Policy-based management for body-sensor networks

Accepted versio

Towards Power Efficient MAC Protocol for In-Body and On-Body Sensor Networks

This paper presents an empirical discussion on the design and implementation of a power-efficient Medium Access Control (MAC) protocol for in-body and on-body sensor networks. We analyze the performance of a beacon-enabled IEEE 802.15.4, PB-TDMA, and S-MAC protocols for on-body sensor networks. We further present a Traffic Based Wakeup Mechanism that utilizes the traffic patterns of the BAN Nodes (BNs) to accommodate the entire BSN traffic. To enable a logical connection between different BNs working on different frequency bands, a method called Bridging function is proposed. The Bridging function integrates all BNs working on different bands into a complete BSN.Comment: 11 pages, 5 figures, 3 tables,KES AMSTA 09, LNAI 5559, pp.335-345, Uppsala, June 2009. arXiv admin note: text overlap with arXiv:0911.150

Ambient health monitoring: the smartphone as a body sensor network component

Inertial measurement units used in commercial body sensor networks (e.g. animation suits) are inefficient, difficult to use and expensive when adapted for movement science applications concerning medical and sports science. However, due to advances in micro-electro mechanical sensors, these inertial sensors have become ubiquitous in mobile computing technologies such as smartphones. Smartphones generally use inertial sensors to enhance the interface usability. This paper investigates the use of a smartphone’s inertial sensing capability as a component in body sensor networks. It discusses several topics centered on inertial sensing: body sensor networks, smartphone networks and a prototype framework for integrating these and other heterogeneous devices. The proposed solution is a smartphone application that gathers, processes and filters sensor data for the purpose of tracking physical activity. All networking functionality is achieved by Skeletrix, a framework for gathering and organizing motion data in online repositories that are conveniently accessible to researchers, healthcare professionals and medical care workers

Real-Time Analysis of Correlations Between On-Body Sensor Nodes

The topology of a body sensor network has, until recently, often been overlooked; either because the layout of the network is deemed to be sufficiently static (”we always know well enough where sensors are”), we always know exactly where the nodes are or because the location of the sensor is not inherently required (”as long as the node stays where it is, we do not need its location, just its data”). We argue in this paper that, especially as the sensor nodes become more numerous and densely interconnected, an analysis on the correlations between the data streams can be valuable for a variety of purposes. Two systems illustrate how a mapping of the network’s sensor data to a topology of the sensor nodes’ correlations can be applied to reveal more about the physical structure of body sensor networks

An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.Comment: 10 figures, 19 page

Optimized Gated Deep Learning Architectures for Sensor Fusion

Sensor fusion is a key technology that integrates various sensory inputs to allow for robust decision making in many applications such as autonomous driving and robot control. Deep neural networks have been adopted for sensor fusion in a body of recent studies. Among these, the so-called netgated architecture was proposed, which has demonstrated improved performances over the conventional convolutional neural networks (CNN). In this paper, we address several limitations of the baseline negated architecture by proposing two further optimized architectures: a coarser-grained gated architecture employing (feature) group-level fusion weights and a two-stage gated architectures leveraging both the group-level and feature level fusion weights. Using driving mode prediction and human activity recognition datasets, we demonstrate the significant performance improvements brought by the proposed gated architectures and also their robustness in the presence of sensor noise and failures.Comment: 10 pages, 5 figures. Submitted to ICLR 201

Design of a Finger Ring Antenna for Wireless Sensor Networks

Body-centric communications have become very active area of research due to ever-growing demand of portability. Advanced applications such as; health monitoring, tele-medicine, identification systems, performance monitoring of athletes, defence systems and personal entertainment are adding to its popularity. In this paper, a novel wearable antenna radiating at 5 GHz for the body-centric wireless sensor networks has been presented. The antenna consists of a conventional microstrip patch mounted on a gold base and could be worn in a finger like a ring. CST Microwave Studio is used for modelling, simulation and optimisation of the antenna. The simulated results show that the proposed antenna has a -10 dB bandwidth of 90.3 MHz with peak gain of 6.9 dBi. Good performance in terms of bandwidth, directivity, gain, return loss and radiation characteristics, along with a miniaturised form factor makes it a very well suited candidate for the body-worn wireless sensor applications