International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / Powered by single-segment switched interconnection, Ethernet can be used in time-critical data acquisition applications. Unlike synchronous time division multiple access, asynchronous packet streams result in congestions and uncertain multiplexing delays. With the delay analysis in the worst case and probabilistic guaranteeing conditions, we restrict the packet-sizes, intervals or traffic burstiness a priori to regulate delay deviations within acceptable scales. Some methods of combinatorics and stochastic theory, e.g. Cumulant Generating Function and the Large Deviation Principle, are used and verified by some simulation-based computations. The influence of time varying delay for telemetry applications is also discussed in some sense.

Active Queue Management (AQM) aims to achieve high link utilization, low queuing delay and low loss rate in routers. However, it is difficult to adapt AQM parameters to constantly provide desirable transient and steady-state performance under highly dynamic network scenarios. They need to be a trade-off made between queuing delay and utilization. The queue size would become unstable when round-trip time or link capacity increases, or would be unnecessarily large when round-trip time or link capacity decreases. Effective ways of adapting AQM parameters to obtain good performance have remained a critical unsolved problem during the last fifteen years. This thesis firstly investigates existing AQM algorithms and their performance. Based on a previously developed dynamic model of TCP behaviour and a linear feedback model of TCP/RED, Auto-Parameterization RED (AP-RED) is proposed which unveils the mechanism of adapting RED parameters according to measurable network conditions. Another algorithm of Statistical Tuning RED (ST-RED) is developed for systematically tuning four key RED parameters to control the local stability in response to the detected change in the variance of the queue size. Under variable network scenarios like round-trip time, link capacity and traffic load, no manual parameter configuration is needed. The proposed ST-RED can adjust corresponding parameters rapidly to maintain stable performance and keep queuing delay as low as possible. Thus the sensitivity of RED's performance to different network scenarios is removed. This Statistical Tuning algorithm can be applied to a PI controller for AQM and a Statistical Tuning PI (ST-PI) controller is also developed. The implementation of ST-RED and ST-PI is relatively straightforward. Simulation results demonstrate the feasibility of ST-RED and ST-PI and their capabilities to provide desirable transient and steady-state performance under extensively varying network conditions.

There have been significant advances in communication technologies over the last decade, such as cellular networks, Wi-Fi, and optical communication. Not only does the technology impact peoples’ everyday lives, but it also helps cities prepare for power outages by collecting and exchanging data that facilitates real-time status monitoring of transmission and distribution lines. Smart grids, contrary to the traditional utility grids, allow bi-directional flow of electricity and information, such as grid status and customer requirements, among different parties in the grid. Thus, smart grids reduce the power losses and increase the efficiency of electricity generation and distribution, as they allow for the exchange of information between subsystems. However, smart grids is not resilient under extreme conditions, particularly when the utility grid is unavailable. With the increasing penetration of the renewable energy sources (RES) in smart grids, the uncertainty of the generated power from the distributed generators (DGs) has brought new challenges to smart grids in general and smart microgrids in particular. The rapid change of the weather conditions can directly affect the amount of the generated power from RES such as wind turbine and solar panels, and thus degrading the reliability and resiliency of the smart microgrids. Therefore, new strategies and technologies to improve power reliability,sustainability, and resiliency have emerged. To this end, in this thesis, we propose a novel framework to improve the smart microgrids reliability and resiliency under severe conditions. We study the transition to the grid-connected operational mode in smart microgrids,in the absence of the utility grid, as an example of emergency case that requires fast and accurate response. We perform a comparative study to accurately predict upcoming grid-connected events using machine learning techniques. We show that decision tree models achieve the best average prediction performance. The packets that carry the occurrence time of the next grid-connected transition are considered urgent packets. Hence, we per-form an extensive study of a smart data aggregation approach that considers the priority of the data. The received smart microgrids data is clustered based on the delay-sensitivity into three groups using k-means algorithm. Our delay-aware technique successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small-cell base stations. As a mitigation action of the utility grid unavailability, we use the electrical vehicles (EVs) batteries as mobile storage units to cover smart microgrids power needs until the utility grid recovery. We formulate a Mixed Integer Linear Programming (MILP) model to find the best set of electrical vehicles with the objective of minimum cost. The EVs participating in the emergency power supply process are selected based on the distance and throughput performance between the base station and the EVs

Timeliness in delivering packets for delay-sensitive applications is an important QoS (Quality of Service) measure in many systems, notably those that need to provide real-time performance. In such systems, if delay-sensitive traffic is delivered to the destination beyond the deadline, then the packets will be rendered useless and dropped after received at the destination. Bandwidth that is already scarce and shared between network nodes is wasted in relaying these expired packets. This thesis proposes that a deterministic per-hop delay can be achieved by using a dynamic queue threshold concept to bound delay of each node. A deterministic per-hop delay is a key component in guaranteeing a deterministic end-to-end delay. The research aims to develop a generic approach that can constrain network delay of delay-sensitive traffic in a dynamic network. Two adaptive queue management schemes, namely, DTH (Dynamic THreshold) and ADTH (Adaptive DTH) are proposed to realize the claim. Both DTH and ADTH use the dynamic threshold concept to constrain queuing delay so that bounded average queuing delay can be achieved for the former and bounded maximum nodal delay can be achieved for the latter. DTH is an analytical approach, which uses queuing theory with superposition of N MMBP-2 (Markov Modulated Bernoulli Process) arrival processes to obtain a mapping relationship between average queuing delay and an appropriate queuing threshold, for queue management. While ADTH is an measurement-based algorithmic approach that can respond to the time-varying link quality and network dynamics in wireless ad hoc networks to constrain network delay. It manages a queue based on system performance measurements and feedback of error measured against a target delay requirement. Numerical analysis and Matlab simulation have been carried out for DTH for the purposes of validation and performance analysis. While ADTH has been evaluated in NS-2 simulation and implemented in a multi-hop wireless ad hoc network testbed for performance analysis. Results show that DTH and ADTH can constrain network delay based on the specified delay requirements, with higher packet loss as a trade-off.