Localization in wireless sensor networks: Classification and evaluation of techniques

Niewiadomska-Szynkiewicz, Ewa

doi:10.2478/v10006-012-0021-x

Cited by 46 publications

(21 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of numerous experiments are presented and discussed in Niewiadomska-Szynkiewicz & Marks (2009), Niewiadomska-Szynkiewicz (2012. In this paper we compare the performance of four localization schemes: trilateration (T), simulated annealing (SA) and two hybrid techniques TSA and TGA.…”

Section: Numerical Verification and Performance Evaluation Of Hplsmentioning

confidence: 99%

“…Moreover, it was observed (see Niewiadomska-Szynkiewicz & Marks (2009), Niewiadomska-Szynkiewicz (2012) that different methods are suitable for different network topologies. Therefore, development of high accuracy localization system that can be applied to various networks requires multiple execution of the localization schemes for different values of parameters specific to the considered algorithms and various test networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High performance wireless sensor network localisation system

Marks¹,

Niewiadomska-Szynkiewicz²,

Kołodziej³

2014

IJAHUC

View full text Add to dashboard Cite

Abstract:In this paper we summarize the results of our research concerned with the development, implementation and evaluation of a software framework for wireless sensor networks localization -High Performance Localization System (HPLS). The system can be used to calculate positions of sensing devices (network nodes) in the deployment area, and to tune and verify various localization schemes through simulation. It provides tools for data acquisition from a workspace, estimation of inter-node distances, calculation of geographical coordinates of all nodes with unknown position and results evaluation. Received Signal Strength measurements are utilized to support the localization process. Trilateration, simulated annealing and genetic algorithm are applied to calculate the geographical coordinates of network nodes. The utility, efficiency and scalability of the proposed localization system HPLS have been justified through simulation and testbed implementation. The calculations have been done in parallel using the map-reduce paradigm and the HPC environment formed by a cluster of servers. The testbed networks were formed by sensor devices manufactured by Advantic Technology (clones of TelosB platform). A provided case study demonstrates the localization accuracy obtained for small-, medium-and large-size multihop networks.

show abstract

Section: Numerical Verification and Performance Evaluation Of Hplsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High performance wireless sensor network localisation system

Marks¹,

Niewiadomska-Szynkiewicz²,

Kołodziej³

2014

IJAHUC

View full text Add to dashboard Cite

show abstract

“…In such a situation, the only option is to increase the number of anchor nodes in the network to increase the coverage [16] . Therefore, the number of anchor nodes, M, is changed for evaluating the effect of M on the localization performance.…”

Section: ⅳ Simulation Results and Discussionmentioning

confidence: 99%

Distributed Sensor Node Localization Using a Binary Particle Swarm Optimization Algorithm

Fatihah¹,

Shin²

2014

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

This paper proposes a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization using the value of the measured distances from three or more neighboring anchors, i.e., nodes that know their location information. The node that is localized during the localization process is then used as another anchor for remaining nodes. The performances of particle swarm optimization (PSO) and BPSO in terms of localization error and computation time are compared by using simulations in Matlab. The simulation results indicate that PSO-based localization is more accurate. In contrast, BPSO algorithm performs faster for finding the location of unknown nodes for distributed localization. In addition, the effects of transmission range and number of anchor nodes on the localization error and computation time are investigated.

show abstract

“…The central station determines the initial position of each node in a network system based on the measurements from GPS or other localization systems described in [13]. Next, the messages with initial goals are distributed among nodes.…”

Section: L1mentioning

confidence: 99%

“…However, in many systems which could use our mobility model the distributed scheme ford i g computing has to be applied. In such scheme the estimate ofd i g is calculated by the i-th node based on current measurements [13], and with regard to the current communication conditions. The measurements can be given by various sensing components of the node, e.g., cameras, force sensors, etc.…”

Section: Reference Distance Estimationmentioning

confidence: 99%

Modeling Mobility in Cooperative Ad Hoc Networks

Niewiadomska-Szynkiewicz

Sikora

Kołodziej

2013

Mobile Netw Appl

Self Cite

View full text Add to dashboard Cite

This paper addresses issues concerned with design and managing of mobile ad hoc networks. We focus on self-organizing, cooperative and coherent networks that enable a continuous communication with a central decision unit and adopt to changes in an unknown environment to achieve a given goal. In general, it is very difficult to model a motion of nodes of a real-life ad hoc network. However, mobility modeling is a critical element that has great influence on the performance characteristics of a cooperative system. In this paper we investigate a novel approach to cooperative and fully connected networks design. We present an algorithm for efficient calculating of motion trajectories of wireless devices. Our computing scheme adopts two techniques, the concept of an artificial potential field and the concept of a particle-based mobility. The utility and efficiency of the proposed approach has been justified through simulation experiments. The results of presented case studies show a wide range of applications of our method starting from simple to more complex ad hoc networks.E. Niewiadomska-Szynkiewicz ( ) Institute

show abstract

Localization in wireless sensor networks: Classification and evaluation of techniques

Cited by 46 publications

References 51 publications

High performance wireless sensor network localisation system

High performance wireless sensor network localisation system

Distributed Sensor Node Localization Using a Binary Particle Swarm Optimization Algorithm

Modeling Mobility in Cooperative Ad Hoc Networks

Contact Info

Product

Resources

About