2,676 research outputs found
Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization
In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conïŹicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network life-time and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), Hybrid-MOEA/D-Ihybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize sub-problems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a signiïŹcant better performance compared with existing algorithms in the literature in terms of all the objectives concerned
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
The Deployment in the Wireless Sensor Networks: Methodologies, Recent Works and Applications
International audienceThe wireless sensor networks (WSN) is a research area in continuous evolution with a variety of application contexts. Wireless sensor networks pose many optimization problems, particularly because sensors have limited capacity in terms of energy, processing and memory. The deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the network. Often, the sensors constituting the network cannot be accurately positioned, and are scattered erratically. To compensate the randomness character of their placement, a large number of sensors is typically deployed, which also helps to increase the fault tolerance of the network. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN. First, we introduce the problem of deployment and then we present the latest research works about the different proposed methods to solve this problem. Finally, we mention some similar issues related to the deployment and some of its interesting applications
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
The deployment of the sensor nodes (SNs) always plays a decisive role in the
system performance of wireless sensor networks (WSNs). In this work, we propose
an optimal deployment method for practical heterogeneous WSNs which gives a
deep insight into the trade-off between the reliability and deployment cost.
Specifically, this work aims to provide the optimal deployment of SNs to
maximize the coverage degree and connection degree, and meanwhile minimize the
overall deployment cost. In addition, this work fully considers the
heterogeneity of SNs (i.e. differentiated sensing range and deployment cost)
and three-dimensional (3-D) deployment scenarios. This is a multi-objective
optimization problem, non-convex, multimodal and NP-hard. To solve it, we
develop a novel swarm-based multi-objective optimization algorithm, known as
the competitive multi-objective marine predators algorithm (CMOMPA) whose
performance is verified by comprehensive comparative experiments with ten other
stateof-the-art multi-objective optimization algorithms. The computational
results demonstrate that CMOMPA is superior to others in terms of convergence
and accuracy and shows excellent performance on multimodal multiobjective
optimization problems. Sufficient simulations are also conducted to evaluate
the effectiveness of the CMOMPA based optimal SNs deployment method. The
results show that the optimized deployment can balance the trade-off among
deployment cost, sensing reliability and network reliability. The source code
is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page
Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors
Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC
Multi-objective 3D topology optimization of next generation wireless data center network
As one of the next generation network technologies for data centers, wireless data center network has important research significance. Smart architecture optimization and management are very important for wireless data center network. With the ever-increasing demand of data center resources, there are more and more data servers deployed. However, traditional wired links among servers are expensive and inflexible. Benefited from the development of intelligent optimization and other techniques, high speed wireless topology for wireless data center network is studied. Through image processing, a radio propagation model is constructed based on a heat map. The line-of-sight issue and the interference problem are also discussed. By simultaneously considering objectives of coverage, propagation intensity and interference intensity as well as the constraint of connectivity, we formulate the topology optimization problem as a multi-objective optimization problem. To seek for solutions, we employ several state-of-the-art serial MOEAs as well as three parallel MOEAs. For the
grouping in distributed parallel algorithms, prior knowledge
is referred. Finally, experimental results demonstrate that,
the parallel MOEAs perform effectively in optimization results and efficiently in time consumption
Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques
A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering
The 3D Deployment Multi-objective Problem in Mobile WSN: Optimizing Coverage and Localization
International audienceThe deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the sensor network. Coverage is an important metric reflecting how well the region of interest is monitored. Random deployment is the sim-plest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. In this paper, we study the positioning of sensor nodes in a WSN in order to maximize the coverage problem and to optimize the localization. First, the problem of deployment is introduced, then we present the latest research works about the different proposed methods. Also, we propose a mathematical formulation and a genetic based approach to solve this problem. Finally, the numerical results of experimentations are presented and discussed. Indeed, this paper presents a genetic algorithm which aims at searching for an optimal or near optimal solution to the coverage holes problem. Our algorithm defines the minimum number and the best locations of the mobile nodes to add after the initial random deployment of the stationary nodes. Compared with random deployment, our genetic algorithm shows significant performance improvement in terms of quality of coverage while optimizing the localization in the sensor network
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