Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, none of the existing technologies delivers optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.
The past few years have witnessed a rapid growth of stationless bike sharing services. The service allows the bikes to be dropped off freely, and to be found through GPS localization. In practice, the bikes are often parked in close proximity to the buildings, where GPS accuracy suffers, making bike search a challenging task. This paper proposes a novel approach to addressing this problem. Inspired by multi-antenna systems, our method tries to collect GPS signals from multiple distributed bikes, by organizing a group of bikes into a network, called a BikeGPS network. Formed by pedestrian users who opportunistically measure inter-bike distance via radio sensing and step tracking, the generated network permits one to map all the nodes? satellite range measurements into a single lead node?s view. By considering both signal and geometry properties of satellite raw measurements, and using an asynchronous coarse time navigation algorithm, the lead node can accurately derive the locations of all the network nodes. Experiments in real world scenarios show that BikeGPS significantly improves the localization performance, in terms of both accuracy and solution availability, compared with the naive GPS approach and a high-level cooperative localization method.
Many virtual machines exist for sensor nodes with only a few KB RAM and tens to a few hundred KB flash memory. They pack an impressive set of features, but suffer from a slowdown of one to two orders of magnitude compared to optimised native code, reducing throughput and increasing power consumption. Compiling bytecode to native code to improve performance has been studied extensively for larger devices, but the restricted resources on sensor nodes mean most modern techniques cannot be applied. Simply replacing bytecode instructions with predefined sequences of native instructions is known to improve performance, but produces code several times larger than the optimised C equivalent, limiting the size of programmes that can fit onto a device. This paper identifies the major sources of overhead resulting from this basic approach, and presents optimisations to remove most of the remaining performance overhead, and over half the size overhead, reducing them to 69% and 91% respectively. While this increases the size of the VM, the break-even point at which this fixed cost is compensated for is well within the range of memory available on a sensor device, allowing us to both improve performance and load more code on a device.
Geomagnetism is promising for indoor localization due to its omnipresence, high stability and availability of magnetometers in smartphones. Previous works often fuse it with pedometer via particles, which are computationally-intensive and make strong user behavior assumptions. To overcome that, we propose Magil, an approach leveraging geomagnetism for indoor localization. To our best knowledge, this is the first piece of work using geomagnetism for smartphone localization without the need of pedometer or user walking model. Magil is applicable to any open or complex indoor environment. In the offline phase, Magil collects and stores geomagnetic fingerprints while surveyors walk indoors. In the online phase, it employs a fast algorithm to match the geomagnetic variation of the target with the stored fingerprints. Given closely-matched segments, Magil constructs user trajectory efficiently with a modified shortest path formulation by selecting and connecting these matched segments. To further improve accuracy and deployability, we propose MagFi, which extends Magil by fusing it with Wi-Fi. MagFi further collects opportunistic Wi-Fi RSSI for fingerprint construction. We have implemented both Magil and MagFi, and conducted extensive experiments in our campus. Results show that our schemes outperform state-of-the-art schemes by a wide margin (often cutting localization error by 30%).
Lawns make up the largest irrigated crop by surface area in North America, and carries with it a demand for over 7 billion gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art irrigation systems do nothing to compensate for areas of turf with heterogeneous water needs. In this work, we overcome the physical limitations of the traditional irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally-computed schedule. A model is then developed to compute moisture movement from runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each sprinkler node in the space. In a turf area covering over 10,000 square feet, two separate deployments with 4 weeks of fine-grained data collection show that DICTUM can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the-art evapotranspiration systems, while substantially improving conditions for plant health. In addition to environmental, social, and health benefits, DICTUM is shown to return its investment in 16-18 months based on water consumption alone.
A mobile crowdsensing platform motivates to employ participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit, i.e., the charge of a sensing task minus the payments to participants. In this paper, we improve the profit via the data reconstruction, which brings new challenges since it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two problems under different situations are studied: (1) for S-POPS, the sensing cost of different sub-areas are the Same. Two mechanisms are designed to tackle this problem, ProSC and ProSC+. An exponential-based quality prediction method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different sub-areas are Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets, the results demonstrate that our proposed mechanisms are more effective and efficient than baselines.
Separation of control and data planes (SCDP) is a desirable paradigm for low-power multi-hop wireless sensor networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. Recently, multi-radio platforms are increasingly available, making the physical separation of the two planes possible. To advance the network architecture design, we leverage on the long-range communication capability of low-power wide-area network radios to form one-hop out-of-band control planes. We choose LoRaWAN to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control (MAC) need to be dealt with to achieve high reliability and efficiency. We design a TDMA-based multi-channel MAC featuring an urgent channel and negative acknowledgment. On a testbed of 16 nodes, we demonstrate applying LoRaCP to physically separate the control-plane network of the Collection Tree Protocol from its ZigBee-based data-plane network. Extensive experiments show that LoRaCP increases packet delivery ratio from 65% to 80% in the presence of external interference, while consuming a per-node radio power of 2.97mW only.
Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate such that almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the costs of permanent physical sensors whilst still achieving fit-for-purpose accuracy and stability. Overall, we were able to reduce the cost of a building sensor network by 60\% to 80\% by replacing physical sensors with virtual ones while still maintaining accuracy of $\leq$1.0\textdegree\,C and fault-tolerance of $\geq 2$ predictors per sensor.
Recognizing in-air hand gestures will benefit a wide range of applications such as sign language recognition, remote control with hand gestures, and ?writing? in the air as a new way of text input. This paper presents AirContour, which focuses on in-air writing gesture recognition with a wrist-worn device. We propose a novel contour-based gesture model which converts human gestures to contours in 3D space, and then recognize the contours as characters. Different from 2D contours, the 3D contours may have the problems such as contour distortion caused by different viewing angles, contour difference caused by different writing directions, and the contour distribution across different planes. To address the above problem, we introduce Principal Component Analysis (PCA) to detect the principal/writing plane in 3D space, and then tune the projected 2D contour in the principal plane through reversing, rotating and normalizing operations, to make the 2D contour in right orientation and normalized size under a uniform view. After that, we propose both an online approach AC-Vec and an offline approach AC-CNN for character recognition. The experimental results show that AC-Vec achieves an accuracy of 91.6% and AC-CNN achieves an accuracy of 94.3% for gesture/character recognition, both outperform the existing approaches.
Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information(CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference(RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.
In this paper, we introduce the concept of Water flow Driven Sensor Networks for leakage and contamination monitoring in urban water distribution systems. The unique aspect of our work is that the sensor network can be deployed in the underground water network with only access to connection points (through manholes) and driven only by water harvested energy without the need for AC power or frequent battery changes. Although water systems may be affected by a large variety of contaminants, only a few sensors can be practically deployed. Thus many types of contaminants are sensed via ``proxy sensing", which may not be 100% reliable. The main problems addressed are (a) adaptation of the network to the available energy in order to maximize leak/contamination detection, and (b) minimal artificial water circulation or leakage to improve detectability during periods of almost zero natural water flow. The paper shows, through extensive simulations, that the proposed approach can drastically reduce the leakage/contamination reporting time (3 hours to ~20 minutes), and the adaptation can reduce this circulation by ~33% and yet enhance the collected/transmitted data by 30%.