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Distributed optimal dispatch strategies are attracting attention to exploit the fast and continuous control capabilities of converter-interfaced small-scale generators connected to power distribution systems. However, their impact on the supporting cyber structure and their scalability to ensure real-time performance need further study. In this paper, a proximal Jacobian distributed optimal power flow model for the dispatch of converter-based power distribution systems is proposed, which features a favorable convergence behavior. A suitable cyber architecture to realize such model is developed through this paper. The corresponding cyber costs are quantified and the proposed architecture’s scalability is analyzed through numerical experiments.

A critical component of the smart grid (SG) infrastructure is the embedded communications network, where an important objective of the latter is the expansion of its throughput, in conjunction with the satisfaction of specified latency and accuracy requirements. For the effective design of the communications network, the user and traffic profiles, such as known-user vs. unknown-user populations and bursty vs. non-bursty data traffics, must be carefully considered and subsequently modeled. This paper relates user and traffic models to the deployment of effective multiple access transmission algorithms in the communications network of the SG.

We consider sparse signals embedded in additive white noise. We study parametrically optimal as well as tree-search sub-optimal signal detection policies. As a special case, we consider a constant signal and Gaussian noise, with and without data outliers present. In the presence of outliers, we study outlier resistant robust detection techniques. We compare the studied policies in terms of error performance, complexity and resistance to outliers.

The objective of a sensor network is the execution of specific signal processing functions on data that are collected in a distributed fashion. The transmission of the data is facilitated by protocols whose operations may be constrained by physical limitations of the network units, while their performance must simultaneously comply with the performance requirements of the deployed signal processing operations. At the same time, the network architecture affects the performance of both the signal processing and the data transmission operations, while some of the sensors may generate high-priority data. In this paper, we consider clustered sensor network topologies deploying a specific stable random access transmission algorithm per cluster, which facilitates high-priority data. We then introduce a dynamic architectural reconfiguration algorithm which controls individual cluster rates for optimal overall network performance. The latter algorithm is facilitated by a high-level traffic rate monitoring protocol.

We present a coherent and systematic review of Random Access Algorithms for packet networks, as developed over three and a half decades. We consider the appropriate user models and we classify the algorithms according to the channel sensing constraints imposed. We also present a review of the analytical methodologies required for the performance analysis of these algorithms.

We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.

We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.

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.

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.

We consider digital wireless multimedia LANs and time-varying traffic rates. To deal effectively with the dynamics of the time-varying traffic rates, a Traffic Monitoring Algorithm (TMA) is deployed to dynamically allocate channel capacities to the heterogeneous traffics. The TMA is implemented as a higher level protocol that dictates the capacity boundaries within two distinct framed transmission techniques: a Framed Time Domain-Based (FTDB) technique and a Framed CDMA (FCDMA) technique. The performance of the TMA in the presence of the FTDB technique is compared to its performance in the presence of the FCDMA technique for some traffic scenarios. The performance metrics used for the TMA-FTDB and TMA-FCDMA combinations are channel capacity utilization factors, traffic rejection rates, and traffic delays. It is found that the TMA-FTDB is superior to the TMA-FCDMA when the speed of the transmission links is relatively low and the lengths of the transmitted messages are relatively short. As the speed of the transmission links and the length of the transmitted messages increase, the TMA-FCDMA eventually outperforms the TMA-FTDB.

We address the accommodation of multimedia traffic by ATM broadband networks. We isolate a single network channel and we search for capacity allocation/transmission multiplexing policies which satisfy all traffic QOSs with simultaneous high utilization of channel capacity resources, in the presence of variable traffic rates. Towards that direction, we superimpose a high-level traffic-monitoring protocol which determines capacity allocations for each traffic category, dynamically. The latter protocol has sequential and implementation-efficient characteristics, and induces moving boundaries within prespecified and fixed channel frames. The channel frames are selected to satisfy the jittering constraints of all the traffics, such as voice, video and high versus low speed data. The high level protocol, in conjunction with the channel frame structure and the specifics of the transmission policy, may satisfy all traffic QOSs and may simultaneously attain high channel utilization, for certain variability conditions of the traffic rates and at relatively low implementation cost. Performance analysis as well as numerical evaluations are provided.

