Anthony Burrell

A comprehensive approach to signaling, transmission, and traffic management for wireless ATM networks

We propose and evaluate a signaling and transmission algorithmic system for wireless digital networks, in conjunction with a Traffic Monitoring Algorithm (TMA) for dynamic capacity allocation in multimedia ATM environments. The deployed signaling protocol is stable, and two transmission techniques are compared: a Framed Time-Domain Based (FTDB) technique and a Framed CDMA (FCDMA) technique. The overall signaling/transmission/traffic monitoring proposed system has powerful performance characteristics, while the TMA-FTDB combination is superior to the TMA-FCDMA combination in environments where message lengths are relatively short and the speed of the transmission lines is relatively low. We also evaluate a system deploying the Ethernet protocol as a signaling protocol, instead. In the presence of relatively tight admission delay constraints in signaling, the latter is significantly inferior to our proposed signaling technique. As the above admission delay constraints diminish, the Ethernet protocol breaks down, while our proposed signaling technique maintains its high performance characteristics.

A class of limited sensing random access algorithms with resistance to feedback errors and effective delay control

We present and analyze a class of limited sensing random access algorithms with powerful properties. The algorithms are implementable in wireless mobile environments and their operational properties are simple. Their throughput in the worst case of the limit Poisson user model is 0.4297, while this throughput degrades gracefully in the presence of channel feedback errors.

The Implementation of Dynamic Rate Allocation in Sensor Networks

We consider sensor networks with a specific signal processing objective. The networks are organized in architectures comprised of sensor clusters whose cluster heads are connected via a backbone network. The data collected by the sensors are finally fused at a fusion center to satisfy the designated signal processing objective. Data operations and their time limitations are dictated by the signal processing objective, in conjunction with the power and life-span constraints of the sensors. The limited life-span of the sensors induce time-varying cluster traffic rates, and, thus dynamics in the operation of any rate allocation schemes. In this paper, we introduce a traffic monitoring algorithmic system, which detects changes in cluster traffic rates and dictates subsequent adaptations in the deployed traffic allocation techniques. We analyze the performance of the monitoring algorithmic system. We also analyze the stability of the coupled monitoring-rate allocation system. We finally present numerical results for some specific system parameters.

Sequential algorithms for detecting changes in acting stochastic processes and online learning of their operational parameters

We present, analyze, and numerically evaluate extended algorithms for detecting changes from an acting stochastic process to a number of possible alternatives. The algorithms are sequential, requiring minimal memory capacity and operational complexity, and they incorporate decision thresholds. The performance of the algorithms is controlled by the selection of the thresholds. An online learning algorithm adapts the thresholds dynamically, to attain prespecified error performance. Asymptotically, the first algorithmic extension detects the acting process correctly, in an expected stopping time sense. In addition, the probability of error induced by a reinitialization algorithmic extension converges asymptotically to zero, when the acting process changes infrequently (with order inversely proportional to the value of the decision thresholds). The presented algorithmic systems are quite powerful and their applications are numerous, ranging from industrial quality control, to identification of changes in patterns, to traffic and performance monitoring in high-speed networks.

Robust Sequential Algorithms for the Detection of Changes in Data Generating Processes

We consider the case where data sequences may be generated by either one of a number of non-parametrically defined processes and where the data generating process may change at any point in time. The objective is to effectively track the latter changes, where each acting process is essentially represented by a whole class of parametrically defined processes. We present, analyze and evaluate robust sequential algorithms which attain the objective for a variety of scenarios. Our robust algorithms consist of appropriate modifications of previously presented parametric sequential algorithms, to mainly resist the occurrence of occasional data outliers in terms of dramatic performance deterioration.

Detecting Software Faults in Distrubted Systems

We are concerned with the problem of detecting faults in distributed software, rapidly and accurately. We assume that the software is characterized by events or attributes, which determine operational modes; some of these modes may be identified as failures. We assume that these events are known and that their probabilistic structure, in their chronological evolution, is also known, for a finite set of different operational modes. We propose and analyze a sequential algorithm that detects changes in operational modes rapidly and reliably. Further more, a threshold operational parameter of the algorithm controls effectively the induced speed versus correct detection versus false detection tradeoff.

Data Fusion in the Sequential Detection of Change

We consider independent and identical sensors connected via a fusion center. The objective is detection of a possible change from an initial data generating process to one of a finite set of alternative processes. We state and analyze the problem and state conditions for asymptotic optimality. We clarify some previously published misconceptions in the process.

The Comparison of two Signaling Protocols for the Wireless

In the wireless environment, the two major stages in the communication between any two users are signaling and transmission. At the signaling stage, the users are not well known to the system, a fact that necessitates the deployment of a Random Access Protocol. The signaling protocol deployed by current wireless systems is ALOHA based and is similar to the currently deployed Ethernet Protocol.In this paper, we compare the performance of the Ethernet Protocol with that of the 2- Cell Limited Sensing Algorithm, when both deployed as the signaling protocol in the Wireless environment. In contrast to the Ethernet Protocol, the 2-Cell Algorithm is Stable. The superiority of the latter algorithm is clearly shown, in terms of induced delays as well as in terms of rejection rates in the presence of admission delay constraints.

Priority Users in a Multi-Channel System

We are considering an environment with an unknown population of users, a portion of who has high priority. The assumed environment contains a number of transmission channels among which each user may select to direct his/her transmission. We allow dynamic channel selection by each one of the high priority users, towards the reduction of their accessing delays. We deploy a class of limited sensing random access algorithms for all the users in the system and analyze performance in terms of throughputs and reduced delays. The accessing of the high priority users is accelerated significantly at the expense of some throughput reduction.

The Signaling Stage for Wireless Networks

We present and evaluate a class of signaling protocols for wireless digital networks. The presented signaling protocols are stable, they induce good delay characteristics and they possess resistance to feed back errors. We also evaluate a system deploying the IEEE 802.11 as a signaling protocol, instead. In the presence of relatively tight admission delay constraints in signaling, the latter is significantly inferior to our proposed signaling technique. As the above admission delay constraints diminish, the IEEE 802.11 MAC protocol breaks down, while our proposed signaling technique maintains its high performance characteristics.