Packet dropping and modification are common attacks that can be launched by an adversary to disrupt communication in wireless multihop sensor networks. Many schemes have been proposed to mitigate or tolerate such attacks, but very few can effectively and efficiently identify the intruders. To address this problem, we propose a simple yet effective scheme, which can identify misbehaving forwarders that drop or modify packets. Extensive analysis and simulations have been conducted to verify the effectiveness and efficiency of the scheme.

When wireless sensors are deployed to monitor the working or life conditions of people, the data collected and processed by these sensors may reveal privacy of people. The actual content of sensory data should be concealed to preserve the privacy, but the data concealment feature may be abused by compromised sensors to modify or ill-process data without being caught. Hence, reconciling privacy preservation and intrusion detection, which apparently conflict with each other, is important. This paper studies this problem in the context of sensory data aggregation, a fundamental primitive for efficient operation of sensor networks. A scheme is proposed that can detect ill-performed aggregation without knowing the actual content of sensory data, and therefore allow sensory data to be kept concealed. The results show that, the actual content of raw and aggregated sensory data can be well concealed. Meanwhile, most of ill-performed aggregations can be detected; the ill-performed aggregations that can escape from being detected have only negligible impact on the final aggregation results.

When deployed for long-term tasks, the energy required to support sensor nodes' activities is far more than the energy that can be preloaded in their batteries. No matter how the battery energy is conserved, once the energy is used up, the network life terminates. Therefore, guaranteeing long-term energy supply has persisted as a big challenge. To address this problem, we propose a node reclamation and replacement (NRR) strategy, with which a mobile robot or human labor called mobile repairman (MR) periodically traverses the sensor network, reclaims nodes with low or no power supply, replaces them with fully charged ones, and brings the reclaimed nodes back to an energy station for recharging. To effectively and efficiently realize the strategy, we develop an adaptive rendezvous-based two-tier scheduling scheme (ARTS) to schedule the replacement/reclamation activities of the MR and the duty cycles of nodes. Extensive simulations have been conducted to verify the effectiveness and efficiency of the ARTS scheme.

When deployed for long-term tasks, the energy required to support sensor nodes' activities is far more than the energy that can be preloaded in their batteries. No matter how the battery energy is conserved, once the energy is used up, the network life terminates. Therefore, guaranteeing long- term energy supply has persisted as a big challenge. To address this problem, we propose a node replacement and reclamation (NRR) strategy, with which a mobile robot or human labor called mobile repairman (MR) periodically traverses the sensor network, reclaims nodes with low or no power supply, replaces them with fully-charged ones, and brings the reclaimed nodes back to an energy station for recharging. To effectively and efficiently realize the strategy, we develop an adaptive rendezvous- based two-tier scheduling (ARTS) scheme to schedule the replacement/reclamation activities of the MR and the duty cycles of nodes. Extensive simulations have been conducted to verify the effectiveness and efficiency of the ARTS scheme.

Packet dropping and modification are common attacks that can be launched by an adversary to disrupt communication in wireless multi-hop sensor networks. Many schemes have been proposed to mitigate the attacks but none can effectively and efficiently identify the intruders. To address the problem, we propose a simple yet effective scheme, which can identify misbehaving forwarders that drop or modify packets. Extensive analysis and simulations using ns2 simulator have been conducted and verified the effectiveness and efficiency of the scheme.

Efficiency and security are two basic requirements for sensor network design. However, these requirements could be sharply contrary to each other in some scenarios. For example, in- network data aggregation can significantly reduce communication overhead and thus has been adopted widely as a means to improve network efficiency; however, the adoption of in-network data aggregation may prevent data from being encrypted since it is a prerequisite for aggregation that data be accessible during forwarding. We address this dilemma by proposing a family of secret perturbation-based schemes that can protect sensor data confidentiality without disrupting additive data aggregation. Extensive simulations are also conducted to evaluate the proposed schemes. The results show that our schemes provide confidentiality protection for both raw and aggregated data items with an overhead lower than that of existing related schemes.

Protecting privacy in sensor networks poses new challenges because of the potential incompatibilities between new privacy-preserving mechanisms and mechanisms already implemented in sensor networks (such as in-network data aggregation). To address this problem, we propose in this paper a set of new privacy-preservation data aggregation schemes. Different from past research, our solutions have the following features: supporting data aggregation for a variety of queries; providing privacy protection for both individual data and aggregate data; being resilient to any number of node collusion; being highly efficient.