On the optimal random deployment of wireless sensor networks in non-homogeneous scenarios

Vales‐Alonso, Javier; Parrado-García, Francisco J.; López-Matencio, Pablo; Alcaraz, Juan J.; González-Castaño, Francisco J.

doi:10.1016/j.adhoc.2012.10.001

Cited by 34 publications

(17 citation statements)

References 22 publications

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“…Inaccessible terrains with large size network cannot follow the deterministic placement hence the only option left is random deployment. J. Vales-Alonso et al in [21] presents random deployment scheme that addresses the coverage and connectivity problem while maximizing the joint sensing coverage of nodes. In proposed scheme, random deployment is optimized analytically by considering the Gaussian clustering which considered some target points and the dispersion.…”

Section: Literature Reviewmentioning

confidence: 99%

Analytical and Simulation Modeling to Analyze Reliability State of Wireless Sensor Networks

Pal

Yogita

Singh

et al. 2015

New Contributions in Information Systems and Technologies

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Abstract. Wireless sensor networks have gained abundant interest due to their potential wide range of applications. Reliability of deployed wireless sensor network is defined by covered area of alive nodes and redundancy of data. Redundancy in data occurs because of overlapped sensed area. An initial reliable wireless sensor network switches to unreliable state because nodes perish in the field randomly. Consequently, the quality of data starts diminishing. It is imperative to know when the network will switch to unreliable state from reliable state so that proper action can be conducted in the field. Work of this paper analyzes and compares analytical and simulation modeling for reliability state of wireless sensor network. Multi-objective genetic algorithm based method is operated for analytical modeling which determines minimum number of nodes (randomly) that covers almost complete area while having required minimum overlapped area. Clustering algorithm, LEACH, is implemented in NS-2 for simulation modeling. Comparative results of analytical and simulation modeling are different because of their different nature but both highlights that reliability of wireless sensor network is salient.

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Section: Literature Reviewmentioning

confidence: 99%

Analytical and Simulation Modeling to Analyze Reliability State of Wireless Sensor Networks

Pal

Yogita

Singh

et al. 2015

New Contributions in Information Systems and Technologies

View full text Add to dashboard Cite

show abstract

“…In accordance with the dimension of network, these studies can be divided into linear network [13], planar network [8,12] and 3D network deployment strategy [14]. Grouped by scenarios, the application scenarios of network deployment include: (1) underwater [14] and (2) the land [15]. From the view of deployment, the optimization goals are: (1) to maximize the network lifetime [7].…”

Section: Related Workmentioning

confidence: 99%

“…Our system model considers the following node deployment features as Ref. [15]: node placement is inherently random, due to airborne sensor deployment, for example. Nevertheless, it is assumed that the network designer can select certain placement parameters, which allows some control over the position of the nodes.…”

Section: The System Modelmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Heuristic Subtraction Deployment Strategy to Guarantee Quality of Event Detection for WSNs

Liu

2014

The Computer Journal

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Cooperative sensing and monitoring event is one of the important applications of sensor networks. Node deployment and duty cycle configuration of event detection in a large wireless sensor networks is an enormous challenge. In this paper, through in-depth analysis we find that there is a tradeoff between node duty cycle and event quality monitoring. That is, adopting larger duty cycle enables network to deploy fewer nodes. Or deploying more nodes can reduce their duty cycle. Both of them can reach the event detecting quality requirement of application. Based on the above findings, a novel subtraction deployment strategy (SDS) combined with the unequal node duty cycle in the network is presented. This strategy improves the duty cycle of nodes in non-hotspots area when reduces the number, thereby minimizing the deployment cost on the premise of meeting the detecting quality of application. We introduce a formal model to indicate the tradeoff between deployment cost and the quality of event detection. Subsequently, we present a heuristic algorithm to find nearoptimal deployment solutions for practical scenarios. Both theoretical and experimental simulation results show that the proposed scheme SDS for event detection can reduce the deployed number of nodes by 11.49% and do no harm to detecting quality. In the deployment of the same node number, the weighted delay of detection can be reduced as much as 47%, and the weighted detection percentage can be increased by 1.56 times without reducing the network lifetime compared with those in uniform deployment scheme.

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“…Results of this chapter can be applied to any AmI network deployment of random nature. This chapter is based on paper [VAPGLMA12].…”

Section: Thesis Structure and Contributionsmentioning

confidence: 99%

Advance in optimal design and deployment of ambient intelligence systems

López-Matencio-Pérez¹

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A brilliant future is forecasted for Ambient Intelligence (AmI) systems. These comprise sensitive environments able to anticipate people's actions, and to react intelligently supporting them. AmI relies on decision-making processes, which are usually hidden to the users, giving rise to the so-called smart environments. Some of those envisioned environments include smart homes, health monitoring, education, workspaces, sports, assisted living, and so forth. Moreover, the complexity of these environments is continuously growing, thereby increasing the difficulty of making suitable decisions in support of human activity. Therefore, decision-making is one of the critical parts of these systems. Several techniques can be efficiently combined with AmI environments and may help to alleviate decisionmaking issues. These include classification techniques, as well as mathematical programming tools. In the first part of this work we introduce two AmI environments where decisionmaking plays a primary role: • An AmI system for athletes' training. This system is in charge of monitoring ambient variables, as well as athletes' biometry and making decisions during a training session to meet the training goals. Several techniques have been used to test different decision engines: interpolation by means of (m, s)-splines, k-Nearest-Neighbors and dynamic programming based on Markov Decision Processes.

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On the optimal random deployment of wireless sensor networks in non-homogeneous scenarios

Cited by 34 publications

References 22 publications

Analytical and Simulation Modeling to Analyze Reliability State of Wireless Sensor Networks

Analytical and Simulation Modeling to Analyze Reliability State of Wireless Sensor Networks

An Efficient Heuristic Subtraction Deployment Strategy to Guarantee Quality of Event Detection for WSNs

Advance in optimal design and deployment of ambient intelligence systems

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