Reactive and adaptive monitoring to secure aggregation in wireless sensor networks

Data aggregation is considered as one of the fundamental distributed data processing procedures for saving the energy and minimizing the medium access layer contention in wireless sensor networks. However, sensor networks are likely to be deployed in an untrusted environment, which make them vulnerable against several attacks. A compromised node may forge arbitrary aggregation value and mislead the base station into trusting a false reading. Secure in-network aggregation can detect such manipulation. But, as long as such subversive activity is, reliable aggregation result can not be obtained. In contrast, the collection of individual sensor node values is robust and solves the problem of availability, but in an inefficient way. Our work seeks to bridge this gap in secure data collection. We propose a framework that enhances availability with efficiency close to that of in-network aggregation avoiding over-reliance on sensors. To achieve this, we design a scheme that is built on one core concept: no trust is supposed in any sensor. Therefore, we design a two hierarchical levels of monitoring to ensure the integrity and the accuracy of aggregate result, only when necessary, i.e. only when malicious activities are detected. Relying on this new type of monitoring mechanism, the framework has the ability to recover from aggregator failure without neglecting energy efficiency, providing thus much higher availability than other security protocols.