Detection of multi‐occupancy using device‐free passive localisation

Abstract

Indoor device-free passive localisation (DfPL) technology uses a received signal strength indication (RSSI)-based method to record variances of a measured signal where a person being tracked is not carrying any electronic device that can be used to estimate the location. The system monitors the changes in the RSSI measurements caused by the presence of a human body in an indoor environment. For example, it is known that the resonance frequency of water is 2.4 GHz and the human body contains >70% water. Thus, the human body attenuates the wireless signal reacting as an absorber. Wireless communication signal strengths between a number of nodes, using IEEE 802.11 or 802.15.4 standards, show that communication links covering distinct areas cannot be affected simultaneously by only one person. Thus, the authors have deployed a novel system that can identify multi-occupants in an environment using patterns of motion from those monitored areas. A pattern recognition neural network was used to identify two people in the environment. No other work based on the DfPL technique has focused on multi-occupancy.

A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments

“…The compound effect of heterogeneous sensors is expected to enable new capabilities to obtain the semantic awareness of surrounding environment. Technologies for opportunistic and transformative radio sensing, such as device free radio localization [13], [14], [54], activity recognition [40], people counting [11] typically focus on the augmentation and transformation of existing radio devices, such as WiFi, machine-type connectivity (MTC) or cellular-wide wireless area networks (WWAN) [17] into human-scale sensors. They generally exploit electromagnetic (EM) fields maintained by different radio sources (i.e., micro-wave, THz bands, infrared).…”

An IoT Framework for Detecting Movement Within Indoor Environments

“…This section provides an overview of an indoor tracking architecture including the web application and the mobile app which we have developed which builds on our previous work [10,11,12,13]. This IoT focused framework allows the use of different indoor tracking technologies to be plugged in and it also allows the use of passive and active Please cite as: Kevin Curran, Gary Mansell, Jack Curran (2018) An IoT Framework for detecting Movement within Indoor Environments.…”

WLAN indoor target intrusion detection algorithm based on adaptive-depth ray tree

“…最后, 表 3 给出了本文方法与现有 MA [8] 、MV [8] 、射线层析成像 [9] 和神经网络 [10] 入侵检测方 法的性能对比. 可以看出, 本文方法在满足较高成功检测概率 (即 97.40%) 的同时, 还能保证较低的漏 检和虚警概率 (即分别为 3.42% 和 7.96%).…”