Sensor Distribution on Coverage in Sensor Networks

Peng, Miao; Xiao, Yang; Chen, Hui; Hao, Qi; Vasilakos, Athanasios V.; Wu, Jie

doi:10.1007/978-3-642-29222-4_24

Cited by 4 publications

(3 citation statements)

References 100 publications

(13 reference statements)

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“…The simulation results demonstrate improved performance of DSS scheme with comparison to SMAC and anycast protocol. Another energy efficient sleep scheduling mechanism is introduced in [14]. This technique is based on Time Division Multiple Access (TDMA) and is referred to as Contiguous Link Scheduling (CLS).…”

Section: Related Workmentioning

confidence: 99%

Energy preserving sleep scheduling for cluster-based wireless sensor networks

Singh

Lobiyal

2013

2013 Sixth International Conference on Contemporary Computing (IC3)

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In sensor network, sleeping technique can be used to conserve energy for maximizing the network lifetime. The sensor nodes are often densely deployed in the sensing field, which increases redundant packet transmissions thereby draining huge amount of sensor energy. However, energy wastage can be preserved by putting some nodes to sleep state, while other nodes remain active in the network. Therefore, a novel way have been determined that allows selected sensor nodes to enter sleep state without degrading sensor coverage of the network. In the proposed Energy Preserving Sleep Scheduling (EPSS) strategy, the sleeping probability of a node varies according to the density of the deployed region. Moreover, the distance of the member nodes from the head node is also taken into account in the sleep policy, thereby balancing energy consumption within a cluster. The simulation result of our proposed sleep scheduling scheme shows significant enhancements over Linear Distance-based Scheduling and Randomized Scheduling schemes respectively.

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Section: Related Workmentioning

confidence: 99%

Energy preserving sleep scheduling for cluster-based wireless sensor networks

Singh

Lobiyal

2013

2013 Sixth International Conference on Contemporary Computing (IC3)

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“…where is the outward normal of . For the differential equation (8), initial condition and boundary condition are given as (9) and (10). So, there is a unique solution for any time and any spot in .…”

Section: Analysis In Mathematicsmentioning

confidence: 99%

“…Furthermore, bottleneck recognition [6,7], which is vital for precise schedule over WSN, requires boundary information. And, in coverage problem, the coverage intensity near the network boundary attracts a lot of research interest [8,9]. So, boundary recognition provides useful information for other WSN applications.…”

Section: Introductionmentioning

confidence: 99%

Boundary Recognition by Simulating a Diffusion Process in Wireless Sensor Networks

Wang

2014

Abstract and Applied Analysis

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Wireless sensor networks (WSN) are becoming increasingly promising in practice. As the predeployment design and optimization are usually unpractical in random deployment scenarios, the global optimum of the WSN’s performance is achievable only if the topology dependent self-organizing process acquires the overview of the WSN, in which the boundary is the most important. The idea of this paper comes from the fact that contours only break on the geometrical boundary and the WSN are discrete sampling systems of real environments. By simulating a diffusion process in discrete form, the end point of semi-contours suggests the boundary nodes of a WSN. The simulation cases show the algorithm is well worked in WSN with average degree higher than 10. The boundary recognition could be very valuable for other algorithms dedicated to optimize the overall performance of WSN.

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