Optimization of the Distribution and Localization of Wireless Sensor Networks Based on Differential Evolution Approach

Abstract

Location information for wireless sensor nodes is needed in most of the routing protocols for distributed sensor networks to determine the distance between two particular nodes in order to estimate the energy consumption. Differential evolution obtains a suboptimal solution based on three features included in the objective function: area, energy, and redundancy. The use of obstacles is considered to check how these barriers affect the behavior of the whole solution. The obstacles are considered like new restrictions aside of the typical restrictions of area boundaries and the overlap minimization. At each generation, the best element is tested to check whether the node distribution is able to create a minimum spanning tree and then to arrange the nodes using the smallest distance from the initial position to the suboptimal end position based on the Hungarian algorithm. This work presents results for different scenarios delimited by walls and testing whether it is possible to obtain a suboptimal solution with inner obstacles. Also, a case with an area delimited by a star shape is presented showing that the algorithm is able to fill the whole area, even if such area is delimited for the peaks of the star.

Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

“…This approach was adopted before testing more elaborated bioinspired techniques for future works, in order to have a baseline and then to determine the feasibility of MODEA with stricter constraints. A previous successful approach with the use of nonconvex constraints and obstacles can be seen in [32]. In future works, such elaborated bioinspired techniques will be tested (more details are available in the Conclusion).…”

“…It is clear that a point should be evaluated against all the nodes present in the network; then [27] DE/Curr-to-Best/1, DE/rand-to-best/1 Power allocation [28] DE/Best/1 Coverage area [29] DE/rand/1, Markov topology control Coverage area [30] DE/rand/1, Voronoi topology control Coverage area [31] DE/rand/1 Coverage area, redundant area [32] DE/rand/1 with rand Coverage area, energy, redundant area [33] Classical and modified DE/rand/1, NSGA-II Coverage area with node elimination [34] DE/Best/1, jDE, JADE Coverage area [35] Modified DE/Curr-to-Best/1 Node position estimation [47] DEA and NSGA-II Routing…”

“…In [32] the same approach of maximizing the coverage area in the presence of obstacles is extended, including the energy consumed in the objective function. The redundant area is also considered in the objective function.…”

“…Compared to the existing bioinspired techniques in the open literature [26,32] and the method here presented, the followed approach is based on a simplified version of the MOEA/D techniques presented in [49], where a set of weights is assigned on each function to be optimized and the summation of them is equal to 1. Such variant is known as Weighted Sum Approach [49,50].…”

Sensor Distribution Optimization for Structural Impact Monitoring Based on NSGA-II and Wavelet Decomposition

“…In recent years, in order to solve the multi-objective optimization problem, many researchers improved the initial heuristic algorithm [11]. Céspedes-Mota uses improved differential evolution algorithm to optimize the distribution of wireless sensor networks according to the distance arranged by sensors [12]. Deb proposes the non-dominated sorting genetic algorithm to solve multi-objective optimization problems [13].…”