Maximum Lifetime of Sensor Networks with Adjustable Sensing Range

Dhawan, Akshaye; Vu, Chinh; Zelikovsky, Alexander; Li, Y.; Prasad, Sushil K.

doi:10.1109/snpd-sawn.2006.46

Cited by 62 publications

(51 citation statements)

References 7 publications

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“…A common approach taken with centralized algorithms is that of formulating the problem as an optimization problem and using linear programming (LP) to solve it [3,6,16,33]. In [41], the authors develop a most-constrained least-constraining heuristic and demonstrated its effectiveness on variety of simulated scenarios.…”

Section: Related Workmentioning

confidence: 99%

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Maximum Lifetime Scheduling in Wireless Sensor Networks

Dhawan¹

2012

Wireless Sensor Networks - Technology and Protocols

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Section: Related Workmentioning

confidence: 99%

“…A similar problem is solved by us for sensors with adjustable ranges in [16]. We present a linear programming based formulation that also uses the (1 + ǫ) Garg-Könemann approximation algorithm [21].…”

Section: Related Workmentioning

confidence: 99%

Maximum Lifetime Scheduling in Wireless Sensor Networks

Dhawan¹

2012

Wireless Sensor Networks - Technology and Protocols

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“…WSNs are consisting of many small and low-cost SNs that form in a self-organized, multi-hop and monitoring network; sensors cooperative with each other, collecting data from the physical medium covered by the sensors and analyzing these data then transferring data through the network reaching to the main SN or to the sink node [1].…”

Section: Introductionmentioning

confidence: 99%

MDFD and DFD Methods to detect Failed Sensor Nodes in Wireless Sensor Network

Mezaal¹,

Kadhim²

2015

IJCA

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Wireless Sensor Network (WSN) contains many low cost and low power sensor nodes (SNs), these nodes may fail to communicate with each other according to some reasons such as battery lifetime or uncontrolled events which will lead to partition the network and reduce the Quality of Service (QoS) as well as the reliability and efficiency of the whole network. The motivation of this paper is detecting these malfunctions using Distributed Fault Detection (DFD) method considered with random proposed network model. Then a modification on DFD method (MDFD) proposed to enhance the efficiency and the reliability of the whole network and handling the error occurred with DFD method. The two methods analyzed and tested using MATLAB ® and they must applied with homogeneous WSNs only that contain only one type of sensors, percentage error of DFD method was about 25% (for three SNs) due to its algorithm limitations in using only half of the neighbor SNs, this percentage error reduced in MDFD method in which all neighbor SNs considered to detect the failed SN reaching full detection accuracy but with latency tradeoff.

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“…Dhawan, et al, [74] also developed algorithms for a similar problem as the one handled by [73]. A mathematical model was proposed and the approximation algorithm of Garg-Konemann was used to determine the approximate solutions.…”

Section: Range-adjustable Sensorsmentioning

confidence: 99%

“…Typical models are the linear, quadratic, cubical and quadruplicate models. The most common are the linear and quadratic models [87], [73].…”

Section: Energy Model For Adjusting Rangementioning

confidence: 99%

Wireless Sensor Network Deployment

Qu¹

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Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% -90% energy savings can be provided in vii different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times.Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without selflocalization systems, developed before our work.viii

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Maximum Lifetime of Sensor Networks with Adjustable Sensing Range

Cited by 62 publications

References 7 publications

Maximum Lifetime Scheduling in Wireless Sensor Networks

Maximum Lifetime Scheduling in Wireless Sensor Networks

MDFD and DFD Methods to detect Failed Sensor Nodes in Wireless Sensor Network

Wireless Sensor Network Deployment

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