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.
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...ijsrd.com
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
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.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
SENSOR SELECTION SCHEME IN WIRELESS SENSOR NETWORKS: A NEW ROUTING APPROACHcsandit
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless
sensor networks, based on data mining formulation. The proposed adapting routing scheme for
sensors for achieving energy efficiency. The experimental validation of the proposed approach
using publicly available Intel Berkeley lab Wireless Sensor Network dataset shows that it is
possible to achieve energy efficient environment monitoring for wireless sensor networks, with a
trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
False Node Recovery Algorithm for a Wireless Sensor NetworkRadita Apriana
This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor
network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm
combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and
more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes
up to 8.7 times, reduces 98.8%, and reduces the rate of energy consumption by approximately 31.1%.
Energy efficient k target coverage in wireless sensor net-2IAEME Publication
This document proposes an energy efficient method for k-target coverage in wireless sensor networks. The method aims to cover targets with the minimum number of sensor nodes to conserve energy. Sensor nodes are deployed randomly and may fail over time. The method selects a supervisor node for each cluster that collects data from sensors and relays it to other supervisor nodes until it reaches the sink. This allows targets to be monitored by k sensors while putting excess sensors into sleep mode to save energy. The document outlines the problem formulation and proposes using an energy consumption model to select active sensors and relay nodes to efficiently monitor targets and extend network lifetime.
Performance Comparison of Sensor Deployment Techniques Used in WSNIRJET Journal
This document compares different sensor deployment techniques for wireless sensor networks (WSNs) and their impact on network lifetime. It implements an artificial bee colony (ABC) algorithm for sensor deployment and compares the results to random deployment and a heuristic technique. Simulation results show that ABC deployment outperforms the other methods, extending network lifetime the most. The network lifetime is maximized when sensors are placed such that targets are covered by many sensors. ABC placement achieves this by iteratively moving sensors to locations with many nearby targets based on a fitness function. The network lifetime is significantly impacted by factors like the number of sensors, sensing range, targets, and area size. ABC deployment performs best across different parameter values tested.
Wireless Sensor Network using Particle Swarm Optimizationidescitation
Wireless sensor network (WSN) is becoming
progressively important and challenging research area. A
Wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical and
environmental conditions and to co-operatively pass their data
through the network to a main location. Wireless sensor
consists of small low cost sensor nodes, having a limited
transmission range and their processing, storage capabilities
and energy resources are limited. The main task of such a
network is to gather information from a node and transmit it
to a base station for further processing.WSN has different
issues such as optimal sensor deployment, node localization,
base station placement, location of target nodes, energy aware
clustering and data aggregation. Recently researchers around
the world are applying bio-inspired optimization algorithm
known as particle swarm optimization (PSO) for increasing
efficiency in the WSN issues. This paper describes the use of
PSO algorithm for optimal sensor deployment in WSN.
Improvising Network life time of Wireless sensor networks using mobile data a...Editor IJCATR
This document discusses improving the lifetime of wireless sensor networks using a mobile data aggregator. It proposes a polling-based scheme where sensor nodes transmit data to nearby polling points (PPs), which then transmit aggregated data to a mobile collector. The mobile collector plans an optimal tour to visit each PP to collect data in sequence. Simulation results show this approach reduces energy consumption and travel time compared to existing approaches, thus improving network lifetime.
This document describes an RSSI (received signal strength indicator) based localization algorithm for wireless sensor networks. It discusses using RSSI values measured from reference nodes to estimate distances and perform trilateration to locate a target sensor node. The algorithm design includes RSSI to distance conversion using a path loss model, trilateration implementation using circle intersections, and simplifying computations for resource-limited sensor node processors through techniques like Taylor series approximations of exponential functions. Pseudocode is provided for RSSI to distance conversion and trilateration calculations.
An implementation of recovery algorithm for fault nodes in a wireless sensor ...eSAT 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
Energy Efficient Target Coverage in wireless Sensor NetworkIJARIIE JOURNAL
This document summarizes research on using artificial intelligence techniques to optimize the deployment of wireless sensor nodes to maximize network lifetime. It first provides background on wireless sensor networks and discusses different types of coverage problems. It then proposes using an artificial bee colony algorithm to reposition sensor nodes such that the minimum network lifetime is maximized. Simulation results show that increasing the number of sensor nodes increases network lifetime, while increasing the number of targets decreases lifetime. The artificial bee colony algorithm effectively optimizes node placement to extend network operating time.
A Fault Tolerant Approach to Enhances Wsn Lifetime in Star TopologyIRJET Journal
This document presents a fault tolerant approach to increase the lifetime of wireless sensor networks using a star topology. It proposes using a gradient diffusion algorithm and fault node recovery algorithm to minimize packet loss and broadcast delay. The fault node recovery algorithm identifies non-functioning sensor nodes using a genetic algorithm and replaces them to extend the network lifetime. Simulation results show the approach increases active nodes by 8-10 times, reduces data loss by 98%, and decreases energy consumption by 27-32% compared to other algorithms. This is achieved by reusing sensor nodes and routing paths to prolong the usability of the wireless sensor network.
This document summarizes research on algorithms for proximity estimation in sensor networks. It discusses using sensor networks to detect events observed by nodes within a certain distance of each other. It proposes an algorithm that utilizes a distributed routing index maintained by nodes in the network to process multiple proximity queries involving different event types. The document reviews several related works on localization algorithms, data-centric sensor networks, geographic routing protocols, and node localization techniques. It evaluates different wireless sensor network simulators and deployment schemes.
Scenarios of Lifetime Extension Algorithms for Wireless Ad Hoc NetworksIJCNCJournal
An Algorithm to extend sensor lifetime and energy is implemented for different scenarios of ad hoc and wireless sensor networks. The goal is to prolong the lifetimes of sensors, covering a number of targeted zones by creating subsets of sensors, in which each subset covers entirely the targeted zones. Probabilistic analysis is assumed in which each sensor covers one or more targets, according to their coverage failure probabilities. Case studies of different sensor subsets arrangements are considered such as load switching, variable target load demands as well as a perturbation in sensor planner locations.
