Confidentiality and integrity for data aggregation in WSN using peer monitoring
Author(s) -
Di Pietro Roberto,
Michiardi Pietro,
Molva Refik
Publication year - 2009
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0122
pISSN - 1939-0114
DOI - 10.1002/sec.93
Subject(s) - computer science , data aggregator , wireless sensor network , scalability , computer network , overhead (engineering) , adversary , confidentiality , base station , resilience (materials science) , computer security , data transmission , distributed computing , physics , database , thermodynamics , operating system
Hop‐by‐hop data aggregation is a very important technique used to reduce the communication overhead and energy expenditure of sensor nodes during the process of data collection in a wireless sensor network (WSN). However, the unattended nature of WSNs calls for data aggregation techniques to be secure. Indeed, sensor nodes can be compromised to mislead the base station (BS) by injecting bogus data into the network during both forwarding and aggregation of data. Moreover, data aggregation might increase the risk of confidentiality violations: If sensors close to the BS are corrupted, an adversary could easily access to the results of the ‘in network’ computation performed by the WSN. Further, nodes can also fail due to random and non‐malicious causes (e.g., battery exhaustion), hence availability should be considered as well. In this paper we tackle the above issues that affect data aggregation techniques by proposing a mechanism that: (i)provides both confidentiality and integrity of the aggregated data so that for any compromised sensor in the WSN the information acquired could only reveal the readings performed by a small, constant number of neighboring sensors of the compromised one; (ii) detects bogus data injection attempts; (iii) provides high resilience to sensor failures. Our protocol is based on the concept of delayed aggregation and peer monitoring and requires local interactions only. Hence, it is highly scalable and introduces small overhead; detailed analysis supports our findings. Copyright © 2009 John Wiley & Sons, Ltd.
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