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As Internet usage increases, new, smarter, networking methods are required to enhance or maintain Quality of Service (QoS). One method, Software-Defined Networking (SDN) offers many advantages by separating the Forwarding and Control Planes. However, heuristic routing algorithms employed by SDN, such as Shortest Path, are not always suited for QoS-based pathfinding. This paper introduces a new Q-Routing algorithm that separates training and pathfinding, utilising two network metrics - latency and bandwidth - instead of latency alone. Two versions of this algorithm are employed, a static and a dynamic version where additional re-training is undertaken to allow Q-Routing to adapt to changing network environments. Both are tested on different size mesh topologies. The results show that static and dynamic Q-Routing are faster at pathfinding compared to K-Shortest Path and on average, find equally good routes.

Q-routing is a promising approach to finding routes in software-defined networks. This paper describes (i) the extension of Q-routing to find paths satisfying multiple Quality of Service metrics such as bandwidth and latency, and (ii) the use of fuzzy matching to allow relaxation of the metrics. Preliminary simulations suggest that fuzzy relaxation of metrics increases the number of flows that can be handled under high network loads.

Wireless sensor networks are often distributedwhich makes detection of cyber-attacks or misconfiguration hard.Topology and data patterns change may result from attacksleading to the compromise of data and service availability orindicate operational problems. Graphs are often used to modeltopology and data paths to describe and compare state of asystem. For anomaly detection, the definition of normal patterns,deviation from normal, and criteria when to declare anomalyare required. In this contribution the process of acquisition ofnormal patterns (ground truth), and criteria when to declareanomaly based on graph comparison are proposed. The anomalydetection is suitable for deployment at the edge of a network.Finally, the inability to define all security threats is addressedby a custom tree-based classifier which only requires normalpatterns for training. A simulated wireless sensor network wasused to acquire data and apply the method. Our experimentsshow that data and topology change can be detected at the edgeof a network.

Current infrastructures are reaching the point where existing networking methods are unable to cope with the exponential growth of traffic and Quality of Service (QoS) requirements. New techniques are necessary to keep pace. One such technique, Software-Defined Networking (SDN) uses a central controller to program many individual network devices. However, SDN uses heuristic algorithms that do not always select the optimal path. This paper looked at creating three Q-Routing algorithms leveraging SDN and Mesh network topologies. Two algorithms used one network metric each (Latency and Bandwidth) and the third used multiple metrics. Results showed that the single metric Q-Routing algorithms on average performed as well as the K-Shortest Path versions while Q-Routing with multiple network metrics failed to match K-Shortest Path (different combination of metrics means these algorithms are not comparable). Results also showed that Q-Routing was able to calculate paths faster than K-Shortest Path in both static and dynamic networks.

The problem of soft information fusion is one of the key challenges in both fields of information fusion and situation awareness. However uncertainty modelling in the context of semantic information fusion is not well-researched.In this paper we present the current state-of-the-art regarding fusion of uncertain semantic information using simple conceptual graphs and propose methods to improve its handling of partial conflict, reliability and expressiveness regarding modal uncertainty. Particular attention is paid to the issues of scalability and tractability. Similarities to other uncertainty handling frameworks such as possibility theory and belief functions are discussed. Potential avenues are identified for further research regarding uncertainty representation in semantic information fusion.