7,147 research outputs found
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
Set It and Forget It: Approximating the Set Once Strip Cover Problem
We consider the Set Once Strip Cover problem, in which n wireless sensors are
deployed over a one-dimensional region. Each sensor has a fixed battery that
drains in inverse proportion to a radius that can be set just once, but
activated at any time. The problem is to find an assignment of radii and
activation times that maximizes the length of time during which the entire
region is covered. We show that this problem is NP-hard. Second, we show that
RoundRobin, the algorithm in which the sensors simply take turns covering the
entire region, has a tight approximation guarantee of 3/2 in both Set Once
Strip Cover and the more general Strip Cover problem, in which each radius may
be set finitely-many times. Moreover, we show that the more general class of
duty cycle algorithms, in which groups of sensors take turns covering the
entire region, can do no better. Finally, we give an optimal O(n^2 log n)-time
algorithm for the related Set Radius Strip Cover problem, in which all sensors
must be activated immediately.Comment: briefly announced at SPAA 201
Movement-efficient Sensor Deployment in Wireless Sensor Networks
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Two key issues in MWSNs - energy consumption, which
is dominated by sensor movement, and sensing coverage - have attracted plenty
of attention, but the interaction of these issues is not well studied. To take
both sensing coverage and movement energy consumption into consideration, we
model the sensor deployment problem as a constrained source coding problem. %,
which can be applied to different coverage tasks, such as area coverage, target
coverage, and barrier coverage. Our goal is to find an optimal sensor
deployment to maximize the sensing coverage with specific energy constraints.
We derive necessary conditions to the optimal sensor deployment with (i) total
energy constraint and (ii) network lifetime constraint. Using these necessary
conditions, we design Lloyd-like algorithms to provide a trade-off between
sensing coverage and energy consumption. Simulation results show that our
algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure
Average Case Network Lifetime on an Interval with Adjustable Sensing Ranges
Given n sensors on an interval, each of which is equipped with an adjustable sensing radius and a unit battery charge that drains in inverse linear proportion to its radius, what schedule will maximize the lifetime of a network that covers the entire interval? Trivially, any reasonable algorithm is at least a 2-approximation for this Sensor Strip Cover problem, so we focus on developing an efficient algorithm that maximizes the expected network lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an expected network lifetime within 12 % of the theoretical maximum. Most of the algorithms that we consider come from a particular family of RoundRobin coverage, in which sensors take turns covering predefined areas until their battery runs out
A Survey of Coverage Problems in Wireless Sensor Networks
Coverage problem is an important issue in wireless sensor networks, which has a great impact on the performance of wireless sensor networks. Given a sensor network, the coverage problem is to determine how well the sensing field is monitored or tracked by sensors. In this paper, we classify the coverage problem into three categories: area coverage, target coverage, and barrier coverage, give detailed description of different algorithms belong to these three categories. Moreover, we specify the advantages and disadvantages of the existing classic algorithms, which can give a useful direction in this area
Distributed Algorithms for Maximizing the Lifetime of Wireless Sensor Networks
Wireless sensor networks (WSNs) are emerging as a key enabling technology for applications domains such as military, homeland security, and environment. However, a major constraint of these sensors is their limited battery. In this dissertation we examine the problem of maximizing the duration of time for which the network meets its coverage objective. Since these networks are very dense, only a subset of sensors need to be in sense or on mode at any given time to meet the coverage objective, while others can go into a power conserving sleep mode. This active set of sensors is known as a cover. The lifetime of the network can be extended by shuffling the cover set over time. In this dissertation, we introduce the concept of a local lifetime dependency graph consisting of the cover sets as nodes with any two nodes connected if the corresponding covers intersect, to capture the interdependencies among the covers. We present heuristics based on some simple properties of this graph and show how they improve over existing algorithms. We also present heuristics based on other properties of this graph, new models for dealing with the solution space and a generalization of our approach to other graph problems
Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain
In this paper, we study the problem of moving sensors on a line to form a
barrier coverage of a specified segment of the line such that the maximum
moving distance of the sensors is minimized. Previously, it was an open
question whether this problem on sensors with arbitrary sensing ranges is
solvable in polynomial time. We settle this open question positively by giving
an time algorithm. For the special case when all sensors have
the same-size sensing range, the previously best solution takes time.
We present an time algorithm for this case; further, if all
sensors are initially located on the coverage segment, our algorithm takes
time. Also, we extend our techniques to the cycle version of the problem
where the barrier coverage is for a simple cycle and the sensors are allowed to
move only along the cycle. For sensors with the same-size sensing range, we
solve the cycle version in time, improving the previously best
time solution.Comment: This version corrected an error in the proof of Lemma 2 in the
previous version and the version published in DCG 2013. Lemma 2 is for
proving the correctness of an algorithm (see the footnote of Page 9 for why
the previous proof is incorrect). Everything else of the paper does not
change. All algorithms in the paper are exactly the same as before and their
time complexities do not change eithe
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
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