Lebanon
Saint Joseph University, Lebanon
ABSTRACT
In a wide range of applications, large amounts of floating-point data are generated by Wireless Sensor
Networks (WSNs). This data is often transferred between several sensor nodes, in a multi-hop fashion,
before reaching its ultimate destination (the base station). It is well known that data communications is the
most energy-consuming task in sensor nodes [1]. This can be a great concern when the nodes are
constrained in energy. Therefore, the amount of data to be transferred between nodes should be reduced to
save energy. In this paper, we investigate data compression for resource-constraint WSNs; we introduce
MAS as a novel adaptive lossless floating-point data compression algorithm for WSNs. MAS exploits the
disproportionality in energy consumption between data transmission and processing. Simulation results,
obtained from OMNeT++ and Atmel Studio, show that MAS surpasses other tested compression algorithms
in terms of compression ratio, compression speed, memory requirements and most importantly energy
savings.
KEYWORDS
Wireless Sensor Networks, Lossless Compression, Floating-point Data, Energy Efficiency
1. INTRODUCTION
In the last few decades, Wireless Sensor Networks (WSNs) has proven to be an interest grabbing
technology, offering great contributions in several application domains. Wireless Sensor
Networks can provide a low cost solution to a variety of real-world problems including but not
limited to health care, industry process control, object tracking, volcanic and seismic monitoring,
smart parking, home automation, etc. Moreover, WSNs can provide enhanced situation awareness
in responding to today’s public safety situations. For example, Sleep Safe project is designed for
monitoring infants while they sleep. Sleep Safe sensor nodes can prevent sudden infant death
syndrome (SIDS) by autonomously detecting the sleeping position of an infant and alerting the
parents wirelessly in real time when the infant is lying on its stomach [2].
Typical WSNs are composed of a relatively high number of sensor nodes communicating through
an infrastructure-less multi-hop wireless network architecture. These nodes usually perform three
main tasks: data collection, data processing and data communication. The nodes capture data
from their surrounding environment, they process it and finally they transfer it to a base station
where decision makers can make use of it.
Sensor nodes are small, cheap and smart devices that are made up of four basic components [3],
as shown in Figure 1:
DOI : 10.5121/ijasuc.2013.4502
13
2. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
1)
2)
3)
4)
A sensing unit which captures a physical quantity from the environment.
A processing unit which processes and analyzes the captured data.
A transceiver which is responsible for data communication.
A power unit which is in most of the cases a battery.
Sensing Unit
Sensor
ADC
Processing
Unit
Processor
Transceiver
Storage
Power Unit
Figure 1. Main Components of a sensor node. [3]
Despite their promising range of applications, most WSNs are constrained in resources; they have
limited amount of energy, limited processing capabilities, short range of communication and
limited memory size. Out of these constraints, energy is considered the primary concern
especially for battery-operated sensors; this is true because when a sensor node is depleted of
energy, it would be useless for the network. This could affect the performance of the whole
network especially if the node is used in critical locations, such as mines, volcanoes, etc.
Instruction Cycles for same
Energy as One Byte
Transmitted
Over the past years, different studies and techniques have been proposed for WSNs to reduce
energy consumption and increase network performance and lifetime. Data compression has been
adopted as a practical technique and reliable solution in terms of energy efficiency in WSNs. The
efficiency of data compression techniques mainly bears on the drastically disproportionate energy
cost between data transmission and processing. This can be seen in Figure 2, which shows how
many compute cycles, on a Texas Instruments MSP430 microcontroller, would be performed for
the same amount of energy required to transmit a single byte over three commonly used radios [7]
(Chipcon CC2420 [4]: short range 125 m, the Chipcon CC1000 [5]: medium range 300 m, and
MaxStream XTend [6]: long range 15 km).
