The wireless sensor network (WSN) is a fundamental component of the Internet of things (IoTs). The limited battery capacities of sensor nodes and network lifetime are major bottlenecks of WSNs and block the further deployment of IoTs. Therefore, a new network architecture, named as battery-free sensor network, was proposed to overcome such obstacle. In an RF-based battery-free sensor network, the battery-free nodes equip no battery and can be recharged by RF-signals. The Dominating Sets (DS) are adopted to maintain the full coverage of such network. However, considering the specific features of RF-based battery-free sensor networks, the DS construction is totally different from that in traditional WSNs. Thus, the problem of constructing DS in a battery-free sensor network is deeply investigated. The NP-Hardness of such problem is proved. Four approximation algorithms are proposed to deal with the snapshot and continuous DS construction requirements, respectively. Furthermore, the electromagnetic interference problem in the RF-based battery-free sensor network is considered and defined. An approximated algorithm is proposed to solve such problem. Finally, extensive simulations are carried out. The experimental results verify that the proposed algorithms have high performance in term of accuracy and efficiency.
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.
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.
Wireless energy transfer has been widely studied in recent decades, existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate. We apply a nonlinear superposition model, and we consider the Fast Charging Scheduling problem (FCS): given multiple chargers and a group of sensors, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor has at least energy E? We prove that FCS is NP-complete and propose a 2-approximation algorithm to solve it in one-dimensional (1D) line. In 2D plane, we first consider a special case of FCS, where the initial phases of all chargers are the same, and propose an algorithm to solve it, which has a bound. Then we propose an algorithm to solve FCS in genera 2D plane. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. Extensive simulations demonstrate that the performance of our algorithm is almost as good as the optimal algorithm.
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.