Efficient Cluster Based Data Collection Using Mobile Data Collector for Wirel...ijceronline
Establishing an efficient data gathering scheme in wireless sensor networks is a challenging task. Lot of researches has been carried out to establish energy efficient data gathering scheme to avoid heavy traffic received by the nodes near the sink. Data gathering scheme is a significant factor in determining the network lifetime. In this paper we propose an efficient data gathering scheme by introducing clustering and mobility into the wireless sensor network. We consider data collection in wireless sensor networks by utilizing mobile data collector and cluster heads. Cluster heads are chosen and clusters are formed to collect data from the sensor nodes. The proposed scheme finds the shortest tour for the mobile data collector to collect data from the cluster heads. The shortest tour saves time and energy in data gathering.
Sensors Scheduling in Wireless Sensor Networks: An Assessmentijtsrd
The wireless sensor networks WSN is a combination of a large number of low power, short lived, unreliable sensors. The main challenge of wireless sensor network is to obtain long system lifetime. Many node scheduling algorithms are used to solve this problem. This method can be divided into the following two major categories first is round based node scheduling and second is group based node scheduling. In this paper many node scheduling algorithm like one phase decomposition model, Tree Based distributed wake up scheduling and Clique based node scheduling Algorithm are analyzed. Manju Ghorse | Dr. Avinash Sharma "Sensors Scheduling in Wireless Sensor Networks: An Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29560.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29560/sensors-scheduling-in-wireless-sensor-networks-an-assessment/manju-ghorse
AN IMPROVED ALGORITHM TO FIRE DETECTION IN FOREST BY USING WIRELESS SENSOR NE...IAEME Publication
Wireless sensor network systems diffuse an intensive, array of small, low-cost
sensors that monitor the environment. The system can be extending anywhere, even in
the place which is inaccessible .This technology can provide observe for forest fires in
the real time. Fire Ignition can be determined quickly, depending on the wake/sleep
table of the system of rules nodes. This research focus on fire detection ability of a
wireless network system. Sub divided system in randomly-spread nodes change the
network from being randomly spread to being arranged, and minimize the period of
working and less energy consumption of each hop. Separate the network into many
sub-networks lead to increases battery lifetime network by 3.7% and increased
performance of power by 69% compared to traditional fire detection networks. The
proposed network requires all nodes to be equipped with temperature sensor. The
analysis of data from sensors can show the fire, also its, behavior, intensity and
direction of deployed, which can assist the firefighting efforts. Traditional fire
detection networks show the fire only so the proposed algorithms show the best result
Anchor Positioning using Sensor Transmission Range Based Clustering for Mobil...ijdmtaiir
This document summarizes and compares two algorithms for selecting anchor points for mobile data collection in wireless sensor networks: Square Grid Clustering (SGC) and Sensor Transmission based Clustering (STC). SGC divides the deployment area into a grid and uses the centroid of each grid cell as an anchor point. STC clusters sensors based on their transmission range and uses the centroid of each cluster as an anchor point. The document finds that STC typically results in fewer anchor points than SGC and lower round-trip times for the mobile data collector. An analysis of the algorithms using sample sensor networks shows that STC outperforms SGC in anchor point selection and data collection efficiency.
Data gathering in wireless sensor networks using intermediate nodesIJCNCJournal
Energy consumption is an essential concern to Wireless Sensor Networks (WSNs).The major cause of the energy consumption in WSNs is due to the data aggregation. A data aggregation is a process of collecting data from sensor nodes and transmitting these data to the sink node or base station. An effective way to perform such a task is accomplished by using clustering. In clustering, nodes are grouped into clusters where a number of nodes, called cluster heads, are responsible for gathering data from other nodes, aggregate them and transmit them to the Base Station (BS).
In this paper we produce a new algorithm which focused on reducing the transmission bath between sensor nodes and cluster heads. A proper utilization and reserving of the available power resources is achieved with this technique compared to the well-known LEACH_C algorithm.
A PROPOSAL FOR IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKIJCNCJournal
The document proposes a new routing protocol for wireless sensor networks that aims to improve network lifetime. The protocol is based on LEACH, an existing energy-efficient clustering protocol, but improves on it by electing cluster heads based on both remaining node energy and distance to the base station. Simulation results show the proposed protocol extends network lifetime by up to 75% compared to LEACH alone by distributing energy usage more evenly across nodes.
In ad hoc networks, routing plays a pertinent role. Deploying the appropriate routing protocol is very important in order to achieve best routing performance and reliability. Equally important is the mobility model that is used in the routing protocol. Various mobility models are available and each can have different impact on the performance of the routing protocol. In this paper, we focus on this issue by examining how the routing protocol, Optimized Link State Routing protocol, behaves as the mobility model is varied. For this, three random mobility models, viz., random waypoint, random walk and random direction are considered. The performance metrics used for assessment of Optimized Link State Routing protocol are throughput, end-to-end delay and packet delivery ratio.
Concept integration using edit distance and n gram match ijdms
Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all
this information not only available to people but also to the machines. Ontology and token are widely being
used to add the semantics in data processing or information processing. A concept formally refers to the
meaning of the specification which is encoded in a logic-based language, explicit means concepts,
properties that specification is machine readable and also a conceptualization model how people think
about things of a particular subject area. In modern scenario more ontologies has been developed on
various different topics, results in an increased heterogeneity of entities among the ontologies. The concept
integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data
processing. There are various techniques to integrate the concepts from different input sources, based on
the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept
(Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept
frequency is used to dominate the integration process. The proposed techniques performance is compared
with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure
& integration efficiency over the different size of concepts. The analysis indicates that edit
distance value based interaction outperformed n-gram integration and semantic similarity techniques.
A bi scheduler algorithm for frame aggregation in ieee 802.11 nijwmn
The document proposes a Bi-Scheduler algorithm to improve throughput in IEEE 802.11n wireless networks using frame aggregation. The algorithm separates frames into different queues based on access category. Delay sensitive frames like voice are directly queued without aggregation. Other frames are aggregated using A-MSDU or A-MPDU based on their characteristics to efficiently transmit frames while meeting their quality of service requirements. The algorithm aims to optimize frame aggregation to improve throughput while maintaining fairness for different traffic types.
Opportunistic and playback sensitive scheduling for video streamingijwmn
Given the strict Quality of Service (QoS) requirements of video streaming, this paper proposes a novel
solution for simultaneous streaming of multiple video sessions over a mobile cellular system. The proposed
solution combines a buffer management strategy with a packet scheduling algorithm. The buffer
management strategy selectively discards packets of a user from base station buffer whereas the packet
scheduling algorithm schedules packets of a user according to its instantaneous channel quality, average
throughput and playback buffer information. Simulation results demonstrate that the proposed solution is
effective in providing a continuous video playback with good perceptual quality for more users. If at least a
good perceptual quality is to be satisfied for all users (QoS constraint of video streaming), then the
proposed solution improves the system capacity by 40% over a conventional packet scheduling algorithm.
