Biblio

Industrial Wireless Sensor Networks (IWSNs) are an extension of the Internet of Things paradigm that integrates smart sensors in industrial processes. However, the unattended open environment makes IWSNs vulnerable to malicious attacks, such as node compromise in addition to eavesdropping. The compromised nodes can again launch notorious attacks such as the sinkhole or sybil attack which may degrade the network performance. In this paper, we propose a lightweight, Secure Directed Diffusion (SDD) protocol. The algorithm for the proposed protocol uses bilinear pairing to derive a location-based key (LK) by binding the ID and geographic location of a node, thereby ensuring neighborhood authentication. Thus, authenticated nodes can prevent eavesdropping, node compromise including sinkhole and sybil attacks while ensuring confidentiality, authenticity, integrity with reduced latency. Finally, through security analysis, we prove that basic security is maintained and above-mentioned attacks are also prevented. We also compute storage, computation and communication overheads which show that SDD performs at least 2.6 times better in terms of storage overhead and at least 1.3 times better in terms of communication overhead over the other state-of-the-art competing schemes for attack preventions in WSN domain.

A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiment results indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% sensor nodes are misbehaving.