3D Deployment Problem in Wireless Sensor Networks resolved by Genetic and Ant Colony Algorithms

Abstract

Wireless Sensor Networks (WSN) is an evolving field of research with various applications. Deploying sensors is a decisive operation that greatly influences the performance and the functioning of the network. Comparing the 2D deployment with the 3D one, the latter is more complex because of its ability to attend more constraints imposed by real problems. On this article, we discuss the 3D node positioning problem, while minimizing the global number of sensors, the consumption levels of energy, and maximizing the covered area and the hybrid localization rate based on the 3D DV-HOP protocol and the RSSI. We seek to solve our problem by choosing the ideal 3D locations to add nomad nodes to achieve the mentioned objectives. An approach based on variants of optimization algorithms based on the NSGA-II algorithm [1] and the ACO algorithm [2] is proposed. The simulation model considers the implementation of the IEEE 802.15.4 standard, and a reactive AODV based routing layer. The results of simulations with OMNeT++ indicate the sustainability of the tested algorithms (NSGA-II, ACO).

WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

“…Furthermore, to account for the influence of signal propagation attenuation on the coverage model, we combine the terrain shading influence criterion with the spherical probability perception model [7].…”

“…A 3D spherical sensing model based on 3D space coverage was proposed to study the coverage problem further [6]. Mnasri [7] compared 2D deployment with 3D deployment and proved that the latter was more complex because it could solve more constraints brought by practical problems. Boufares [8] proposed a distributed algorithm based on virtual force to actualize autonomous coverage of the sensor nodes in the 3D region.…”

IAM-TSP: Iterative Approximate Methods for Solving the Travelling Salesman Problem

“…Moreover, the application fields of TSP include any problem involving the search for the shortest paths. These fields vary in domains from big data classification [8,9,10], deployment and routing of IoT networks [11,12,13,14,15,16], financial prediction [17,18], image processing [19], and [20], computer vision [21], [22], and [23]. TSP can be defined by finding a Hamiltonian cycle that visits each vertex precisely once, with the least amount of total weight feasible, given a full undirected weighted graph G = (V, E) with vertex set V and edge set E. The total weight of a cycle is equal to the weights of each of its individual edges.…”