Performance analysis and implementation for nonbinary quasi cyclic ldpc decod...ijwmn
Non-binary low-density parity check (NB-LDPC) codes are an extension of binary LDPC codes with
significantly better performance. Although various kinds of low-complexity iterative decoding algorithms
have been proposed, there is a big challenge for VLSI implementation of NBLDPC decoders due to its high
complexity and long latency. In this brief, highly efficient check node processing scheme, which the
processing delay greatly reduced, including Min-Max decoding algorithm and check node unit are
proposed. Compare with previous works, less than 52% could be reduced for the latency of check node
unit. In addition, the efficiency of the presented techniques is design to demonstrate for the (620, 310) NBQC-
LDPC decoder.
USER CENTRIC NETWORK SELECTION IN WIRELESS HETNETSijwmn
This document discusses user-centric network selection in heterogeneous wireless networks. It proposes using game theory based algorithms to allow users to select the optimal network based on their individual needs and preferences. Specifically, it analyzes Bush-Mosteller and Boltzmann-Gibbs reinforcement learning algorithms for network selection among integrated UMTS, WLAN and WiMAX networks. Simulation results show the impact on metrics like throughput, delay and load for each network under different user conditions and applications.
Performance evaluation of different spectrum sensing techniques for realistic...ijwmn
In this paper, the performance assessment of five different detection techniques from spectrum sensing
perspective in cognitive radio networks is proposed and implemented using the realistic implementation
oriented model (R-model) with signal processing operations. The performance assessment of the different
sensing techniques in the existence of unknown or imprecisely known impulsive noise levels is done by
considering the signal detection in cognitive radio networks under a non-parametric multisensory detection
scenario. The examination focuses on performance comparison of basic spectrum sensing mechanisms as,
energy detection (ED) and cyclostationary feature detection (CSFD) along with the eigenvalue-based
detection methods namely, Maximum-minimum eigenvalue detection (MMED), Roy’s largest Root Test
(RLRT) which requires knowledge of the noise variance and Generalized Likelihood Ratio Test (GLRT)
which can be implemented as a test of the largest eigenvalues vs. Maximum-likelihood estimates a noise
variance. From simulation results it is observed that the detection performance of the GLRT method is
better than the other techniques in realistic implementation oriented model.
The aims of PDHPE focus on physical activities, healthy lifestyles and positive relationships. During physical activity time, students will gain benefits like reduced stress, increased confidence and creativity. It is important to make healthy decisions and say no to junk food to maintain positive lifestyles. PDHPE can help students make correct choices. Relationships are vital, so students must learn how to improve their personalities, avoid conflicts, and develop communication skills. PDHPE is important for primary education as it provides benefits in both physical and psychosocial ways.
Design and analysis of high gain diode predistortionijwmn
This paper presents the design and analysis of a high gain, broadband Schottky and PIN diode based RF
pre-distortion linearizer for TWTA. The circuit is using ABCD matrix approach. The simulation is
performed using Agilent ADS software. We have proposed a new linearizer circuit which can achieve a
high gain compared to existing linearizer designs.
An ann based call handoff management scheme for mobile cellular networkijwmn
Handoff decisions are usually signal strength based because of simplicity and effectiveness. Apart from the
conventional techniques, such as threshold and hysteresis based schemes, recently many artificial
intelligent techniques such as Fuzzy Logic, Artificial Neural Network (ANN) etc. are also used for taking
handoff decision. In this paper, an Artificial Neural Network based handoff algorithm is proposed and it’s
performance is studied. We have used ANNhere for taking fast and accurate handoff decision. In our
proposed handoff algorithm, Backpropagation Neural Network model is used.The advantages of
Backpropagation method are its simplicity and reasonable speed. The algorithm is designed, tested and
found to give optimum results.
A voronoi diagram based approach for analyzing area coverage of various node ...ijasuc
With recent advances in electronics and wireless communication technologies, the Wireless Sensor
Networks (WSNs) are being used in wide range of applications of military, ecological, health related areas.
Due to their resource constraints such as, sensor node’s size, memory, and processing capabilities, the
scale of deployment of WSNs requires careful decisions with respect to various performance measures. In
the last few years, there has been a tremendous interest on development of large-scale wireless sensor
networks as a basic issue to be addressed as it can influence the performance metrics of WSNs such as,
coverage, connectivity, resilience and scalability requirements. Many deployment schemes have been
proposed for wireless sensor networks. In this paper, we survey six deployment models random,
rectangular-grid, square-grid, triangular-grid, hexagonal-grid, and hybrid deployments schemes and
analyze their implications on network area coverage in WSNs. More generally, under some deployment
assumptions on an irregular geographical map for a defense monitoring, we show the analytical and
simulation-based results of a WSN made up of mica2 motes using the deployment knowledge to motivate
the use of these emerging paradigms in order to achieve higher network area coverage. To apply
deployment schemes on irregular geographical target area, we propose to include the concept of voronoi
diagram based approach in WSNs, to provide a way of dividing an irregular geographical area into a
number of regular regions. We have been configured the sensor node parameters such as sensing,
temperature, energy capabilities using mannasim based on NS-2.34.
DYNAMIC OPTIMIZATION OF OVERLAP-AND-ADD LENGTH OVER MIMO MBOFDM SYSTEM BASED ...ijwmn
An important role performed by Zero Padding (ZP) in multi-band OFDM (MB-OFDM) System. This role
show for low-complexity in résistance against multipath interference by reducing inter-carrier interference
(ICI) and eliminating the inter-symbol interference (ISI) Also, zero-padded suffix can be used to eliminate
ripples in the power spectral density in order to conform to FCC requirements. At the receiver of MB-OFDM system needs to use of a technique called as overlap-and-add (OLA). Which maintain the circular convolution property and take the multipath energy of the channel.In this paper, we proposed a method of performing overlap-and-add length for zero padded suffixes. Then,we studied the effect of this method, dynamic optimization of overlap-and-add (OLA) equalization, on the performance of MIMO MBOFDM system on Bit Error Rate (BER) with AWGN channel and SalehValenzuela (S-V) Multipath channel Model.In the dynamic optimization OLA, the Length of ZP depends on length of channel impulse response (CIR).
