Paul G. Fitzpatrick

Learn More
—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)
We then introduce the Link Adaptive Signature Estimation (LASE) technique to minimize the number of NSSs needed to create diagnostic systems that have generalization capability for coping with communication link variations. To achieve this, we create Feature Estimator Functions (FEFs) using multivariate regression techniques and a minimal number of(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)