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Traffic signals are ubiquitous devices that first appeared in 1868. Recent advances in information and communications technology (ICT) have led to unprecedented improvements in such areas as mobile handheld devices (i.e., smartphones), the electric power industry (i.e., smart grids), transportation infrastructure, and vehicle area networks. Given the trend towards interconnectivity, it is only a matter of time before vehicles communicate with one another and with infrastructure. In fact, several pilots of such vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and parking spaces) communication systems are already operational. This survey of autonomous and self-organized traffic signaling control has been undertaken with these potential developments in mind. Our research results indicate that, while many sophisticated techniques have attempted to improve the scheduling of traffic signal control, either real-time sensing of traffic patterns or a priori knowledge of traffic flow is required to optimize traffic. Once this is achieved, communication between traffic signals will serve to vastly improve overall traffic efficiency.

Optimal block designs for additive models achieve their efficiency by dividing experimental units among relatively homogenous blocks and allocating treatments equally to blocks. Responses in many modern experiments, however, are drawn from distributions such as the one- and two-parameter exponential families, e.g., RNA sequence counts from a negative binomial distribution. These violate additivity. Yet, designs generated by assuming additivity continue to be used, because better approaches are not available, and because the issues are not widely recognised. We solve this problem for single-factor experiments in which treatments, taking categorical values only, are arranged in blocks and responses drawn from a Poisson distribution. We derive expressions for two objective functions, based on D_A- and C-optimality, with efficient estimation of linear contrasts of the fixed effects parameters in a Poisson generalised linear mixed model (GLMM) being the objective. These objective functions are shown to be computational efficient, requiring no matrix inversion. Using simulated annealing to generate Poisson GLMM-based locally optimal designs, we show that the replication numbers of treatments in these designs are inversely proportional to the relative magnitudes of the treatments' expected counts. Importantly, for non-negligible treatment effect sizes, Poisson GLMM-based optimal designs may be substantially more efficient than their classically optimal counterparts.

The primary contribution of this work is to examine the energy efficiency of pulse coupled oscillation for time synchronization in a realistic wireless network environment and to explore the impact of mobility on convergence rate. Energy coupled oscillation is susceptible to interference; this approach uses reception and decoding of short packet bursts to eliminate this problem. The energy efficiency of a commonly used timestamp broadcast algorithm is compared and contrasted with pulse-coupled oscillation. The emergent pulse coupled oscillation technique shows greater energy efficiency as well as robustness with mobility. A proportion of the sensors may be integrated with GPS receivers in order to obtain a master clock time.

We present progress on the experimental validation of a fundamental and universally applicable vulnerability analysis framework that is capable of identifying new types of vulnerabilities before attackers innovate attacks. This new framework proactively identifies system components that are vulnerable based upon their Kolmogorov Complexity estimates and it facilitates prediction of previously unknown vulnerabilities that are likely to be exploited by future attack methods. A tool that utilizes a growing library of complexity estimators is presented. This work is an incremental step towards validation of the concept of complexity-based vulnerability analysis. In particular, results indicate that data types (semantic types) can be identified by estimates of their complexity. Thus, a map of complexity can identify suspicious types, such as executable data embedded within passive data types, without resorting to predefined headers, signatures, or other limiting a priori information.

A fundamental understanding of gain provided by motion prediction in wireless ad hoc routing is currently lacking. This paper examines benefits in routing obtainable via prediction. A theoretical best-case non-predictive routing model is quantified in terms of both message overhead and update time for non-predictive routing. This best- case model of existing routing performance is compared with predictive routing. Several specific instances of predictive improvements in routing are examined. The primary contribution of this paper is quantification of predictive gain for wireless ad hoc routing., Comment: Introduces flash routing

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis.

This research concentrates on the design and analysis of an algorithm referred to as Virtual Network Configuration (VNC) which uses predicted future states of a system for faster network configuration and management. VNC is applied to the configuration of a wireless mobile ATM network. VNC is built on techniques from parallel discrete event simulation merged with constraints from real-time systems and applied to mobile ATM configuration and handoff. Configuration in a mobile network is a dynamic and continuous process. Factors such as load, distance, capacity and topology are all constantly changing in a mobile environment. The VNC algorithm anticipates configuration changes and speeds the reconfiguration process by pre-computing and caching results. VNC propagates local prediction results throughout the VNC enhanced system. The Global Positioning System is an enabling technology for the use of VNC in mobile networks because it provides location information and accurate time for each node. This research has resulted in well defined structures for the encapsulation of physical processes within Logical Processes and a generic library for enhancing a system with VNC. Enhancing an existing system with VNC is straight forward assuming the existing physical processes do not have side effects. The benefit of prediction is gained at the cost of additional traffic and processing. This research includes an analysis of VNC and suggestions for optimization of the VNC algorithm and its parameters., Comment: PhD Thesis

This paper describes the design of a control and management network (orderwire) for a mobile wireless Asynchronous Transfer Mode (ATM) network. This mobile wireless ATM network is part of the Rapidly Deployable Radio Network (RDRN). The orderwire system consists of a packet radio network which overlays the mobile wireless ATM network, each network element in this network uses Global Positioning System (GPS) information to control a beamforming antenna subsystem which provides for spatial reuse. This paper also proposes a novel Virtual Network Configuration (VNC) algorithm for predictive network configuration. A mobile ATM Private Network-Network Interface (PNNI) based on VNC is also discussed. Finally, as a prelude to the system implementation, results of a Maisie simulation of the orderwire system are discussed., Comment: author's web page at http://www.crd.ge.com/people/bush

This paper extends a stochastic theory for buffer fill distribution for multiple ``on'' and ``off'' sources to a mobile environment. Queue fill distribution is described by a set of differential equations assuming sources alternate asynchronously between exponentially distributed periods in ``on'' and ``off'' states. This paper includes the probabilities that mobile sources have links to a given queue. The sources represent mobile user nodes, and the queue represents the capacity of a switch. This paper presents a method of analysis which uses mobile parameters such as speed, call rates per unit area, cell area, and call duration and determines queue fill distribution at the ATM cell level. The analytic results are compared with simulation results., Comment: author's web page at http://www.crd.ge.com/people/bush

The goal of this paper is to define and analyze systems which exhibit brittle behavior. This behavior is characterized by a sudden and steep decline in performance as the system approaches the limits of tolerance. This can be due to input parameters which exceed a specified input, or environmental conditions which exceed specified operating boundaries. An analogy is made between brittle commmunication systems in particular and materials science.