These measures, based on SNR, insert the ZP according to the measurement.Dynamic optimization of length of ZP improves the Performance of MIMO MBOFDM system. In fact wedeveloped a technique to select the length of ZP as function of SNR and CIR estimate. In our simulation
this technique improve to 0.6 dB at BER=10-2 with a multipath channels CM4
AN E XAMINATION OF T HE E FFECTIVENESS OF T EACHING D ATA M ODELLING C ONCEPTSijdms
The effective teaching of data modelling concepts i
s very important; it constitutes the fundament of d
ata-
base planning methods and the handling of databases
with the help of database management languages,
typically SQL. We examined three courses. The stude
nts of two courses prepared for the exam by solving
tests, while the students of the third course prepa
red by solving tasks from a printed exercise book.
The
number of task for the second course was 2.5 times
more than the number of task for the first course.
The
main purpose of our examination was to determine th
e effectiveness of the teaching of data modelling c
on-
cepts, and to decide if there is a significant diff
erence between the results of the three courses. Ac
cording to
our examination, with increasing the number of test
tasks and with the use of exercise book, the resul
ts
became significantly better
Adaptive congestion control protocol (accp) for wireless sensor networksijwmn
In Wireless Sensor Networks (WSN) when an event is detected there is an increase in data traffic that might
lead to packets being transmitted through the network close to the packet handling capacity of the WSN.
The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction in
throughput. In this paper we developed an adaptive congestion control algorithm that monitors network
utilization and adjust traffic levels and/or increases network resources to improve throughput and conserve
energy. The traffic congestion control protocol DelStatic is developed by introducing backpressure
mechanism into NOAH. We analyzed various routing protocols and established that DSR has a higher
resource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm to
trigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulation
results, ACCP protocol does not only improve throughput but also conserves energy which is critical to
sensor application survivability on the field. Our Adaptive Congestion control achieved reliability, high
throughput and energy efficiency.
T HE I MPACT OF TCP C ONGESTION W INDOW S IZE ON THE P ERFORMANCE E VA...ijwmn
A mobile ad hoc network (MANET) is a temporary coll
ection of mobile nodes randomly moved within a
limited terrain area. The nodes are connected to fo
rm a wireless network without use any communication
infrastructure. Because of the limiting resources o
f MANET nodes, multiple hops
scheme is proposed for
data exchange
across the network. Varieties of mobile ad hoc rout
ing protocols have been developed to
support the multi-hop scheme of ad hoc networks. A
popular Transmission Control Protocol (TCP)
provides a reliable connection in a computer networ
k environment; it sets its congestion window size i
n
response to the behavior of the network to achieve
the best performance. This work aims to investigate
and
compare the MANET protocol
performance, such as DSDV, AODV and DSR in terms of
network
throughput, average routing load, the packet delive
ry ratio (PDR), and average end-to-end delay by
varying the maximum congestion window size. Our si
mulation has been implemented using a well-known
NS-2.35 network simulator. The simulated results sh
ow that the demonstrates of the concepts of MANET
routing protocols with respect to TCP congestion wi
ndow size in MANET environment
RESOURCE ALLOCATION ALGORITHMS FOR QOS OPTIMIZATION IN MOBILE WIMAX NETWORKSijwmn
This document summarizes research on resource allocation algorithms for quality of service (QoS) optimization in mobile WiMAX networks. It discusses the Swapping Min-Max (SWIM) algorithm and Cooperative Multicast Scheduling (CMS) technique. SWIM performs scheduling for real-time polling service to meet QoS criteria like optimal throughput, latency guarantees, minimal delay jitter and number of bursts. CMS enhances throughput for multicast video by dividing transmission bursts into two phases where selected stations retransmit to nearby members for cooperation. Simulation results show SWIM has less bursts, zero jitter and optimal throughput, while CMS further improves throughput for each multicast group member.
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
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
ADAPTIVE SENSOR SENSING RANGE TO MAXIMISE LIFETIME OF WIRELESS SENSOR NETWORK IJCNCJournal
Wireless Sensor Network (WSN) is commonly used to collect information from a remote area and one of the most important challenges associated with WSN is to monitor all targets in a given area while maximizing network lifetime. In wireless communication, energy consumption is proportional to the breadth of sensing range and path loss exponent. Hence, the energy consumption of communication can be minimized by varying the sensing range and decreasing the number of messages being sent. Sensing energy can be optimized by reducing the repeated coverage target. In this paper, an Adaptive Sensor Sensing Range (ASSR) technique is proposed to maximize the WSN Lifetime. This work considers a sensor network with an adaptive sensing range that are randomly deployed in the monitoring area. The sensor is adaptive in nature and can be modified in order to save power while achieving maximum time of monitoring to increase the lifetime of WSN network. The objective of ASSR is to find the best sensing range for each sensor to cover all targets in the network, which yields maximize the time of monitoring of all targets and eliminating double sensing for the same target. Experiments were conducted using an NS3 simulator to verify our proposed technique. Results show that ASSR is capable to improve the network lifetime by 20% as compared to other recent techniques in the case of a small network while achieving an 8% improvement for the case of a large networks.
Adaptive Sensor Sensing Range to Maximise Lifetime of Wireless Sensor NetworkIJCNCJournal
The document describes an Adaptive Sensor Sensing Range (ASSR) technique proposed to maximize the lifetime of a Wireless Sensor Network (WSN). The ASSR technique allows sensors to vary their sensing range to minimize energy consumption while ensuring all targets are monitored. It considers a WSN with sensors randomly deployed that can modify their sensing range to save power. The objective of ASSR is to find the optimal sensing range for each sensor to cover all targets, maximizing monitoring time and eliminating duplicate monitoring. Simulation results show ASSR can improve network lifetime by 20% for small networks and 8% for large networks compared to other recent variable sensing range techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR
Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault
tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased
longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only
geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault
tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This
protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors
from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors,
distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the
protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous
protocols such as LEACH in the simulation results.
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.
Bottleneck Detection Algorithm to Enhance Lifetime of WSNjosephjonse
In recent years, a wireless sensor network is gaining much more importance due to its immense contribution in numerous applications. Deployment of sensor nodes that would reduce computation, minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization, reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random additional sensor node deployment and Targeted additional sensor node deployment are proposed to enhance network lifetime. Deployment strategies are compared with respect to network parameters such as throughput, packet delivery fraction and network lifetime.
