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Driven by a wide range of real-world applications, several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. Even if the repetitive motions are fairly well detectable through the wireless signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. To tackle these challenges, we propose a novel wireless sensing system, called Motion-Fi $\ ^+$ + , which marries battery-free wireless backscattering and device-free sensing in one clean sheet. Motion-Fi $\ ^+$ + is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-user performing certain motions simultaneously because of the relatively short transmission range of backscattered signals and dedicated signal separation method. We implement a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5 percent count error. With little efforts in learning the patterns, our method could achieve 95.2 percent motion-recognition accuracy for a variety of 7 typical motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100 percent with only three repetitions. Our experiments also show that the motions of multiple persons separating by around $ 2$ meters cause little accuracy reduction in the counting process.

Real-time handwriting tracking is important for many emerging applications such as Artificial Intelligence assisted education and healthcare. Existing movement tracking systems, including those based on vision, ultrasound or wireless technologies, fail to offer high tracking accuracy, no learning/training/calibration process, low tracking latency, low cost and easy to deploy at the same time. In this work, we design and evaluate a wireless backscattering based handwriting tracking system, called MilliBack, that satisfies all these requirements. At the heart of MilliBack are two Phase Differential Iterative (PDI) schemes that can infer the position of the backscatter tag (which is attached to a writing tool) from the change in the signal phase. By adopting carefully-designed differential techniques in an iterative manner, we can take the diversity of devices out of the equation. The resulting position calculation has a linear complexity with the number of samples, ensuring fast and accurate tracking. We have put together a MilliBack prototype and conducted comprehensive experiments. We show that our system can track various handwriting traces accurately, in some testings it achieve a median error of 4.9 mm. We can accurately track and reconstruct arbitrary writing/drawing trajectories such as equations, Chinese characters or just random shapes. We also show that MilliBack can support relatively high writing speed and smoothly adapt to the changes of working environment.

With the proliferation of Smart Home and Artificial Intelligence of Things (AIoT), humanized while effective HumanComputer Interface (HCI) has been the central focus of research to bridge user’s daily life and ever-growing hardware capability. In this context, ultrasound-based acoustic sensing is being widely adopted due to its nature of contact-less and platform-agnostic. In order to fully exploit the potential of ultrasonic bandwidth and kill two birds with one stone, we propose SignID, a multi-task learning boosted system capable of jointly recognizing user gestures and identification. The system only needs a pair of speaker and microphone, which is available on various smart devices, to emit sound wave and receive the Doppler-shifted echo. To handle ambient noise and lower the computation overheads, we adopt a bandpass filter to remove the frequency band out of interest, then apply undersampling to compress the measurement for the subsequent FFT processing. After segmentation and spectrogram extraction, a multi-task deep neural network based on cross-stitch units is responsible for jointly learning representation for both identification and gesture recognition. The proposed system is evaluated based on our implementation in a real-life scenario, and the overall accuracy is over 95%. We also test the impact of different environmental factors, including user distance, ambient noise, body gesture, and training data volume. The results prove our model’s robustness to various disturbances.

In this paper, we propose a method of using low-power small cameras to detect shelf status. For shopping malls, especially large shopping malls, when to replenish the shelves is very important. Whether they can replenish them immediately or not is directly related to their turnover. Now most shopping malls use manual inspection mode, that is, a waiter is responsible for certain shelves. They will replenish shelves in time when they are need. But this method wastes too much manpower. In our scheme, we use low-power IoT C ameras to M onitor S tore S helves (CMSS). Firstly, we put a specific photo on the back of the shelf and put the camera in front of the shelf to face the photo. When the goods on the shelf are emptied, the camera recognizes the graphics of the photos posted on the shelf in advance, and will send a message to inform the manager that replenishment is needed. For image recognition, we use an ultra low-power FPGA chip and run a convolutional neural networks(CNN) model on it to achieve. The chips and cameras we use are low power consumption. Their power consumption is milliwatts level, which is also the lowest power consumption device we know of running CNN. The power consumption of the entire system is less than 6 mW, and the price is about 22 dollars. Our experimental results show that on a shelf with a width of 40cm, the maximum lateral distance that the system can recognize is about 40cm. Compared with the existing solutions, our solution has the advantages of lower cost, low power consumption and easy maintenance.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

Wireless localization based on commodity WiFi infrastructures has become one of the most important functions provided by ubiquitous mobile devices. Unfortunately, it heavily relies on the accuracy of the CSI measurements, which contain several kinds of phase distortion. Specifically in this work, we explore the source of CSI errors for WiFi APs with multiple antennas, which are the phase offset between RF chains and have a π phase shift. In order to mitigate these errors in an accurate and automatic way, we devise a customized hardware design with a carefully designed algorithm, which could ensure accurate and automatic error calibration. We implement and validate our system, and conduct extensive evaluation tests. The results show that our system can achieve a median AoA error of 8 degrees in LOS scenarios. The state-of-the-art indoor localization system SpotFi can benefit from our system, which achieves an improvement of nearly 15cm in median localization error and 40 cm in 90th percentile tail localization error.

In this work, we present iLoc, a non-invasive localization system for mobile devices with COTS WiFi access points (APs). We designed and implemented this working system, which only uses COTS APs to locate mobile devices without hardware nor software modifications. Moreover, iLoc is user-friendly because it need not the initial location of the users and need not any settings relating to it. iLoc mainly achieves the following three favorable merits. First, iLoc takes advantage of the fact that all data packets of the 802.11n protocol are broadcast packets, and modifies the protocol stack in the monitoring APs. This modification ensures the application layer receives the channel state information (CSI) for localization without affecting the regular communications in wireless local area network (WLAN). Second, iLoc converts the collected CSI matrixes into the Hankel matrixes and brings them into the super-resolution MUSIC algorithm to get the angle of arrival (AOA). Finally, iLoc employs triangulation for localization and correlates the MAC address and CSI of the device by modifying the Linux kernel to distinguish different mobile devices. iLoc also uses GPU, Kafka queue, and multi-threading technologies to improve the processing efficiency. Our experiments in different environments show that the localization accuracy of iLoc for mobile devices can reach 90cm on average.

Intelligent diagnosis and treatment service is the core content of hospital Internet of Things. Due to the complexity of the hospital environment, the disregard of the system design leads to the uncertainty of data and the instability of system. Timed automata provides an effective method for formal modeling of Internet of Things systems. Therefore, based on the theory of timed automata, introducing the related concepts such as environmental entities, the interaction between Internet of Things services and external environment and the time attribute of services are fully considered, and a method for modeling and verifying hospital intelligent diagnosis and treatment services under the Internet of Things environment is proposed to improve the stability and reliability of system to ensure its correctness. Firstly, on the basis of discussing common scenes of intelligent diagnosis and treatment in hospitals, the application composition is analyzed, and an intelligent diagnosis and treatment service providing framework based on environment interaction is built. Secondly, based on the formal description of two types of environmental entities, the three atomic services, that is, perception type, control type and processing type are modeled respectively. On this basis, the modeling of composite services is completed based on the timed automata network, so as to build a hospital intelligent diagnosis and treatment service model based on timed automata. Finally, taking the application scenario of hospital intelligent diagnosis room as an example, the effectiveness and feasibility of the above-mentioned modeling method are analyzed, and its timeliness correctness is verified by UPPAAL model detection tool. Practice shows that the method can accurately analyze the model during system design, avoid design errors, reduce design defects, and provide effective support for the implementation of hospital intelligent diagnosis and treatment services in the Internet of Things.

Fall detection is one of the most important issues for taking good care of the elder people. Previous solutions are mainly focusing on the fall detection accuracy to avoid unnecessary false alarm with wearable devices, which provide the detection ability after the fall events. In this study, we propose ART, an accurate and real-time fall detection algorithm using COTS smart watch, which could provide accurateter that, we extract the threshold of the acceleration and angle by using the method of SVM. Evaluation results show that, ART can detect fall effectively before the impact, and the accuracy of the forward fall, backward fall and the lateral fall are 100%,95.5% and 95.7% respectively. The average processing time is 276ms, which can provide relatively sufficient time for fall prevention.

The widely deployed WiFi APs and ZigBee devices make the coexistence for these two networks more pervasive than ever. Previous solutions focus on the contention resolution in time domain, but would lead to the underutilized frequency resources. However, for frequency domain schemes, to enable efficient coexistence for WiFi and ZigBee networks, the WiFi nodes or ZigBee nodes have to select the appropriate channel for transmission. Such scheme will need strict cooperations among these two independent systems, and could not work with the conventional legacy systems seamlessly. In this paper, we propose a frequency overlay approach named COFFEE (COexist wiFi For zigbEE networks), where subcarriers interfering with ZigBee transmissions are nullified by WiFi. According to our basic evaluations, due to the relatively robust feature in WiFi/OFDM design, nullifying small portion of subcarriers would affect the WiFi performance slightly, but could improve the capability for coexistence significantly. Thus by using COFFEE, WiFi and ZigBee nodes can transmit their packets concurrently without any coordination. Also, COFFEE and conventional legacy WiFi systems could work together seamlessly. Furthermore, we implement COFFEE with USRP software radio platform and evaluate the performance under real wireless network scenarios. The extensive evaluations show that compared to the coexistence schemes in time domain, COFFEE is able to increase the network throughput by more than 300%. Even when the ZigBee node never knows the transmission time of WiFi packet, it still can provide concurrent lossless transmission by using COFFEE with only 10% to 15% WiFi throughput reduction.

Wireless energy transfer technologies have played an important role in the development of Internet of Things. Most of the previous studies focus on scheduling mobile chargers efficiently for rechargeable sensor nodes. In this paper, we investigate the deployment problem for wireless charging stations (WCSs) in urban areas with respect to the users detouring cost when they move to the candidate WCSs. With pre-known user’s trajectories and given number of WCSs, we deploy the WCSs to maximize the number of recharged users with guaranteed probability. We convert our deployment problem into an NP-hard weighted maximum coverage problem, and prove the objective function is a maximum submodular set function. To this end, a simple but efficient greedy algorithm with approximation factor of $\left({1 - \frac {1}{e}}\right)$ is proposed for the threshold detouring mode. In addition, an improved algorithm with an approximation factor of $\left({1 - \frac {1}{\sqrt {e}}}\right)$ is presented for the linear/nonlinear detouring mode. Finally, we evaluate the performance of our algorithms by comparing them with two typical heuristic algorithms ( flow-centric and random-based ), and the impacts of different detouring thresholds on our algorithms by synthetic traces. Moreover, real trace-driven evaluations validate that our algorithm improves the coverage quality by 75% when compared to the two aforementioned algorithms.

In this paper, we develop a wireless data gathering model for a multi-drone system in traditional wireless sensor networks (WSNs), where each drone serves as a data collector in the extremely large and densely deployed area. Previous solutions usually consider how to schedule the traveling route among nodes, but fail to optimize the data transmission time. We focus on extending the data collection issue over Shannon model which involves the factors such as the transmission bandwidth and the SNR between a drone and a sensor node. A novel and typical system model is formulated and we investigate the corresponding Route Selection and Communication Association (RSCA) problem, that is, given a set of candidate flight routes and a fixed number of deployed nodes, we determine which routes should be selected for traveling and which nodes should be associated such that the overall energy consumption for data gathering could be minimized . We prove the RSCA problem is NP-hard by reduction from the Vertex Cover problem and then devise an efficient and accessible $O(\log \log n)$ approximation algorithm within the time complexity bound by $O(p^{2}\log n\log \log n)$ , where $n$ is the number of sensor nodes and $p$ is the number of routes in WSNs. Extensive simulations are carried out to investigate the performance of our designed algorithm by comparing with the brute-force and random methods. The proposed algorithm achieves 54% more energy consumption at most and 45% more on average comparing with the optimal solution. Furthermore, real-world trace-driven evaluations have been conducted to show that our obtained solution would hold 34% more at most and 25% more on average comparing with the optimal energy consumption and eventually validate our algorithm.

Recent years have seen major innovations in cooperative wireless networks. Despite the fact that throughput gains have been achieved in packet recovery, hardly any of these technologies could effectively decode signals when the number of signal sources is greater than available antennas in each AP. Thus, conventional cooperative methods rely on adaptive scheduling for interference-free data packets. Deploying scheduling-free cooperative signal recovery requires prompt processing with low overheads. Yet potentially, the concurrent client transmissions could overwhelm any single AP, i.e., the number of concurrent transmissions are more than that of antennas. This paper presents the first step towards breaking this stalemate, by enabling symbol alignment and constellation reinforcement instead of relying on scheduling. We present POLYPHONY, a scheduling-free cooperative design, where decoding process could be coordinated without over-the-air scheduling, and coupled signals are decoded promptly after deep cooperations. We implement POLYPHONY prototype with GNURadio/USRP platform, and deploy it with a 16-node enterprise network. Particularly, we demonstrate how it manages the complex interactions with scheduling-free signal enforcement, and enables a beyond node-DoF (Degree of Freedom) decoding with AP coordinations. Furthermore, we show how the cooperative decoding process improves radio access among clients. Our results demonstrate a gain of nearly 200 percent for network throughput, which is a significant improvement for heavy contending networks.

Recent researches show that cross technology interference management plays an important role in the emergence of innovative applications for nowadays wireless networks. Existing algorithms either suffer from the constraint on the need of clean preamble or require technological similarities among cross technology systems. In this paper, we ask the question: “whether the packet with interfered preamble can be decoded?" To answer this question, we investigate the property of preamble and propose Narthil, an innovative coexistence algorithm which based on imperfect interference management, handling packet detection, symbol synchronization, and channel estimation with interfered preambles. Narthil applies a simple Butterworth band-stop filter for imperfect interference filtering, and leverages the residual signals for packet detection and symbol synchronization, as well as channel estimation. The insight is the inherent properties of preamble such as periodicity and modulation scheme, and the frequency continuity in OFDM signal could be effectively leveraged for important processes such as packet detection, symbol synchronization, and channel estimation. These favorable properties could be used to further enhance the performance of other cross technology signal coexistence algorithms. We implement Narthil algorithm on our USRP/GNU Radio platform, and evaluate its performance by using 15 USRP-N210 devices through the real deployment experiments. The results demonstrate that Narthil can effectively detect the incoming packet and achieves a high accuracy symbol synchronization, as well as channel estimation of the interfered preamble, and also achieve a good performance on packet decoding.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.

We propose a novel motion sensing/recognition system, called DF-Mose, which marries low-power wireless backscattering and device-free sensing in one clean sheet. DF-Mose is an accurate, interference tolerable motion-recognition system that counts repetitive motions without using scenario-dependent templates or profiles within 5% count error and enables multiuser to perform certain motions simultaneously based on the nature of backscattered signals and dedicated signal separation method. With little efforts in learning the patterns, our method could achieve 95.2% motion-recognition accuracy for a variety of 7 typical motions.

WiFi has become a pervasive communication medium in connecting various devices of WLAN and IoT. However, WiFi connections are vulnerable to the impersonation attack from rogue access points (AP) or devices, whose SSID and/or MAC/IP address are identical to the legitimate devices. This kind of attack is difficult to countermeasure with traditional network security mechanisms. In this paper, we present a novel security mechanism to detect and identify rogue WiFi devices or AP using environment-independent characteristics extracted from channel state information (CSI), and refuse their connections. We find that nonlinear phase errors of different subcarriers change with WiFi network interface cards (NIC), due to the I/Q imbalance and imperfect oscillator of each WiFi NIC. Validated by our experiments, this phase feature across subcarriers is consistent and invariant to location and external environment, and can be extracted to build an essential signature of the NIC itself. Such signature of the transmitter can be calculated in real-time by the receiver and cannot be forged by rogue devices. Extensive experiments with dozens of WiFi devices demonstrate that the proposed mechanism can reliably detect the rogue WiFi connections and prevent impersonation in various scenarios. The speed of identification is 8$\times$ faster than that of the state-of-the-art solution. Moreover, the accuracy of rogue connection detection is up to 96% and false alarm rate is shown below 2%.

Wireless energy transfer technologies have attracted increasing attention on empowering the wireless sensor nodes in recent years. In this paper, we consider a typical wireless rechargeable network, where a mobile charging vehicle is scheduled to charge a wireless sensor network with practical nodes' deployment restrictions that may result in low charging efficiency for sensor nodes by charging vehicle. In our model, we take the effects of charging distance and angle on charging efficiency into consideration. Intuitively, there is an inevitable tradeoff between the charging distance and the angle. First of all, the scheduling traveling path of charging vehicle in previous studies has been proved to be NP-hard. Even worse, the nonlinear property between the charging distance and angle makes the problem even harder. For these concerns, we aim at minimizing the recharging cycle time, which contains the traveling time and recharging time. To this end, we prove that the charging vehicle would travel along the shortest Hamiltonian cycle. And we show the optimal charging location for each wireless charging incident. Experimental results demonstrate that our proposed solution could improve the charging efficiency around two times compared with the baseline scheme without optimization for angle and distance.

