Energy saving mechanism for a Smart Wearable System: monitoring infants during the sleep

In Smart Wearable Systems (SWS), the wearable devices are powered by batteries with very limited energy available. These emergent systems have strong Quality of Service (QoS) requirements, with focus on reliable communication and low power consumption. This is the scope of the Baby Night Watch, a project developed in the context of the European Texas Instruments Innovation Challenge (TIIC) 2015. This Project consists of a monitoring tool for infants, which matches different emergent research fields. SWSs require energy saving mechanism to reduce the energy wasting during wireless communications. A Transmission Power Control (TPC) mechanism that changes its characteristics according to the scenario of operation, is proposed. It uses sensors to determine the position of the infant and, based on that, predicts the current state of the channel. Other TPC algorithms are implemented and their performance are compared with our novel mechanism. The proposed TPC mechanism outperforms the existing ones in terms of the energy saving.Duarte Fernandes and André G. Ferreira are supported by FCT (grant SFRH/BD/92082/2012 and SFRH/BD/91477/2012 respectively). This work was partially funded by FCT within the Project Scope: Pest-OE/EEI/UI0319/2014, and partially funded by -Programa Operacional Factores de Competitividade – COMPETE and National funds through FCT – Fundação para a Ciência e a Tecnologia- under the project UID/CTM/00264

Simple Prediction-Based Power Control for the On-Body Area Communications Channel

Methods for transmit power control based on simple long-term channel prediction for the general body-area communications channel are presented. The power control methods are based on large sets of empirical every-day activity data. Numerous transmit-receive pair (Tx-Rx) locations on the human body, i.e. on-body, for a typical body-area-network (BAN) are considered. With the use of a simple prediction method based on held samples, and an enhanced held simple prediction method that uses short term mean path loss with the held sample, optimal power allocation for long-term transmit power control is described. When tested, according to the draft IEEE 802.15.6 BAN radio standard, on empirical data, both power allocation methods are shown to be more reliable, and also more energy efficient in terms of transmit circuit power consumption, than systems that use typical set Tx power levels for BAN

The Internet of Humans: Optimal Resource Allocation and Wireless Channel Prediction

Recent advances in information and communications technologies (ICT) have accelerated the realization of the Internet of Humans (IoH). Among the many IoH applications, Wireless Body Area Networks (BANs) are a remarkable solution that are revolutionising the health care industry. However, many challenges must be addressed, including: a) unavoidable inter-BAN interference severely degrading system performance. b) The non-stationarity and atypical dynamics of BAN channels make it extremely challenging to apply predictive transmit power control that improves the energy efficiency of the network. In this context, this thesis investigates the use of intelligent and adaptive resource allocation algorithms and effective channel prediction to achieve reliable, energy-efficient communications in BAN-enabled IoH. Firstly, we investigate the problem of co-channel interference amongst coexisting BANs by proposing a socially optimal finite repeated non-cooperative transmit power control game. The proposed method improves throughput, reduces overall power consumption and suppress interference. The game is shown to have a unique Nash equilibrium. We also prove that the aggregate outcome of the game is socially efficient across all players at the unique Nash equilibrium, given reasonable constraints for both static and slowly time-varying channels. Secondly, we address the problem of overlapping transmissions among non-coordinated BANs with multiple access schemes through intelligent link resource allocation methods. We present two non-cooperative games, employed with a time-division multiple access (TDMA) based MAC layer scheme that has a novel back-off mechanism. The Link Adaptation game jointly adjusts the sensor node's transmit power and data rate, which provides robust transmission under strong inter-BAN interference. Moreover, by adaptively tuning contention windows size an alternative game, namely a Contention Window game is developed, which significantly reduces latency. The uniqueness and existence of the games' Nash Equilibrium (NE) over the action space are proved using discrete concavity. The NE solution is further analysed and shown to be socially efficient. Motivated by the emergence of deep learning technology, we address the challenge of long-term channel predictions in BANs by using neural networks. Specifically, we propose Long Short-term Memory (LSTM)-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2s over non-stationary BAN on-body channels. An incremental learning scheme, which provides continuous and robust predictions, is also developed. We also propose a lightweight NN predictor, namely 'LiteLSTM', that has a compact structure and higher computational efficiency. When implemented on hand-held devices, 'LiteLSTM' remains functional with comparable performance. Finally, we explore the theoretical connections between BAN on-body channels' characteristics and the performance of NN-based power control. To analyse wide-sense stationarity (WSS) characteristics, different stationarity tests are performed for a range of window lengths for on-body channels. Following from this, we develop test benches for NN-based methods at corresponding window lengths using empirical channel measurements. It is observed that WSS characteristics of the BAN on-body channels have a significant impact on the performance of NN-based methods