Péter Schaffer

Position-Based Aggregator Node Election in Wireless Sensor Networks

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 Girao et al. (2007). PANEL ensures load balancing, and it supports intra and intercluster routing allowing sensor-to-aggregator, aggregator-to-aggregator, base station-to-aggregator, 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.

RANBAR: RANSAC-based resilient aggregation in sensor networks

We present a novel outlier elimination technique designed for sensor networks. This technique is called RANBAR and it is based on the RANSAC (RANdom SAmple Consensus) paradigm, which is well-known in computer vision and in automated cartography. The RANSAC paradigm gives us a hint on how to instantiate a model if there are a lot of compromised data elements.However,the paradigm does not specify an algorithm and it uses a guess for the number of compromised elements, which is not known in general in real life environments. We developed the RANBAR algorithm following this paradigm and we eliminated the need for the guess. Our RANBAR algorithm is therefore capable to handle a high percent of outlier measurement data by leaning on only one preassumption,namely that the sample is i.i.d. in the unattacked case. We implemented the algorithm in a simulation environment and we used it to filter out outlier elements from a sample before an aggregation procedure. The aggregation function that we used was the average. We show that the algorithm guarantees a small distortion on the output of the aggregator even if almost half of the sample is compromised. Compared to other resilient aggregation algorithms, like the trimmed average and the median, our RANBAR algorithm results in smaller distortion, especially for high attack strengths.

PANEL: Position-based Aggregator Node Election in Wireless Sensor Networks

In this paper, 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. PANEL ensures load balancing, and it supports intra-and inter-cluster routing allowing sensor to aggregator, aggregator to aggregator, base station to aggregator, and aggregator to base station communications. We also present simulation results showing that PANEL is very energy efficient.

Survey Secure and reliable clustering in wireless sensor networks: A critical survey

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.

Resilient aggregation with attack detection in sensor networks

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

CORA: Correlation-based resilient aggregation in sensor networks

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.

Spontaneous cooperation in multi-domain sensor networks

Sensor networks are large scale networks consisting of several nodes and some base stations. The nodes are monitoring the environment and send their measurement data towards the base stations possibly via multiple hops. Since the nodes are often battery powered, an important design criterion for sensor networks is the maximization of their lifetime. In this paper, we consider multi-domain sensor networks, by which we mean a set of sensor networks that co-exist at the same physical location but run by different authorities. In this setting, the lifetime of all networks can be increased if the nodes cooperate and also forward packets originating from foreign domains. There is a risk, however, that a selfish network takes advantage of the cooperativeness of the other networks and exploits them. We study this problem in a game theoretic setting, and show that, in most cases, there is a Nash equilibrium in the system, in which at least one of the strategies is cooperative, even without introducing any external incentives (e.g., payments).

Cora: correlation-based resilient aggregation in sensor networks

In this paper we consider the problem of resilient data aggregation, namely, when aggregation has to be performed on a compromised sample. We present a statistical framework that is designed to mitigate the effects of an attacker who is able to alter the values of the measured parameters of the environment around some of the sensor nodes. Our proposed framework takes advantage of the naturally existing correlation between the sample elements, which is very rarely considered in other sensor network related papers. The algorithms presented are to be applied without assumption on the sensor networks sampling distribution or on the behaviour of the attacker. The effectiveness of the algorithms is formally evaluated.

Exploiting resource heterogeneity in delay-tolerant networks

Routing in delay and disruption-tolerant networks (DTNs) relies on intermediary nodes, called custodians, to deliver messages to destination. However, nodes usually differ significantly in terms of available resources: energy, buffer space and bandwidth. Routing algorithms need to make the most efficient use of custodian resources while also making sure those in limited supply are not exhausted. This paper proposes a distributed scheme for calculating resources available in node vicinity as a tool to support meaningful routing decisions. A generic model is developed first and is then applied to individual network assets. The model is based on a sparse network, where resources are potentially not uniformly distributed. It uses recent encounters to estimate resource availability in node vicinity. It is shown that a store-carry-forward scheme may benefit from accessing vicinity resource estimates. This knowledge allows nodes to implement meaningful custodian election and queue management strategies, approached here from a holistic perspective. It is demonstrated that routing protocols not only use up fewer resources overall but also consume resources preferentially from nodes with higher resource levels, sparing nodes with limited supplies. As a result, disparities in available resources across the node population are significantly reduced, and nodes are less likely to leave the network as a consequence of resource depletion. Copyright

Exploiting Resource Heterogeneity in DTN

Routing in delay and disruption-tolerant networks (DTNs) relies on intermediary nodes, called custodians, to deliver messages to destination. However, nodes usually differ significantly in terms of available resources: energy, buffer space and bandwidth. Routing algorithms need to make the most efficient use of custodian resources while also making sure those in limited supply are not exhausted. This paper proposes a distributed scheme for calculating resources available in node vicinity as a tool to support meaningful routing decisions. A generic model is developed first and is then applied to individual network assets. The model is based on a sparse network, where resources are potentially not uniformly distributed. It uses recent encounters to estimate resource availability in node vicinity. It is shown that a store-carry-forward scheme may benefit from accessing vicinity resource estimates. This knowledge allows nodes to implement meaningful custodian election and queue management strategies, approached here from a holistic perspective. It is demonstrated that routing protocols not only use up fewer resources overall but also consume resources preferentially from nodes with higher resource levels, sparing nodes with limited supplies. As a result, disparities in available resources across the node population are significantly reduced, and nodes are less likely to leave the network as a consequence of resource depletion. Copyright