BOTTLENECK DETECTION ALGORITHM TO ENHANCE LIFETIME OF WSNijngnjournal
The document proposes a bottleneck detection algorithm to identify weak areas in a wireless sensor network and enhance the network lifetime. It detects bottleneck and minimal bottleneck zones where sensor nodes deplete their energy quickly. The algorithm identifies these weak zones and two additional sensor node deployment strategies are proposed - random deployment that places extra nodes everywhere and targeted deployment that places nodes in identified bottleneck areas. Simulations show the deployment strategies increase network lifetime parameters like throughput and packet delivery compared to the existing system. The bottleneck detection algorithm and additional node placements help balance energy usage and form stable links to prolong the wireless sensor network lifetime.
Bottleneck Detection Algorithm to Enhance Lifetime of WSNjosephjonse
In recent years, a wireless sensor network is gaining much more importance due to its immense contribution in numerous applications. Deployment of sensor nodes that would reduce computation, minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization, reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random additional sensor node deployment and Targeted additional sensor node deployment are proposed to enhance network lifetime. Deployment strategies are compared with respect to network parameters such as throughput, packet delivery fraction and network lifetime.
Design and Implementation a New Energy Efficient Clustering Algorithm Using t...ijmnct
Wireless Sensor Networks are consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, one of the most important issues that need to be enhanced in order to improve the life span of the network is energy efficiency. to overcome this demerit many research have been done. The clustering is the one of the representative approaches. In this paper, we introduce a dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design and system optimization by genetic algorithm is presented approach to select the best cluster head in sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The results of evaluations is representative of a reduction the MSE metric in proposed method in comparison with LEACH method for select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless sensor network and can cause the reduce energy consumption and increase network lifetime.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Design and Implementation a New Energy Efficient Clustering Algorithm Using t...ijmnct
Wireless Sensor Networks are consist of small battery powered devices with limited energy resources.Once
deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy
source is not feasible. Hence, one of the most important issues that need to be enhanced in order to
improve the life span of the network is energy efficiency. to overcome this demerit many research have
been done. The clustering is the one of the representative approaches. In this paper, we introduce a
dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design
and system optimization by genetic algorithm is presented approach to select the best cluster head in
sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented
in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in
comparison with LEACH method is calculated in select the cluster head. The results of evaluations is
representative of a reduction the MSE metric in proposed method in comparison with LEACH method for
select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless
sensor network and can cause the reduce energy consumption and increase network lifetime.
Performance of energy balanced territorial predator scent marking algorithm b...eSAT 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
ENERGY EFFICIENT DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
An Energy Efficient Mobile Sink Based Mechanism for WSNs.pdfMohammad Siraj
Network lifetime and energy efficiency are crucial performance metrics used to evaluate
wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be
employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering
is to decrease the energy consumption of the network. In fact, the clustering technique will be
considered effective if the energy consumed by sensor nodes decreases after applying clustering,
however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this
paper, the energy consumption of nodes, before clustering, is considered to determine the optimal
cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of
cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent
problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from
the asynchronous energy depletion of nodes located in different layers of the network.
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
Spatial correlation based clustering algorithm for random and uniform topolog...eSAT 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
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
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Increasing the Network life Time by Simulated Annealing Algorithm in WSN with Point
1. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
DOI : 10.5121/ijasuc.2013.4203 31
Increasing the Network life Time by Simulated
Annealing Algorithm in WSN with Point
Mostafa Azami1
, Manij Ranjbar2
, Ali Shokouhi rostami3
, Amir Jahani Amiri4
1, 2
Computer Department, University Of Kurdistan, Sanandaj, Iran
m.azami@b-iust.ac.ir
3,4
Electrical Department, University Of tamishan, Behshahr, Iran
a.shokouhi@b-iust.ac.ir
Abstract. 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.
Keywords: Asymmetrical Sensor Networks, Energy, Lifetime.
1 Introduction
HERE have been remarkable uses of cheap sensor networks with lower energy. The main
challenge in designing wired and wireless systems emerges from the two significant
sources: communication bandwidth, the energy of a system. Overcoming these restrictions
requires designing new communication techniques to augment the needed bandwidth for each
user and innovating energy-efficient protocols. Designs would be diverse in different
applications due to the expectations of a system. For instance, the energy used in performance
rounds must be optimized and the lifetime of a network ought to be maximized. Since battery
replacement is not appropriate in many applications, low power consumption is a crucial
requirement in these networks. The lifetime of a sensor can be efficiently increased by
optimizing power consumption [3]. Power-efficient designs have found widespread uses in
these networks. They are generally in the spotlight from the perspective of hardware, algorithm
and protocol design. An approach to reduce energy consumption is to decrease the number of
sensors in the sensing area in a way that target detection is guaranteed in the given area. If the
network is scalable, the algorithm can optimally be employed to decrease the number of nodes
[4].
Many concepts have been released for the word “Point” due to the variety in the number of
sensors, their types and applications. Hence, point can be defined generally as a parameter of
service quality in a wireless network. For example, it may be asked that what the quorum is for
a wireless network to supervise a specific region or what the probability of detecting an
incident is in a specific time interval. Besides, relations given for coverage find the weak
T
2. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
32
points of a sense field and introduce amending designs to improve service quality of the
network [2]. Some of the targets, with definite positions that must be controlled, are considered
in the scenario of point coverage. Many of sensors are distributed randomly and they send
achieved information to the central processor. Each target must be controlled by at least one
sensor. One way of reducing energy consumption is decreasing the number of active sensors in
a covered area by supposing that each sensor can control all existing targets in its own area. A
method to increase the lifetime of a sensor network is dividing sensors into some separated
sets. Each set must cover all targets. These separated sets are activated consecutively in a way
that only one set is active at a moment. In addition to lifetime increase and decrease in the
number of active sensors, the following provisions must be satisfied:
_the number of sensors must be in a way that target detection is guaranteed in the given area.
_each sensor must be able to connect to the center.
A sensor network can be deployed in three ways: random[3,16],controlled placement
[2,6,7,10,14,18] and uniform distribution[19].
Random method is a method of network generation places a seed node at the origin . Sensors
are usually randomly distributed over a wide sensing area .When the environment is unknown,
random placement must be used and sensors may be randomly dropped from aircraft. sensors
are initially position on distributed over the network domain that sensor locations are
independent[3,16].
