In event-driven wireless Visual Sensor Networks (wVSNs), video nodes have access to additional data from scalar-sensors such as temperature or motion. The scalar-data may be used locally by the nodes instead of (or in conjunction with) vision technologies to control the potentially energy-costly transmission and storage of video frames and must thus be reliable. In this work we focus on the detection of occasional errors in such scalar-data sensors under both the scenario of harsh environmental conditions, and the scenario of hostile conditions involving an attacker. In the hostile case, the attack statistics may not be known to the cluster-head performing the error detection. We hence propose the use of a count detector in conjunction with Nash equilibrium analysis for the hostile case. We compare the detection performance of the count detector in hostile conditions to the performance of the optimal Neyman-Pearson (NP ) detector which may be used under harsh conditions (scenario where the error statistics may be estimated). Through analysis and simulations we conclude that in this severe regime of attack with missing statistics, the count detector performs reasonably well compared with the optimal NP detector with significance for reliable event-driven wVSN.
There is a critical need to provide privacy assurances for distributed vision-based sensor networking in applications such as building surveillance and healthcare monitoring. To effectively address protection and reliability issues, secure networking and processing must be considered from system inception. This paper presents attacks that affect the data privacy in visual sensor networks and proposes privacy-promoting security solutions based on opponent detection via game-theoretic analysis and keyless encryption.Index Terms-Visual sensor networks, privacy of visual data, network security, keyless privacy.
The gathering of surveillance data such as visual intelligence from potentially hostile areas has long played a pivotal role in attaining various safety and security objectives. The methodology of gathering such surveillance is increasingly shifting towards rapid-deployment autonomous networks that limit the need for human exposure, and that cover large unattended areas while operating over extended periods of time. To achieve the surveillance objectives, such networks must be dependable and secure even in the presence of a potentially hostile counter-surveillance opponent. In this work we explicitly model and consider the presence of such an opponent in the form of a hostile sensor network with eavesdropping and actuation capabilities. We present a methodology for addressing the security and dependability issues arising in such extreme settings, which we collectively refer to as G-E-M. Specifically, we wish to ensure the legitimacy and authenticity of the gathered (G-E-M) visual surveillance in the presence of a hostile network engaged in stealthy disinformation activities. We also wish to ensure that the collected surveillance can be encrypted (G-E-M) for transmission even if keys between the nodes and the sink are temporarily compromised or otherwise unavailable. Finally we wish to ensure that the network design both inherently prolongs the lifetime of the network and also mitigates (G-E-M) deliberate energy drains. These issues are not typically examined collectively though the dependability of all these components is required to maintain the functionality and longevity of the network. Though developed and presented for the case of an attacker in the form of a hostile network, the methodologies have applicability to networks with a subset of subverted nodes that behave maliciously.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.