Surveillance Wireless Sensor Networks: Deployment Quality Analysis

Onur, Ertan; Ersoy, Cem; Delic, H.; Akarun, Lale

doi:10.1109/mnet.2007.4395110

Cited by 91 publications

(46 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Default Value Noise figure (F Identify node on/off status based on coverage and connectivity: To create a coverage map of the building, WSN-Maintain currently supports two types of coverage models, i.e., the binary detection model and Elfes's model [14]. These models can be used to approximate, for example, passive infrared (PIR) sensors.…”

Section: Parametersmentioning

confidence: 99%

A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network

Sitanayah

Sreenan

Fedor

2016

JSAN

View full text Add to dashboard Cite

Abstract:Contiki's Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep.

show abstract

Section: Parametersmentioning

confidence: 99%

A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network

Sitanayah

Sreenan

Fedor

2016

JSAN

View full text Add to dashboard Cite

show abstract

“…Barrier coverage with wireless sensors concentrates on detecting intruders that attempt to penetrate a specific area [18], [19], where a chain of sensor nodes are deployed on or along the virtual border to form a network [20]. With the requirement of protecting sensitive facilities and national borders increasing, barrier coverage has a growing number of applications.…”

Section: Related Workmentioning

confidence: 99%

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Zhang

Lee

et al. 2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Abstract-Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC.Index Terms-Barrier coverage, evolutionary algorithms, multiobjective optimization, wireless sensor networks (WSNs).

show abstract

“…However, as mentioned before, in some applications (such as urban applications), the deployment of nodes at pre-specified positions is feasible (Onur et al, 2007). Hence, this subsection focuses on geometric distributions instead of random distribution and their effect on maximizing the network's lifetime.…”

Section: Geometric Distributionsmentioning

confidence: 99%