DARE: evaluating Data Accuracy using node REputation

Typical wireless sensor networks (WSNs) applications are characterized by a certain number of different requirements such as: data accuracy, localization, reputation, security, and confidentiality. Moreover, being often battery powered, WSNs face the challenge of ensuring privacy and security despite power consumption limitations. When the application scenario allows their use, data aggregation techniques can significantly reduce the amount of data exchanged over the wireless link at the price of an increased computational complexity and the potential exposition to data integrity risks in the presence of malicious nodes. In this paper, we propose DARE, an hybrid architecture combining WSNs with the wireless mesh networking paradigm in order to provide secure data aggregation and node reputation in WSNs. Finally, the use of a secure verifiable multilateration technique allows the network to retain the trustworthiness of aggregated data even in the presence of malicious node. Extensive performance evaluations carried out using simulations as well as a real-world prototype implementation, show that DARE can effectively reduce the amount of data exchanged over the wireless medium delivering up to 50% battery lifetime improvement to the wireless sensors

Efficient Large-scale Trace Checking Using MapReduce

The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval for which logged events must be buffered to check satisfaction of the specification. The former issue can be addressed by distributed and parallel trace checking algorithms that can take advantage of modern cloud computing and programming frameworks like MapReduce. Still, the latter issue remains open with current state-of-the-art approaches. In this paper we address this memory scalability issue by proposing a new semantics for MTL, called lazy semantics. This semantics can evaluate temporal formulae and boolean combinations of temporal-only formulae at any arbitrary time instant. We prove that lazy semantics is more expressive than standard point-based semantics and that it can be used as a basis for a correct parametric decomposition of any MTL formula into an equivalent one with smaller, bounded time intervals. We use lazy semantics to extend our previous distributed trace checking algorithm for MTL. We evaluate the proposed algorithm in terms of memory scalability and time/memory tradeoffs.Comment: 13 pages, 8 figure