Abstract A wireless sensor network can detect single (or atomic) events or composite events. Since sensors are battery powered and in general, it is hard to recharge them, energy management is always an important issue. In this paper, we study the SCED problem; given a wireless sensor network deployed for watching a composite event x1, x2, … , xM, design a sensor scheduling mechanism such that the set of active sensors ensure the coverage and connectivity conditions and WSN lifetime is maximized. Network lifetime is organized in rounds. Each round has two phases: initialization and data collection. In the initialization phase, the goal is to choose a set of active sensors as sensing nodes or relay nodes such that to achieve both coverage and connectivity requirements. Sensors which are not chosen to be active go to sleep to save energy. In the data collection phase, active sensors perform sensing and data relaying. When another round begins, a new set of active sensors are determined. Two solutions are proposed for the SCED problem, a grid‐based distributed algorithm and a localized algorithm. We analyze their performance through simulations. Copyright © 2010 John Wiley & Sons, Ltd.

Smartphones are envisioned to provide promising applications and services. At the same time, smartphones are also increasingly becoming the target of malware. Many emerging malware can utilize the proximity of devices to propagate in a distributed manner, thus remaining unobserved and making detections substantially more challenging. Different from existing malware coping schemes, which are either totally centralized or purely distributed, we propose a Community-based Proximity Malware Coping scheme, CPMC. CPMC utilizes the social community structure, which reflects a stable and controllable granularity of security, in smartphone-based mobile networks. The CPMC scheme integrates short-term coping components, which deal with individual malware, and long-term evaluation components, which offer vulnerability evaluation towards individual nodes. A closeness-oriented delegation forwarding scheme combined with a community level quarantine method is proposed as the short-term coping components. These components contain a proximity malware by quickly propagating the signature of a detected malware into all communities while avoiding unnecessary redundancy. The long-term components offer vulnerability evaluation towards neighbors, based on the observed infection history, to help users make comprehensive communication decisions. Extensive real- and synthetic-trace driven simulation results are presented to to evaluate the effectiveness of CPMC.

Delay tolerant networks (DTNs) are envisioned to provide promising applications and services. One critical issue in DTNs is efficiently forwarding the messages within the delay requirements while avoiding the cost associated with blind flooding. To guide the forwarding process, nodes can evaluate their relationships with each other, in terms of ``closeness'', which summarizes both temporal and spacial information, based on contact history. However, due to the uncertainty in nodal mobility, the contact history usually contains fuzziness and incomplete information. In this paper, we first define and utilize a fuzzy trust evaluation system for nodes to summarize their relationships to other nodes, in terms of closeness. We then propose the fuzzy clustering to organize nodes into overlapped fuzzy communities based on nodes' evaluations of closeness. On top of the fuzzy communities, a novel fuzzy-weight-based delegation forwarding scheme is proposed to propagate the messages into all communities while avoiding repeated forwarding in the same community. Extensive simulation results based on real traces are presented to support the effectiveness of our scheme.

In mobile ad hoc networks, nodes have the inherent ability to move. Aside from conducting attacks to maximize their utility and cooperating with regular nodes to deceive them, malicious nodes get better payoffs with the ability to move. In this paper, we propose a game theoretic framework to analyze the strategy profiles for regular and malicious nodes. We model the situation as a dynamic Bayesian signaling game and analyze and present the underlining connection between nodes' best combination of actions and the cost and gain of the individual strategy. Regular nodes consistently update their beliefs based on the opponents' behavior, while malicious nodes evaluate their risk of being caught to decide when to flee. Some possible countermeasures for regular nodes that can impact malicious nodes' decisions are presented as well. An extensive analysis and simulation study shows that the proposed equilibrium strategy profile outperforms other pure or mixed strategies and proves the importance of restricting malicious nodes' advantages brought by the flee option.

Good sensor deployment is vital for wireless sensor networks (WSNs). To improve the initial deployment and to prolong network lifetime, one approach is to relocate sensors in different densities which vary with the distance to the sink. Since sensors located closer to the sink are involved in more data forwarding, sensors in this region should have a higher density. In this paper, we address the problem of Movement-assisted Sensor Positioning (MSP) to increase network lifetime with the objective to achieve the theoretical sensor densities while minimizing sensor movement. We propose three solutions: an Integer-Programming formulation, a localized matching method, and a distributed corona-radius scanning algorithm. Simulation results are presented to evaluate the proposed solutions.

Sensor deployment in mobile sensor networks has received significant attention in recent years. Goals during sensor deployment include improving coverage, achieving load balance, and prolonging the network lifetime. To improve the initial deployment, one possible method is to use mobile sensors, thus allowing sensors to relocate. In this paper, we present a sensor deployment strategy in mobile sensor networks. Our goal is to improve coverage and prolong network lifetime through sensor relocation after the initial deployment. The problem in this paper is defined as Movement-assisted Sensor Positioning (MSP) for network lifetime increase problem. With the observation that the sensors closer to the sink tend to consume more energy than those farther away from the sink, we first compute the desired non-uniform sensor density in the monitored area to reduce the energy holes near the sink and to prolong network lifetime. Assuming that sensors can move only once, we then propose a centralized algorithm to relocate mobile sensors to satisfy the density requirement with minimum cost. We construct the virtual multi-flow graph and solve the sensor relocation problem using a maximum-flow minimum-cost algorithm. The improvement in network lifetime is proved both mathematically and by simulations.