10000000
1000000
100000
10000
1000
100
10
1
CC2420
CC1000
Xtend
Radio
Figure 2. Number of TI MSP430F1611 compute cycles that can be performed for the same amount of
energy as transmitting one byte over three radios. [7]
It is obvious that the most energy consuming part in WSNs is the communication. Approximately
80% of power consumed in each sensor node is used for data transmission [1]. Thus to save
14
3. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
energy and maximize network lifetime, data transmissions should be minimized without losing
vital information. The lower the size of the transmitted data the lower the number of required
transmissions. In our work, we study data compression as the technique for minimizing data size.
In this paper, we propose an energy efficient floating-point data compression algorithm for WSNs
called MAS. MAS is a new adaptive streaming lossless compression algorithm that relies on an
accommodative coding technique to achieve compression at low processing costs. MAS offers
great contributions to WSNs, because it is one of the first algorithms to specifically compress
floating-point data. By focusing on floating-point data, it is possible to achieve much better
compression ratios because we can exploit the characteristics and the nature of numbers to build
our algorithm. In fact, floating-point data is generated in a wide range of applications such as
weather monitoring (temperature, pressure…), healthcare (blood pressure, cardiac activity…),
localization and tracking (position, height, coordinates…), industry (temperature, vibrations,
radioactivity…), etc.
The remainder of this paper is organized as follows: Section 2 gives an overview of common
compression schemes specifically designed for WSNs. Section 3 presents our newly proposed
algorithm. Section 4 presents an evaluation of the presented algorithms. Section 5 concludes this
paper while Section 6 presents future works.
2. RELATED WORK
Energy efficiency has been a major concern in the design and development of WSNs. Since radio
communication is known to be the main source of energy consumption, most of the proposed
techniques in the literature, which aim to increase energy savings, have focused on reducing data
communication (transmission/reception). Data compression is such a technique, which is often
used in conjunction with data collection techniques to transmit the collected data in an energy
efficient manner.
Due to the distributed nature of WSN applications, and the resource-constrained nature of sensor
nodes, traditional data compression techniques cannot be easily used. It may not be feasible to run
sophisticated data compression algorithms on sensor nodes. The limited resources available in
these nodes demand the development of specifically designed algorithms. In this section, we
present two famous compression algorithms used in WSNs: SLZW [7] and K-RLE [8]. We also
present our proposed compression algorithm MAS [9] and compare it with these algorithms.
2.1. S-LZW
S-LZW [7] (Sensor-LZW) is an adaptation of the popular lossless data compression algorithm
LZW [10]. S-LZW follows the same procedure used by the LZW algorithm, but with little
restrictions regarding the size of the used data structures. The added restrictions ensure that the
requirements of the algorithm are still within the bounds of the available resources in sensor
nodes.
Before heading into the details of the modification, it is first important to understand why LZW is
not suitable for WSN. LZW is a dictionary-based compression algorithm; it works by converting
strings of symbols into integer codes. LZW does not use a static dictionary; instead, it builds the
dictionary on the fly in a special way to allow both the encoder and the decoder to be able to
generate the same dictionary from the input data. First, both the encoder and the decoder initialize
the dictionary with 256 entries containing the symbols in the ASCII code. Then the dictionary
continues to grow while parsing the input.
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4. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
The dictionary is the main obstacle preventing LZW from being applicable to WSN. Throughout
the compression mechanism, the dictionary keeps growing and can reach sizes much higher than
the available RAM on sensor nodes, and this can clearly disrupt the stability of the system.
Another problem that LZW faces is that it requires a predefined data volume, i.e. in order for it to
start the compression procedure a significant amount of data must already be available. That is
why S-LZW can only be used in delay tolerant networks.
Several modifications were done on LZW to make it portable to WSNs. Most of these
modifications focus on reducing the amount of RAM required for LZW to operate. Here is a list
of modifications that gave the birth to S-LZW:
•
S-LZW uses a 512-entry dictionary. As we mentioned before, this dictionary will be
initialized with 256 ASCII code symbols. With this size, the dictionary may get full while
compressing or decompressing certain datasets. There are two protocols to follow when the
dictionary fills, either fix the dictionary to its state whence it get full, or reset the dictionary to
the 256 entries. The authors of S-LZW [7] proved that using the fixed protocol produces
better results when compressing data of small block sizes (528 bytes).