In this paper, we investigate the task offloading issue in mobile social networks. Although the “d-choice” paradigm in “ball and bin” theory had shown the power of random choice in load balancing with a random walk model, its performance could be fairly poor where real-trace data sets are concerned. According to our preliminary evaluation results with “MobiClique,” the “d-choice” scheme could not achieve well-balanced allocations in a real-trace data set. Nevertheless, it would bring fundamental challenges to task reassignment in the following aspects: First of all, some of the friendships are relatively stable, which would lead to a more imbalanced task assignment, even if the “d-choice” scheme is applied for balancing. Second, some users would meet quite infrequently, which could inevitably lead to intolerable time delay and unfair task allocations. In tackling these difficulties, we revisit the real data sets for exploring the contact property among users. We find that the frequently met users could be leveraged for efficient task execution due to higher task priority. To this end, we propose the “ $i$ Top-K” algorithm, leveraging the basic concept, i.e., “your friends are more powerful than you,” which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With careful selections of “Top-K” friends, we achieve balanced load and guaranteed performance at the same time. Experimental studies verify our scheme and show the effectiveness with three typical data trace sets, including “MobiClique.” In this typical networking scenario, ours outperforms the conventional random choice scheme up to 15 $\times$ and the social relationship assignment without priority method up to 9 $\times$ . Moreover, the “Top-K” scheme could be adaptive, even when no intimate friends are available. By scaling the “K” factor to larger values, our scheme outperforms random assignment and could be inspiringly close to the optimal solution. In summary, ours could effectively explore the social relationship and leverage it for efficient task assignment, which would further encourage more mobile users to work together under the rule of social contacts.

In this work, we address the spectrum utilization problem in cognitive radio (CR) networks, in which a CR can only utilize spectrum opportunities when the channel is idle. One challenge for a CR is to balance exploring new channels and exploiting existing channel, due to the fact that the channel availability and channel quality, potentially heterogeneous and time-dependent, are often unknown in advance due to the large number of channels, and the limited hardware capability of single CR. In this work, we propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity $O({N}\cdot {u})$ for a network with ${N}$ channels and ${u}$ secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus nonstochastic), it results in a total regret uniformly upper bounded by $\Theta (\sqrt{T {N}\,\log {N}})$ , w.h.p , for communication lasts for $T$ timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

Recent studies show that Wi-Fi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or Wi-Fi nodes. However, massive deployment of ZigBee nodes and uncooperative Wi-Fi users call for innovative cross-technology coexistence without intervening legacy systems. In this work, we investigate the Wi-Fi and ZigBee coexistence when ZigBee is the interested signal. Typically, the duration of transmitting a ZigBee data packet is longer than that of a Wi-Fi packet. Mitigating short duration Wi-Fi interference (called flash ) in long duration ZigBee data (called smog ) is challenging. To address these challenges, we propose ZIMO: a sink-based MIMO design for harmony coexistence of ZigBee and Wi-Fi networks with the goal of protecting the ZigBee data packets from being interfered by high-power cross-technology signals. The key insight is to properly exploit opportunities resulted from differences between Wi-Fi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of Wi-Fi and ZigBee will enhance other coexistence technologies such as TIMO [1] . We implement a prototype in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve ZigBee network throughput up to 1.9 $\times$ , with 1.5 $\times$ in media, and 1.1 $\times$ to 1.9 $\times$ for Wi-Fi network as byproduct in ZigBee signal recovery.

Recently several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery-free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separated by around 2 meters cause little accuracy reduction in the counting process.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we propose Hitchhike , a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with five nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to $1.9\times$ , with median throughput gain of $1.2\times$ .

In opportunistic channel access, the user needs to make real time decisions on when and which channel to access with uncertainty. Assuming perfect channel statistics, several studies have applied optimal stopping theory to derive control strategy for sequential sensing/probing based opportunistically accessing ( s -SPA), exploiting temporary opportunities among multiple channels. Meanwhile, numerous multi-arm bandit (MAB)-based approaches have been proposed for online learning of channel selection in periodical sensing/accessing system, however, these schemes fail to exploit the opportunistic diversity in short term. In this paper, we investigate online learning of optimal control in s -SPA systems, where both statistics learning and temporary opportunity utilization are jointly considered. An effective and efficient online policy, so called IE-OSP, is proposed, which theoretically guarantees system converges to the optimal s -SPA strategy with bounded probability. Experimental results further show that, the regret of IE-OSP is almost in optimal logarithmic increasing rate over time, and is sub-linear with the increasing number of channels. Compared with existing solutions, our proposed algorithm achieves 25 ∼ 30% throughput gain in typical scenarios.

With the tremendous development of drone systems, several studies have demonstrated the trend and need of leveraging drones to collect data in traditional wireless sensor networks. Unfortunately, it is intrinsically difficult to incorporate drones or drones to wireless sensor network efficiently due to high-density sensor deployment in an extremely large area. Previous solutions mainly focus on scheduling the data collection route among nodes, but overlooking the data transmission time for each node. In this paper, we suppose the data transmission time for data collection over Shannon channel model and consider how to optimize the data collection time for multiple drones, when data transmission time should be respected. We propose a pre-defined route scheme and formulate it into route selection and communication association, a.k.a. RSCA (Route Selection and Communication Association) problem. Then we devise an efficient and accessible greedy algorithm with log(m) approximation ratio, where m is the number of cluster-heads in WSNs. Extensive simulations have been carried out to evaluate our proposed algorithm and our simulation results show our greedy algorithm is 154% at most and 145% on average of optimal solution, thus validate the advantages of our heuristic solution.

