Guoxing Zhan

Design and Implementation of TARF: A Trust-Aware Routing Framework for WSNs

The multihop routing in wireless sensor networks (WSNs) offers little protection against identity deception through replaying routing information. An adversary can exploit this defect to launch various harmful or even devastating attacks against the routing protocols, including sinkhole attacks, wormhole attacks, and Sybil attacks. The situation is further aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or efforts at developing trust-aware routing protocols do not effectively address this severe problem. To secure the WSNs against adversaries misdirecting the multihop routing, we have designed and implemented TARF, a robust trust-aware routing framework for dynamic WSNs. Without tight time synchronization or known geographic information, TARF provides trustworthy and energy-efficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both simulation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions. Further, we have implemented a low-overhead TARF module in TinyOS; as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. Based on TARF, we also demonstrated a proof-of-concept mobile target detection application that functions well against an antidetection mechanism.

TARF: a trust-aware routing framework for wireless sensor networks

Multi-hop routing in wireless sensor networks (WSNs) offers little protection against deception through replaying routing information. This defect can be taken advantage of by an adversary to misdirect significant network traffic, resulting in disastrous consequences. It cannot be solved solely by encryption or authentication techniques. To secure multi-hop routing in WSNs against intruders exploiting the replay of routing information, we propose TARF, a trust-aware routing framework for WSNs. Not only does TARF significantly reduce negative impacts from these attackers, it is also energy-efficient with acceptable overhead. It incorporates the trustworthiness of nodes into routing decisions and allows a node to circumvent an adversary misdirecting considerable traffic with a forged identity attained through replaying. Both our empirical and simulated experimental results indicate that TARF satisfactorily performs routing and is resilient against attacks by exploiting the replay of routing information.

SPA: a smart phone assisted chronic illness self-management system with participatory sensing

The medical system has not been able to effectively adapt to the dramatic transformation in public health challenges; from acute to chronic and lifestyle-related illnesses. Although acute illnesses can be treated successfully in an office or hospital, chronic illnesses comprise the bulk of health care needs and require a very different approach. There is overwhelming consensus that the prevention and treatment of cardiovascular disease, diabetes, hypertension, chronic pain, obesity, asthma, HIV, and many other chronic illnesses require substantial patient self-management [2].

SensorTrust: A resilient trust model for wireless sensing systems

Wireless sensor networks (WSNs) are prone to failures and malicious attacks. Traditional approaches from encryption and authentication are not sufficient to solve the problems. Trust management of WSNs is bringing new approaches. However, it is still a challenge to establish a trust environment for WSNs. To conquer that challenge, we propose a resilient trust model with a focus on data integrity, SensorTrust, for hierarchical WSNs. SensorTrust integrates past history and recent risk to accurately identify the current trust level. It employs a Gaussian model to rate data integrity in a fine-grained style and a flexible update protocol to adapt to varied context. With acceptable overhead, SensorTrust proves resilient against varied faults and attacks.

Towards Location and Trajectory Privacy Protection in Participatory Sensing

The ubiquity of mobile devices has facilitated the prevalence of participatory sensing, whereby ordinary citizens using their private mobile devices to collect regional information and share with participators. However, such applications may endanger users’ privacy by revealing their locations and trajectories information. Most of existing solutions, which hide a user’s location information with a coarse region, are under k-anonymity model. Yet, they may not be applicable in some participatory sensing applications which require precise location information for high quality of service. In this paper, we present a method to protect the user’s location and trajectory privacy with high quality of service in some participatory sensing applications. Then, we utilize a new metric, called Slope Ratio (SR), to evaluate the method we proposed. The analysis and simulation results show that the method can protect the user’s location and trajectory privacy effectively.

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

It is often important to obtain the real-time location of a small-sized ground robotic vehicle when it performs autonomous tasks either indoors or outdoors. We propose and implement LOBOT, a low-cost, self-contained localization system for small-sized ground robotic vehicles. LOBOT provides accurate real-time, 3D positions in both indoor and outdoor environments. Unlike other localization schemes, LOBOT does not require external reference facilities, expensive hardware, careful tuning or strict calibration, and is capable of operating under various indoor and outdoor environments. LOBOT identifies the local relative movement through a set of integrated inexpensive sensors and well corrects the localization drift by infrequent GPS-augmentation. Our empirical experiments in various temporal and spatial scales show that LOBOT keeps the positioning error well under an accepted threshold.

SensorTrust: a resilient trust model for WSNs

We present SensorTrust, a trust model to evaluate the trustworthiness of nodes in hierarchical wireless sensor networks, focusing on data integrity.

Data Quality and Failures Characterization of Sensing Data in Environmental Applications

Environmental monitoring, targeting at discovering and understanding the environmental laws and changes, is one of the most important sensor network application domains. Environmental monitoring is one of the most important sensor network application domains. The success of those applications is determined by the quality of the collected data. Thus, it is crucial to carefully analyze the collected sensing data, which not only helps us understand the features of monitored field, but also unveil any limitations and opportunities that should be considered in future sensor system design. In this paper, we take an initial step and analyze one-month sensing data collected from a real-world water system surveillance application, focusing on the data similarity, data abnormality and failure patterns. Our major findings include: (1) Information similarity, including pattern similarity and numerical similarity, is very common, which provides a good opportunity to trade off energy efficiency and data quality; (2) Spatial and multi-modality correlation analysis provide a way to evaluate data integrity and to detect conflicting data that usually indicates appearances of sensor malfunction or interesting events; and (3) External harsh environmental conditions may be the most important factor on inflicting failures in environmental applications. Communication failures, mainly caused by lacking of synchronization, contribute the largest portion among all failure types.

StressBar: a system for stress information collection

The causes of stress and how it affects our behaviors are generally not well understood. The stress research usually requires a large amount of data to analyze possible stress-related factors. The data collection process traditionally is time-consuming and cost-ineffective. To help medical researchers collect the stress information, we propose a tool named StressBar utilizing the powerful data collection capacity of smart phones. In this paper, we will show our design and implementation considerations of StressBar.