•
S-LZW divides input data into block sizes of 528 bytes, and then it compresses these blocks
individually. It is important to note that S-LZW requires 528 bytes of data to be available in
order to compress it, if this amount of data is not available, it has to wait until data
accumulate and reach the required size because compressing data of smaller size will be
inefficient as shown in [7]. WSN data sampling rate is relatively low and it may take some
time to collect 528 bytes of data to be able to start the compression. This is why this
algorithm can only be used in delay tolerant networks.
•
The last modification enhances S-LZW by allowing it to benefit from the similarity of data
generated by sensor nodes. This is done by adding a mini-cache, which is a hash-indexed
dictionary of size N, where N is a power of two, which stores recently used and created
dictionary entries. The authors show that it is best to use mini-caches of sizes 32 or 64
dictionary entries.
2.2. K-RLE
K-RLE [8] is a new compression algorithm whose idea is inspired from the lossless data
compression algorithm RLE [11]. RLE stands for Run-Length Encoding, which is a very basic
and simple compression algorithm that works in this way: if a data item d occurs n consecutive
times in the input stream, we replace the n occurrences with a single pair nd.
RLE itself is very simple and can be used in WSN without any major changes, its RAM and
processing requirements are very low. However, there is a major limiting constraint in RLE, for
RLE to achieve good compression ratio, the input data must contain long sequences of repeated
characters, and this rarely occurs in the data generated from sensors. To solve this problem, KRLE algorithm has been proposed; K-RLE means RLE with K precision.
The idea behind this algorithm is: let K be a number, if a data item d, d+K, or d-K occur n
consecutive times in the input stream, we replace the n occurrences with a single pair nd [8]. The
new addition in K-RLE allows it to achieve higher compression ratios than RLE but at an even
cost, which is data loss. In contrary to RLE, K-RLE is a lossy compression algorithm, and the
amount of data loss is strongly related to the K parameter. Higher values for K means better
compression ratio but more data loss, while lower values of K means lower compression ratio but
less data loss.
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5. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
There are two main advantages of K-RLE over S-LZW:
1) K-RLE uses much less amount of RAM than S-LZW, so it can be used in several sensor
platforms where S-LZW cannot be used.
2) K-RLE has the streaming feature, which means it does not need to buffer data before
being able to start the compression process. So K-RLE can be used in networks that
cannot tolerate delay.
The main two disadvantages of K-RLE are:
1) It is a lossy algorithm, so it is not suitable for some applications
2) It requires the input data to contain long sequences of similar characters in order to have a
good compression ratio.
3. MAS COMPRESSION ALGORITHM
MAS stands for Minimalist, Adaptive and Streaming compression algorithm. Minimalist means
that it uses the minimum possible amount of resources. Adaptive means it generates variable-size
output according to the number of digits in the input, and Streaming means that it does not require
buffering of the data before starting the compression process.
MAS is a specialized lossless compression algorithm that only compresses single-precision
floating-point data. MAS’ implementation does not require any correlation or similarity in the
input data, which makes it general and applicable in various domains.
MAS can encode any floating-point number satisfying the following two conditions:
1) The number of significant digits should be at most 7, if a floating-point number exceeds 7
significant digits it would be truncated.
2) The floating-point number when put in scientific notation must have a power of 32 or
less.
Although these conditions mean that some numbers representable by IEEE standard 754 [12] will
not be representable in MAS. However, these numbers almost do not exist in the data generated
by WSN. Numbers that have decimal powers of more than ±32 are almost not found in any
application in WSN.
One of the greatest merits of MAS is that it does not require any floating-point operation
(addition, subtraction, multiplication, division) to compress floating-point numbers. This is very
important because most microcontrollers and processors in sensor platforms are not equipped
with an FPU (Floating point unit). The FPU is responsible for carrying out operations on floatingpoint numbers. In the absence of an FPU, these operations are emulated in software but at the cost
of time and cycles, which could lead to higher energy consumption. Internally, MAS treats
floating-point numbers as strings of characters to carry out the needed operations with a low
number of cycles.
3.1. MAS Encoding Technique
The first step in encoding a floating-point number is to write it in scientific notation. Scientific
notation allows the representation of very small or big numbers with ease. So any number is first
written under the following format (d = digit, e = exponent):
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6. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
± ݀. ݀݀݀݀݀݀ ±01 ݔ
To encode a number, the different parts in the above format must be encoded. MAS encodes them
in five sections detailed below and are shown in Figure 3 from left to right:
•
•
•
•
•
Number of significant digits (n) (number of d’s): represented on 3 bits because its
maximum value is 7.
The exponent (e): represented on 5 bits because its maximum value is 32.
Number sign (ns): represented on 1 bit. (0 for positive, 1 for negative).
Exponent sign (es): represented on 1 bit. (0 for positive, 1 for negative).
The integer formed by the d’s without the decimal point (ddddddd): variable bit size
depending on the number of d’s (maximum 24 bits). Details are in Table 1.
n n n | e e e e e | ns | es | _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
Figure 3. MAS encoding of a real number.
Table 1.Number of bits needed to represent an integer in binary formed by a certain number of digits.
Number of digits
1
2
3
4
5
6
7
Number of needed bits
4
7
10
14
17
20
24
It is clear that MAS exploits the significant number of digits to represent floating point numbers,
for example: -0.0001 is represented on 14 bits while 92301.1 is represented on 30 bits.
Integers form a large part of real numbers, but the above representation may be unfair for integers
because there is no need for the exponent and its sign when representing integers. Therefore, a
special encoding has been chosen for integers, but there should be a discriminator for the decoder
to know which type of number it is going to decode. We exploit the fact that the number of
significant digits cannot be zero, and use this as a discriminator between the 2 encodings. Integer
representation thus has four parts as shown in Figure 4:
•
•
•
•
3 bits that are all zeroes acting as a discriminator.
Number of significant digits (n) in the integer: 3 bits.
Number sign (ns): 1 bit.
The integer: variable bit size following Table 1 (maximum 24 bits).
0 0 0 | n n n | ns | _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
Figure 4. MAS encoding of an integer.
We also exploit the fact that in an integer the number of significant digits cannot be zero to make
a special representation for the actual zero. A zero is represented in MAS as 6 zero bits: 000000.
Finally, since our algorithm generates codes of variable size, we need to make sure that the
overall output is aligned to memory; we do this by filling the required number of bits by 1s. The
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7. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
number of alignment bits is between 1 and 7, and they are added only once at the end of the
output.
3.2. MAS Decoding Technique
The decoding technique is straightforward and is done following these steps:
1) Read the first three bits.
2) If the bits are zeroes, the encoded number is an integer.
a. Read the next three bits to extract the number of significant digits.
b. Read the next bit to determine the number’s sign.
c. Read the number of bits required to extract the number referring to Table 1, then
extract the integer.
d. Combine the readings so far to decode the integer.
3) If the bits are non-zeros, the encoded number is a real.
a.
b.
c.
d.
e.
f.
The already read bits represent the number of significant digits.
Read the next five bits to extract the exponent.
Read the next bit to determine the number’s sign.
Read the next bit to determine the exponent’s sign.
Read the number of bits required to extract the number referring to Table 1, then
extract the integer.
Combine the readings so far to decode the real number.
The explanation of MAS’ encoding and decoding techniques clarifies why MAS is considered a
streaming algorithm. MAS can compress or decompress even one single value, and does not
require predefined data volume as S-LZW.
4. EVALUATION
The evaluation metrics of any compression algorithm for WSN are based on the resource
limitations of sensor nodes. Thus, we chose the following metrics to evaluate the presented
algorithms: compression ratio, processing cost, memory requirements, and energy savings.
In the following sub-sections, we will start by describing the chosen platform and the chosen
simulators, and then we will move to present the simulation results and evaluate the algorithms
performance.
4.1. Platform Overview
Our chosen platform is the Waspmote [13] because of its interesting characteristics. A Waspmote
can be connected to 60 sensor types and can support up to 8 different wireless technologies.
