A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs

Information-Centric Fog Computing enables a multitude of nodes near the end-users to provide storage, communication, and computing, rather than in the cloud. In a fog network, nodes connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within that network topology. The centrality is then used to distinguish nodes in the fog network, or to prioritize some nodes over others to participate in the caching fog. We argue that, for an Information-Centric Fog Computing approach, graph centrality is not an appropriate metric. Indeed, a node with low connectivity that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce acontent-based centrality (CBC) metric which takes into account how well a node is connected to the content the network is delivering, rather than to the other nodes in the network. To illustrate the validity of considering content-based centrality, we use this new metric for a collaborative caching algorithm. We compare the performance of the proposed collaborative caching with typical centrality based, non-centrality based, and non-collaborative caching mechanisms. Our simulation implements CBC on three instances of large scale realistic network topology comprising 2,896 nodes with three content replication levels. Results shows that CBC outperforms benchmark caching schemes and yields a roughly 3x improvement for the average cache hit rate

Cooperative power control approaches towards fair radio resource allocation for wireless network

Performance optimization in wireless networks is a complex problem due to variability and dynamics in network topology and density, traffic patterns, mutual interference, channel uncertainties, etc. Opportunistic or selfish approaches may result in unbalanced allocation of channel capacity where particular links are overshadowed. This degrades overall network fairness and hinders a multi-hop communication by creating bottlenecks. A desired approach should allocate channel capacity proportionally to traffic priority in a cooperative manner. This work consists of two chapters that address the fairness share problem in wireless ad hoc, peer-to-peer networks and resource allocation within Cognitive Radio network. In the first paper, two fair power control schemes are proposed and mathematically analyzed. The schemes dynamically determine the viable resource allocation for a particular peer-to-peer network. In contrast, the traditional approaches often derive such viable capacity for a class of topologies. Moreover, the previous power control schemes assume that the target capacity allocation, or signal-to-interference ratio (SIR), is known and feasible. This leads to unfairness if the target SIR is not viable. The theoretical and simulation results show that the capacity is equally allocated for each link in the presence of radio channel uncertainties. In the second paper, based on the fair power control schemes, two novel power control schemes and an integrated power control scheme are proposed regarding the resource allocation for Cognitive Radio network to increase the efficiency of the resource while satisfying the Primary Users\u27 Quality of Service. Simulation result and tradeoff discussion are given --Abstract, page iv

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains