Improved ACO-Based Sweep Coverage Scheme Considering Data Delivery

Abstract

The newly introduced sweep coverage scheme uses mobile sensors to implement network coverage in wireless sensor networks (WSNs) and has attracted much attention from researchers. However, data buffer and moving speed of the mobile sensor are limited in sweep coverage. Thus, scheduling the minimum number of mobile sensors to efficiently implement dynamical network coverage while considering data delivery is still a challenging problem. To provide steady and efficient data gathering from sensors, an improved ant colony optimization-based sweep coverage (IACOSC) scheme supporting data delivery was proposed. In IACOSC, the artificial ants were used to create the initial coverage routes for points of interest. Then, a novel metric called route coverage efficiency was used to evaluate the routes. Finally, a local search algorithm based on route deletion and node insertion was employed to optimize these routes. Algorithm analysis shows that the time complexity of IACOSC is O(n 3 ). Simulation results show that, compared with existing sweep coverage approaches considering data delivery, IACOSC significantly reduces the computational complexity and decreases the computation time by 50 % while reducing the mobile sensors by 16.73 % in the same network scenarios. The results obtained in this study can be applied to optimal deployment of WSN using the sweep coverage scheme.

Cooperation of Simulation and Data Model for Performance Analysis of Complex Systems

“…The platform model includes a structure of cluster and hardware models. The network model describes a topology model, thus indicating connections between the clusters [29]. When these models are modelled by data modelling, details can be highly abstracted.…”

An ABC Algorithm with Recombination

“…The SI refers to establish mathematical model to simulate the social behaviors from nature. In the past decades, different SI algorithms have been proposed, including ant colony optimization (ACO) [8,17,25], particle swarm optimization (PSO) [22], artificial bee colony (ABC) [9], firefly algorithm (FA) [20], hybrid algorithm (HA) [14], bat algorithm (BA) [2], and cuckoo search (CS) [5,30].…”