Cognitive privacy for personal clouds

This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure

Flexible and dynamic network coding for adaptive data transmission in DTNs

Existing network coding approaches for Delay-Tolerant Networks (DTNs) do not detect and adapt to congestion in the network. In this paper we describe CafNC (Congestion aware forwarding with Network Coding) that combines adaptive network coding and adaptive forwarding in DTNs. In CafNC each node learns the status of its neighbours, and their egonetworks in order to detect coding opportunities, and codes as long as the recipients can decode. Our flexible design allows CafNC to efficiently support multiple unicast flows, with dynamic traffic demands and dynamic senders and receivers. We evaluate CafNC with two real connectivity traces and a realistic P2P application, introducing congestion by increasing the number of unicast flows in the network. Our results show that CafNC improves the success ratio, delay and packet loss, as the number of flows grows in comparison to no coding and hub-based static coding, while at the same time achieving efficient utilisation of network resources. We also show that static coding misses a number of coding opportunities and increases packet loss rates at times of increased congestion

Enabling rapid and cost-effective creation of massive pervasive games in very unstable environments

Pervasive gaming is a new form of multimedia entertainment that extends the traditional computer gaming experience out into the real world. Through a combination of personal devices, positioning systems and other sensors, combined with wireless networking, a pervasive game can respond to player's movements and context and enable them to communicate with a game engine and other players. We review our recent deployment examples of pervasive games in order to explain their distinctive characteristics as wireless ad-hoc networking applications. We then identify the network support challenges of scaling pervasive games to include potentially mass numbers of players across extremely heterogeneous and unreliable networks. We propose a P2P overlay capable of storing large amount of game related data, which is the key to combating the loss of coverage and potential dishonesty of players. The proposed protocol decreases the deployment costs of the gaming infrastructure by self organization and utilizing storage space of users' devices. We demonstrate scalability and increased availability of data offered by the proposed protocol in simulation based evaluatio