Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art

Anchor Nodes Placement for Effective Passive Localization

Wireless sensor networks are composed of sensor nodes, which can monitor an environment and observe events of interest. These networks are applied in various fields including but not limited to environmental, industrial and habitat monitoring. In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to find sensor node's positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. I do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. I show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity. The more the non-linearity, the better the localization. The localization for our network design proves better when I place anchor nodes at right angles

Accurate range free localization in multi-hop wireless sensor networks

To localize wireless sensor networks (WSN)s nodes, only the hop-based data have been so far utilized by range free techniques, with poor-accuracy, though. In this thesis, we show that localization accuracy may importantly advantage from mutual utilization, at no cost, of the information already offered by the advancing nodes (i.e., relays) between all anchors (i.e., position-aware) and sensor nodes join up. In addition, energy-based informant localization approaches are generally established corresponding to the channel path-loss models in which the noise is mostly expected to shadow Gaussian distributions. In this thesis, we signify the applied additive noise by the Gaussian mixture model and improve a localization algorithm depend on the received signal intensity to attain the greatest likelihood location, estimator. By employing Jensen’s inequality and semidefinite relaxation, the originally offered nonlinear and nonconvex estimator is relaxed into a convex optimization difficulty, which is able to be professionally resolved to acquire the totally best solution. Moreover, the resultant Cramer–Rao lower bound is originated for occurrence comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks. The performance is evaluated in terms of RMSE in terms of three algorithms WLS, CRLR, and GMSDP based on using the Monte Carlo simulation with account the number of anchors that varying from anchor=4 to anchor =20. Finally, the GMSDP- algorithm achieves and provides a better value of RMSEs and the greatest localization estimation errors comparing with the CRLR algorithm and WLS algorithm

Assessment of indoor localization algorithm performance under realistic conditions

Wireless sensor networks localization is one of most vibrant research field since it has more advantageous than a global positioning system (GPS) in aspect of cost and indoor usability. In wireless sensor networks (WSN), predetermined location of a sensor can sometimes be meaningless because a sensor can be deployed in such an area where is hardly reachable. Therefore, a sensor must be able to localize itself and provides its location information to other sensor nodes. Non-cooperative localization, also called traditional localization, like local positioning system (LPS) has been done by communications only between anchor nodes and an agent node. However, a sensor requires to have a sensor that has unlimited power transmission capability, which seems unrealistic. Furthermore, accuracy is also limited due to communication only between an anchor node and an agent node. To deal with these problems, cooperative localization technique has been suggested and studied. Cooperative localization is the technique including not only measurement between anchor nodes and an agent node but also the measurement between agent nodes for location estimation so as to increase positioning accuracy and robustness. Among various sensory modalities (acoustic, seismic, infrared and so on) the modality of measurement made in wireless sensor networks throughout this thesis is radio frequency (RF). Simulation results of both non-cooperative and cooperative localization accuracy will be observed in order to prove that cooperative localization outperforms non-cooperative localization. In addition, simulated data is compared to data which is actually measured in indoor environment to give the idea how reliable the simulation is in indoor

Comparative node selection-based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co-operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade-off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering