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We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) called aBeacon (short for alibaba Beacon). aBeacon is pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04), aBeacon consists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of $\$ $ 600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operate aBeacon from its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing. [ABSTRACT FROM AUTHOR]

Hierarchical Modulation (HM) is quite efficient for multimedia broadcast in wireless networks. It exploits the wireless broadcast advantage and allows a transmission to reach different users with various qualities. However, existing HM related schemes are based on specially designed hardware, leading to extra hardware and deployment cost in practice. As a remedy for this, we design and implement a software-based HM scheme, named as SoftHM. By carefully tuning the Physical Layer input of a packet (a sequence of “0” or “1” bits, known as PHY payload), a transmitter simply broadcasts the packet with a single rate (e.g., 54Mbps, the highest rate in 802.11g WLAN), while diverse receivers can decode and extract different amount of information (with different reception rate, e.g., 12Mbps or 54Mbps) from it based on their link qualities. Reverse engineering techniques are adopted so that SoftHM works in a plug-in mode, offering it high potentials for wireless systems without any changes in existing modulation and coding modules. We implement SoftHM based on Universal Software Radio Peripheral (USRP) B210 and Commercial of the Shelf (COTS) Wi-Fi devices. Both rigorous analysis on its performance and comprehensive implementation-based experiments show that compared with the traditional time-sharing approach, to support low-quality users with the same bandwidth, high-quality users in SoftHM achieve 111.1% to 616.7% higher throughput. Likewise, to serve high-quality users with the same bandwidth, low quality users in softHM achieve 176.9% to 390.9% higher throughput. [ABSTRACT FROM AUTHOR]

Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we propose a near optimal scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than $(1-\epsilon)$ of the optimal solution to PESA with a smaller EMR threshold $(1-\epsilon /2)R_{t}$ and a larger EMR coverage radius $(1+\epsilon /2)D$. We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the comparison algorithms. [ABSTRACT FROM AUTHOR]

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging, a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging, we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process. [ABSTRACT FROM AUTHOR]

Neighbor discovery is essential for mobile devices to find each other before communications. Traditional discovery protocols are constrained by the in-technology communication paradigm. Cross-Technology Communications enable ZigBee nodes to be coordinated by a WiFi node without any hardware changes or gateway equipment, which sheds the light on more efficient neighbor discovery schemes. In this work, we introduce a new direction for neighbor discovery based on Cross-Technology Communication technique and propose a physical-layer technique called NewBee. NewBee takes the advantages of coordinations from a WiFi node to assist ZigBee nodes for neighbor discovery. A simple yet effective Countdown Mechanism is employed so that ZigBee nodes are coordinately waked up according to the beacons in the Countdown Mechanism. We give a rigorous analysis on NewBee and the worst-case discovery latency is significantly reduced from the traditional O(n2) to O(n), where n indicates the reciprocal of node's duty-cycle. The experimental results show that NewBee only needs 11%, 10%, 2.5%, 2.4% and 4.4% percent of discovery time compared with the state-of-the-art BlindDate, SearchLight, Disco, Quorum, and Birthday protocols with 5% nodes’ duty-cycle, respectively. [ABSTRACT FROM AUTHOR]

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold Rt . We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a series of distributed algorithms, including a fully distributed algorithm that provably achieves $(1-\epsilon)$ approximation ratio and requires only communications with neighbors within a constant distance for each charger. Through extensive simulation and testbed experiments, we demonstrate that our proposed algorithms can outperform the set-cover algorithm by up to 17.05%, and has an average performance gain of 41.1% over the existing algorithm in terms of the overall charging utility. [ABSTRACT FROM PUBLISHER]

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology, which allows the power to be transferred from a charger to ambient devices wirelessly, receives intensive interests. Existing schemes mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the safe charging problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold Rt . We show that SCP is NP-hard and propose a solution, which provably outperforms the optimal solution to SCP with a relaxed EMR threshold (1-\epsilon )Rt . Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when $\epsilon = 0.1$ , while a naive greedy algorithm is 34.6% below our solution. [ABSTRACT FROM PUBLISHER]

Association link network (ALN) can organize massive Web information to provide many intelligent services in our big data society. Effective semantic layered technologies not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of related information systems such as Web information system and industrial information system. How to realize the layer division of association semantic by the hierarchy analysis of ALN is an important research topic. To solve this problem, this paper proposes a hierarchy-cutting model of association semantic. First, experiments of four types of keywords with different linking roles are conducted to discover the possible distribution law. Experimental results show that these keywords with association role reveal previous power-law distribution. Then, based on the discovered power-law distribution, up-cutting and down-cutting points are presented to divide the association semantic into three layers. At the same time, theories of the hierarchy-cutting model are presented. Finally, examples of current core topic and permanent topics belonging to a domain are given. The experiments show that hierarchy-cutting points have high accuracy. The multilayer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle sizes on ALNs. [ABSTRACT FROM PUBLISHER]

An urban emergency event requires an immediate reaction or assistance for an emergency situation. With the popularity of the World Wide Web, the internet is becoming a major information provider and disseminator of emergency events and this is due to its real-time, open, and dynamic features. However, faced with the huge, disordered and continuous nature of web resources, it is impossible for people to efficiently recognize, collect and organize these events. In this paper, a crowdsourcing based burst computation algorithm of an urban emergency event is developed in order to convey information about the event clearly and to help particular social groups or governments to process events effectively. A definition of an urban emergency event is firstly introduced. This serves as the foundation for using web resources to compute the burst power of events on the web. Secondly, the different temporal features of web events are developed to provide the basic information for the proposed computation algorithm. Moreover, the burst power is presented to integrate the above temporal features of an event. Empirical experiments on real datasets show that the burst power can be used to analyze events. [ABSTRACT FROM AUTHOR]

Among various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is prone to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile during run-time. In a practical dynamic environment, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove that, this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and modify it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is about $1$ meter. Compared with the traditional RSS-based approaches in dynamic environments, the accuracy improves up to $10$ times. [ABSTRACT FROM AUTHOR]

Recent pioneer work has shown that packet receptions on adjacent links are correlated, which contradicts the long held assumption that wireless links are statistically independent. Since wireless link correlation affects a wide range of protocol designs, it is essential to quantify the impact generically. In particular, this paper focuses on a unified transmission cost metric for diversity-based routing schemes, including opportunistic routing, network coding, and hybrid routing. This paper covers both unicast and broadcast. Compared with the legacy metrics, our metric provides a direct and accurate estimation of the transmission cost in the presence of link correlation. The new metric helps a wide range of routing algorithms determine when they can benefit from reception diversity, and how to maximize the benefit, at negligible costs. We evaluate the metric on one 802.11 test bed and three 802.15.4 test beds running TelosB, MICAz, and GreenOrbs nodes. The experimental results show that our metric 1) reduces 92% and 94% of the estimation error of the transmission cost in unicast and broadcast and 2) outperforms the link independent metric in both unicast and broadcast applications. [ABSTRACT FROM PUBLISHER]

Recent work has shown that wireless links are not independent, and that transmissions from a transmitter to multiple receivers are correlated. This finding has profound implications for the performance of network protocols such as broadcast, multicast, opportunistic routing, and network coding. In this paper, we show how link correlation can significantly impact broadcast. We present the design and implementation of CorLayer, a general supporting layer for energy-efficient reliable broadcast that carefully blacklists certain poorly correlated wireless links. The design uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of our work is CorLayer's broad applicability and effectiveness. We integrate CorLayer transparently with 16 state-of-the-art broadcast protocols specified in 13 publications on three physical testbeds running TelosB, MICAz, and GreenOrbs nodes, respectively. The experimental results show that CorLayer significantly improves energy efficiency across a wide spectrum of broadcast protocols and that the total number of packet transmissions can be reduced consistently by 47% on average. [ABSTRACT FROM AUTHOR]

Geocast in vehicular networks aims to deliver a message to a target geographical region, which is useful for many applications such as geographic advertising. This is a highly challenging task in vehicular network environments due to the rare encounter opportunities and uncertainty caused by vehicular mobility. As more vehicles are equipped with on-board navigation systems, vehicle trajectories are ready for exploitation. We observe that a vehicle has a higher capability of delivering a message to the target region if its own future trajectory or trajectories of those vehicles to be encountered overlap the target region. Motivated by this observation, we develop a message forwarding metric, called coverage capability, to characterize the capability of a vehicle to successfully geocast the message. When calculating the coverage capability, we are facing the major challenge raised by the absence of accurate vehicle arrival time. Through an empirical study using real vehicular GPS traces of 2,600 taxis, we verify that the travel time of a vehicle, which is modeled as a random variable, follows the Gamma distribution. The travel time modeling helps us to make accurate predictions for inter-vehicle encounters. We perform extensive trace-driven simulations and the results show that our approach achieves 37.4 percent higher delivery ratio and 43.1 percent lower transmission overhead comparing with GPSR which is a representative geographic routing protocol. [ABSTRACT FROM AUTHOR]

Reducing transmission redundancy is key to efficient broadcast in wireless networks. A standard approach to achieving this goal is to create a network backbone consisting of a subset of nodes that are responsible for data forwarding, while other nodes act as passive receivers. On top of this, network coding (NC) is often used to further reduce unnecessary transmissions. The main problem with existing backbone and NC combinations is that the backbone construction process is blind of what is needed by NC, thus may produce a structure that limits the power of NC algorithms. To address this problem, we propose Coding Opportunity Aware Backbone (COAB) metrics, which seek to maximize coding opportunities when selecting backbone forwarders. We show that the backbone construction process guided by our metrics leads to significantly increased coding frequency, at the cost of minimal localized information exchange. The highlight of our work is COAB's broad applicability and effectiveness. We integrate the COAB metrics with ten state-of-the-art broadcast algorithms specified in eight publications [1]-[8], and evaluate COAB with a running testbed of 30 MICAz nodes and extensively simulations. The experimental results show that our design outperforms the existing schemes substantially. [ABSTRACT FROM PUBLISHER]