In [16] a method for achieving an appropriate coverage is suggested. It has a random
distribution and sensor density is the variable parameter in the network. The optimum density
of a sensor is achieved by defining an upper bound for the probability of point coverage and
finding a relation between sensor density and the average of the area that is not covered. In [3]
a method for increasing network lifetime is proposed based on maximizing the number of
sensor groups with random distribution and on the basis of the optimization of energy
consumption. In this method, each node is allowed to be the member of more than one group
which results in network lifetime increase.
Second type of sensor distribution is the controlled placement approach; sensors can be
carefully deployed on a sensor field, if the terrain properties are predetermined. Consequently,
the controlled placement can be planned to meet the requirements of various levels of services.
For example, surveillance, target positioning and target tracking. If the planning process is
subject to some resource constraints (such as, the deployment cost) and to achieve some
specific goals, the sensor deployment will be considered as an optimization problem
[2,6,7,10,14,18]. For example Carbunar and his colleagues have devised a method for
decreasing energy consumption by determining the position of sensor nodes and decreasing
their overlap area [2]. A method to decrease power consumption is released in [14] with
controlled placement. At first, sensors are divided into two groups in this method. One of them
is just active at a moment. Their activeness inactiveness approach is iterated periodically. The
algorithm for decreasing the number of nodes can be employed optimally if the network is
scalable. The energy is not distributed homogeneously in sensors for the networks with static
distributions [6, 7]. In [10] an approach has been presented to access a scalable coverage which
is utilized in the case of the existence of overhead and high computational complexity for
3. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
33
boosting energy efficiency. A wireless network must be stable in the case of abnormality. In
[18] a method is released for a three-dimensional coverage by considering a node to be able to
be omitted or displaced. It’s a self-amending method which optimizes the energy consumption
of a network.
Third type of sensor distribution is the uniform distribution approach; in this approach Sensor
nodes were randomly distributed using uniform distribution for their X and Y coordinates. In
this topology, all sensor nodes were assumed stationary during the simulation. A uniform
distribution is one for which the probability of occurrence is the same for all values of X. It is
sometimes called a rectangular distribution. For example, if a fair die is thrown, the probability
of obtaining any one of the six possible outcomes is 1/6. Since all outcomes are equally
probable, the distribution is uniform. If a uniform distribution is divided into equally spaced
intervals, there will be an equal number of members of the population in each interval. . In [19]
a method to decrease the number of sensors and abate energy consumption is released on the
basis of biological algorithms. Uniform distribution used in this method and our study is a
privilege to other methods.
The limited energy supply of wireless sensor networks poses a great challenge for the
deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with
simulated annealing to increase lifetime. In scalable networks, optimal algorithms are used to
reduce the number of nodes in the network. This paper, by using SA method has been
discussed evaluation and optimal selection of sensor nodes to cover the environment, So that
the sensors will have the maximum amount of silence. The advantage of this method compared
to other methods is that selection sensors starting from an uniform state with high temperature
and then with gradual cooling, we will discover Convergence to number of active minimum
sensors and minimizing the energy. Thus reducing the energy consumption per lifetime will
lead to maximize the network lifetime.
Both sensor deployment and energy conservation are key issues for WSNs. This work
considers the problem of constructing an energy-efficient sensor network for surveillance and
target positioning services using the uniform distribution approach. The design goals are to
achieve target positioning as well as to prolong sensor network lifetime. To support positioning
functionality, the sensor field must be completely covered and each unit in the field is
discriminable. It requires deploying more sensors than to support surveillance functionality.
However, to keep all sensors in active to support the target positioning service is not necessary
and waste sensors’ energy if intrusion events occur infrequently. Actually, the surveillance
service is enough when there isn’t any intruder in the sensor field.
The proposed grouping algorithm is presented in the second section. The third section is
devoted to sensor network protocol design. Simulation results are discussed in the fourth
section. The Last section is about total results.
2 Point coverage in WSN
The objective is to cover a set of points in the Point Coverage approach. Fig. 1 depicts a set of
sensors which are deployed uniformly to cover a set of targets (the square nodes). The
connected black nodes form a set of active sensors that is the result of the timing mechanism
[14].
4. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
34
Fig. 1. Coverage Point
Timing protocol is one of the posed grouping protocols in sensor networks. This algorithm is
based on the timing protocol of activity duration of the networks. It uses a two-phase
mechanism (initiative and executive) and works on the basis of data communication in the
shape of single-hop or multi-hop. Each group includes some super nodes, relay and monitoring
sensors. In this protocol, group selection is done by using the fit function designed in the
protocol.
In the initiative phase, some of nodes call themselves “sensor” and send their propagation
messages to their neighbors. The second phase (executive) is known as the stable phase. In this
phase, data reception or transmission is done from sensor nodes to relay nodes and from relay
nodes to destination. Fig. 3 shows the schedule of the protocol operation.
Figure. 2. Sample of selected set for covering targets - sensor nodes (red circles), relay nodes (green circles) and
super node (blue hexagon)
Figure. 3. Schedule of the protocol operation.
The nodes of a super node transmit data carefully, like LEACH algorithm, due to the schedule.
The energy is saved by grouping in the rest time of inactive nodes. In grouping protocols due to
the periodic circulation of active sensors, energy consumption is steady in the whole network.
Hence, we used this feature in our paper. As shown in Fig. 2 each round includes two phases:
5. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
35
initiative phase and executive phase. The initiative phase includes two parts. The former is
devoted to the selection of monitoring sensors. The latter is for selecting relay sensors. It’s so
obvious that using super nodes increases the lifetime of the network.
2.1 Energy Model
The energy model for transmitting and receiving one bit of data has been assumed to be in
accordance with LEACH energy model [14]. Assume that the distance between a transmitter
and a receiver is d in the energy model mentioned above. If d is more than d0, the multi-path
model (with path loss coefficient 4) is used. Otherwise the open space model (with pass loss
coefficient 2) is used.
