Abstract

Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.

Highlights

  • Wireless sensor networks (WSNs) are nowadays ubiquitous

  • Motivated by the advantages of Mobile wireless sensor networks (MWSNs) and the vital role of the MEP problem, this paper investigates the problem of finding the minimal exposure path in a MWSN

  • To prove the efficiency of HPSO-MMEP, we compare the algorithm with two existing algorithms, which are GAMEP [39] and HPSO [33], in various experimental scenarios

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Summary

Introduction

Wireless sensor networks (WSNs) are nowadays ubiquitous. They are used in various different domains: military, medical, environment, etc. Sensors can be attached to larger machines like mobile robots or can be a self-contained miniature system with the ability to move to desired areas Such MWSNs are extremely valuable in situations where traditional static wireless sensor network (SWSN) deployment mechanisms fail or are not suitable; for instance, a hostile environment where sensors cannot be manually deployed, and must be air-dropped. Thanks to the mobility of sensor nodes, MWSNs can improve coverage quality, prolong the network lifetime, optimize use of resources and be relocated very efficiently [20,21,22]. Due to high dimensionality and non-linearity of the objective function, and the mobility feature of sensors, the MMEP problem is much more challenging than the traditional MEP problem in SWSNs. we propose to integrate genetic algorithm into particle swarm optimization to form an efficient algorithm named HPSO-MMEP algorithm.

Related Works
Sensing Model
Sensor Field Intensity Model
Exposure
Problem Formulation
Proposed Algorithm
Individual Representation
Control-Point Initialization
Integration of GA into PSO Algorithm to Form HPSO-MMEP
Crossover Operator
Mutation Operator
Complexity Analysis
Experimental Results
Data Settings
Parameters and System Settings
Computation Results
Effects of Several Factors of Mmep Problem on the Solution
Conclusions
Full Text
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