A Waspmote uses an 8-bit AVR microcontroller called ATmega1281 [14], which is low power
microcontroller provided by Atmel [15]. It is supplied with a 128 KB flash (program) memory
and an 8 KB RAM. The microcontroller can have frequencies between 0 and 16 MHz at an
operating voltage of 1.8/5.5 V. In the active mode, as shown in Figure 5, the microcontroller
consumes 1 mA at a voltage of 3.3 V and frequency of 1 MHz; this means this microcontroller
consumes 3.3 nJ for one computation cycle in its active mode. This microcontroller does not have
an FPU unit.
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8. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
2.5
5.5V
2
5.0V
4.5V
4.0V
1
3.3V
2.7V
0.5
Icc ( mA)
1.5
1.8V
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Frequency (MHz)
Figure 5. Active Supply Current vs. frequency (0.1MHz - 1.0MHz) on ATmega1281 microcontroller. [14]
We chose the wireless technology to be CC2420 RF [16] transceiver, which complies with the
IEEE 802.15.4 standard [17]. This transceiver is designed for low-power and low-voltage
wireless applications. It has low current consumption; for transmission, it consumes 17.4 mA and
for reception, it consumes 18.8 mA. It has an effective data rate of 250 kbps.
4.2. Simulators Overview
In order to calculate the total energy consumed, we need to calculate two different kinds of
energy, the computation energy, and the communication energy. To calculate the computation
energy, we calculate the number of cycles needed to achieve the required computation and this is
done by using the Atmel AVR Studio [18]. To calculate the communication energy, we calculate
the transmission energy only at the node performing the compression using OMNeT++ [19].
Atmel AVR Studio [18] is an Integrated Development Environment (IDE) for writing and
debugging AVR/ARM applications. It supports the complete range of Atmel AVR tools and
devices. The simulator in AVR Studio can simulate the CPU, including all instructions, interrupts,
and most of the on-chip I/O modules. We use Atmel AVR Studio to calculate the required
number of cycles as well as the memory requirements of the algorithms.
OMNeT++ [19] is an object-oriented modular discrete-event network simulation framework.
OMNeT++ itself is not a simulator, but rather provides infrastructure and tools for writing
simulations. It is considered the best simulation framework for WSN as demonstrated in [20]. In
order to simulate WSN, we use MiXiM [21], which is an OMNeT++ modelling framework
created for mobile and fixed wireless networks. We use OMNeT++ to calculate transmission
energy consumption and to model realistic behaviour of nodes in an environment close to reality.
4.3. Datasets
To make our results more realistic, we use real-world datasets from various application domains.
Our datasets include carbon dioxide monthly measurements in ppm above Mauna Loa (CO2),
monthly mean water levels in meters in the lake of the wood at Warroad (Water), the radioactivity
in the ground at one minute intervals over one day (Radio) [22], temperature measurements in a
garden (Temp) [23], average humidity at Limoges (Hum), sea low level pressure (Pressure) [24].
Table 2 shows the sizes in bytes of the used datasets.
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9. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
Table 2. The different used datasets and their sizes in bytes.
CO2
3070
Dataset
Size (bytes)
Hum
264
Radio
49450
Temp
10192
Pressure
2405
Water
5395
4.4. Simulation Scenario
Our OMNeT++ simulation model consists of two sensor nodes that are 100 meters apart. One of
these nodes sends the data in compressed form while the other one only receives them and replies
with MAC acknowledgements. We assume that the data is transmitted in packets having payloads
of 64 bytes. No noise or interference was added to the simulation model so no packets were
dropped. Sensor nodes are equipped with battery having a nominal voltage of 3.3 V and a
nominal capacity of 1000 mAh. Regarding the algorithms, we use S-LZW-MC32 (mini cache of
size 32) and K-RLE with K = 2.
4.5. Simulation Results and Analysis
The following subsections present the simulation results along with their respective analysis.
