One fundamental issue in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal...
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ISBN:
(纸本)9781581137644
One fundamental issue in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a predefined value. The sensing ranges of sensors can be unit disks or non-unit disks. We present polynomial-time algorithms, in terms of the number of sensors, that can be easily translated to distributed protocols. The result is a generalization of some earlier results where only k=1 is assumed. Applications of the result include: (i) positioning applications, (ii) situations which require stronger environmental monitoring capability, and (iii) scenarios which impose more stringent fault-tolerant capability.
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with...
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ISBN:
(纸本)9781509003051
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.
coverage and connection problem have been the question which worth exploring in wireless sensor networks (WSNs). How to use connected sensors to cover the designate area is the major goal. Currently, some peoples have...
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ISBN:
(纸本)9781479953905
coverage and connection problem have been the question which worth exploring in wireless sensor networks (WSNs). How to use connected sensors to cover the designate area is the major goal. Currently, some peoples have studied this problem, but their proposed methods focused on 2D ideal plane or 3D full space. However, the actual terrain is rugged and uneven. Therefore, the exist solutions cannot obtain the fairest information, so we use Spline function to shape the FoI(Field of Interest) from the undulate terrain. Furthermore, we use the feature of convex hull of Spline function to ensure the full coverage for FoI. And we propose a shorter transmission path algorithm to reduce network latency and energy consumption between sensors. In evaluation results, our proposed algorithm provides more efficient connection and a guarantee of high coverage in the same time.
It is well known that connectivity and coverage are the important elements in the Wireless sensor networks (WSNs). In order to make a complete WSNs, scholars have begun to study how to use connected sensors to cover t...
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It is well known that connectivity and coverage are the important elements in the Wireless sensor networks (WSNs). In order to make a complete WSNs, scholars have begun to study how to use connected sensors to cover the designate area, but their proposed methods focused on 2D ideal plane or 3D full space, however, the actual terrain is rugged and uneven. It means that their proposed method may not suitable for a real environment. In addition, most of studies only consider single target that many researchers separated the connectivity problem and coverage problem. However, those two problems have very close relationship, so they must be considered in a same time, otherwise, the results may fall into a local optimum. For instance, several researches presented that use of transmission range adjustment to reduce redundant coverage so that they miss many chance to pass the message by shortest path since the many neighbors cannot be covered when coverage is decreased. Therefore, we use Spline function to shape the irregular FoI (Field of Interest) as well as use feature of convex hull of Spline function to ensure the full coverage and then design a shorter transmission path algorithm according to the coverage pattern to reduce network latency and energy consumption. The simulation results show that the proposed method indeed taking into account coverage and connection problem so that it certainty suitable for real WSNs environment.
Energy-efficient coverage enhancement (EEC) is a highly nonconvex and challenging optimization problem in wireless sensor networks (WSNs) deployment. Traditional intelligent optimization algorithms often suffer from p...
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Energy-efficient coverage enhancement (EEC) is a highly nonconvex and challenging optimization problem in wireless sensor networks (WSNs) deployment. Traditional intelligent optimization algorithms often suffer from premature convergence and low efficiency when solving the EEC problem. In this article, a competitive learning optimizer (CLO) is proposed and upgraded to a multiobjective competitive learning optimizer (MOCLO), which is inspired by the behavior of human competitive learning in workplace. In order to verify the single-objective optimization capability of CLO, it is applied to the coverage problem in 2-D and 3-D WSNs, and the simulation results indicate that CLO achieves the overall best results in terms of solution accuracy, stability and convergence by comparing with five well-regarded optimization algorithms. For solving the EEC problem in 2-D and 3-D WSNs, MOCLO is applied. By comparing three popular multiobjective optimization algorithms, it is found that MOCLO can consume less energy to achieve higher coverage, which proves the effectiveness and superiority of MOCLO in addressing the EEC problem.
Wireless sensor networks consist of a large number of sensor nodes with limited power and resource. To prolong network lifetime, the energy consumption must be somehow reduced In this paper, we propose a localized den...
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ISBN:
(纸本)9781424423019
Wireless sensor networks consist of a large number of sensor nodes with limited power and resource. To prolong network lifetime, the energy consumption must be somehow reduced In this paper, we propose a localized density control algorithm for energy savings. The goals are to maintain a minimal number of active sensor nodes and to reduce radio-traffic intensity while conserving the sensing coverage of the network. Our localized algorithm is based on a greedy solution of a weighted set-cover problem. Each node locally computes whether to sleep or to stay active. Given that the local decision might worsen the sensing coverage, we also introduce a voting scheme for selecting active nodes to assure that a node can sleep if and only if its, sensing area is completely covered by its active neighbors. We have implemented our localized algorithm and voting scheme on Tiny OS and evaluated on TOSSIM. The result indicates that our algorithm is efficient and viable for practical use.
