1,332 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
Emitter Location Finding using Particle Swarm Optimization
Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error
Target localization using RSS measurements in wireless sensor networks
The subject of this thesis is the development of localization algorithms for target localization in
wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS
(QRSS) measurements.
In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many
existing works on RSS localization assumes that the shadowing components are uncorrelated.
However, here, shadowing is assumed to be spatially correlated. It can be shown that
localization accuracy can be improved with the consideration of correlation between pairs of RSS
measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function,
a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative
technique based on Newtons method is utilized to give a solution. Numerical simulations show
that the proposed algorithms achieves better performance than existing algorithms with reasonable
complexity.
In chapter 4, target localization with an unknown path loss model parameter is investigated. Most
published work estimates location and these parameters jointly using iterative methods with a good
initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization
algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function.
By combining PSO with a consensus algorithm, the centralized estimation problem is extended to
a distributed version so that can be implemented in distributed WSN. Although suboptimal, the
distributed approach is very suitable for implementation in real sensor networks, as it is scalable,
robust against changing of network topology and requires only local communication. Numerical
simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound
(CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with
respect to the centralized PSO.
In chapter 5, a distributed gradient algorithm for RSS based target localization using only
quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an
initial estimate for the gradient algorithm. A practical quantization threshold designer is presented
for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate
at each node is also quantized. The RSS measurements and the local estimate at each sensor
node are quantized in different ways. By using a quantization elimination scheme, a quantized
distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the
local estimate is gradually eliminated with each iteration. Simulations show that the performance
of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a
small number of bits can achieve the performance of the distributed gradient algorithm using
unquantized data
AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM
Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 mete
Monte Carlo localization algorithm based on particle swarm optimization
In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70 m/s
Nature Inspired Range Based Wireless Sensor Node Localization Algorithms
Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining
the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO
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