Neighbor Anchors Research Articles

Range-Free Localization Schemes for Wireless Sensor Networks

<span lang="EN-US">Localization of nodes is one of the key issues of wireless sensor network (WSN) that gained a wide attention in recent years. The existing localization techniques can be generally categorized into two types: range-based and range-free. Compared with rang-based schemes, the range-free schemes are more cost-effective, because no additional ranging devices are needed. As a result, we focus our research on the range-free schemes. In this paper we study three types of range-free location algorithms to compare the localization error and energy consumption of each one. Centroid algorithm requires a normal node has at least three neighbor anchors, while DV-hop algorithm doesn’t have this requirement. The third studied algorithm is the amorphous algorithm similar to DV-Hop algorithm, and the idea is to calculate the hop distance between two nodes instead of the linear distance between them .The simulation results show that the localization accuracy of the amorphous algorithm is higher than that of other algorithms and the energy consumption does not increase too much. </span>

Range-free 3D node localization in anisotropic wireless sensor networks

In this paper, we propose two computationally efficient ‘range-free’ 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.

High Efficient Localization Algorithm Applied in Complicated 3D Wireless Sensor Networks (ELCV)

This paper proposes a high e‐cient localization algorithm that can be used in complicated vile 3D Wireless Sensor Networks (WSNs), which is called ELCV. A general Gaussian noise model is introduced to characterize the non-ideal nature of listened background noise observed in WSNs. Meanwhile sensor nodes with random communication range are placed in the WSNs to better adapt complicated multivariant wireless environment. A spatial central point algorithm (ELCV) is addressed to provide robust estimation of acoustic source locations in the presence of assistant anchors. In this algorithm sensor nodes are equipped with random communication range that can be changed during a set scope. With ELCV, each individual unknown node will acquire data packages from anchors and then solve for a spatial node on some given point in cube space formed by eight neighbor anchors. With other three related anchor nodes around symmetric tetrahedron can be formed. Finally in this symmetric tetrahedron, centroid algorithm is added to compute estimated node position with accuracy and robustness. The localization mechanism has been implemented in Matlab simulation software. The efiectiveness and robustness of ELCV algorithm are clearly demonstrated through extensive simulation results.

An Improved Sensor Positioning for Wireless Sensor Networks

Sensor positioning is a fundamental block in various location-dependent applications of wireless sensor networks. In order to improve the positioning accuracy without increasing the complex and cost of sensor nodes, an improve sensor positioning method is proposed for wireless sensor networks. In the method, after receiving the broadcasting message of the neighboring anchor nodes, the sensor nodes calculate a modifying factor of the change of the signal strength. And they modify the distances between themselves and neighboring anchor nodes with the modifying factor. Simulation results show that the proposed method can obtain a high positioning accuracy.

Binary Particle Swarm Optimization (BPSO) Algorithm for Distributed Node Localization

In this paper, we propose a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization under the distance measurement from three or more neighboring anchors. The node that gets localized will be used as a reference for remaining nodes. A comparison of the performances of PSO and BPSO in terms of localization error and computation time is presented using simulations in Matlab.

Research of ELCV 3D Localization Algorithm in Ship Area Networks

We propose a 3D localization algorithm used in ship area networks (SANs) in the literature before. In this paper in order to explore more performances of ELCV we made some other comprehensive simulations. In ELCV a classic noise model is introduced to characterize the acoustic background noise observed in SANs. Meanwhile random communication range nodes are placed in the SANs. ELCV is addressed to provide robust estimation of unknown nodes in the presence of outliers. In this algorithm sensor nodes are also equipped with random communication range that can be changed during a set scope. With ELCV, each individual unknown node will acquire data packages from anchors and then solve for a spatial node on some given point in cube space formed by eight neighbor anchors. With other three related anchor nodes around symmetric tetrahedron can be formed. Finally by centroid algorithm, in this symmetric tetrahedron, estimated node positions with accuracy and robustness are obtained. In this work more parameters are changed and different environment arguments are taken into account and then simulation results are given. By these simulation results we further prove the accuracy, efficiency and robustness in SANs. Meanwhile simulation processes are finished in MATLAB software. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2704 Full Text: PDF

Performance analysis of new centroid localization algorithm in wireless sensor network

Centroid Localization(CL) algorithm is a representative range-free localization algorithm in Wireless Sensor Network(WSN).To improve the localization accuracy of CL when anchors are unevenly distributed,Smallest Enclosing Polygon Localization(SEPL) algorithm was presented.In SEPL,the centroid of the smallest polygon which enclosed the neighbor anchors of the unknown node was regarded as the estimated location of the unknown node.The simulation results show that SEPL is robust when the topology of anchors is not uniform.The proposed algorithm outperforms CL by an average of 15% in localization accuracy.

Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.

A Node Localization Algorithm Based on Adaptive Inertia Weight Particle Swarm Optimization

Node localization is a key technology of wireless sensor network applications. Considering the resource constraints of senor nodes, node localization is transformed into an unconstrained optimization problem, then one distributed localization algorithm based on particle swarm with adaptive inertia weight is put forward. The unknown nodes construct particle region using the received information of their neighbor anchor nodes, which can reduce the search scope and save the computational cost of the algorithm. Mean deviation of the distances which are the particles to the global optima can be computed to characterize the distribution of particles, and is used to adaptively adjust the inertia weight to avoid falling into the local optima. Simulation shows that the proposed algorithm has a good localization performance.