Autonomous deployment for load balancing k-surface coverage in sensor networks

published_or_final_versio

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved

A Novel Skeleton Extraction Algorithm for 3d Wireless Sensor Networks

Wireless sensor network design is critical and resource allocation is a major problem which remains to be solved satisfactorily. The discrete nature of sensor networks renders the existing skeleton extraction algorithms inapplicable. 3D topologies of sensor networks for practical scenarios are considered in this paper and the research carried out in the field of skeleton extraction for three dimensional wireless sensor networks. A skeleton extraction algorithm applicable to complex 3D spaces of sensor networks is introduced in this paper and is represented in the form of a graph. The skeletal links are identified on the basis of a novel energy utilization function computed for the transmissions carried out through the network. The frequency based weight assignment function is introduced to identify the root node of the skeleton graph. Topological clustering is used to construct the layered topological sets to preserve the nature of the topology in the skeleton graph. The skeleton graph is constructed with the help of the layered topological sets and the experimental results prove the robustness of the skeleton extraction algorithm introduced. Provisioning of additional resources to skeletal nodes enhances the sensor network performance by 20% as proved by the results presented in this paper

3D Surface Covering with Virtual Forces

International audienceThe deployment of Wireless Sensor Network (WSNs) is widely used in monitoring applications. Depending on the entity to be monitored, sensor nodes are deployed in a two-dimension (2D) area or a three-dimension (3D) area. In addition, sensor nodes can be deployed either in a distributed way or their positions are precomputed by a central entity and they are placed at their positions using mobile robots. In this paper, we are interested in the deployment of WSN in a 3D-surface using autonomous and mobile nodes. Our goal is to ensure full 3D-surface coverage and maintain network connectivity. To reach our goal we propose 3D-DVFA-SC, a distributed deployment algorithm based on virtual forces strategy to move sensor nodes. Simulation results show that 3D-DVFA-SC provides a very good coverage rate while maintaining network connectivity

Covering a 3D flat surface with autonomous and mobile wireless sensor nodes

International audienceWireless Sensor Networks (WSNs) are used in a wide range of applications due to their monitoring and tracking abilities. Depending on the applications goals, sensor nodes are deployed either in a two dimensional (2D) area or in a three-dimensional (3D) area. In addition, WSN deployment can be either in a distributed or a centralized manner. In this paper, we are interested in a fully distributed deployment of WSN in several 3D-flat-surface configurations using autonomous and mobile nodes. Our goal is to ensure full 3D flat surfaces coverage and maintain network connectivity for these surfaces. To reach our goal we propose 3D-DVFA-FSC, a distributed deployment algorithm based on virtual forces strategy to move sensor nodes over different 3D-flat-surface shapes. Simulation results show that 3D-DVFA-FSC provides a full coverage rate regardless of the 3D-flat-surface configuration while maintaining network connectivity

A Novel Deployment Scheme Based on Three-Dimensional Coverage Model for Wireless Sensor Networks

Coverage pattern and deployment strategy are directly related to the optimum allocation of limited resources for wireless sensor networks, such as energy of nodes, communication bandwidth, and computing power, and quality improvement is largely determined by these for wireless sensor networks. A three-dimensional coverage pattern and deployment scheme are proposed in this paper. Firstly, by analyzing the regular polyhedron models in three-dimensional scene, a coverage pattern based on cuboids is proposed, and then relationship between coverage and sensor nodes’ radius is deduced; also the minimum number of sensor nodes to maintain network area’s full coverage is calculated. At last, sensor nodes are deployed according to the coverage pattern after the monitor area is subdivided into finite 3D grid. Experimental results show that, compared with traditional random method, sensor nodes number is reduced effectively while coverage rate of monitor area is ensured using our coverage pattern and deterministic deployment scheme