4.5.1. Compression Ratio
The compression ratio is a very important metric when comparing compression algorithms. In
WSNs, having higher compression ratios means lesser amount of data to be transmitted, which
means more energy savings. The compression ratio is calculated according to the following
equation:
− 1 = ݅ݐܽݎ ݊݅ݏݏ݁ݎ݉ܥ
ܿ݁ݖ݅ݏ .݀݁ݏݏ݁ݎ݉
݁ݖ݅ݏ .݈ܽ݊݅݃݅ݎ
Compression Ratio (%)
Compression Ratios on Various Datasets
80%
70%
60%
50%
40%
30%
20%
10%
0%
CO2
Hum
MAS
Radio
S-LZW
Temp
Pressure
Water
K-RLE
Figure 6. Compression ratios on various datasets.
The results shown in Figure 6, show that MAS beats both S-LZW and K-RLE in all the datasets.
MAS’ highest compression ratio is about 68.7% and its lowest compression ratio is 57.5%, SLZW highest compression ratio is 57.7% and its lowest compression ratio is 31.1%, K-RLE does
not perform well in compressing these data sets, its highest compression ratio is 8.3% while its
lowest is 0%. This could be justified by the fact that numbers found in datasets generated by
sensor nodes often do not contain long sequences of repeated symbols.
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10. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
These results show a great advantage of using MAS to compress floating-point data, since it
always achieves better results than the other algorithms.
4.5.2. Computation Time and Energy
After compression ratios have been presented it is important to see how much computation cycles
does the microcontroller run in order to achieve these results. From computation cycles, we can
calculate the compression time required by the microcontroller by assuming that the
microcontroller is operating at a frequency of 1 MHz.
Figure 7 shows that in all cases K-RLE requires the least number of computation cycles. This is
justified by the fact that K-RLE compression ratios are low, thus the algorithm is not performing
all the required procedures. The results for the K-RLE algorithm are not reliable to be used for
comparison with the other algorithms since they do not reflect the actual performance of the KRLE algorithm. For the other algorithms, we notice that S-LZW requires more computation
cycles than MAS in all the datasets. In most cases, MAS requires about one-third the amount
required by S-LZW. The same justification applies to compression time and computation energy
(shown in Figure 8) since they are directly proportional to computation cycles.
Compression time is calculated using the following formula:
= ݁݉݅ݐ ݊݅ݏݏ݁ݎ݉ܥ
ܰݏ݈݁ܿݕܿ ݊݅ݐܽݐݑ݉ܿ ݂ ݎܾ݁݉ݑ
ݕܿ݊݁ݑݍ݁ݎܨ
In our experimentation, the microcontroller has in its active state a frequency of 1 MHz.
As for the computation energy, it is calculated using the following formula:
݈݁ܿݕܿ ݁݊ ݂ ݕ݃ݎ݁݊ܧ ݔ ݏ݈݁ܿݕܿ ݊݅ݐܽݐݑ݉ܥ = ݕ݃ݎ݁݊݁ ݊݅ݐܽݐݑ݉ܥ
The energy of one cycle is calculated in section 4.1, and was found to be 3.3 nJ.
16
14
12
10
8
6
4
2
0
16
14
12
10
8
6
4
2
0
CO2
Hum
Radio
MAS
Temp
S-LZW
Pressure
Compression Time (s)
Cycles (million cycle)
Computation Cycles and Compression Time
Water
K-RLE
Figure 7. Required computation cycles and compression time on ATmega1281 microcontroller.
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11. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
Computation Energy Cost
Energy (mJ)
50
40
30
20
10
0
CO2
Hum
MAS
Radio
S-LZW
Temp
Pressure
Water
K-RLE
Figure 8. Computation energy cost.
4.5.3. Transmission Energy
Computation energy alone is not sufficient to reflect the energy efficiency of an algorithm. The
energy efficiency depends on both computation and transmission energy. Transmission energy is
the energy consumed by the sensor to send the compressed data wirelessly. Figure 9 shows the
energy required to transmit the compressed form of the datasets.
