Ultra wideband: applications, technology and future perspectives

Ultra Wide Band (UWB) wireless communications offers a radically different approach to wireless communication compared to conventional narrow band systems. Global interest in the technology is huge. This paper reports on the state of the art of UWB wireless technology and highlights key application areas, technological challenges, higher layer protocol issues, spectrum operating zones and future drivers. The majority of the discussion focuses on the state of the art of UWB technology as it is today and in the near future

Channel estimation and transmit power control in wireless body area networks

Wireless body area networks have recently received much attention because of their application to assisted living and remote patient monitoring. For these applications, energy minimisation is a critical issue since, in many cases, batteries cannot be easily replaced or recharged. Reducing energy expenditure by avoiding unnecessary high transmission power and minimising frame retransmissions is therefore crucial. In this study, a transmit power control scheme suitable for IEEE 802.15.6 networks operating in beacon mode with superframe boundaries is proposed. The transmission power is modulated, frame-by-frame, according to a run-time estimation of the channel conditions. Power measurements using the beacon frames are made periodically, providing reverse channel gain and an opportunistic fade margin, set on the basis of prior power fluctuations, is added. This approach allows tracking of the highly variable on-body to on-body propagation channel without the need to transmit additional probe frames. An experimental study based on test cases demonstrates the effectiveness of the scheme and compares its performance with alternative solutions presented in the literature

On-Body Channel Measurement Using Wireless Sensors

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/TAP.2012.219693

Study of MAC Protocols for Mobile Wireless Body Sensor Networks

Wireless Body Area Networks (WBAN) also referred to as a body sensor network (BSN), is a wireless network of wearable computing devices. It has emerged as a key technology to provide real-time health monitoring of a patient and diagnose many life threatening diseases. WBAN operates in close vicinity to, on, or inside a human body and supports a variety of medical and non-medical applications. The design of a medium access control is a challenge due to the characteristics of wireless channel and the need to fulfill both requirements of mobility support and energy efficiency.  This paper presents a comparative study of IEEE 802.15.6, IEEE 804.15.4 and T-MAC in order to analyze the performance of each standard in terms of delay, throughput and energy consumption. Keywords: Biomedical, IEEE 802.15.6; T-MAC, IEEE 802.15.4, mobility, low-power communication, wireless body sensor networks, implantable sensors, healthcare applications, biosensors

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Artificial Neural Network Based Prediction Mechanism for Wireless Network on Chips Medium Access Control

As per Moore’s law, continuous improvement over silicon process technologies has made the integration of hundreds of cores on to a single chip possible. This has resulted in the paradigm shift towards multicore and many-core chips where, hundreds of cores can be integrated on the same die and interconnected using an on-chip packet-switched network called a Network-on-Chip (NoC). Various tasks running on different cores generate different rates of communication between pairs of cores. This lead to the increase in spatial and temporal variation in the workloads, which impact the long distance data communication over multi-hop wire line paths in conventional NoCs. Among different alternatives, due to the CMOS compatibility and energy-efficiency, low-latency wireless interconnects operating in the millimeter wave (mm-wave) band is nearer term solution to this multi-hop communication problem in traditional NoCs. This has led to the recent exploration of millimeter-wave (mm-wave) wireless technologies in wireless NoC architectures (WiNoC). In a WiNoC, the mm-wave wireless interconnect is realized by equipping some NoC switches with an wireless interface (WI) that contains an antenna and transceiver circuit tuned to operate in the mm-wave frequency. To enable collision free and energy-efficient communication among the WIs, the WIs is also equipped with a medium access control mechanism (MAC) unit. Due to the simplicity and low-overhead implementation, a token passing based MAC mechanism to enable Time Division Multiple Access (TDMA) has been adopted in many WiNoC architectures. However, such simple MAC mechanism is agnostic of the demand of the WIs. Based on the tasks mapped on a multicore system the demand through the WIs can vary both spatially and temporally. Hence, if the MAC is agnostic of such demand variation, energy is wasted when no flit is transferred through the wireless channel. To efficiently utilize the wireless channel, MAC mechanisms that can dynamically allocate token possession period of the WIs have been explored in recent time for WiNoCs. In the dynamic MAC mechanism, a history-based prediction is used to predict the bandwidth demand of the WIs to adjust the token possession period with respect to the traffic variation. However, such simple history based predictors are not accurate and limits the performance gain due to the dynamic MACs in a WiNoC. In this work, we investigate the design of an artificial neural network (ANN) based prediction methodology to accurately predict the bandwidth demand of each WI. Through system level simulation, we show that the dynamic MAC mechanisms enabled with the ANN based prediction mechanism can significantly improve the performance of a WiNoC in terms of peak bandwidth, packet energy and latency compared to the state-of-the-art dynamic MAC mechanisms