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Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to misreport their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment.We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.

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Abstract-We consider a scenario where a sophisticated jammer jams an area in a single-channel wireless sensor network. The jammer controls the probability of jamming and transmission range to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by a monitoring node in the network, and a notification message is transferred out of the jamming region. The jammer is detected at a monitor node by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network computes channel access probability in an effort to minimize the jamming detection plus notification time. In order for the jammer to optimize its benefit, it needs to know the network channel access probability and number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of one another, and the case where the jammer or the network lack this knowledge. The latter is captured by formulating and solving optimization problems, the solutions of which constitute best responses of the attacker or the network to the worst-case strategy of each other. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a intuitive heuristic jamming strategy for that case.

The pervasiveness of wireless devices and the architectural organization of wireless networks in distributed communities, where no notion of trust can be assumed, are the main reasons for the growing interest in the issue of compliance to protocol rules. Reliable and timely detection of deviation from legitimate protocol operation is recognized as a prerequisite for ensuring efficient and fair use of network resources and minimizing performance losses. Nevertheless, the random nature of protocol operation together with the inherent difficulty of monitoring in the open and highly volatile wireless medium poses significant challenges. In this paper, we consider the fundamental problem of detection of node misbehavior at the MAC layer. Starting from a model where the behavior of a node is observable, we cast the problem within a minimax robust detection framework, with the objective to provide a detection rule of optimum performance for the worst-case attack. The performance is measured in terms of required number of observations in order to derive a decision. This framework is meaningful for studying misbehavior because it captures the presence of uncertainty of attacks and concentrates on the attacks that are most significant in terms of incurred performance losses. It also refers to the case of an intelligent attacker that can adapt its policy to avoid being detected. Although the basic model does not include interference, we show that our ideas can be extended to the case where observations are hindered by interference due to concurrent transmissions. We also present some hints for the problem of notifying the rest of the network about a misbehavior event. Our work provides interesting insights and performance bounds and serves as a prelude to a future study that would capture more composite instances of the problem.

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