Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user's context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.

Crowdsensing applications require individuals toshare local and personal sensing data with others to produce valuableknowledge and services. Meanwhile, it has raised concernsespecially for location privacy. Users may wish to prevent privacyleak and publish as many non-sensitive contexts as possible.Simply suppressing sensitive contexts is vulnerable to the adversariesexploiting spatio-temporal correlations in users' behavior.In this work, we present PLP, a crowdsensing scheme whichpreserves privacy while maximizes the amount of data collectionby filtering a user's context stream. PLP leverages a conditionalrandom field to model the spatio-temporal correlations amongthe contexts, and proposes a speed-up algorithm to learn theweaknesses in the correlations. Even if the adversaries are strongenough to know the filtering system and the weaknesses, PLPcan still provably preserves privacy, with little computationalcost for online operations. PLP is evaluated and validated overtwo real-world smartphone context traces of 34 users. Theexperimental results show that PLP efficiently protects privacywithout sacrificing much utility.

Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, if not carefully designed, taxi-sharing may cause more harm than benefit - it becomes harder to strike the balance between driver's profit and passenger's quality of service (e.g. travel time, number of strangers that share a taxi, etc.). In this paper, we propose a QoS-aware taxi-sharing system design - QA-Share - by addressing two important challenges. First, QA-Share aims to maximize driver profit and user experience at the same time. Second, QA-Share continuously optimizes these two metrics by dynamically adapting its schedule as new requests arrive, without entering an oscillation state. To address these two challenges, we have formulated the optimization problem using integer linear programming, and derived the optimal solution under a small system scale. When the number of requests and taxis becomes large, we have devised a heuristic algorithm that has a much faster execution time. We have also studied how to minimize oscillations caused by schedule re-calculations by dynamically tuning the update threshold. We have evaluated our approach with real-world dataset in a Chinese city - ZhenJiang - which contains the GPS traces recorded by over 3,000 taxis during a period of three months in 2013. Our results show that the QoS and profit is increased by 38% compared to earlier schemes.

How to shorten the time for channel waiting is critical to avoid network contention. Traditional MAC protocols with CSMA often assume that a transmission must be deferred if the channel is busy, so they focus more on the optimization of serial transmission performance. Recent advances in physical layer, however, allows a receiver to reengage onto a stronger incoming signal from an ongoing transmission or interference, and thus shows the potential of parallel transmissions. Indeed, even if the channel is busy, a node has opportunities to carry out a successful transmission. In this study, we propose opportunistic concurrency (OPC), a new MAC layer scheme, which enables sensor nodes to capture the opportunistic concurrency and carry out parallel transmissions instead of always waiting for a clear channel. Based on local concurrency map, which encodes the interactions among different links, OPC utilizes concurrency control algorithm to make transmission decision distributedly. Our experiments on a testbed consisting of 60 TelosB sensor motes identify the transmission opportunities in WSNs with OPC. Evaluation results show that OPC achieves a 17 percent reduction in packet latency, a 9.4 percent addition in throughput and a 10 percent reduction in power consumption compared with existing approaches.

In large-scale wireless sensor networks, faulty link detection plays a critical role in network diagnosis and management. Most potential network bottlenecks such as network partition and routing errors can be detected by link scan. Since sequentially checking all potential links incurs high transmission and storage cost, we propose a passive scheme Link Scanner (LS) for monitoring wireless links. As we know, to maintain a sensor network running in a normal condition, many applications in flooding manner are necessary, such as time synchronization, reprogramming, protocol update, etc. During such regular flooding processes that for other purposes originally, LS passively collects hop counts of received probe messages at sensor nodes. Based on the observation that faulty links can result in mismatch between received hop counts and network topology, LS deduces all links' status with a probabilistic model. We evaluate our scheme by carrying out experiments on a testbed with 60 TelosB motes and conducting extensive simulation tests. A real outdoor system is also deployed to verify that LS can be reliably applied to surveillance networks.

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.

In a large-scale wireless sensor network, thousands of sensor nodes periodically generate and forward data back to the sink. In our recent outdoor deployment, we observe that some bottleneck nodes can greatly determine other nodes' data collection ratio, and thus affect the whole network performance. To figure out the importance of a node in data collection, the manager needs to understand the interactive behaviors among the parent and child nodes. To address this issue, we present a management tool BOND (Bottleneck Node Detector). We introduce the concept of Node Dependence to characterize how much a node relies on each of its parent nodes. BOND models the routing process as a Hidden Markov Model, and uses a machine learning approach to learn the state transition probabilities in this model based on the observed traces. BOND utilizes Node Dependence to explore the hidden bottleneck nodes in the network. Moreover, we can predict how adding or removing the sensor nodes would impact the data flow, thus avoid data loss and flow congestion in redeployment. We implement our tool on real hardware and deploy it in an outdoor system. Our extensive experiments show that BOND infers the Node Dependence with an average accuracy of more than 85%.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring future developments and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses for a complete stack of protocols at large scale. Specifically, very little is known about: 1) when, where, and under what kind of circumstances packet losses occur; 2) why packets are lost. As a step toward addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined causal graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power ad hoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments .

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring further development and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses with respect to a complete stack of protocols at large scales. Specifically, very little is known about (1) When, where, and under what kind of circumstances packet losses occur. (2) Why packets are lost. As a step towards addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined casual graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power adhoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments.

Routing dynamics are intrinsic characteristics of operational wireless sensor networks (WSNs). We present the measurement and analysis results for routing dynamics in a large-scale WSN. We seek to answer several fundamental questions: How dynamically are current routing protocols performing? What causes routing dynamics? What is the impact of routing dynamics? Answers to the above questions are critical to understanding the interactions among multiple network elements, evaluating protocol design strategies, and improving system performances. However, measurements in large-scale WSNs are challenging due to the lack of dedicated log information (be analogous to configuration files, syslog messages used in Internet). We propose an approach to identify the routing dynamics based on limited information and correlate them with system events to find out the root causes. The key findings of our study include: parent change events mainly affect local nodes, i.e. They do not cause routing instability on far-away nodes, environment dynamics and routing loops have large impact on routing, small portion of parent changes might not be necessary, while a large portion of parent changes are effective in improving network performance.

In this paper, we investigate the predictability of packet arrivals in asynchronous duty-cycling wireless sensor networks (WSNs). We conduct statistical analysis on data traces collected in both outdoor large-scale and indoor testbed WSN to show that traditional well-known traffic models, e.g., Poisson process and Self-similarity process, do not fit well for modeling packet arrivals in both networks. According to our observations, some key characteristics such as sleeping interval and sampling rate have significant impact on the traffic patterns under Low power listening (LPL) models. Hence, we raise a question: could we achieve accurate prediction on the packet arrivals in asynchronous duty-cycling WSNs? To answer this question, we design a novel data-driven predictor P2IT focusing on two prediction goals: (i) the arrival time of the next packet (ii) the number of arrival packets within a short time interval. We conduct extensive trace-driven experiments to demonstrate that our predictor achieves high acuracies on both prediction goals under various experimental settings.

Driven by rising application demands, the scale of Wireless Sensor Networks has grow rapidly in recent years. Thus, the traditional network management pattern, in which the sink is responsible for managing the entire network, reveals many problems in terms of both performance and efficiency. We propose an energy-efficient scheme to improve the performance of online network management and diagnosis services based on the quality of interactive communications in large-scale sensor networks. By abstracting the uncertain network model from our deployed sensor systems, we define the manageable nodes. Under different practical constraints, we then design algorithms in which multiple management centers can work in cooperation to cover as many manageable nodes as many as possible. Using the data obtained from our urban sensing sensor network system, CitySee with 494 nodes, we conduct trace-driven simulations. The experimental results verify the feasibility and effectiveness of this design.