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. 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 traffic and performance monitoring in highspeed networks.

We consider multimedia wireless networks in environments where traffic characterizations and traffic rates are generally time-varying. For such networks we propose a CDMA transmission policy, in conjunction with a moving boundaries concept induced by a traffic monitoring high-level protocol. The proposed transmission/traffic management technique is analyzed and numerically evaluated.

This paper describes and analyzes a medium access protocol for wireless personal communication networks serving integrated traffic. A multi-priority mechanism, which is implemented and controlled explicitly by the users and without any involvement from the network is incorporated into the protocol. We examine the proposed protocol under reservation and non-reservation policies and evaluate its performance when handling integrated voice and data traffic. The proposed protocol is suitable to be used in Personal Communication Systems aiming to provide integrated (voice/data) services.

A critical piece of the Smart Grid (SG) infrastructure is the supporting Communication Networks that facilitate data gathering, monitoring and control of the electric grid. Over the years, electric utilities built robust communication networks for their transmission and distribution systems, but stopped short of the “last mile” connections to the end user. A primary goal of the SG is to expand the communications network throughout and beyond the Distribution Network (DN); thus, enabling a holistic management and control of the electric grid from generation to consumption. For the realization of the Smart Grid goals, two-way communication capabilities must extend beyond the Distribution Network to the end user, allowing two-way data flow, e.g., real-time energy pricing as well as real-time demand data back to the utility operators. Selecting an appropriate communications architecture is the first and most important decision in designing a communications network since it will have lasting effects on the efficiency, reliability and cost of the network. This paper presents and evaluates various communication architectures for deployment of the SG within the DN and to the end users.

The problem of binary hypothesis testing is revisited in the context of distributed detection with feedback. Two basic distributed structures with decision feedback are considered. The first structure is the fusion center network, with decision feedback connections from the fusion center element to each one of the subordinate decisionmakers. The second structure consists of a set of detectors that are fully interconnected via decision feedback. Both structures are optimized in the Neyman-Pearson sense by optimizing each decision-maker individually. Then, the time evolution of the power of the tests is derived. Definite conclusions regarding the gain induced by the feedback process and direct comparisons between the two structures and the optimal centralized scheme are obtained through asymptotic studies (that is, assuming the presence of asymptotically many local detectors). The behavior of these structures is also examined in the presence of variations in the statistical description of the hypotheses. Specific robust designs are proposed and the benefits from robust operations are established. Numerical results provide additional support to the theoretical arguments. >

Random multiple access (RMA) protocols comprise an important class for the accessing/signaling stage in multimedia ATM wireless networks. Within the RMA class, the ALOHA algorithm is most widely used at this point in time, while, as is well known, the latter algorithm becomes unstable, as the user population increases. In this paper, we consider the part-and-try algorithm, instead, for a system with different priorities. Based on this algorithm, we construct a mixed priority protocol whose total throughput-for all priorities combined-equals 0.487. In the special and important for ATM case of two, low, and high priorities, with respective Poisson intensities /spl lambda//sub L/ and /spl lambda//sub H/. We also present an analytical methodology which allows the computation of the average packet delays as functions of the rates /spl lambda//sub L/ and /spl lambda//sub H/. >

Two fundamental distributed decision network structures are considered: the first system consists of finite number of sensors, each collecting asymptotically many data, while the second one employs asymptotically many sensors, each collecting a single datum. For binary hypothesis testing, the Neyman-Pearson criterion is utilized and justified via information theoretic arguments. An asymptotic relative efficiency performance measure is used to establish tradeoffs between the two structures, by comparing the performance characteristics of the decentralized detection systems to their centralized counterparts. >

We consider object tracking and monitoring implemented via the use of distributed wireless sensor networks. We view the signal processing and communications operations performed in such networks, in conjunction with the time constraints imposed on their signal processing objectives and the limited life-spans of their sensors. We identify some of the object identification and network monitoring functions that are embedded in such systems. We subsequently focus on a core algorithm whose various manifestations may serve effectively a variety of network functions, and propose its novel application for image and sound object tracking. We also propose a new distributed version of the algorithm for the monitoring of data rates in the network.

The design of hybrid transmission algorithms for the multiplexing of voice and data over a common digital channel is of interest to various communication networks, including cellular radio and high-speed topologies. In environments where the characteristics of the voice and data traffics may vary dynamically, the issue is the deployment of hybrid multiplexing algorithms (HMAs) which satisfy the constraints imposed by the voice traffic, while they simultaneously attain high channel utilization and induce low implementation overhead. In this paper, we propose, evaluate, and compare two HMAs: a semidynamic and a dynamic. The former induces lower implementation overhead than the latter, but it is applicable only to environments where the rate of the voice traffic may vary relatively slowly and its statistics are parametrically known. The semidynamic HMA induces frame structures, where the capacity allocation per frame, for the voice versus data traffic, is dictated by a superimposed traffic monitoring algorithm. The dynamic HMA, on the other hand, assigns each channel slot to voice versus data packets dynamically; it requires no statistical knowledge about the voice traffic, at the expense of significantly increased implementation overhead.

We consider a decentralized hypothesis testing structure with asymptotically many sensors, each collecting a single datum. The sensors deploy robust test functions that are designed for outlier classes of hypotheses. The sensor outputs are transmitted to the fusion center for the global decision. In this paper, we concentrate on sensor-level decision making, and study the asymptotic performance of the decentralized detection scheme described above. In particular, we utilize the asymptotic relative efficiency performance measure, defined as the ratio of the number of sensors needed by the decentralized structure over the number of data needed by the centralized one to attain the same performance level. Our results indicate the superiority and the necessity of using robust statistical techniques.

Two feedforward neural structures intended for binary hypothesis testing are considered. The first structure, FFS1, is a tandem structure, while the second structure, FFS2, involves cumulative feedforward feedback. Both parametric and robust designs for the two structures are considered and analyzed in terms of induced false alarm and power probabilities. The inferiority of the FFS1 is rigorously proved in terms of the rate with which the induced power probability increases with respect to the number of the neural elements. Asymptotic results are presented, as well as numerical results, with emphasis on the Gaussian and location parameter nominal hypotheses model. Learning algorithms for the parameter involved in the robust network designs are discussed as well. >

In cellular radio, accessing requests are transmitted through signaling channels in the form of minipackets. The accessing requests are generated by a variety of mobile users that impose different constraints. The authors consider data and high-priority public-safety cellular users and isolate a single channel that is shared by the mixed user population. They devise and analyze a mixed protocol that satisfies the constraints of the high-priority public-safety users, while maintaining the data traffic with satisfactory delays. For the high-priority users, a well-defined and finite but bursty population and a number of different priorities are considered. The population of data users is assumed time-varying and not well-defined, and is modeled as limit Poisson and in the analysis of the protocol. >

Two mutually independent traffic streams whose arrivals are modeled as renewal processes are considered. The arrivals are stored in an infinite capacity buffer. One of the processes requires a strict upper bound on the total delay per arrival, and it is assumed that the processing time per arrival for both processes is a constant. Given the above model, the class of nonpreemptive scheduling policies which satisfy the delay constraint while maintaining the system stability is found. The delays induced by the latter policy are analyzed via a methodology based on the regenerative theorem. A numerical example is given in which the policy that minimizes the expected delays for both traffic streams is also considered. >