This paper addresses problems in controlling quality of service (QoS) in wireless sensor networks
(WSNs). QoS is defined as the number of awakened sensors in a WSN. Sensor deaths (caused by battery
failure) and sensor replenishments (caused by redeployment of new sensors) contribute to the difficulty of
controlling QoS in WSNs. A previous research developed a QoS control scheme based on the Gur Game
algorithm. However, this scheme does not consider the energy consumption of sensors, which shortens
WSN lifetime. This paper proposes a novel QoS control scheme that periodically swaps active and
sleeping sensors to balance power consumption. Our study also uses the distribution manner of the
previous work. Our scheme significantly extends WSN lifetime and maintains desired QoS. Simulations
that compared our scheme with previous schemes in various environments show that our scheme build a
robust and long-lasting sensor network capable of dynamically adjusting active sensors.
Throughput analysis of energy aware routing protocol for real time load distr...
Abstract Wireless sensor network (WSNs) are self-organized systems that depend on highly distributed and scattered low cost tiny devices. These devices have some limitations such as processing capability, memory size, communication distance coverage and energy capabilities. In order to maximize the autonomy of individual nodes and indirectly the lifetime of the network, most of the research work is done on power saving techniques. Hence, we propose energy-aware load distribution technique that can provide an excellent data transfer of packets from source to destination via hop by hop basis. Therefore, by making use of the cross-layer interactions between the physical layer and the network layer thus leads to an improvement in energy efficiency of the entire network when compared with other protocols and it also improves the response time in case of network change. Keywords:- wireless sensor network, energy-aware, load distribution, power saving, cross layer interactions.
The document summarizes an efficient cluster head selection scheme and dynamic sleep time approach for reducing energy consumption in wireless sensor networks. It proposes two algorithms: 1) the Associated Cluster Head Array algorithm which selects cluster heads based on residual node energy in a way that balances energy load. 2) the Dynamic Sleep time for Aggregationed Data algorithm which dynamically adjusts sleep intervals based on network conditions rather than using fixed sleep times, reducing wasted energy from idle listening. Simulation results show the proposed methods significantly reduce energy compared to previous clustering routing algorithms for sensor networks.
This document discusses a proposed cost-based energy efficient routing algorithm for wireless body area networks (WBANs). WBANs use wireless sensor nodes placed on or inside the body to monitor vital health signals. The proposed algorithm aims to maximize network lifetime by selecting optimal forwarding nodes based on a cost function that considers residual energy and distance to the sink node. Simulation results show the proposed algorithm performs better than traditional routing methods by achieving a longer stability period with more uniform energy consumption across nodes.
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document proposes modifications to the Sparsity-aware energy efficient clustering protocol (SEEC) to improve its energy efficiency and ability to handle failures in underwater sensor networks. Key alterations to SEEC include transmission range management, separate reclustering for each group, and sensor sleeping modes. A new simulator called USNeT was developed to test the modified SEEC protocol. Simulation results indicate the modified SEEC provides better performance than the original SEEC in terms of energy efficiency and network survivability during cluster head failures or disrupted sensor connectivity.
THRESHOLD BASED DATA REDUCTION FOR PROLONGING LIFE OF WIRELESS SENSOR NETWORK
ABSTRACT
Wireless sensor network is a set of tiny elements i.e. sensors. WSN is used in the field of Health Monitoring, Civil Construction, Military Applications and Agricultural etc., for monitoring environmental parameters.The WSN is having the challenges like less processing power, less memory, less bandwidth and battery
powered. The data monitored through the sensors would be sent to the sink for data processing. The data sent from sensor node can be controlled for saving the energy, as maximum energy is consumed for transmission of data and it is not possible to replace the batteries frequently. In this work threshold based and adaptive threshold based data reduction techniques with energy efficient shortest path are used for minimizing the energy of sensor node and the network. Adaptive approach saves energy and reduce data by 30% to 40% as compared to threshold based approach.
SINK RELOCATION FOR NETWORK LIFETIME ENHANCEMENT METHOD IN WSN
The document proposes an energy-aware sink relocation (EASR) method to enhance the lifetime of wireless sensor networks. The EASR method uses information on the residual battery energy of sensor nodes to adaptively change their transmission ranges and determine where the sink should relocate to. It incorporates an energy-aware transmission range adjustment that sets smaller ranges for nodes with lower battery levels, and a sink relocation mechanism that moves the sink to alleviate energy consumption at hotspot nodes nearing energy depletion. Analysis and simulations show the EASR method can significantly extend network lifetime compared to traditional strategies.
Review Paper on Energy Efficient Protocol in Wireless Sensor Network
Wireless sensor network (WSN) is a system composed of a large number of low-cost micro-sensors. This network is used to collect and send various kinds of messages to a base station (BS). WSN consists of low-cost nodes with limited battery power, and the battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long network life time. The lifetime of Wireless Sensor Networks (WSN) is crucial. To achieve the aim, we need not only to minimize total energy consumption but also to balance WSN load. Hence, this paper aims to study different energy balance routing protocols of WSNs. In this paper, we have compared different protocols of WSN, ensuring maximum network lifetime by balancing the load as equally as possible
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKS
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
This document discusses improving the performance of mobile wireless sensor networks using a modified DBSCAN clustering algorithm. It first provides background on wireless sensor networks and discusses challenges related to mobility. It then reviews several existing works related to clustering, mobility, and extending network lifetime. The paper proposes using a modified DBSCAN algorithm that takes into account mobility, remaining energy, and distance to base station to select cluster heads. It evaluates the performance of this approach based on throughput, delay, and packet delivery ratio, finding improvements over other methods.
