We introduce PANEL a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings are fetched by the base stations after some delay. In particular, the motivation for the design of PANEL was to support reliable and persistent data storage applications, such as TinyPEDS; see the study by . PANEL ensures load balancing, and it supports intra and intercluster routing allowing sensor-to-aggregator, aggregator-to-aggregator, base station-toaggregator, and aggregator to-base station communications. We also compare PANEL with HEED; see the study by Younis and Fahmy (2004) in the simulation environment provided by TOSSIM, and show that, on one hand, PANEL creates more cohesive clusters than HEED, and, on the other hand, that PANEL is more energy efficient than HEED.

In the past few years, research interest has been increased towards wireless sensor networks (WSNs) and their application in both the military and civil domains. To support scalability in WSNs and increase network lifetime, nodes are often grouped into disjoint clusters. However, secure and reliable clustering, which is critical in WSNs deployed in hostile environments, has gained modest attention so far or has been limited only to fault tolerance. In this paper, we review the state-of-the-art of clustering protocols in WSNs with special emphasis on security and reliability issues. First, we define a taxonomy of security and reliability for cluster head election and clustering in WSNs. Then, we describe and analyze the most relevant secure and reliable clustering protocols. Finally, we propose countermeasures against typical attacks and show how they improve the discussed protocols.

We introduce PANEL a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings are fetched by the base stations after some delay. In particular, the motivation for the design of PANEL was to support reliable and persistent data storage applications, such as TinyPEDS; see the study by . PANEL ensures load balancing, and it supports intra and intercluster routing allowing sensor-to-aggregator, aggregator-to-aggregator, base station-toaggregator, and aggregator to-base station communications. We also compare PANEL with HEED; see the study by Younis and Fahmy (2004) in the simulation environment provided by TOSSIM, and show that, on one hand, PANEL creates more cohesive clusters than HEED, and, on the other hand, that PANEL is more energy efficient than HEED.

In this paper, we propose a new model of resilient data aggregation in sensor networks, where the aggregator analyzes the received sensor readings and tries to detect unexpected deviations before the aggregation function is called. In this model, the adversary does not only want to cause maximal distortion in the output of the aggregation function, but it also wants to remain undetected. The advantage of this approach is that in order to remain undetected, the adversary cannot distort the output arbitrarily, but rather the distortion is usually upper bounded, even for aggregation functions that were considered to be insecure earlier (e.g., the average). We illustrate this through an example in this paper.

In this paper, we consider the problem of resilient data aggregation in sensor networks, namely, how to aggregate sensor readings collected by the base station when some of those sensor readings may be compromised. Note that an attacker can easily compromise the reading of a sensor by altering the environmental parameters measured by that sensor. We present a statistical framework that is designed to mitigate the effects of the attacker on the output of the aggregation function. The main novelty of our approach compared to most prior work on resilient data aggregation is that we take advantage of the naturally existing correlation between the readings produced by different sensors. In particular, we show how spatial correlation can be represented in the sensor network data model, and how it can be exploited to increase the resilience of data aggregation. The algorithms presented in this paper are flexible enough to be applied without any special assumption on the distribution of the sensor readings or on the strategy of the attacker. The effectiveness of the algorithms is evaluated analytically considering a typical attacker model with various parameters, and by means of simulation considering a sophisticated attacker.