A genetic algorithm for sensor deployment based on two-dimensional operators

Greedy Algorithms for Target Coverage Lifetime Management Problem in Wireless Sensor Networks

“…Many variants of this model have been proposed in the literature, depending most of times, on the design choices linked to the problem. Some approximation algorithms like greedy algorithms [14][15], genetic algorithms [16][17] have been proposed, with the aim to efficiently solve the problem, when it is no longer possible to perform an exhaustive search. Approximation algorithms propose sub-optimal solutions that can avoid the complexity of the exhaustive search, which can solve efficiently the problem and in a reasonable running time.…”

A dominance-based stepwise approach for sensor placement optimization

“…Other, more realistic scenarios have been created, such as the Art Gallery problem [11,21,26], where there are fixed obstacles. In this case, the purpose of sensor placement is to achieve maximum coverage with a minimum number of sensors taking these obstacles into account, as shown in Fig.…”

“…Because more sensors mean higher cost for sensor placement, it does not make much sense for two sensors to cover the same area, or if too many sensors are used, making the coverage of some of them redundant. The compounding effect of these two problems makes sensor placement optimization an NP-hard problem, which some practitioners may try to resolve simply by using generic optimization methods, such as Evolutionary Algorithms [3,6,26]. As far as we know, there are no specific algorithms designed to solve a topology-aware sensor placement problem focusing on coverage redundancy among sensors.…”

“…In most cases, sensor placement optimization is regarded as an overly complex problem, and so generic heuristic algorithms are often used for the optimization task [6,26].…”

Topography-Aware Sensor Deployment Optimization with CMA-ES

“…Some prior work has been conducted with such paradigms [3,16], but using more or less the same over-simplifying assumptions as the deterministic approaches. Among available evolutionary algorithms, we chose the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [8] for its good performance and stability [6,7].…”