Transmission Energy Cost
Energy (mJ)
500
400
300
200
100
0
CO2
Hum
MAS
Radio
S-LZW
Temp
Pressure
Water
K-RLE
Figure 9. Transmission energy cost
Regarding transmission energy consumption, it is obvious that K-RLE consumes the most energy.
This is because K-RLE compression ratio is low, thus it is transmitting larger amounts of data
than the other algorithms. Again, MAS consumes the least amount of transmission energy and
beats the other algorithm.
4.5.4. Total Consumed Energy
The total energy consumed could better reflect the energy efficiency of the three algorithms.
Despite the fact that K-RLE consumes the least amount of computation energy, results in Figure
10 show that it consumes the most amount of total energy. This is because it sends large amounts
of data over the network. For the other two algorithms, MAS consumes the least energy and thus
proves to be a strong candidate for compression in WSN.
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12. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
Energy (mJ)
Total Energy Cost when Using Compression
450
400
350
300
250
200
150
100
50
0
CO2
Hum
MAS
Radio
S-LZW
Temp
Pressure
Water
K-RLE
Figure 10. Total energy cost when using compression.
To be able to calculate energy savings of each algorithm, we have to calculate the energy
consumed when not using any compression algorithm. The energy consumed when not using a
compression algorithm is the energy required to send the data in uncompressed form, so it
depends greatly upon the data sizes. That is why we see, in Figure 11, that the Radioactivity
dataset is consuming the most energy since it has the largest size.
Energy (mJ)
Total Energy Cost when not Using Compression
500
450
400
350
300
250
200
150
100
50
0
CO2
Hum
Radio
Temp
Pressure
Water
Figure 11. Total energy cost when not using compression.
4.5.5. Energy Savings
Figure 12 shows the percentage of energy saved when using each of the three compression
algorithm. Energy savings is calculated according to the following formula:
ܵܽ− 1 = ݕ݃ݎ݁݊݁ ݀݁ݒ
ݐ݅ݓ ݕ݃ݎ݁݊ܧℎ ܿ݊݅ݏݏ݁ݎ݉
ݐ݅ݓ ݕ݃ݎ݁݊ܧℎ݊݅ݏݏ݁ݎ݉ܿ ݐݑ
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13. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
Energy Savings
70%
Saved Energy (%)
60%
50%
40%
30%
20%
10%
0%
-10%
CO2
Hum
MAS
Radio
S-LZW
Temp
Pressure
Water
K-RLE
Figure 12. Energy saved when using compression algorithms.
It is clear that MAS achieves the most energy savings with results better than both S-LZW and KRLE. MAS’ energy savings are more than that of S-LZW by an average of 20%, and better than
that of K-RLE by an average of 48%.
In some datasets, when using the K-RLE algorithm, the saved energy is negative, it means that
the compression algorithm is not saving energy; instead, it is leading to more energy
consumption. This is due to the low compression ratios of K-RLE on some datasets.
4.5.6. Memory Requirements
To complete the evaluation we must calculate the amount of memory consumed by each
algorithm. As a reminder, our platform has a flash memory of 128 KB and a RAM of 8 KB. It is
important to note that the memory results are independent of the datasets used. This is because
these results are obtained just when building the algorithm and before running any operation or
procedure. So these values represent the amount of memory allocated by the algorithm when they
are loaded into RAM and before operating on any dataset. Figures 13 and 14 show the absolute
and relative memory consumption of each algorithm for flash memory and RAM respectively.
Memory (Bytes)
Flash Memory Consumption
4000
[VALUE], 2.8%
3000
[VALUE], 1.5%
2000
[VALUE], 0.9%
1000
0
MAS
S-LZW
K-RLE
Figure 13. Flash memory consumption.
MAS consumes the largest amount in the flash memory. In fact, MAS program code is a little
long since it has two representations for integers and real numbers. Flash memory consumption is
not important and it is not a concern, since flash memory is always of a large size, and MAS is
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14. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
only using 2.8% of that memory. This does not introduce any problems in performance since the
flash memory is reserved for program code and not for random access.
Memory (Bytes)
RAM Consumption
4000
[VALUE], 39.6%
3000
2000
1000
[VALUE], 0.5%
0, 0.0%
0
MAS
S-LZW
K-RLE
Figure 14. RAM consumption.
In terms of RAM usage, which is the important memory concern, MAS consumes only 44 bytes
while K-RLE consumes almost zero bytes. S-LZW turns out to be the most RAM consuming
algorithm, and this is because of the dictionary it uses, S-LZW consumes 3240 bytes, which is
equivalent to about 40% of the RAM.
These results prove that MAS is a strong candidate for compression in WSNs, since it beats the
other algorithms in all the proposed metrics. It achieves compression ratios better than S-LZW by
an average of 13% and better than K-RLE by an average of 59%. MAS saves the most amount of
energy, it saves by an average of 20% more than S-LZW and 54% more than K-RLE. In terms of
memory, MAS and K-RLE use a very little amount of RAM, MAS uses only 0.5% of the total
amount of RAM, while K-RLE consumes almost zero bytes of RAM. S-LZW uses the most
amount of RAM; it consumes 3240 bytes that is equivalent to 39.6% of the total available RAM.
5. CONCLUSIONS
In this paper, we propose MAS, a new lossless floating-point data compression algorithm for
WSNs. MAS is applicable to a variety of sensor hardware and platforms due to its low memory
and processing requirements.
Simulation results show that MAS’ energy savings are on average 54% on all the tested datasets,
while maintaining the highest compression ratios. MAS surpasses the other tested compression
algorithms in terms of compression ratio, compression speed, memory requirements and energy
savings. These results, which are obtained from accurate and trustworthy simulators, present
MAS as a strong and competing candidate for data compression in WSN.
6. FUTURE WORKS
As a short-term step, we would like to improve MAS to exploit the correlation and the similarity
in the data generated by sensor nodes. Such an improvement would allow MAS to achieve higher
compression ratios. We would also like to implement a transformation that aims at reducing the
number of digits in the input. This transformation is expected to increase MAS’ compression ratio
since MAS relies mainly on the number of digits in the data to achieve compression
As a long-term step, we would like to study the efficiency of using MAS with an aggregation
technique. The main challenge here is to prove that introducing MAS to aggregated networks
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15. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.5, October 2013
does not lead to more energy consumption at the level of aggregators, which are supposed to
follow this cycle to achieve their job: decompression – aggregation – compression.
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AUTHORS
EL ASSI Maher is a PhD student starting from October 2013. His PhD is arranged by joint supervision
between the University of Franche Comte, Besançon – France, and the Lebanese University, Beirut –
Lebanon. He received M.S. degree in the Lebanese University in 2012. He also graduated as a Civil
Engineer in 2012 from the Lebanese University. His research interests include Internet of Things (IoT),
Wireless Sensor Networks (WSN), energy efficiency in embedded devices, network modelling and
simulation and lightweight data analysis techniques.
GHADDAR Alia has PhD in Computer Science with more than 6 years of teaching experience and 4 years
of web development. Obtained her Master degree in Computer Science from the Lebanese University. She
started her PhD at the University of Lille-1, Science and technology in France. During that time, she was
member in POPS-project team; A joint project of INRIA, University of Lille-1 and CNRS. Her interests
now lie in the Internet of things, mobile sensors, data communication and knowledge discovery in the
wireless sensor networks.
TAWBI Samar, PhD in Computer Science. She is an associate professor in the Computer Sciences
department in the Lebanese University since 2005. She received the M.S. degree in “Mathematical
Modelling and Scientific Software Engineering” by the Lebanese University and the universities of Rennes,
Reims (France) and EPFL (Switzerland). She obtained her PhD in 2004 at ‘Paul Sabatier’ University,
France. Her research interests include data aggregation and communication in wireless sensors networks,
auto configurable WSN, Internet of things and Cloud computing.
GHADDAR Fadi, MS in Network and System Security. He has more than 15 years of experience in
software analysis and development, network and system security and team leading. He obtained recently
his Master degree from Saint Joseph University. His research interests include information security, data
aggregation and security in wireless sensors networks.
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