We introduce Harmonium, a novel ultra-wideband RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive, lightweight, and FCC-compliant ultra-wideband transmitter or tag, fixed infrastructure anchors with known locations, and centralized processing that calculates the tags position. Anchors employ a new frequency-stepped narrowband receiver architecture that rejects narrowband interferers and extracts high-resolution timing information without the cost or complexity of traditional ultra-wideband approaches. In a complex environment, 90% of position estimates obtained with Harmonium exhibit less than 31cm of error with an average 9cm of inter-sample noise. In non-line-of-sight conditions (i.e. through-wall), 90% of position error is less than 42cm. The tag draws 75mW when actively transmitting, or 3.9mJ per location fix at the 19Hz update rate. Furthermore, VLSI-based design concepts are identified for a simple, low-power realization of the Harmonium tag to offer a roadmap for the realization of Harmonium concepts in future integrated systems. Harmonium introduces a new design point for indoor localization and enables localization of small, fast objects such as micro quadrotors, devices previously restricted to expensive optical systems.
We propose a new algorithm for the classical averaging problem for distributed Wireless Sensors Networks. This subject is well studied and there are many clever algorithms in the literature. These algorithms are based on the idea of local exchange of information. They behave well in dense networks (for example in networks which connections form a complete graph), but their convergence to the real average is very slow in linear or cyclic graphs. Our solution is different: in order to calculate the average, we first build an approximate histogram of observed data, and then from this histogram we estimate the average. In our solution we use the extreme propagation technique and probabilistic counters. It allows to find the approximation of the average of a set of measurements done by sensor network with arbitrary precision, controlled by two parameters. Our method requires O(D) rounds, where D is the diameter of the network. We study the message complexity of this algorithm and we show that it is of order O(log n) for each node, where n is the size of the network.
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.
Wireless energy transfer has recently emerged as a promising alternative to realize the long standing vision of perpetual embedded wireless networks. However, this technology transforms the notion of energy from merely a nodes local commodity to, similar to data, a deployment-wide shareable resource. The challenges of managing a shareable energy resource are much more complicated and radically different from the research of the past decade: Besides energy efficient operation of individual devices, we also need to optimize network wide energy distribution. To counteract these challenges, we propose an energy stack, a layered software model for energy management in future transiently powered embedded networks. An initial specification of the energy stack, which is based on the historically successful layered approach for data networking, consists of three layers: (i) the transfer layer; dealing with the physical transfer of energy, (ii) the scheduling layer; optimizing energy distribution over a single hop, and (iii) the network layer; creating a global view of the energy in the network for optimizing its network-wide distribution. As a contribution, we define the interfacing APIs between these layers, delineate their responsibilities, identify corresponding challenges, and provide a first implementation of the energy stack.
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.
Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this paper, we review the state-of-the-art in energy harvesting WSNs for environmental monitoring applications including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy harvesting-based WSNs, along with some future work directions to address these challenges.