5,548 research outputs found
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Study of 3D Wireless Sensor Network Based on Overlap Method
Wireless sensor network as a new information acquisition technology has profound impact on people's work and life style, has the very high research value. Energy issues important factor restricting the development of the deep WSN is node, sensor nodes for processing data collected information and communication between nodes will speed up the energy consumption of nodes. Cover the deployment strategy is directly related to the optimal distribution of target area monitoring the degree of perception and limited resources of wireless sensor network, determines the service quality of the wireless sensor network to improve the. How to design an efficient coverage algorithm directly affects the coverage and network lifetime, because the actual environment of 3D wireless sensor network is more close to people, so the 3D WSN. Covering research has more realistic significance. At present, about the research of wireless sensor network many 3D covering literature, according to the general configuration of nodes is divided into deterministic coverage and random covering two aspects. This paper presents a wireless sensor network node for 3D scene coverage model and its deployment method, based on analyzing the common regular polyhedron models used in 3D space coverage, proposed a model based on covering the structure, on the basis of this theory to derive a quantitative relationship between coverage model and node sensing radius, more based on the quantitative relationship between the further calculation of network area remains fully covering the minimum number of nodes are required, the network regional 3D mesh finite mesh node coverage model in accordance with the deployment
An efficient genetic algorithm for large-scale planning of robust industrial wireless networks
An industrial indoor environment is harsh for wireless communications
compared to an office environment, because the prevalent metal easily causes
shadowing effects and affects the availability of an industrial wireless local
area network (IWLAN). On the one hand, it is costly, time-consuming, and
ineffective to perform trial-and-error manual deployment of wireless nodes. On
the other hand, the existing wireless planning tools only focus on office
environments such that it is hard to plan IWLANs due to the larger problem size
and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
industrial indoor environments. To fill this gap, this paper proposes an
overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
algorithm for deploying large-scale robust IWLANs. As a progress beyond the
state-of-the-art wireless planning, two full coverage layers are created. The
second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
deployment cost is reduced by minimizing the number of access points (APs); the
hard constraint of minimal inter-AP spatial paration avoids multiple APs
covering the same area to be simultaneously shadowed by the same obstacle. The
computation time and occupied memory are dedicatedly considered in the design
of GAOD for large-scale optimization. A greedy heuristic based
over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
benchmarks. In two vehicle manufacturers with a small and large indoor
environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
outputted up to 25% less APs than a random OD algorithm. Furthermore, the
effectiveness of this model and GAOD was experimentally validated with a real
deployment system
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