≥+
〈+
=+= −−
0
4
0
2
),()(),(
dddllE
dddllE
dlElEdlE
fselec
fselec
ampTxelecTxTx
ε
ε
(1)
electE is the required energy to activate the electrical circuit. mpε and fsε are the activation
energies for power amplifiers in multi-path and open space cases, respectively and q is
represented as (2)
α
qdpdlETx +=),( (2)
On the receiver side, the consumed energy to receive one bit of data is as (3).
plElElE elecelecRxRx === − )()( (3)
In the presented asymmetrical networks, the initial energy of super nodes is assumed to be
several times greater than the initial energy of typical sensors. The consumption energy of a
monitoring node and a relay in each round are denoted by 1Es and 1Ec , respectively.
3 Proposed Method
The problem is how to design a protocol to increase network lifetime and decrease energy
consumption in the existence of these nodes. The benchmarks are trying to use the energy of
common sensors to the most.
In covering networks, the physical positions of nodes and the number of the times of using
them should be considered in protocol designing. How many times a sensor is used and also
the distance between the selected node (in fact its relay path) and the super node has a crucial
role for the energy consumption of that group. Therefore, we should be seeking for a relation
between these two parameters and their energy consumption. At first, we state the problem and
considered provisions. Then, simulation parameters that include timing algorithm based on the
super nodes (for point coverage) will be explained. Our network has N sensors named 1S
to NS . We have M super nodes named 1uS to uMS (M<N). The proposed timing algorithm
divides the time interval into specific rounds and identical intervals rT . Selected group is only
active during the time rT and other groups are off during a round. The duration of a round
rT can be computed by considering grouping time, the energy of groups, conjectured lifetime
physical parameters and the types of common sensors used in the network.
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36
3.1 Provisions dominating the network
The provisions of the network are listed below:
_ there are K targets with definite positions in the network composed of sensor nodes and super
nodes. In the considered scenario, sensor nodes and super nodes are deployed uniformly. The
schedule of the activities of sensor nodes must guarantee the following conditions after running
the algorithm for network lifetime:
• There are targets 1aT to akT which must always be covered.
• There are nodes 1S to NS which perform the monitoring task and are deployed uniformly.
• The super nodes to are deployed uniformly.
• There will be chosen sets of nodes 1C to jC . Each jC is a set of active nodes. It is
constructed in each round by the protocol.
• Each set jC is necessary and sufficient to cover all the k targets.
In fact, the objective is to divide sensor nodes into active and inactive groups. Active sensors
must be able to do connectivity and coverage. The objective to use this algorithm is
maximizing the groups to reduce energy consumption and increase network lifetime. In each
performing round, it should be checked whether a node is active, a sensor node or a relay node.
- Each common sensor has the initial iE and a limited processing power. Unlike common
sensors, super nodes have higher energy, greater lifetime and higher processing power.
- All super nodes are connected to each other so there is at least one path between two super
nodes.
- Each active sensor exists in one of the jC groups and must be connected to a super node by
relay nodes. It’s connected to at least one super node through a path to transmit its own
information to the super node.
- Sensor nodes possess initial energy iE , communication range cR , and sensing
range )( scs RRR ≥ .
- This selection must be local and distributed. Decision making is done by using the data of
neighboring node with a fixed multi-hop distance.
Definition 1:
In defining point coverage, it should be said that when the Euclidean distance between our
node and the target is less than or the same as sR , the target is covered.
Definition 2:
Sensors can connect with each other or super nodes if their Euclidean distance is less than cR .
Definition 3:
Network lifetime is defined as the time interval in which all k targets are covered by a set of
active sensor nodes that are connected to super nodes.
3.2 Sensing Nodes Selection Algorithm
As indicated before, designed grouping algorithm, executed in the beginning of each
performance round, includes two sections. The first section is the selection of active nodes.
7. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
37
The second section is attributed to gathering data from nodes and sending the data through
relay nodes.
In the first section, one of jC groups is formed in a way that the above provisions are satisfied.
When this group is active, all other nodes are inactive (Sleep Mode) and use little energy. They
will be evaluated in the next phase. This evaluation is done by considering a series of the
physical factors of sensors during a round.
3.2.1 System Specifications
The network is supposed to be in a squared environment. There are kaT targets in the
environment that must be covered to produce a connected covering network. nTars Includes all
the targets in the sensing domain of nS . They are not covered by other apt nodes. The number
of targets located in the sensing range of the node 1S is shown by 1m .
The initial energy of common sensors is iE and the initial energy of super nodes is three times
greater than iE . The energy consumed in each round is called 1Es and the consumed energy of a
relay in each round is called 1Ec .
The first section including sensor node selection, fit function checking for evaluation and
selecting active monitoring nodes takes w time units (Second is the time unit here). The
waiting time of the node nS is computed by a function measuring the physical parameters of
the sensor nS . Waiting time is stated as a coefficient multiplied by the whole time of a round by
using the parameters of a node: remained energy, initial energy and the number of the targets
observed in the range of a sensor. A sensor decides to sleep or remain awake after passing
waiting time. If 11 csn EEE +< ( nE is the remained energy of the sensor node nS ) then the node
cannot be converted to a sensor node. So waiting time is not computed and nt , the waiting time
of the node n, is equivalent to w. It means that the node is not a sensor one. Otherwise,
when 11 csn EEE +> , nt is computed and inspected. When nt finishes and φ≠nTars ,
nS introduces itself as a sensor node and joins active nodes in the group. Then, new selected
node says its position to the two-hop neighboring nodes. If there is a node such as jS at the end
of the round that φ≠nTars and 11 csn EEE +< , the node sends the “no coverage” message to its
super node. It means the lifetime of
the network is finished. At this time, a message containing “no complete coverage” is sent to
super nodes and the network sends this message to the final monitoring destination.
3.2.2 Simulated Annealing Algorithm
SA algorithm is a random hill-climbing movement which shows sort of efficiency. To perceive
it better, suppose that a ping-pong ball is inside the deepest hole of a rough surface. By shaking
the surface, we can change the position of the ball to a minimum local place. But, it should not
be much intense that make the ball far from a considered distance. SA’s goal is to start with
intense shakes (high temperature) and then reduce the intensity (Temperature Reduction).
8. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
38
Procedure Simulated Annealing:
S1 = Choose an initial solution
T = Choose an initial temperature
REPEAT
REPEAT
S2 = Generate a neighbor of the S1
UNTIL s2 establish criteria
∆E = objective ( S2 ) – objective ( S1)
IF ( ∆E > 0 ) THEN // s2 better than s1
S1 = s2
ELSE with probability EXP( ∆E/ T )
S1 = s2
END IF
Decrease T
UNTIL meet the stop criteria
End
Instead of choosing the best movement, a random movement is chosen in this algorithm. If the
movement improves the case, it will be accepted all the time. Otherwise, the algorithm accepts
the movement with the probability value of 1 ( ). Also, probability reduces due to
temperature (T) reduction. Bad movements may be allowed when the temperature is high and
decrease as the temperature reduces.
