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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.

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.

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.

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 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, 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 $1.2\times$ .

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.

The 2.4 GHz ISM band is becoming increasingly over crowded with the ubiquitous usage of WiFi and ZigBee systems. WiFi Access Points (APs) are widely used in urban area for ubiquitous Internet access. Most of them, such as IEEE 802.11g [7] system, are leveraging OFDM communication scheme, and working at the frequency band from 2.402 to 2.482 GHz. The OFDM systems divide the 20 MHz bandwidth into 64 orthogonal subcarriers, and use 52 of them for data transmission. Meanwhile, the Wireless Senor Networks (WSNs) are also applied for real time and long term environmental monitoring in urban area such as urban sensing (e.g., City See) [10], as well as medical health care applications [13]. Unfortunately, the WSNs also select the crowded 2.4 GHz ISM band for communications, which is an over crowded spectrum band.

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.

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.

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.

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.

In this work, we address the spectrum utilization problem in cognitive radio networks. The difficulty of the problem comes from the need to balance exploring new channels and exploiting existing channel, and the fact that the channel availability and channel quality, which are potentially heterogeneous and time-dependent, are unknown or hard to know for CRs due to the large number of channels and the limited hardware capability of single CR. In this demo, we present a practical joint sensing/probing/accessing schemes, which demonstrates how to sense, probe and access spectrum opportunities. Our methods are proved to be workable in USRP networks and evaluated in real network scenarios.

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.

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.