Wireless Body Area Networks (WBANs) are seen as the enabling technology for developing new generations of medical applications, such as remote health monitoring. As such it is expected that WBANs will predominantly transport mission-critical and delay sensitive data. A key strategy towards building a reliable WBAN is to ensure such networks are highly immune to interference. To achieve this, new and intelligent wireless spectrum allocation strategies are required not only to avoid interference, but also to make bestuse of the limited available spectrum. This paper presents a new spectrum allocation scheme referred to as Smart Channel Assignment (SCA), which maximizes the resource usage and transmission speed by deploying a partially-orthogonal channel assignment scheme between coexisting WBANs as well as offering a convenient trade-off between spectral reuse efficiency, transmission rate and outage. Detailed analytical and experimental studies verify that the proposed SCA strategy is robust to variations in channel conditions, increase in sensor node-density within each WBAN and an increase in number of coexisting WBANs.
Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are also not entirely convex in shape. In this paper, we propose a heuristic-based Kamada-Kawai algorithm called W-KK-MS for boundary node detection problem, which is capable of aligning node positions while maintaining fast convergence rate in producing a visual drawing from the input network topology. The algorithm put forward in this paper selects and assigns weights to top-k nodes in each iteration to increase the convergence rate in detecting boundary nodes for CNCAH networks. We also propose a novel approach to detect and unfold stacked regions in CNCAH networks. Experiment results show that the proposed algorithms can achieve fast convergence on boundary node detection in CNCAH networks and able to successfully unfold stacked regions. The design and implementation of a prototype system called ELnet for analyzing CNCAH networks is also described in this paper. The ELnet system is capable of generating synthetic networks for testing, integrating with force-directed algorithms, and visualizing and analyzing of algorithms outcome.
Smartphones have become the most pervasive device in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, including accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.
Mobile crowdsensing can facilitate environmental surveys by leveraging sensor-equipped mobile devices that carry out measurements covering a wide area in a short time, without bearing the costs of traditional field work. In this paper, we examine statistical methods to perform an accurate estimate of the mean value of an environmental parameter in a region, based on such measurements. The main focus is on estimates produced by taking a ``snapshot'' of mobile device readings at a random instant in time. We compare stratified sampling with different stratification weights to sampling without stratification, as well as an appropriately modified version of systematic sampling. Our main result is that stratification with weights proportional to stratum areas can produce significantly smaller bias, and gets arbitrarily close to the true area average as the number of mobiles and the number of strata increase. The performance of the methods is evaluated for an application scenario where we estimate the mean area temperature in a linear region that exhibits the so-called Urban Heat Island effect, with mobile users moving in the region according to the Random Waypoint Model.
Incorrect muscle activation can lead to sub-optimal performance, muscle imbalance and eventually bodily injury. Consequently, assessing muscle activation is important for both excelling in sports and general well-being of sports and exercise participants. Existing techniques for assessing muscle activation such as Electromyography (EMG) are invasive, requiring needles inserted directly into the muscle or electrodes that have considerable placement requirements (shaving, gels etc.), making them unsuitable for exercise environments. As a result, such systems have been explored more in clinical than sports-based scenarios. Consequently, in a real world sports and exercise environment such systems fail because of factors such as body motion noise which gets induced in the sensor data due to the high motion and high impact movement nature of active sports and exercise. We present MyoVibe, a system for sensing and determining muscle activation in high mobility, high impact exercise scenarios. MyoVibe senses and interprets multiple muscle vibration signals obtained from a wearable network of accelerometers to determine muscle activation. By utilizing diverse feature set combined with the simple yet effective motion artifact mitigation technique, MyoVibe can reduce inertial sensor noise in these high mobility exercises. As a result, MyoVibe can predict muscle activation with greater than 97% accuracy.
Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In-situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed, but are less practical due to need for large infrastructure, lack of adaptivity and limited coverage. Controlled-mobility in sensor networks, i.e. the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this paper, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve 3-D sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.