Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 9, Issue 8, Pages 1119-1133Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2010.75
Keywords
Jamming; security; jamming detection and mitigation; optimization; wireless multiple access; wireless sensor network
Funding
- ARO PECASE [W911NF-05-1-0491]
- ARL CTA [DAAD19-01-2-001]
- ARO MURI [W911NF-07-1-0287]
- US Army Research Laboratory [DAAD19-01-2-0011]
- European Commission [FP7-ICT-224218, FP7-ICT-216715]
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We consider a scenario where a sophisticated jammer jams an area in which a single-channel random-access-based wireless sensor network operates. The jammer controls the probability of jamming and the transmission range in order to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by the network (namely by a monitoring node), and a notification message is transferred out of the jammed region. The jammer is detected by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network defends itself by computing the channel access probability to minimize the jamming detection plus notification time. The necessary knowledge of the jammer in order to optimize its benefit consists of knowledge about the network channel access probability and the number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of each other and the case where the jammer and the network lack this knowledge. The latter is captured by formulating and solving optimization problems where the attacker and the network respond optimally to the worst-case or the average-case strategies of the other party. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a meaningful heuristic algorithm for an efficient jamming strategy. Our results provide valuable insights about the structure of the jamming problem and associated defense mechanisms and demonstrate the impact of knowledge as well as adoption of sophisticated strategies on achieving desirable performance.
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