Paul G. Fitzpatrick

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—Large-scale, self-organizing wireless sensor and mesh network deployments are being driven by recent technological developments such as The Internet of Things (IoT), Smart Grids and Smart Environment applications. Efficient use of the limited energy resources of wireless sensor network (WSN) nodes is critically important to support these advances, and(More)
We investigate performance improvements through TCP window size optimisation achievable when TCP Reno is used over a highly heterogeneous network, such as an 802.11b Wireless LAN or a GPRS-based internet connection. Initially, our modelling focuses on a constant rate, buffered access link, based on a loss-less wireless channel and with a bandwidth at least(More)
—We present an automated solution for rapid diagnosis of client device problems in private cloud environments: the Intelligent Automated Client Diagnostic (IACD) system. Clients are diagnosed with the aid of Transmission Control Protocol (TCP) packet traces, by (i) observation of anomalous artifacts occurring as a result of each fault and (ii) subsequent(More)
Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be labor-intensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create(More)
Business process reengineering (BPR) has been a popular business improvement strategy for the past decade. However, Holland and Kumar (Getting past the obstacles to successful reengineering, Business Horizons, 1995, p. 79) noted that 60–80% of BPR initiatives have been unsuccessful. An extensive review of the literature revealed significant gaps in research(More)
—We present an automated solution for rapid diagnosis of both known and unknown " soft-failures " in network User Devices (UDs). A multiclass classifier is first trained with the known faults and during diagnosis, the unknown faults are clustered to determine the existence of a new fault. Then, in an iterative process, the classifier is retrained with the(More)