To reduce the temperature, T is multiplied by a coefficient between 0 and 1. Fast decrease of
the temperature makes us encounter the problem of local optimality. So we choose a value near
1 for this parameter (for example 0.998).
• The steps of SA
Start and Preparation:
Entering problem information and adjusting parameters.
1. Producing an answer near the current answer that accepted criteria.
2. Evaluating this answer
2-1) the neighbor is better than the current answer so go to a new answer.
2-2) the probability is greater than the random number so go to a new answer.
Otherwise get back to the first step.
3. Updating the parameters of the problem and the algorithm. Move to the step 1.
Initially, the number of sensors is requested from the user. A binary chromosome generated
randomly. In SA step, Optimized chromosome created from Primary chromosome. In this step,
for create a neighbor chromosome, Two indexes will be chosen randomly. Then, these indexes,
that represent two sensors, will be changed.Then Dikestra algorithm is used to determination
relay nodes. If the sensors of a chromosome cover the whole network through this method
reduce energy of active sensors and add the value 1 to the lifetime number, and this loop
continues until 11 csn EEE +< .
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The values of simulation parameters are presented in table 1.
Table 1. Used values in simulation
Parameter Value
Network Size 500 * 500 m
SNodes Location Uniform Distribution
Nodes Location Uniform Distribution
Nodes Initial Energy 0.1 J
Super Node Initial
Energy
0.5 J
Communication Range 90 m
Sensing Range 60 m
Number of Nodes 300
Number of SNodes 25
Number of Target 20
Elect 50 nJ/bit
Life Time
Time Life=0
Reduce energy
Increase Time life
SA
Dikestra algorithm
Random
Terminate
Check
cover
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40
S1=S2
initialize T
initial S1
Generate a neighbor of the
solution S1
S1
Lower temperature F(S1)
<F(S2)
T < Stop
temperature
P(∆F) > Random(0-1)
Check
criteria
11. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
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4. Results
In Figure 4 a sample of the response of the protocol to the selection of monitoring nodes, relay
nodes and selected path is depicted for a round. The black squares are the targets. Typical
/nodes and super nodes are shown by green-colored stars and red-colored circles, respectively.
To investigate the performance of the presented algorithm, C# has been used. In this section,
the proposed algorithm is compared with the algorithms in [9, 1, 3, 11, and 12]. Compared its
algorithms with other two algorithms. It was better than them.
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Figure 4. A sample of the response of the algorithm in a round
Figure. 5. Comparison of network lifetime for new algorithms
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42
Figure. 6. Active sensor number per lifetime
Figure. 7. Total Network Energy consumption with algorithms per active sensor number
Figure. 8. Change of lifetime with increase of number of targets
As seen in the simulations, it shows that the SA algorithm is applied for selection of
monitoring sensors in a point coverage network with increase in life time and reduction of
average useful energy consumption. Amount of Increase in efficiency is significant than [11-
13] Resources.
As it is shown in Table 1, this experiment was performed on a 500 x 500 environment; we
distribute 300 nodes and 250 super nodes in this environment evenly to cover 20 targets which
13. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
43
are distributed in the environment randomly. Communication range of each node is 90 m and
its sensing range is 60 m also the amount of super nodes primary energy are 5 times greater
than nodes. In this paper, to reduce the energy consumption and get smart sensor, two
conditions have been considered:
- If the target was observed by several sensors, the sensor that sees more targets is selected for
sensing.
- To reduce the activation interface nodes between target observer node and the nearest super
node, Dijensra algorithm is used.
Simulations conducted in this paper for a point coverage network has been in C # language and
has been tested by the MATLAB software. As shown in Figure 5, a simulation based on 300
sensors was done on the basis of the parameters in Table 1, which is reached to 67 rounds in
lifetime of the network.
Also in Figure 5 we compare the change in network lifetime number for changes in nodes from
300 to 450 sensors; the green line is the result of 450 sensors and the blue line shows the state
of 300 sensors.
Calculations done are based on implementation of the algorithm in 10 times and averaging the
values of the lifetime of this number. The test has reached to 130 rounds for 450 sensors and
similar condition with Table 1. According to Figure 6, the chart of average energy
consumption, the consumed energy per round increases reasonably and amount of consumption
has gentle ascending trend.
Figure 7 shows the number of active sensors for complete coverage network. Amount of
Increase in active sensors has been about 76 sensors in the beginning of network to 108 sensors
at end of network lifetime.
Before activation of 76 sensors, the amount of network remaining energy is fixed and by the
first lifetime, network remaining energy is decreased. As it is evident in the figure, Energy
reduction development has been slow in the early implementation of the program, but
gradually according to energy completion of some sensors we had to use more sensors to cover
the entire environment that leads to drastic reduction of network remaining energy in the last
lifetimes.;
The lifetime of network has drawn In Figure 8 by changing the number of objectives and
implementation of algorithms to 10 times, that comparing with [11-13] it is seen that the SA
for a point coverage network has identical condition and in some cases also has the advantages
to 10 percent. According to increase in lifetime factor, the increase has also seen to 14 percent
compared to other researches.
5. Conclusions
In this paper, we presented a method to select active sensors in each round in asymmetrical
wireless covering networks. In previous methods, this selection was dependent on the
parameters of each sensor. In the proposed algorithm, this selection is according to the contest
between neighboring nodes. Energy consumption in this method is more balanced than other
similar methods. Generally, diagrams illustrate that the proposed method for the selection of
monitoring sensors in point coverage is more energy-efficient than GSA, EEDG and EDTC.
Also, it causes a longer lifetime.
According to simulation results, it is observed that this algorithm is well acted to solve this
problem and optimization of a wireless sensor network in large-scale and it is able to provide a
good and implementable response for network design and we can achieve better energy
14. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.4, No.2, April 2013
44
efficiency by organizing the network nodes and classifying them. Higher performance of
network leads to increasing network life time.
Using SA algorithm, the categories were chosen so that the energy consumption in the
network is minimized. Creating balance and uniformity in energy consumption of nodes and
prolonging network life time is the Outcome of using the algorithm.
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