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Abstract
Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.
Highlights
In recent years, with the development of micro-electromechanical systems (MEMS) directional sensor networks (DSNs) have received much attention due to their wide and significant applications [1], which offer important economic benefits
As can be seen from the figure, with the increase in the iteration number, the solutions obtained by learning automata-based coral reef optimization algorithm (LACRO) approximate optimal, which verify the effectiveness of the algorithm in solving multi-objective coverage optimization problems in heterogeneous directional sensor network (HDSN)
Work Featured by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes, this paper presents a novel multi-objective optimization problem of HDSN which guarantees the coverage rate of the monitor area, the financial cost of deployment and the directional sensor network communication connectivity
Summary
With the development of micro-electromechanical systems (MEMS) directional sensor networks (DSNs) have received much attention due to their wide and significant applications [1], which offer important economic benefits. A DSN is a wireless network which is equipped with directional sensors such as video sensors, ultrasound and infrared sensors. Differing from the sensor nodes of the traditional wireless sensor networks (WSN) which have omni-directional sensing ranges, the sensing range of DSN sensor nodes are restricted by their directions and specific angular dimensions. Since directional sensing and directional communication have great impact on the performance of a DSN, several difficulties have emerged in DSN protocol design. One of the most fundamental problems is the coverage issue. There are extensive amounts of research about the coverage problems in WSNs, these WSN research results cannot be directly applied to DSNs
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