Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

Abstract

In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.

Review of nature-inspired methods for wake-up scheduling in wireless sensor networks

“…A set of methods based on Learning Automata (LA) has been proposed to address the target coverage problem [74,75] and to maximize network lifetime while maintaining good coverage [76][77][78]. Target coverage problem in WSN [75] and WSN with directional sensors [74] (i.e.…”

A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

“…In the literature, several studies can be found, which have used the scheduling technique for solving the target coverage problem in WSNs (see [5,6,7,8,9,10,11,12]). For the first time, Cardei and Du [5] addressed the problem of target coverage and proved its NP-completeness.…”

“…In [9], the authors proposed an iterative approximation based on Lagrangean relaxation and subgradient optimization to solve the problem and prolong the network lifetime. LA were also employed to solve the target coverage problem (e.g., [10,11,12,24]). The above-mentioned studies attempted to solve efficiently the target coverage problem in networks where sensors had a single power level.…”

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

“…Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, mainly including smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. In traditional wireless sensor networks [1,18,26,39,41] and IoT(Internet of Things) [9,14,20,22,38], the deployment of sensor nodes could be sort into two parts, including the random deployment and the purposeful deployment based on the specific environment. In random deployment scheme, due to the random characteristic, targeted at achieving the highest coverage rate, more redundancy nodes are needed.…”