Wireless sensor networks (WSNs) have recently attracted a great deal of attention due to their numerous attractive applications in many different fields. Sensors and WSNs possess a number of special characteristics th...
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Wireless sensor networks (WSNs) have recently attracted a great deal of attention due to their numerous attractive applications in many different fields. Sensors and WSNs possess a number of special characteristics that make them very promising in a wide range of applications, but they also put on them lots of constraints that make issues in sensor network particularly challenging. These issues may include topology control, routing, coverage, security, data management and many others. Among them, coverage problem is one of the most fundamental ones for which a WSN has to watch over the environment such as a forest (area coverage) or set of subjects such as collection of precious renaissance paintings (target of point coverage) in order for the network to be able to collect environment parameters, and maybe further monitor the environment. In this dissertation, we highly focus on the area coverage problem. With no assumption of sensors’ locations (i.e., the sensor network is randomly deployed), we only consider distributed and parallel scheduling methods with the ultimate objective of maximizing network lifetime. Additionally, the proposed solutions (including algorithms, a scheme, and a framework) have to be energy-efficient. Generally, we investigate numerous generalizations and variants of the basic coverage problem. Those problems of interest include k-coverage, composite event detection, partial coverage, and coverage for adjustable sensing range network. Various proposed algorithms. In addition, a scheme and a framework are also suggested to solve those problems. The scheme, which is designed for emergency alarming applications, specifies the guidelines for data and communication patterns that significantly reduce the energy consumption and guarantee very low notification delay. For partial coverage problem, we propose a universal framework (consisting of four strategies) which can take almost any complete-coverage algorithm as an input to generate an algorithm
Wireless sensor networks (WSNs) consist of battery-limited sensor nodes which have the ability of sensing the environment, communicating with other nodes and processing the data. Large number of sensor node deployment...
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Wireless sensor networks (WSNs) consist of battery-limited sensor nodes which have the ability of sensing the environment, communicating with other nodes and processing the data. Large number of sensor node deployment over a geographical area imposes some constraints on the retrieval of the data. The use of mobile sinks (e. g., unmanned aerial vehicle, UAV) is an effective solution method for such large-scale networks. However, depending on the path and altitude of the UAV, and the type of radios in use, coverage problem arises where some nodes cannot get connected to the UAV. In this paper, the coverage problem is examined where UAV is used as mobile sink node. On the basis of our analysis, a dynamic and distributed clustering approach is proposed. Evaluations are performed with a realistic simulation environment. Performance results show that proposed approach reduces the energy-consumption and construct more stable and well balanced clusters that connect the uncovered nodes to the UAV.
We present a methodology for analyzing coverage properties in dynamic sensor networks. The dynamic sensor network under consideration is studied through a series of snapshots, and is represented by a sequence of simpl...
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We present a methodology for analyzing coverage properties in dynamic sensor networks. The dynamic sensor network under consideration is studied through a series of snapshots, and is represented by a sequence of simplicial complexes, built from the communication graph at each time point. A method from computational topology called zigzag persistent homology takes this sequence of simplicial complexes as input, and returns a 'barcode' containing the birth and death times of homological features in this sequence. We derive useful statistics from this output for analyzing time-varying coverage properties. In addition, we propose a method which returns specific representative cycles for these homological features, at each point along the birth-death intervals. These representative cycles are then used to track coverage holes in the network, and obtain size estimates for individual holes at each time point. A weighted barcode, incorporating the size information, is then used as a visual and quantitative descriptor of the dynamic network coverage. (C) 2015 Elsevier B.V. All rights reserved.
The scientific location of earthquake emergency supply warehouses is conducive to the effective distribution of emergency relief resources and improved rescue efficiency in earthquake hazard. Comprehensively consideri...
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The scientific location of earthquake emergency supply warehouses is conducive to the effective distribution of emergency relief resources and improved rescue efficiency in earthquake hazard. Comprehensively considering the regional population as well as coverage quality at the demand points, this paper aims to divide the coverage thresholds of earthquake emergency rescue and logistic supplies according to their time-series features,and to build a location model for supply warehouses according to the variety and amount of stored supplies considering their time-series features, in hope of optimizing the set covering issue of earthquake relief supply warehouses. The solution is approached with two methods: the target deviation rate minimization model and NSGA-Ⅱ algorithm. The results obtained by solving the target deviation rate minimization model can balance every target. The branch and bound algorithm can find the global optimal solution at a certain calculation scale with high calculation efficiency, but its efficiency decreases significantly when the operation scale increases. The NSGA-Ⅱ algorithm is more suitable for large-scale solution calculations with high calculation efficiency, and it can output a set of non-inferior solutions for decision makers to select from according to different preference. Taking Aba Prefecture in Sichuan Province as illustration, the feasibility of the model is validated;meanwhile, the effectiveness and benefits of the two approaches in solving the problem of multi-objective set covering of the warehouses are compared and analyzed.
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