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.
Battery-Free Wireless Sensor Networks (BF-WSNs) are newly emerging Wireless Sensor Networks (WSNs) to break through the energy limitations of traditional WSNs. In BF-WSNs, the broadcast scheduling problem is more challenging than that in traditional WSNs. This paper investigates the broadcast scheduling problem in BF-WSNs with the purpose of minimizing broadcast latency. The Minimum-Latency Broadcast Scheduling problem in BF-WSNs (MLBS-BF) is formally defined and its NP-completeness is proved. Three progressively improved approximation algorithms for solving the problem are proposed. The broadcast latency of the broadcast schedules produced by the proposed algorithms is analyzed. The correctness and approximation ratio of the proposed algorithms are also proved. Finally, extensive simulations are conducted to evaluate the performances of the proposed algorithms. The simulation results show that the proposed algorithms have high performance.
This paper presents Whisper, a fast and reliable protocol to flood small amounts of data into a multi-hop network. Whisper makes use of synchronous transmissions, a technique first introduced by the Glossy flooding protocol. In contrast to Glossy, Whisper does not let the radio switch from receive to transmit mode between messages. Instead, it makes nodes continuously transmit identical copies of the message and eliminates the gaps between subsequent transmissions. To this end, Whisper embeds the message to be flooded into a signaling packet that is composed of multiple packlets ? where a packlet is a portion of the message payload that mimics the structure of an actual packet. A node must intercept only one of the packlets to detect that there is an ongoing transmission and that it should start forwarding the message. This allows Whisper to speed up the propagation of the flood, and thus, to reduce the overall radio-on time of the nodes. Our evaluation on the Flocklab testbed shows that Whisper achieves comparable reliability but 2x lower radio-on time than Glossy. We further show that by embedding Whisper in an existing data collection application, we can more than double the lifetime of the network.
Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle's lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle's speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this paper we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle's current lane. We employ a deep learning based technique to classify the vehicle's lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real world datasets collected in developed and developing regions. DeepLane can detect vehicle's lane position with an accuracy of over 90% and we have implemented DeepLane as an Android-app.
Patients with respiratory diseases require accurately measuring and frequently monitoring blood oxygen level. Existing techniques however either need a dedicated hardware or fails to predict low saturation levels. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO2, using camera and flashlight functions that are readily available on today's off-the-shelf smartphones. Since phone's camera and flashlight are not made for this purpose, utilizing them for oxygen level estimation poses many difficulties. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A near field-based pressure detection and feedback mechanism are also proposed to mitigate the negative impacts of user's behavior during the measurement. We also derive a non-linear referencing model with an outlier removal technique that allows PhO2 to accurately estimate the oxygen level from color intensity ratios produced by smartphone's camera. An evaluation on COTS smartphone with 6 subjects shows that PhO2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. In addition, our evaluation in hospitals presents high correlation with ground-truth qualified by the 0.83/1.0 Kendall Ä coefficient
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.
The design of MAC protocol for WSNs with both limited energy consumption and data delivery time is crucial for industrial and control applications. Since TDMA MAC eliminates the collision occurrence and seeks into the minimization of the number of time-slots assigned to each node, the energy consumption of the nodes is reduced. Furthermore, with the proper allocation of the time-slots to the nodes, the transmission delay can be significantly reduced. In this paper, we propose TDMA scheduling algorithm for Cluster-Tree topology WSNs that meets the timeliness and the energy demands. The algorithm adopts an elegant approach that expresses the timing constraints of the data transmissions as an integer multiple of the length of the schedule period. Moreover, since the distributed algorithm is well-suited to the scarce resources of the WSNs, we focus on the distributed methods that allow each cluster to come up with its allocated time-slots. The algorithm is based on graph theory, such as distributed shortest path, distributed topological ordering, and distributed graph coloring algorithms. The efficiency of the algorithm, regarding the elapsed time to construct the schedule and the energy consumption, is evaluated over benchmark instances up to several thousands of nodes.
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.
Modern protocols for wireless sensor networks efficiently support multi-hop upward traffic from many sensors to a collection point, e.g., for monitoring applications. However, ever-evolving low-power wireless scenarios increasingly require support also for downward traffic, e.g., for actuation. The IETF Routing Protocol for Low-power and Lossy Networks (RPL) is among the few tackling both traffic patterns. Unfortunately, its support for downward traffic is significantly unreliable and inefficient compared to its upward counterpart. We tackle this problem by extending RPL with mechanisms inspired by opposed, yet complementary, principles: retain the route-based operation of RPL and devise techniques allowed by the standard vs. rely on flooding as the main networking primitive. We define three base mechanisms, integrate them in a popular RPL implementation, analyze their individual and combined performance, and elicit the tradeoffs in scalability, reliability, and energy consumption. The evaluation relies on simulation, with real-world topologies from a smart city scenario and synthetic grid ones, and on testbed experiments validating simulations. Results show that the combination of all three mechanisms into a novel protocol, T-RPL, i) yields high reliability, close to flooding, ii) with low energy consumption, similar to route-based approaches, and iii) improves remarkably the scalability of RPL w.r.t. downward traffic.
Massive multi-input multi-output (MIMO) which is well known as an enabling technology for 5G systems has lot of advantages, such as huge spectral efficiency gain, significant reduction of latency, and robustness to interference etc. However, to get these benefits, accuracy of the channel state information (CSI) obtained at the transmitter is required. It is shown that CSI estimation based on direction-of-arrival (DOA) provides the best performance in terms of error bound. This paper proposes a blind joint DOA/symbols estimation approach for 3-D millimeter wave (mmW) massive MIMO systems base on tensor decompositions. We show that the received MIMO signal satisfies a parallel profiles with linear dependencies (PARALIND) model, where the symbols and DOA matrices can be viewed as two independent factor matrices. Such a hybrid tensorial modeling enables a blind joint estimation of DOA/symbols.We develop the delta bilinear ALS (DBALS) algorithm that exploits the increment values between two iterations of the factor matrices, refine these predictions by using the enhanced line search and use these refined values to initialize for two factor matrices. Moreover, we propose a new algorithm Vandermonde constrained DBALS (WC-DBALS) that takes into account the potential Vandermonde nature structure of the DOA matrix for DBALS.
Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile-time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala's specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces run time by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail.
Transportation and distribution (T&D) of fresh food products is a substantial and increasing part of the economic activity throughout the world. Unfortunately, fresh food T&D not only suffers from significant spoilage and waste, but also from dismal efficiency due to tight transit timing constraints between the the availability of harvested food until its delivery to the retailer. Fresh food is also easily contaminated, and together with deteriorated fresh food is responsible for much of Food Borne Illnesses. The logistics operations are ongoing rapid transformation on multiple fronts including infusion of information technology in the logistics operations, automation in the physical product handling, standardization of labeling, addressing and packaging, and shared logistics operations under 3rd party-logistics (3PL) and related models. In this paper, we discuss how these developments can be exploited to turn fresh food logistics into an intelligent cyberphysical system driven by online monitoring and associated operational control to enhance food freshness and safety, reduce food waste, and increase T&D efficiency. Some of the issues discussed in this context are fresh food quality deterioration processes, food quality/contamination sensing technologies, communication technologies for transmitting sensed data through the challenging fresh food media, intelligent management of T&D pipeline, and other operational issues.