Device Free Localisation Techniques in Indoor Environments

The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised

Fine-Grained Localization for Multiple Transceiver-Free Objects by using RF-Based Technologies

In traditional radio-based localization methods, the target object has to carry a transmitter (e. g., active RFID), a receiver (e.g., 802.11 x detector), or a transceiver (e. g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow the tracking of a transceiver-free object. Based on radio signal strength indicator (RSSI), which is readily available in wireless communication, three centralized tracking algorithms, and one distributed tracking algorithm are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has higher tracking accuracy but requires calibration. The probabilistic cover algorithm is based on distributed dynamic clustering. It can dramatically improve the localization accuracy when multiple objects are present. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the localization accuracy for single object can reach about 0.8 m and for multiple objects is about 1 m

Device-Free, Radio-based Activity Recognition using Smart Home Wireless Communication Technologies

This dissertation demonstrates the use of received signal strength to infer human activities. It has the following contributions: 1) Reference design of a device-free, 2.4GHz IEEE 802.15.4-based sensor system for Activity Recognition; 2) Fundamental description of influences affecting Activity Recognition performance; 3) Software design pattern for device-free, radio-based Inference Systems; 4) Development and characterization of three specialized device-free, radio-based Inference System