In this work, we leverage the reflection property explored in our experimental results and propose an efficient and effective algorithm for the reconstruction of the layout. The proposed algorithm identifies the reflective path, which is caused by obstacle, and evaluated by the difference of Angle of Arrival (AoA) at each transceiver deployed at the room. Then with the knowledge of AoA corresponding to the affected reflective path, the reflection points can be calculated. These reflection points could be used to outline the surface of obstacle. Moreover, we can estimate the shape, size, and location of the obstacle by using AlphaShape algorithm. The simulation results prove the effectiveness of our algorithm.

Wireless energy transfer technologies have played an important role in the development of Internet of Things (IoTs). Most of previous studies focus on scheduling mobile chargers efficiently for rechargeable sensor nodes. In this paper, we consider optimizing the deployment for Wireless Charging Stations (WCSs) in urban area. We respect the users detouring cost, when they need move to the candidate WCSs. Given a number of WCSs and users trajectories, we aim at optimizing the WCSs deployment to maximize the number of users for recharging with guaranteed probability. We convert our deployment problem into the weighted maximum coverage problem, which has been proved to be NP-hard. We have also proved that our objective function is a maximum submodular set function. Then a simple but efficient greedy algorithm could be applied with guaranteed approximation ratio (1−1/e ). Finally, we evaluate the performance of our algorithm by comparing with two effective algorithms, and the impacts of different parameters on our algorithm. The evaluation results show that our algorithm improves the number of covered users with 30% comparing with two aforementioned algorithms.

With the increasing ubiquitous usage of the smart phone, participatory sensing networks become applicable for urban sensing applications. Notably, in pollution monitoring applications, untrained participants and heterogeneously unreliable sensors bring much noise into the reported data. Status quo researches fail to identify truthful pollution sources when the parameters are not known. In this paper, we cluster the reported data and make parameter estimations of the pollution sources. Inspired by the expectation maximization method, we estimate the existence of the pollution sources and leverage these results for noise estimations iteratively. The key insight is that, we can identify the truthful pollution sources with the noisy data mitigation iteratively. Further, theoretical analysis also shows that, we can achieve optimal estimation in the sense of maximum likelihood (ML). Simulation results show that, compared with basic ML algorithm, we improve the false positive and false negative of identification by 99% and 82%, respectively, at the same time, the mean error and the deviation error of noise estimation by 49% and 70%, respectively.

Wireless energy transfer has provided a promising technology to extend the lifetime of wireless rechargeable sensor network. Most of previous studies focus on scheduling chargers or deploying stationary charging stations to replenish energy for rechargeable sensors. These methods could not be applicable when real deployment is concerned, because the roadway needs to be fully respected for mobile chargers in typical urban area. In dealing with this difficulty, we investigate the problem of scheduling mobile chargers with mobility constraints in the scenario of a city graph. First of all, we aim at optimizing the traveling path for chargers to minimize the traveling cost. Consequently, we convert our scheduling problem into edge coverage problem, which is quite different from the point coverage problem. Then classical problem CARP (Capacitated Arc Routing Problem), which has been proved NP-hard, is applied to solve aforementioned problem. To this end, a simple but efficient genetic algorithm cooperated with decoding algorithm Split is proposed. Finally, we evaluate the impacts of different parameters on our algorithm and get the near optimal solution.

In this paper, we consider typical wireless energy harvesting network, where a mobile charging vehicle is scheduled to serve a wireless sensor network. Specifically, for practical considerations such as deployment restrictions, the charging vehicle could not provide full efficiency energy supply to sensor nodes. For wireless energy harvesting, there is an inevitable tradeoff between the charging distance and the angle. We focus on optimizing the charging efficiency when the distance and angle factors are concerned. Scheduling charging vehicle for rechargeable nodes in previous studies has been proved to be NP hard. Even worse, the non-linear property between the charging distance and angle should be carefully considered, which makes the problem even harder. We investigate how to minimize the recharging cycle for all the deployed sensors in network, which contains the traveling time and recharging time. With this problem, we show the charging vehicle is required to move at the shortest Hamiltonian cycle. And we present optimal charging location for each wireless charging incident. Experimental results demonstrate that, our proposed solution could enhance the charging efficiency up to 2 times comparing with the baseline scheme without optimization for angle.

