Indoor localisation by using wireless sensor nodes

This study is devoted to investigating and developing WSN based localisation approaches with high position accuracies indoors. The study initially summarises the design and implementation of localisation systems and WSN architecture together with the characteristics of LQI and RSSI values. A fingerprint localisation approach is utilised for indoor positioning applications. A k-nearest neighbourhood algorithm (k-NN) is deployed, using Euclidean distances between the fingerprint database and the object fingerprints, to estimate unknown object positions. Weighted LQI and RSSI values are calculated and the k-NN algorithm with different weights is utilised to improve the position detection accuracy. Different weight functions are investigated with the fingerprint localisation technique. A novel weight function which produced the maximum position accuracy is determined and employed in calculations. The study covered designing and developing the centroid localisation (CL) and weighted centroid localisation (WCL) approaches by using LQI values. A reference node localisation approach is proposed. A star topology of reference nodes are to be utilized and a 3-NN algorithm is employed to determine the nearest reference nodes to the object location. The closest reference nodes are employed to each nearest reference nodes and the object locations are calculated by using the differences between the closest and nearest reference nodes. A neighbourhood weighted localisation approach is proposed between the nearest reference nodes in star topology. Weights between nearest reference nodes are calculated by using Euclidean and physical distances. The physical distances between the object and the nearest reference nodes are calculated and the trigonometric techniques are employed to derive the object coordinates. An environmentally adaptive centroid localisation approach is proposed.Weighted standard deviation (STD) techniques are employed adaptively to estimate the unknown object positions. WSNs with minimum RSSI mean values are considered as reference nodes across the sensing area. The object localisation is carried out in two phases with respect to these reference nodes. Calculated object coordinates are later translated into the universal coordinate system to determine the actual object coordinates. Virtual fingerprint localisation technique is introduced to determine the object locations by using virtual fingerprint database. A physical fingerprint database is organised in the form of virtual database by using LQI distribution functions. Virtual database elements are generated among the physical database elements with linear and exponential distribution functions between the fingerprint points. Localisation procedures are repeated with virtual database and localisation accuracies are improved compared to the basic fingerprint approach. In order to reduce the computation time and effort, segmentation of the sensing area is introduced. Static and dynamic segmentation techniques are deployed. Segments are defined by RSS ranges and the unknown object is localised in one of these segments. Fingerprint techniques are applied only in the relevant segment to find the object location. Finally, graphical user interfaces (GUI) are utilised with application program interfaces (API), in all calculations to visualise unknown object locations indoors

Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

Radiometric Wireless Sensor Network Monitoring of Partial Discharge Sources in Electrical Substations

A wireless sensor network (WSN) with the potential to monitor and locate partial discharge (PD) in high-voltage electricity substations using only received signal strength (RSS) is proposed. The advantages of an RSS-based operating principle over more traditional methods (e.g., time-of-arrival and time-difference-of-arrival) are described. Laboratory measurements of PD that emulate the operation of a PD WSN are presented. The hardware architecture of a prototype PD WSN is described and the particular challenges of an RSS-based location approach in an environment with an unknown, and spatially varying, path-loss index are discussed. It is concluded that an RSS-based PD WSN is a plausible solution for the monitoring of insulation integrity in electricity substations

Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well