The Energy hole problem is a major problem of
data collection in wireless sensor networks. The sensors near the
static sink serve as relays for remote sensors, which reduce their
energy rapidly, causing energy holes in the sensor field. This
project has proposed a customizable mobile sink based adaptive
protected energy efficient clustering protocol (MSAPEEP) for
improvement of the problem of energy holes along with that we
also characterize and made comparison with the previous
existing protocols. A MSAPEEP uses the adaptive protected
method (APM) to discover the best possible number of cluster
heads (CHs) to get better life span and constancy time of the
network. The effectiveness of MSAPEEP is compared with
previous protocols; specifically, low energy adaptive clustering
hierarchy (LEACH) and mobile sink enhanced energy efficient
PEGASIS based routing protocol using network simulator(NS2).
Examples of simulation result show that MSAPEEP is more
reliable and removes the potential of energy hole and enhances
the stability and life span of the wireless sensor network(WSN).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Throughput analysis of energy aware routing protocol for real time load distr...eSAT Journals
Abstract Wireless sensor network (WSNs) are self-organized systems that depend on highly distributed and scattered low cost tiny devices. These devices have some limitations such as processing capability, memory size, communication distance coverage and energy capabilities. In order to maximize the autonomy of individual nodes and indirectly the lifetime of the network, most of the research work is done on power saving techniques. Hence, we propose energy-aware load distribution technique that can provide an excellent data transfer of packets from source to destination via hop by hop basis. Therefore, by making use of the cross-layer interactions between the physical layer and the network layer thus leads to an improvement in energy efficiency of the entire network when compared with other protocols and it also improves the response time in case of network change. Keywords:- wireless sensor network, energy-aware, load distribution, power saving, cross layer interactions.
The document summarizes an efficient cluster head selection scheme and dynamic sleep time approach for reducing energy consumption in wireless sensor networks. It proposes two algorithms: 1) the Associated Cluster Head Array algorithm which selects cluster heads based on residual node energy in a way that balances energy load. 2) the Dynamic Sleep time for Aggregationed Data algorithm which dynamically adjusts sleep intervals based on network conditions rather than using fixed sleep times, reducing wasted energy from idle listening. Simulation results show the proposed methods significantly reduce energy compared to previous clustering routing algorithms for sensor networks.
This document discusses a proposed cost-based energy efficient routing algorithm for wireless body area networks (WBANs). WBANs use wireless sensor nodes placed on or inside the body to monitor vital health signals. The proposed algorithm aims to maximize network lifetime by selecting optimal forwarding nodes based on a cost function that considers residual energy and distance to the sink node. Simulation results show the proposed algorithm performs better than traditional routing methods by achieving a longer stability period with more uniform energy consumption across nodes.
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document proposes modifications to the Sparsity-aware energy efficient clustering protocol (SEEC) to improve its energy efficiency and ability to handle failures in underwater sensor networks. Key alterations to SEEC include transmission range management, separate reclustering for each group, and sensor sleeping modes. A new simulator called USNeT was developed to test the modified SEEC protocol. Simulation results indicate the modified SEEC provides better performance than the original SEEC in terms of energy efficiency and network survivability during cluster head failures or disrupted sensor connectivity.
THRESHOLD BASED DATA REDUCTION FOR PROLONGING LIFE OF WIRELESS SENSOR NETWORKpijans
ABSTRACT
Wireless sensor network is a set of tiny elements i.e. sensors. WSN is used in the field of Health Monitoring, Civil Construction, Military Applications and Agricultural etc., for monitoring environmental parameters.The WSN is having the challenges like less processing power, less memory, less bandwidth and battery
powered. The data monitored through the sensors would be sent to the sink for data processing. The data sent from sensor node can be controlled for saving the energy, as maximum energy is consumed for transmission of data and it is not possible to replace the batteries frequently. In this work threshold based and adaptive threshold based data reduction techniques with energy efficient shortest path are used for minimizing the energy of sensor node and the network. Adaptive approach saves energy and reduce data by 30% to 40% as compared to threshold based approach.
SINK RELOCATION FOR NETWORK LIFETIME ENHANCEMENT METHOD IN WSNEditor IJMTER
The document proposes an energy-aware sink relocation (EASR) method to enhance the lifetime of wireless sensor networks. The EASR method uses information on the residual battery energy of sensor nodes to adaptively change their transmission ranges and determine where the sink should relocate to. It incorporates an energy-aware transmission range adjustment that sets smaller ranges for nodes with lower battery levels, and a sink relocation mechanism that moves the sink to alleviate energy consumption at hotspot nodes nearing energy depletion. Analysis and simulations show the EASR method can significantly extend network lifetime compared to traditional strategies.
Review Paper on Energy Efficient Protocol in Wireless Sensor NetworkIJERA Editor
Wireless sensor network (WSN) is a system composed of a large number of low-cost micro-sensors. This network is used to collect and send various kinds of messages to a base station (BS). WSN consists of low-cost nodes with limited battery power, and the battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long network life time. The lifetime of Wireless Sensor Networks (WSN) is crucial. To achieve the aim, we need not only to minimize total energy consumption but also to balance WSN load. Hence, this paper aims to study different energy balance routing protocols of WSNs. In this paper, we have compared different protocols of WSN, ensuring maximum network lifetime by balancing the load as equally as possible
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Energy Optimization in Heterogeneous Clustered Wireless Sensor NetworksIRJET Journal
1) The document discusses energy optimization in heterogeneous clustered wireless sensor networks. It proposes a new method called Energy optimized heterogeneous clustered wireless sensor networks (EEHC) to improve network lifetime by reducing energy consumption.
2) The EEHC method selects cluster heads based on node energy levels and connectivity to balance energy usage. It uses different transmission techniques within and between clusters to minimize energy usage.
3) Simulation results show the EEHC method improves network lifetime compared to LEACH and AEEC clustering protocols for wireless sensor networks.
Load Balancing for Achieving the Network Lifetime in WSN-A SurveyAM Publications
a wireless sensor network is network form of sense compute, and communication elements which helps to
observe, events in a specified environment. Sensor nodes in wireless sensor network are depends on battery power they
have limited transmission range that’s why energy efficiency plays a vital role to minimize the overhead through which
the Network Lifetime can be achieved. The lifetime of network, depends on number of nodes, strength, range of area
and connectivity of nodes in the network. In this paper we are over viewing techniques which are used in wireless sensor
network for load balancing. Wireless sensor network having different nodes with different kind of energy which can be
improve the lifetime of the network and its dependability. This paper will provide the person who reads with the
groundwork for research in load balancing techniques for wireless sensor networks.
This document provides a literature review of various methods proposed by researchers to improve energy efficiency and security in wireless sensor networks (WSNs). It summarizes several key energy efficient routing protocols like LEACH, PEGASIS and TEEN, as well as security threats like denial of service attacks, wormhole attacks, and Sybil attacks. The document reviews several studies that have developed algorithms and schemes to reduce energy consumption through techniques like dynamic clustering, mobile agent clustering, and randomized routing. It also discusses schemes to prevent security issues like false data injection and improve data authentication. The conclusion states that future work needs to focus on improving battery power and providing better fault tolerance and protection from severe security threats in WSNs.
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.
Performance Advancement of Wireless Sensor Networks using Low Power Technique...AM Publications
In this paper, we present optimization techniques for WSNs. Our main goal is to minimize the power consumption and
latency. We address the problem of minimizing the energy consumption in WSNs including hardware. ZigBee protocol is used to
design nodes on WSN to achieve a very low power consumption rate. Furthermore, we propose to use IRS protocol in WSN within
a ZigBee technique to discover information from unaware locations and achieve efficiency of energy and sacrifices latency. Our
main idea is to support WSNs with both ZigBee technique and IRS protocol. In addition, we address the problem of efficient node
placement for congestion control in WSNs. Thus, we evaluate the performance of specific routing and some algorithms of
congestion control when wireless sensor nodes are deployed under different placements of network. To demonstrate the strength of
the used algorithms, our simulation in C# proves that ZigBee-IRS- ESRT-Flooding approaches reduce the power consumption
from 10% to 19% when compared to existing techniques of low Power and node placement.
1) The document proposes an NSGA-III based energy efficient clustering and tree-based routing protocol for wireless sensor networks.
2) It forms clusters based on remaining energy of nodes initially, then uses NSGA-III to improve inter-cluster data aggregation and select the shortest path between cluster heads and the sink.
3) Simulation results show the proposed protocol significantly improves network lifetime, throughput, and residual energy over other techniques.
Efficient Data Gathering with Compressive Sensing in Wireless Sensor NetworksIRJET Journal
This document discusses using compressive sensing for efficient data gathering in wireless sensor networks. It proposes using a random walk algorithm to collect random measurements along multiple random walks, allowing for non-uniform sampling unlike traditional compressive sensing theory. The random walk approach can help address constraints like path constraints in wireless sensor networks. It provides the mathematical foundations to reconstruct sparse signals from random measurements collected in a random walk manner using graph theory and l1 minimization. Simulation results show the random walk approach can significantly reduce communication costs and noise compared to other data gathering schemes.
2013 2014 ieee final year students me,mtech dotnet project titlesRICHBRAINTECHNOLOGIES
This document provides contact information for Richbrain Technologies and lists IEEE final year project titles in various areas like wireless sensor networks, cloud computing, data mining, and mobile computing. Richbrain Technologies offers IEEE projects for students pursuing BE, BTech, ME, MTech degrees in computer science, information technology, electronics and communication engineering, and electrical engineering. It provides mobile number and email address to contact for details about these projects. The document then lists several project titles under different topics with brief descriptions.
CTAS is a collaborative two-level task scheduling algorithm for wireless sensor nodes with multiple sensing units:
1) It performs coarse-grain scheduling at the group level, scheduling event types and data transmissions for neighboring sensor nodes based on their overlapping sensing areas.
2) It performs fine-grain scheduling at the individual node level, scheduling the tasks of the assigned event types for each sensor node.
3) Simulation results show CTAS significantly improves energy consumption by up to 67% and reduces event misses by 75% compared to existing techniques.
2013 2014 ieee final year be,btech students for cse,it dotnet project titlesRICHBRAINTECHNOLOGIES
This document provides information about IEEE final year projects available through Richbrain Technologies. It lists over 100 potential project titles across several topics including mobile computing, cloud computing, wireless sensor networks, data mining and more. Contact information is provided to inquire further about specific projects.
The document is composed of repeated phrases of "Example text" and "This is an example text". It provides examples of social communication text but no actual social communication content. The document suggests replacing the example text with one's own text but provides no other context or information.
2013 2014 ieee final year students cse,it dotnet project titlesRICHBRAINTECHNOLOGIES
This document provides contact information for Richbrain Technologies and lists IEEE final year project titles and topics in various areas like wireless sensor networks, cloud computing, data mining, and mobile computing. Richbrain Technologies offers IEEE projects for students in fields such as computer science, information technology, electronics and communication engineering. The project titles provided cover technologies including wireless networks, distributed systems, machine learning, and databases.
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.
1) The document discusses the opportunity for technology to improve organizational efficiency and transition economies into a "smart and clean world."
2) It argues that aggregate efficiency has stalled at around 22% for 30 years due to limitations of the Second Industrial Revolution, but that digitizing transport, energy, and communication through technologies like blockchain can help manage resources and increase efficiency.
3) Technologies like precision agriculture, cloud computing, robotics, and autonomous vehicles may allow for "dematerialization" and do more with fewer physical resources through effects like reduced waste and need for transportation/logistics infrastructure.
