Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper

Device-Free Localization for Human Activity Monitoring

Over the past few decades, human activity monitoring has grabbed considerable research attentions due to greater demand for human-centric applications in healthcare and assisted living. For instance, human activity monitoring can be adopted in smart building system to improve the building management as well as the quality of life, especially for the elderly people who are facing health deterioration due to aging factor, without neglecting the important aspects such as safety and energy consumption. The existing human monitoring technology requires additional sensors, such as GPS, PIR sensors, video camera, etc., which incur cost and have several drawbacks. There exist various solutions of using other technologies for human activity monitoring in a smartly controlled environment, either device-assisted or device-free. A radio frequency (RF)-based device-free indoor localization, known as device-free localization (DFL), has attracted a lot of research effort in recent years due its simplicity, low cost, and compatibility with the existing hardware equipped with RF interface. This chapter introduces the potential of RF signals, commonly adopted for wireless communications, as sensing tools for DFL system in human activity monitoring. DFL is based on the concept of radio irregularity where human existence in wireless communication field may interfere and change the wireless characteristics

AI-Based Analytics for Hawkers Identification in Video Surveillance for Smart Community

Street hawking is a widespread phenomenon in urban areas globally, presenting challenges for local authorities such as traffic congestion, waste management, and negative impacts on the city's image. This research addresses key issues faced by authorities in managing hawkers, including the resistance to formalization, maintaining urban aesthetics, waste disposal, and understanding user preferences. The study investigates the performance of the You Only Look Once (YOLO) algorithm, utilizing Convolutional Neural Networks (CNN) for real-time object detection. To achieve thisobjective, the YOLOv5 algorithm is trained with a custom image dataset collected from the same camera along the street in the city area to detect five classes of objects, namely umbrella, table, stool, car, and people. Real images that were captured via camera and video surveillance were compiled as datasets which are then used to train and test the algorithm. The study aims to provide insights into the data collection process of hawkers along the street around the areas and the development of real-time hawker detection for the smart city application

Inline 3D volumetric measurement of moisture content in rice using regression-based ML of RF tomographic imaging

The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos

Enhanced experimental investigation of threshold determination for efficient channel detection in 2.4 GHz WLAN cognitive radio networks

This paper presents an experimental investigation of threshold determination for efficient channel detection in wireless LAN (WLAN) based cognitive radio (CR) networks. The spectrum saturation problem is a critical issue in wireless communication systems worldwide due to on growing user demands day by day with many new applications to the limited frequency spectrum. Hence, present demand is an efficient and intelligent spectrum management and allocation system. In this paper, we proposed an adaptive threshold determination technique based on free space path loss (FSPL) model to detect the presence or absence of PUs. The model is designed especially for Android based smartphones and tablets. The smartphones act as secondary users (SUs) and existing 2.4 GHz WLAN channels as PUs. The network is prepared in a usual noisy lab/outdoor environment and tested for the robustness of the proposed model. Results show the desired range of usable threshold and the channel detection performance depends on the noise floor level of the surrounding environment

Cancer detection using an electronic nose: A preliminary study on detection and discrimination of cancerous cells

Lack of effective tools to diagnose lung cancer at an early stage has caused high mortality in cancer patients especially in lung cancer patients. Electronic nose (E-Nose) technology is believed to offer non-invasive, rapid and reliable analytic approach by measuring the odour released from cancer to assist medical diagnosis. In this work, using a commercial E-nose (Cyranose-320), we aimed to detect the volatile organic compounds (VOCs) emitted by different types of cancerous cells. The lung cancer cell (A549) and breast cancer cell (MCF-7) were used for this study. Both cells were cultured using Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% of Fetal Bovine Serum (FBS) and incubated for three days. The static headspace of cell cultures and blank medium were directly sniffed by Cyranose-320. The preliminary results from this study showed that, the E-nose is able to detect and distinguish the presence of VOCs in cancerous cells with accuracy of 100% using LDA. To this end, the VOCs emitted from cancerous cells can potentially used as biomarker

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model