This paper focuses on the automatic placement of actors within wireless sensor-actor networks with the conscious to minimize the cost and maximize the sensor-actor binding ratio. A wireless sensor-actor network (WSAN) comprises of sensors to gather information from their surrounding environment and actors to perform pertinent actions upon the environment, after processing the sensors' data. The resourcerich actors are deployed to increase the processing and communication capabilities, thereby saving the energy and extending the lifespan of WSAN. We have assumed that sensor nodes are to communicate directly to the respective actors. The actor placement problem is to select a minimal set of actors and their optimal locations within WSAN, keeping in mind the communication requirements between sensors and actors. We have encoded the actor placement problem into the Simulated Annealing (SA) approach, to optimize the total number of actors covering as many sensors as possible subject to cost and performance constraints. Our experimental results demonstrate the feasibility of SA in searching the space of the actor placement problem by maintaining 80% sensor-actor coverage range throughout the experiments with optimal network cost. We also have carried out a sensitivity analysis in the selection of SA parameter, such as the cooling rate (α), to study its effect on the performance.
PurposeContinuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.Design/methodology/approachThe authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.FindingsThe experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.Practical implicationsThe proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.Originality/valueThe paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.
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