An algorithm for fault node recovery of wireless sensor networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A multi-hop routing protocol for an energy-efficient in wireless sensor networkIJECEIAES
The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
IRJET- Sink Mobility based Energy Efficient Routing Protocol for Wireless Sen...IRJET Journal
The document describes a proposed sink mobility based energy efficient routing protocol for wireless sensor networks. The protocol uses both a static centralized sink and a mobile sink that follows a predetermined path with 4 sojourn locations. This is aimed to improve network lifetime by balancing energy load across nodes. Simulation results show that the proposed approach with a mobile sink performs better than the Threshold sensitive Energy Efficient sensor Network (TEEN) protocol alone in terms of number of alive nodes, number of cluster heads, and number of packets sent to the base station over multiple rounds. Using a mobile sink helps scatter the energy load in the network and extends lifetime compared to only using a static sink.
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 novel energy efficient routing algorithm for wireless sensor networks using...ijwmn
There are numerous applications for wireless sensor networks which are inevitable now a day in our daily
life. Majority of such applications which use wireless sensor networks will be in areas where the direct
human intervention is impossible. So the limited energy available in such sensors is a threat for prolonging
the life of the entire network. The need of energy efficiency in wireless sensor networks is a hot research
topic in which lot of new strategies for improvement in energy efficiency has been sought after. As
communication process consumes more energy, an energy efficient routing strategy can probably reduce
the energy consumption to a great extend. This paper gives an overview of the different routing techniques
in which mobile sinks are used to facilitate the routing process which can effectively reduce the energy use.
A new routing strategy with mobile sinks and a static sink is proposed and is compared based on the
matrices life time and average energy of the nodes with the existing Shortest Hop path (SH) algorithm. The
simulation results shows the proposed algorithm is more energy efficient than the existing one.
This document discusses performance evaluation of sensor node scalability using a reactive modified I-LEACH protocol. It begins with an abstract that introduces the challenges of wireless sensor networks including limited power, computing, and storage capacity of sensor nodes. It then reviews related work on improving the LEACH protocol. The paper aims to increase network lifetime by using a reactive I-LEACH protocol and compares its performance to LEACH and I-LEACH based on power usage and lifetime. It finds that the proposed technique shows more effective results, even with increased node scalability.
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...Editor IJCATR
In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with
specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication
distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to
form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won’t be
disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the
simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other
method such as Traditional clustering or Simulated Annealing
Maintain load balancing in wireless sensor networks using virtual grid based ...zaidinvisible
This document summarizes a routing protocol called the Virtual Grid Based Routing Protocol (VGRP) that aims to maximize the lifetime of wireless sensor networks by balancing the data traffic load evenly among sensor nodes. The VGRP splits the sensor field into a grid of equal sized sub-cells and uses clustering and chain techniques to collect sensed data. It is compared to the CFDASC routing protocol through simulation, and results show VGRP outperforms CFDASC in terms of network stability and load balancing. The document provides background on wireless sensor networks and reviews several related grid-based and load balancing routing protocols.
Energy efficient routing in wireless sensor network based on mobile sink guid...IJECEIAES
In wireless sensor networks (WSNs), the minimization of usage of energy in the sensor nodes is a key task. Three salient functions are performed by WSNs’ sensor nodes namely data sensing, transmitting and relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for data sensing and transmission. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Experimentation is done on the network simulator 2 Platform. The existing routing techniques like threshold sensitive energy efficient sensor network, energy-efficient low duty cycle, and adaptive clustering protocol are compared with the obtained results of chosen algorithm. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.
Survey: energy efficient protocols using radio scheduling in wireless sensor ...IJECEIAES
An efficient energy management scheme is crucial factor for design and implementation of any sensor network. Almost all sensor networks are structured with numerous small sized, low cost sensor devices which are scattered over the large area. To improvise the network performance by high throughput with minimum energy consumption, an energy efficient radio scheduling MAC protocol is effective solution, since MAC layer has the capability to collaborate with distributed wireless networks. The present survey study provides relevant research work towards radio scheduling mechanism in the design of energy efficient wireless sensor networks (WSNs). The various radio scheduling protocols are exist in the literature, which has some limitations. Therefore, it is require developing a new energy efficient radio scheduling protocol to perform multi tasks with minimum energy consumption (e.g. data transmission). The most of research studies paying more attention towards to enhance the overall network lifetime with the aim of using energy efficient scheduling protocol. In that context, this survey study overviews the different categories of MAC based radio scheduling protocols and those protocols are measured by evaluating their data transmission capability, energy efficiency, and network performance. With the extensive analysis of existing works, many research challenges are stated. Also provides future directions for new WSN design at the end of this survey.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
Increasing the Network life Time by Simulated Annealing Algorithm in WSN wit...ijasuc
Since we are not able to replace the battery in a wireless sensor networks (WSNs), the issues
of energy and lifetime are the most important parameters. In asymmetrical networks, different sensors
with various abilities are used. Super nodes, with higher power and wider range of communication in
comparison with common sensors, are used to cause connectivity and transmit data to base stations in
these networks. It is crucial to select the parameters of fit function and monitoring sensors optimally in a
point covering network. In this paper, we utilized an algorithm to select monitoring sensors. The
selection is done by using a novel algorithm that used by simulated annealing. This selection takes
remained energy into consideration. This method increases lifetime, decreases and balances energy
consumption as confirmed by simulation results.
AN EFFICIENT SLEEP SCHEDULING STRATEGY FOR WIRELESS SENSOR NETWORKijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Routing Algorithm for Wireless Sensor Network Using Particle Swarm O...IOSR Journals
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APPLICATION OF PERIODICAL SHUFFLE IN CONTROLLING QUALITY OF SERVICE IN WIRELESS SENSOR NETWORKS
1. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
DOI : 10.5121/ijasuc.2013.4202 17
APPLICATION OF PERIODICAL SHUFFLE IN
CONTROLLING QUALITY OF SERVICE
IN WIRELESS SENSOR NETWORKS
Hao-Li Wang1
and Rong-Guei Tsai2
1
Department of Computer Science and Information Engineering,
National Chiayi University, Chiayi, Taiwan
haoliwang@gmail.com
2
Department of Engineering Science,
National Cheng Kung University, Tainan, Taiwan
sig73ma@gmail.com
ABSTRACT
This paper addresses problems in controlling quality of service (QoS) in wireless sensor networks
(WSNs). QoS is defined as the number of awakened sensors in a WSN. Sensor deaths (caused by battery
failure) and sensor replenishments (caused by redeployment of new sensors) contribute to the difficulty of
controlling QoS in WSNs. A previous research developed a QoS control scheme based on the Gur Game
algorithm. However, this scheme does not consider the energy consumption of sensors, which shortens
WSN lifetime. This paper proposes a novel QoS control scheme that periodically swaps active and
sleeping sensors to balance power consumption. Our study also uses the distribution manner of the
previous work. Our scheme significantly extends WSN lifetime and maintains desired QoS. Simulations
that compared our scheme with previous schemes in various environments show that our scheme build a
robust and long-lasting sensor network capable of dynamically adjusting active sensors.
KEYWORDS
Wireless Sensor Network, Quality of Service, Gur Game
1. INTRODUCTION
Rapid developments in wireless communication, distributed signal processing, and ubiquitous
computing make wireless sensor networks (WSNs) popular in the last few years. A WSN
consists of a large number of small sensors and a sink (base) station. Sensors are small devices
with limited energy supply and low computational capability. They are used for covering and
monitoring a sensing field to collect useful information. Sensor networks are widely used in a
variety of domains, such as environmental observation, health care, and military monitoring.
Sensors are usually placed randomly in a sensor field. The concept of redundancy is applied to
WSNs to achieve a high degree of reliability. One or more sensors may cover the same region
and gather similar data; thus, numerous redundant data are sent to the sink. Redundant data
collection should be avoided to conserve energy in WSNs. Sensors are scheduled to be
periodically active and idle. Only several sensors are active in a given period of time, resulting
in high reliability and low data redundancy. The assumption that a sensor network has a fixed
number of nodes is unreasonable because sensor nodes usually have limited lifetimes. Therefore,
adding and deleting sensor nodes have to be taken into consideration.
2. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
18
New questions arise based on the addition and deletion of sensor nodes, such as the selection of
active nodes in all sensors, which may be added or deleted randomly. This subject is known as
quality of service (QoS) control in WSNs.
In WSN, QoS has different meanings in different applications. For applications of event
detection and target tracking, QoS means the coverage of the WSN. For applications in sensing
harsh environments, QoS means observation accuracy. For multimedia applications in WSN,
QoS means information transportation related parameters. In this research, we consider the
applications of event detection, and thus define QoS as the number of active sensors that can
send information at any given time.
We have two goals regarding QoS control design. The first involves maximizing the lifespan of
the sensor network. The second is concerned with having enough working sensors to send
packets toward the sink. The lifespan of a sensor network is defined as the period of time until
the first sensor in the network runs out of energy. Other researchers gave a different definition
for network lifetime, which they described as the duration until the active nodes can no longer
perform the required task. We chose the first definition in this paper because it is the most
commonly used definition in WSNs.
Most studies on WSNs focused on medium access control, routing, data aggregation, and sensor
deployment. Only a few studies discussed QoS control. A previous research introduced a QoS
control approach based on the Gur Game algorithm. The Gur Game-based scheme maintains
QoS without knowing the total number of sensors. However, the Gur Game-based scheme does
not consider power consumption and causes short sensor lifetimes. Many studies have been
conducted on power-saving issues in WSNs. Some studies scheduled sensors to sleep longer,
whereas others reduced transmission data. Moreover, most of these studies proposed centralized
methods to achieve power consumption savings. However, centralized methods cannot be
applied in the Gur Game-based scheme because they may destroy the potential distribution
manner of the scheme. Therefore, we propose an enhanced QoS control scheme that balances
power consumption and maintains the potential distribution manner of the Gur Game-based
scheme.
Our contributions are threefold. (1) This paper recognizes hidden reasons for short sensor
lifetime in the Gur Game-based scheme. Sensor networks are able to last longer after the cause
of energy inefficiency is removed. (2) This paper enhances prior work by balancing power
consumption. To keep the potential distribution manner of the Gur Game-based scheme, our
method avoids centralized schemes, which were widely used in most prior studies on power
saving. (3) Our method significantly improves sensor lifetime. Simulations that evaluate shuffle
in various environments show that sensors exhibit great improvements in lifetime with our
method. We clarify the proposed mechanism is the revised version of our previous research [3]
to include extensive simulation results.
The remainder of this paper is organized as follows. Section II presents several related studies.
Section III describes the system model, problem definition, and the proposed solution. Section
IV displays the simulation results. Finally, Section V concludes the paper.
2. RELATED WORKS
This section presents a number of previous literatures on QoS control in WSNs, and then
introduces a Gur Game-based QoS control scheme, which is the first and the most related QoS
control scheme in this field.
3. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
19
2.1. Previous literature on QoS control in WSNs
WSNs have attracted the attention of researchers’ for the past years. A huge amount of general
literature on WSNs exists. However, not too many studies focused on controlling the number of
power-on sensors to a desired target number. This subject is called QoS control. Although QoS
control is a hot topic in WSNs, previous studies on this topic still exist. Iyer and Kleinrock first
defined the QoS control problem and proposed a QoS control approach based on the Gur Game
algorithm[1]. That study motivated our work in this paper. A short introduction of the Gur
Game-based scheme is provided later in this section.
Some researches extend the study of Iyer and Kleinrock in different ways [2–9]. Some studies
discussed the energy conservation in QoS control scheme [2–5], whereas others extend QoS
scheme to cluster structures [6–9]. Besides, WSN lifetime is defined in [7-9] as the maintenance
duration of the desired QoS.
Other related works are briefly introduced as follows. A novel WSN taxonomy with QoS is
proposed in [10], where a reference model that enables the classification of WSNs is also
established. A survey of QoS-aware routing techniques in WSNs is proposed in [11]; a number
of middleware approaches and certain open issues for QoS support in WSNs are also explored.
A traffic engineering model that relies on delay, reliability, and energy-constrained paths to
achieve reliable and energy-efficient transmission of information routed by a WSN is proposed
in [12]. This paper adopts multipath routing to improve reliability and packet delivery in WSNs
while maintaining low power-consumption levels. QoS requirement and the minimum number
of active nodes are explored in [13] because the former is usually inversely proportional to
energy consumption. A QoS protocol for WSNs that controls topology based on analytical
results is proposed in [13]. Besides, a dynamic clustering algorithm is presented to achieve the
optimal assignment of active sensors while maximizing the number of regions covered by the
sensors [2]. Ant algorithm and genetic algorithm are considered in the design of QoS control. A
trade-off between sensing coverage and network lifetime necessitate the use of a routing
protocol, which was proposed in [14], to accommodate both energy-balance and coverage-
preservation for sensor nodes in WSNs. Both energy consumption for radio transmission and
residual energy over the network are discussed. Although references [2–5] are concerned with
energy conservation in the QoS control scheme, they do not focus on imbalances in power
consumption.
Although several aspects of QoS control in WSNs have been extensively investigated, however,
unbalanced power consumption is relatively unexplored. To the best of our knowledge, the
current research is the first attempt in solving the problem of unbalanced power consumptions
in QoS control.
2.2. A Gur Game-based QoS control scheme
The Gur Game algorithm in controlling QoS is presented in this section. In short, the principle
of the Gur Game algorithm is based on biased random walks of finite-state automata. The
automata describe a set of states with assigned meanings and a set of rules to determine
switches from one state to another. Figure 1 is a simple example of a finite-state automaton with
four states for the Gur Game algorithm. Each state has its own meaning. States -1 and -2 are
sleep modes, whereas states 1 and 2 are active modes.
4. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
20
Figure 1. The automaton of Gur Game with 4 states
The reward function is the key in the Gur Game scheme and responsible for measuring
performance of the system. Following equation is an example of the reward function.
R*(t) = 0.2 + 0.8exp (-0.002(Kt-n)2
)
where Kt is the number of active nodes and n is the desired QoS value. As shown in Figure 2,
when Kt is close to n, the R value approaches the top value (i.e. 1). Figure 2 presents an example
of the reward function with Kt =35.
Figure 2. Examples of reward function with Kt =35, 50, and 70
The sink counts the number of received data packets from the active sensors and determines the
number of active sensors. Then, the sink uses the information and reward functions to derive the
reward value R, and broadcasts R to all sensors. The sensors can then decide whether to be
active or idle in the next iteration based on the received R, its finite-state automaton, and the
current state. Finally, the Gur Game algorithm can make the number of active sensors to reach
the target after a certain number of iterations.
3. THE PROPOSED SCHEME: SHUFFLE
3.1. Problem description
The Gur Game-based scheme initially determines the state of each sensor node randomly. All
sensors are uniformly distributed in all states with half of the sensors being active. The number
5. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
21
of active sensors approaches desired QoS target after a certain number of runs. Finally, the
number of active sensors equals that in the desired QoS, thus making the whole system stable.
The probability of transition is equal to one when the desired QoS is achieved, thus keeping
sensors in a steady state and maintaining the stability of the system.
Although the goal of QoS control is achieved, a potential problem still exists. All sensors are in
a steady state, thus active sensors are always identical. These identical active sensors may expire
soon because of energy depletion. By contrast, sleep sensors are always asleep in steady states.
This imbalance in power consumption significantly reduces the lifetime of WSNs.
A periodical sleeping mechanism is adopted to solve this problem. However, periodical sleeping
is not applicable to the Gur Game-based scheme. Moreover, a centralized scheduling scheme
may control sensors quickly and effectively but may encounter scalability problems when the
number of sensors increases. Therefore, maintaining the characteristics of the Gur Game scheme
and avoiding unbalanced power consumption is our goal.
Figure 3 shows an example of the unbalanced power consumption in Gur Game-based scheme.
Figure 3 shows an example of unbalanced power consumption in the Gur Game-based scheme.
Figure 3a presents the initial states of all sensors, which are distributed uniformly in four states
(-2, -1, +1, and +2). Figure 3b displays the node states at the 200th epoch. Ninety-seven percent
of the sensors are in the edge states, that is, states -2 and +2. Figure 3c provides the node states
at the 500th epoch, when almost all sensors are in the edge states.
(a) 1st epoch (b) 200th epoch (c) 500th epoch
Figure 3. States of sensors at different epoch
3.2. Proposed scheme: Shuffle
We first thought of exchanging active nodes for sleep nodes to avoid unbalanced power
consumption. We considered a simple and easy method in the beginning. The base specifies
several nodes to exchange states. However, this method is not suitable when fairness and
scalability are concerned.
Moreover, exchanging sensor states may result in unstable systems. In particular, QoS
approaches the target number and promotes system stability after a certain period of QoS
vibrations. However, a long stable duration also implies unbalanced power consumption.
Exchanging sensor states may help balance power consumption; however, this method may
disrupt network stability.
6. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
22
We thought of using the Gur Game scheme once more to help the system return to stability after
exchanging sensor states. The self-optimization characteristic of the Gur Game scheme enables
QoS to return automatically to the desired value after a certain period of QoS vibrations.
Based on this idea, we propose an enhanced QoS control scheme called Shuffle, which
periodically applies the Gur Game scheme to maintain network stability after the exchange of
sensor states. Shuffle swaps the sensor states located in the two edge states (-2 and +2). In
particular, all sensors in the two edge states are swapped. Subsequently, Shuffle uses the Gur
Game scheme to help the system return to stability and achieve the desired QoS. Shuffle
attempts to modify the Gur Game-based scheme as less as possible and to maintain its
characteristic.
The time complexity of the Gur Game scheme is signified by O(one) because the number of
nodes does not affect the computation in the reward function. The time complexity of Shuffle is
denoted as O(s), where s is the number of times the sensors are shuffled.
The duration of the exchange of sensor states is an important issue in Shuffle. Stability will not
be achieved if sensor states are exchanged too often. By contrast, long sensor state exchanges
may lead to unbalanced power consumption. We observe QoS vibration to determine the period
of shuffle. The system becomes stable after about 450 epochs of QoS vibration (Figure 4). Thus,
we suggest a shuffle period larger than 500 epochs. The shuffle period in Figure 4 is 1000
epochs. The QoS clearly reverts to the desired value (35) after the second QoS vibration at the
1000th epoch. Moreover, Figure 4 shows that frequent shuffles result in system instability in a
short period of time.
Figure 4. Number of awake sensors of modified scheme with period of 700, 1000, and 1400.
7. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
23
4. RESULTS AND DISCUSSION
The performance of the proposed scheme, Shuffle, in terms of residual energy and lifetime is
compared with the Gur Game-based scheme.
4.1. Simulation environment
One hundred sensors are randomly deployed in a 100 m × 100 m area with a sink at the center
upon simulation. All sensors can hear the broadcast message from the sink. Time is divided into
discrete intervals, i.e. epochs. For each epoch, each active sensor can transmit one data to the
sink though multi-hops. Sensors do not exchange messages among themselves. Specifically,
sensors only transmit to and receive data from sinks.
Since this research does not focus on energy model, we assume a general energy model to
evaluate the lifetime of proposed scheme in the simulation. A sensor has an initial 10000 units
of battery power. For a power-on sensor, each round takes one unit of battery power. A power-
off sensor does not use one unit of battery power in this round. A sensor is considered dead if its
battery power is exhausted.
The Gur Game-based scheme is compared with the Shuffle scheme because the former is the
most related to our proposed scheme. The simulation model is implemented with Java, and the
two measured parameters are defined as follows:
Residual energy: The amount of energy for each sensor when the first sensor is dead.
Network lifetime: A period that begins from network initialization to the first instance of
sensor death.
4.2. Simulation results
Simulated residual energy against various sensor IDs is shown in Figure 5, in which the left part
is the Gur Game-based scheme, whereas the right part is the Shuffle. On average, the Gur
Game-based scheme has more residual energy than the Shuffle (Figure 5). Difference in residual
energy values implies that the Gur Game-based scheme cannot conserve energy efficiently. The
unbalanced power consumption of the Gur Game-based scheme leaves a large amount of
unused energy at the end of network lifetime. The values in the two sections of Figure 5 are all
measured at the end of the lifetime. The lifetime of the Gur Game-based scheme is 10142 (when
sensor #17 dies), whereas the lifetime of Shuffle is 16923 (when sensor #80 dies). By contrast,
residual energy values of the sensors in Shuffle are more balanced and lower compared with the
sensors in the Gur Game-based scheme.
(a) gurgame-based scheme (b) shuffle
Figure 5. Residual energy at the end of lifetime
8. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
24
Figure 6 plots network lifetime against total number of sensors. The desired number of power-
on sensors in this experiment depends on the total number of sensors. In particular, the ratio of
the desired number of power-on sensors to the total number of sensors is fixed. Therefore, if the
number of sensors is 100 and 200, then the desired number is 35 and 70, respectively. The
exchange period in the simulation is 100 epochs. Figure 6 shows that Shuffle can have longer
network lifetime compared with the Gur Game-based scheme. Network lifetime increases with
the increasing total number of sensors because more sensors are available to be powered on.
Figure 6 also shows that higher frequencies state exchanges (that is, shorter exchange periods)
lead to longer network lifetime because frequent exchanges result in more balanced power
consumptions.
Figure 6. Comparison of lifetime in different number of sensors
Simulation results of network lifetime against the exchange period are shown in Figure 7. The
network lifetime of the Gur Game-based scheme is independent from the exchange period; thus,
the Gur Game-based network lifetime is close to a horizontal line. The network lifetime of
Shuffle decreases with increasing exchange period because frequent exchanges result in more
balanced power consumptions. The network lifetime of Shuffle with a very long exchange
period is very close to that of the Gur Game-based scheme.
9. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
25
Figure 7. Comparison of lifetime in different period of shuffle
Simulation results shown in Figure 8 are those of network lifetime against desired QoS. Shuffle
with different exchange periods has a longer lifetime than the Gur Game-based scheme (Figure
8). The network lifetime of Shuffle reaches its peak when the desired QoS is close to 50. This
condition occurs because all sensors are easily devised into two disjoint groups that take turns in
working. Moreover, Shuffle with a shorter exchange period has a longer network lifetime.
Figure 8. Comparison of lifetime in different desired QoS
All the aforementioned simulations are conducted under a static network, whereas, the
following simulations are conducted under a dynamic network. No sensor failures and renewals
are observed in the 100 sensors in the static network. By contrast, sensor failures, renewals, and
transmission delays are experienced by the 100 sensors in the dynamic network. New sensors
are added into the system with exponentially distributed times between births with mean 100
seconds for sensor failures and renewals. All sensors remain alive for an exponentially
distributed time with mean 101 seconds. Packet delay for each sensor is uniformly distributed
from zero to five epochs [1].
10. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
26
Figure 9. QoS performance against epoch for shuffle in a realistic environment (1)only sensor
birth-and-death (2) only packet delay(3) both sensor birth and dead and packet delay
Figure 9 shows a trace of the number of active sensors versus the sample run time of 2000
epochs. Three figures are exhibited in Figure 9. The top figure only presents the results of
sensor birth and death. Sensor birth and death leads to longer convergence time. Active sensors
may still change after converging because of sensor birth and death. The middle figure shows
the results of additional packet delay. Similar to sensor birth-and-death, packet delays lead to a
long convergence time. However, active sensors lock once they converged. The bottom figure
shows the results of adding sensor birth and death as well as packet delay, which makes the
curve more unbalanced.
11. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
27
Figure 10. WSN lifetime against total number of sensors for a realistic environment considering
sensor birth-and-death
Figure 10 plots WSN lifetime against different sensor numbers in the network with sensor birth
and death. Shuffle is shown to have a longer lifetime than the Gur Game-based scheme (Figure
10). We can classify the six curves into three groups: (1) Gur Game-based scheme, (2) proposed
scheme without sensor birth and death, and (3) proposed scheme with sensor birth and death.
In the first group, the lifetime of the Gur Game-based scheme with sensor birth and death is
very close to that of the Gur Game-based scheme without sensor birth and death. The Gur
Game-based scheme is not significantly affected by sensor birth and death, which only change
active sensors slightly. The lifetime of the Gur Game-based scheme with sensor birth and death
is slightly longer than that of the Gur Game-based scheme without sensor birth and death when
the total number of sensors is large. The difference in lifetime is caused by the dynamics of
sensor birth and death, which leads to disruptions and node state exchanges between active and
sleep sensors.
The second group is Shuffle with different exchange periods (1000 and 1400) and without
sensor birth and death. Lifetime reaches its peak at sensor number 70, which is double that of
the desired QoS (35). This condition happens because of the easy behavior of the sensor in
Shuffle when the desired QoS is half that of the sensor number. Two groups, each with half the
number of sensors, take turns in waking up. This process wastes less energy during convergence
and results in a longer lifetime. In addition, lifetime is longer with large sensor numbers (85)
than with small sensor numbers (55). A longer lifetime is the result of more sensors taking turns
in waking up. Furthermore, a smaller shuffle period causes sensors to change states more
frequently.
The third group is Shuffle with different exchange periods (1000 and 1400) and with sensor
birth and death. No lifetime peak is observed at sensor number 70 in this group because sensor
birth and death break the balance of the two groups and cause active sensors to be inactive.
Instead, the two curves increase with increasing sensor numbers. Similar to the second group,
the third group exhibits larger sensor numbers or lower exchange periods with longer lifetime.
12. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
28
Figure 11. WSN lifetime against total number of sensors for a realistic environment considering
sensor transmission delay
Figure 11 plots the WSN lifetime against different sensor numbers in the network with packet
delay. Shuffle is observed to have a longer lifetime than Gur Game-based scheme (Figure 11).
We can classify the six curves in Figure 11 into two groups: (1) Gur Game-based scheme and (2)
Shuffle.
The lifetime of the Gur Game-based scheme is not affected by packet delay. The lifetime of
Shuffle is not affected by packet delay either. Similar to the aforementioned results, Shuffle has
the highest lifetime when the desired QoS is half of the sensor number. Furthermore, lifetime is
longer when the sensor number is large because more sensors can take turns waking up. In
addition, shorter exchange periods correspond to longer Shuffle lifetime because power
consumption is more balanced.
Figure 12. WSN lifetime against total number of sensors for a realistic environment considering
both sensor birth-and-death and transmission delay
13. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
29
Simulation results of lifetime against total number of sensors in the network with packet delay,
sensor failures, and sensor renewals are shown in Figure 12. Figure 12 shows results similar to
Figure 10 except that larger fluctuations are caused by packet delay.
In conclusion, the results for the dynamic environment show that Shuffle lifetime is always
larger than that of the Gur Game-based scheme regardless of transmission delay, sensor failure,
and sensor renewal.
Based on all simulations, we conclude that Shuffle can effectively prolong lifetime by
periodical shuffling regardless of the total number of sensors, desired QoS, and the period of
Shuffle.
5. CONCLUSIONS
This paper focuses on the design of a QoS control scheme for WSNs. First, we recognize that
sensors move to the edge state in the Gur Game automaton, resulting in a limited lifetime for a
prior Gur Game-based scheme. Furthermore, this paper presents an enhanced QoS control
scheme, called Shuffle, which balances power consumption and maintains the strength of the
Gur Game-based scheme similar to self-optimization. The evaluation of Shuffle in various
environments shows that Shuffle significantly improves network lifetime. Further simulation
results show that the gains of Shuffle are dependent on the period of shuffles. A short shuffle
period achieves a high degree of balance on power consumption, whereas frequent shuffles
cause system instability in a short period.
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Authors
Hao-Li Wang received his BE and MS from National Cheng Kung University,
Taiwan. He received his PhD from Iowa State University, United States. At
present, he is a faculty in National Chiayi University, Taiwan. His research
interests are wireless sensor networks and wireless LANs.
Rong-Guei Tsai received his MS from National Chiayi University, Taiwan.
He is presently pursuing his PhD in National Cheng Kung University,
Taiwan. His research interests are Quality of Service control and coverage in
wireless sensor networks.