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iCapture: Facilitating Spontaneous User-Interaction with Pervasive Displays using Smart Devices

Abstract. The eCampus project at Lancaster University is an inter-disciplinary project aiming to deploy a wide range of situated displays across the University campus in order to create a large per-vasive communications infrastructure. At present, we are conducting a series of parallel research activities in order to investigate how the pervasive communications infrastructure can support the daily needs of staff, students and visitors to the University. This paper introduces one of our current research investigations into how one is able to mediate spontaneous interaction with the pervasive display infrastructure through camera equipped mobile phones (i.e. smart devices).

Intrusion Detection Systems for Community Wireless Mesh Networks

Wireless mesh networks are being increasingly used to provide affordable network connectivity to communities where wired deployment strategies are either not possible or are prohibitively expensive. Unfortunately, computer networks (including mesh networks) are frequently being exploited by increasingly profit-driven and insidious attackers, which can affect their utility for legitimate use. In response to this, a number of countermeasures have been developed, including intrusion detection systems that aim to detect anomalous behaviour caused by attacks. We present a set of socio-technical challenges associated with developing an intrusion detection system for a community wireless mesh network. The attack space on a mesh network is particularly large; we motivate the need for and describe the challenges of adopting an asset-driven approach to managing this space. Finally, we present an initial design of a modular architecture for intrusion detection, highlighting how it addresses the identified challenges

A privacy preserving approach to energy theft detection in smart grids

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements