Real-time self-stabilizing scheme for the localization of faults in wireless sensor networks
- Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard
- Date: 2014
- Type: Text , Book chapter
- Relation: Recents advances in Image, Audio and Signal Processing p. 233-242
- Full Text: false
- Reviewed:
- Description: Reliable acquisition of data from massively dense wireless sensor networks (WSN) is a challenge due to the unpredictable behaviour of nodes responsible for collecting and disseminating datasets of interest. Therefore, accurate sensing of events from nodes depend on several microscopic and macroscopic factors such as distance of a node from the sink, radio signal strength and connectedness of network for routing datasets to the nearest sink. Several Clustering schemes have been proposed for routing datasets, where major focus was on finding the next cluster-head with maximum energy for routing data. Such schemes are not suitable for the real-time dissemination of datasets because electing the next cluster-head is a computational intensive process. A new energy-efficient self-stabilizing sliding rectangle protocol (ESSRP) is proposed in this paper for ensuring reliability and connectedness of regions for minimizing data loss and prolonging network life. The proposed scheme not only looks at the energy-balance of a particular cluster but also ensures fault-localization and tolerance by providing self-stabilization to network in the event of nodes or links failure using Green’s Theorem. The WSN rectangular regions should be oriented counter-clockwise, piecewise regular and continuously differentiable so that faults can be efficiently localized, identified and rectified in a particular region
Detection of CAN by ensemble classifiers based on Ripple Down rules
- Authors: Kelarev, Andrei , Dazeley, Richard , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Book chapter
- Relation: Knowledge Management and Acquisition for Intelligent Systems p. 147-159
- Full Text: false
- Reviewed:
- Description: It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.