Unfortunately, we had mixed up the institution and address of author “Qiongzheng Lin” with others. The correct institution and address of author “Qiongzheng Lin” are given here.

Recent studies show that cross technology interference is critically important for emergence of innovative applications in today’s wireless networks. Existing approaches either suffer from the constraint of the need of clean preamble, or requiring technological similarities among cross technology systems.

Cooperative design is becoming especially import for loosely coupled MIMO system. Cooperative AP selection is one of the most important issue need to be addressed. In this study, we investigate the efficient AP selection for cooperative MIMO system. The major concern in this work is to reduce the coordinative overhead in network. Two key challenges need to be formally addressed before achieving aforementioned goal. First, the decoding symbols in network is moderately large, which will lead to considerably high network traffic. Second, the decoding performances are highly correlated with the decoding symbols. Reducing symbol transfer will inevitably lead to low decoding rate. Thus, high quality decoding data, say, selecting the appropriate AP for decoding and performing efficient symbol transfer would be a possible solution to tackle aforementioned difficulties. In our design, we fully evaluate the performance of symbol level decoding for cooperative MIMO system, and explore the relationship between the cooperative AP selection with overall decoding. We propose WISE: a simple but effective scheme for AP selection, which is almost optimal and aggressive with moderately low complexity. Our algorithm is implemented and evaluated with USRP, an open software radio network design platform, which enable us to evaluate and validate our scheme more effectively. Experimental results show that, ours could significantly reduce the network overhead, and maintain relatively high decoding rate for cooperative MIMO system.

Software radio systems are playing fundamentally important role for developing intelligent and advanced wireless network system. For cooperative network design, especially loosely coupled system, e.g., the multi-user MIMO system for clients, and the coordinations over ethernet for APs, frequency offset is one of the most important issues needs to be addressed.

In this work, we investigate the distributed and balanced task reassignment in mobile social networks. Previous studies have shown the power of random choice in load balancing with random walking model. Inspired by the `2-choice' paradigm in `ball and bin' theory, we evaluate this simple but effective scheme with real trace data `MobiClique'. According to the preliminary evaluation results, we find that, social relationship significantly differs from pure random walk model, and will bring challenges in task reassignment in the followings: First, friendships are relatively stable, which will lead to imbalanced task assignment. Second, some users meet quite infrequently, which will lead to intolerable time delay and uneven task distribution. In tackling with these challenges, we propose `iTop-K', leveraging the basic concept, i.e., your friends are more powerful than you, which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With the selection of `top-K' friends, we can achieve load balancing and guaranteed network performance at the same time. Experimental studies verify our scheme and show the effectiveness. In typical working scenario, where real-trace driven simulation is applied, ours outperforms the conventional random choice up to 15×, and the social relationship assignment without priority method up to 9×.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we propose Hitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with 5 nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., Side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Neighbor discovery is the very first step for many mobile applications. Previous neighbor discovery protocols that use single channel may be failed when faced with unreliable channel environment, whereas most of multi-channel schemes do not focus on neighbor discovery in low duty cycle wireless networks. The key challenge for multi-channel slotted neighbor discovery is how to effectively assign the channel during each active slot. We propose McDisc, an energy-efficient and reliable duty-cycle-based neighbor discovery protocol that is built on two basic idea. First, it leverages randomized approach to establish the multi-channel discovery schedule, where the node is assigned to the chosen channel during each active slot randomly. Randomized approach may suffer from extremely low discovery probability in worst case. In tackling this problem, we then employ deterministic channel assignment that can ensure bounded discovery latency. The theoretical analysis and simulation results confirm that McDisc can cope with unreliable channel environment ensuring reliable and low latency discovery efficiently. In terms of 60% packet loss ratio, McDisc outperforms U-Connect [1] by approximate 100% in the worst-case discovery latency.

Multichannel accessing is a basic and important function in wireless sensor networks, where intelligent and low-cost methods are needed for energy-efficient spectrum utilization. Unfortunately, for energy constrained nodes, methods from cognitive radio technologies are not applicable in WSN multichannel systems due to computation and communication overhead, as well as extremely large energy consumptions. In this work, we propose a spectrum learning and utilizing paradigm, where sensor nodes can sense, probe, access and switch among channels automatically without prior knowledge. Under this basic and important learning method, we present a two-stage learning and utilization algorithm, which constitutes of two interleaving parts: `transmitting while learning' and `learning while transmitting'. Building these two parts is a non-trivial work, which need efficient channel learning and utilizing scheme working together seamlessly. In the first `transmitting while learning' stage, we propose the `non-call-back' policy and leverage the Lagrangian method for the additional energy cost considerations. Under this heuristic policy, an improved version for the traditional Gittins Index model is provided. After that, the heuristic algorithm is proved `2-approximate' to the optimal result. Furthermore, leveraging the channel correlations, the learning complexity is reduced from $O(T)$ stages to $O(\log T)$ stages, where $T$ is the number of time slots for channel learning. Secondly, we make a structural analysis on the `learning while transmitting' stage, leveraging the 2-dimensional optimal stopping theory. Notably, we use the a $16$-node TelosB sensor network and an $8$-node USRP network for performance evaluation. The USRP nodes are playing as interfering and monitoring nodes for controllable network states design, making network performance evaluation convincible. The proposed algorithm is designed in real implementations with experimental results, showing the efficiency of the proposed scheme.

We propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N·k) for a network with N channels and k secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus non-stochastic), it results in a total regret uniformly upper bounded by Θ(√TN logN), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

The main challenge in DTNs is how to deal with path uncertainty in achieving a reliable routing scheme. All Erasure coding based routing algorithms make the assumption that the underlying path probabilities are known previously and remain constant, which is unpractical. On the other hand, the overall behavior of path probability tends to be stable with the increasing number of paths, which can be used to increase the stability of Erasure coding based schemes. Bearing this in mind, we present Inter-Coding: Inter-Coding is designed to fully combine the reliability of erasure coding, and the stability of interleaving to cope with uncertainties. We evaluate our approach in terms of delivery ratio under different level of uncertainty as well as different interleaving policy, and validate that Inter-Coding offers reliable and stable performance even the path uncertainty and dynamic is high.

In extremely low-duty-cycle wireless sensor networks, a sender has to wait for a certain period of time to forward a packet until its receiver becomes active, which will result in longer end-to-end delay than ever. Many works have been done to improve delivery ratio but lack of the consideration on energy efficient delivery delay. In addition, unreliable links is another challenge in wireless sensor networks. Redundancy and multiple paths can be used to cope with unreliability, but neither of them is energy efficient. Even worse, both of them have poor performance on delivery delay. In this work, we introduce a novel way of allocating erasure coded blocks over multiple paths to improve energy efficient delivery delay while achieving comparably high delivery ratio. We evaluate our algorithm with extensive simulations. Evaluations show that our design decreases delivery delay greatly with slight decrease in delivery ratio.

In wireless sensor networks, there are many link quality measurement metrics such as RSSI (Received Signal Strength Indicator), LQI (Link Quality Indicator) and PRR (Packet Reception Rate), which can be used under different channel quality and application scenarios. As in time synchronization, channel quality would be essential to the clock synchronization. The broadcast nature of time synchronization algorithm makes channel quality measurement complex. Moreover, the measurement on channel quality would be costly. We make an experimental study on time synchronization in wireless sensor network. Firstly, we use the RSSI and PRR with different packet lengths, secondly, we find that, the PRR would be accurate but costly, and the RSSI is not accurate enough for time synchronization algorithm evaluation. In the end, we propose a channel clustering and categorization mechanism in dealing with the channel measurement difficulties. And we find that, in dealing with the lossy links, the compensation model for synchronization errors is more important than reliable transmissions. Also, the proposed measurement would be helpful to the time synchronization algorithm, especially the FTSP (Flooding Time Synchronize Protocol).

Since traditional TDM based voice switches can not be efficiently applied in the packet networks, improvements on data voice multiplexing based on TDM are needed for appropriate usage in some extent. Transforming TDM voice data into IP packets can realize flexible data switching, and in this work, we present a data voice multiplexing based scheme on the embedded micro processor MPC8280 to implement the data processing. Moreover, a series of thorough tests have been done under this prototype for performance evaluations.

In this work, we are striving for a perfect balance on utilisation between reliable channels and unreliable channels, where the network throughput can be maximised. We propose 'p-stable', a distributed Multi-phase Maximum Weighted Matching algorithm. We also apply channel bundles to effectively improve stability in the network, and prove the problem NP-hard. The time complexity is O(Δ³ + (log. *n)²), and overhead complexity is O(Δ × (Δ + n) + n α + log n), with Δ denoting maximum number of node degree in an n nodes network. Simulation results show that p-stable design effectively improves network stability, especially upon the existence of large number of unreliable channels.