Biblio

There is a growing movement to retrofit ageing, large scale infrastructures, such as water networks, with wireless sensors and actuators. Next generation Cyber-Physical Systems (CPSs) are a tight integration of sensing, control, communication, computation and physical processes. The failure of any one of these components can cause a failure of the entire CPS. This represents a system design challenge to address these interdependencies. Wireless communication is unreliable and prone to cyber-attacks. An attack upon the wireless communication of CPS would prevent the communication of up-to-date information from the physical process to the controller. A controller without up-to-date information is unable to meet system's stability and performance guarantees. We focus on design approach to make CPSs secure and we evaluate their resilience to jamming attacks aimed at disrupting the system's wireless communication. We consider classic time-triggered control scheme and various resource-aware event-triggered control schemes. We evaluate these on a water network test-bed against three jamming strategies: constant, random, and protocol aware. Our test-bed results show that all schemes are very susceptible to constant and random jamming. We find that time-triggered control schemes are just as susceptible to protocol aware jamming, where some event-triggered control schemes are completely resilient to protocol aware jamming. Finally, we further enhance the resilience of an event-triggered control scheme through the addition of a dynamical estimator that estimates lost or corrupted data.

Wireless Sensor Network (WSN) applications range from domestic Internet of Things systems like temperature monitoring of homes to the monitoring and control of large-scale critical infrastructures. The greatest risk with the use of WSNs in critical infrastructure is their vulnerability to malicious network level attacks. Their radio communication network can be disrupted, causing them to lose or delay data which will compromise system functionality. This paper presents Antilizer, a lightweight, fully-distributed solution to enable WSNs to detect and recover from common network level attack scenarios. In Antilizer each sensor node builds a self-referenced trust model of its neighbourhood using network overhearing. The node uses the trust model to autonomously adapt its communication decisions. In the case of a network attack, a node can make neighbour collaboration routing decisions to avoid affected regions of the network. Mobile agents further bound the damage caused by attacks. These agents enable a simple notification scheme which propagates collaborative decisions from the nodes to the base station. A filtering mechanism at the base station further validates the authenticity of the information shared by mobile agents. We evaluate Antilizer in simulation against several routing attacks. Our results show that Antilizer reduces data loss down to 1% (4% on average), with operational overheads of less than 1% and provides fast network-wide convergence.

As the number of small, battery-operated, wireless-enabled devices deployed in various applications of Internet of Things (IoT), Wireless Sensor Networks (WSN), and Cyber-physical Systems (CPS) is rapidly increasing, so is the number of data streams that must be processed. In cases where data do not need to be archived, centrally processed, or federated, in-network data processing is becoming more common. For this purpose, various platforms like DRAGON, Innet, and CJF were proposed. However, these platforms assume that all nodes in the network are the same, i.e. the network is homogeneous. As Moore's law still applies, nodes are becoming smaller, more powerful, and more energy efficient each year; which will continue for the foreseeable future. Therefore, we can expect that as sensor networks are extended and updated, hardware heterogeneity will soon be common in networks - the same trend as can be seen in cloud computing infrastructures. This heterogeneity introduces new challenges in terms of choosing an in-network data processing node, as not only its location, but also its capabilities, must be considered. This paper introduces a new methodology to tackle this challenge, comprising three new algorithms - Request, Traverse, and Mixed - for efficiently locating an in-network data processing node, while taking into account not only position within the network but also hardware capabilities. The proposed algorithms are evaluated against a naïve approach and achieve up to 90% reduction in network traffic during long-term data processing, while spending a similar amount time in the discovery phase.