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SUMMARYExisting pervasive applications are based on time series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. In this paper we analyze complex-event semantic correlation that examines epistemic uncertainty in computer networks by using Dempster-Shafer theory to support a high-volume, event-based, in-network and non-deterministic pervasive network management. We consider imprecision and uncertainty when an event is detected and associate a belief parameter with the semantics and the detection of composite events. The approach taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. In the end, we establish that a lightweight, distributed, largevolume, event-based technique which exploits epistemic uncertainty to correlate events along contextual dimensions provides a successful technique for enabling management of large-scale and pervasive contemporary and future computer networks.

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A distributed hash table (DHT) based approach for supporting forensic capability in Mobile Ad Hoc Networks (MANETs) is presented. The DHT-based approach has been modified to inhibit recursive increase in bandwidth consumption due to forensic activity -the process of logging is associated with that of packet delivery via customizable decreasing functions. Simulation has revealed that this approach limits the bandwidth requirement for forensic activities, although it requires a trade-off between bandwidth consumption and effective logging. The focus is to design a self-organized logging system over networks with dynamic topology.

Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications.This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer vii instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management.Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities.Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.