The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). In most IoT application scenarios, billions of things are interconnected together using standard communication protocols to provide services for different applications in the healthcare industry, smart cities, transportation, and food supply chain. Despite their advantage of connecting things anywhere, anytime, and anyplace, IoT presents many challenges due to the heterogeneity, density, the power constraints of things, and the dynamic nature of the network that things might connect and disconnect at any time. All of these increase the communication delay and the generated data, and it is thereby necessary to develop resource management solutions for the applications in IoT. One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step. To overcome the communication cost overhead in the network and to solve the problem efficiently, the nodes use distributed learning algorithms. Next, the problem is extended to include energy constraints on the transmission decisions of the nodes, where each one of them has a battery of finite capacity, which is replenished by an energy-harvesting process. This constrained problem is solved using energy-aware channel access games under different scenarios of perfect and imperfect information. In the distributed sensing part, a traffic-monitoring system, integrated into a WSN, is proposed as a potential application to implement the channel access solution. This system maximizes the privacy of the sensed traffic by using low-cost and low-power sensor devices that integrate passive infrared sensors (PIR) and ultrasonic range finders. To estimate the parameters required to solve the real-time monitoring problem (vehicle detection, classification, and speed estimation), the measurements of these sensors are analyzed using a set of optimized machine-learning algorithms. The selection of these algorithms is due to the continuous variation of the sensed environment over time, the lack of the system state dynamic models, and the limitation in the resources.

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Manual video analysis is apparently a tedious task. An efficient solution is of highly importance to automate the process and to assist operators. A major goal of video analysis is understanding and recognizing human activities captured by surveillance cameras, a very challenging problem; the activities can be either individual or interactional among multiple objects. It involves extraction of relevant spatial and temporal information from visual images. Most video analytics systems are constrained by specific environmental situations. Different domains may require different specific knowledge to express characteristics of interesting events. Spatial-temporal trajectories have been utilized to capture motion characteristics of activities. The focus of this dissertation is on how trajectories are utilized in assist in developing video analytic system in the context of surveillance. The research as reported in this dissertation begins real-time highway traffic monitoring and dynamic traffic pattern analysis and in the end generalize the knowledge to event and activity analysis in a broader context. The main contributions are: the use of the graph-theoretic dominant set approach to the classification of traffic trajectories; the ability to first partition the trajectory clusters using entry and exit point awareness to significantly improve the clustering effectiveness and to reduce the computational time and complexity in the on-line processing of new trajectories; A novel tracking method that uses the extended 3-D Hungarian algorithm with a Kalman filter to preserve the smoothness of motion; a novel camera calibration method to determine the second vanishing point with no operator assistance; and a logic reasoning framework together with a new set of context free LLEs which could be utilized across different domains. Additional efforts have been made for three comprehensive surveillance systems together with main contributions mentioned above.

As an example, the thesis proposes a real-time route guidance system to show how it supports other transportation services, which can then automatically guide vehicles by voice. It illustrates the system architecture and describes the establishment of each part. The concept of agent network is introduced to build up the system. Furthermore, a dynamic route algorithm is presented in brief. A communication system integrating the existing infrastructure is discussed and simulation results are provided to testify the applicability of the proposed wireless data communication system. / Finally, the thesis sums up the contributions achieved and proposes some future works. / For the communication network, the main challenging problems are the large scale of the network, the movement of vehicles that may cause the levity of the network structure, and the large demands on communication capacity. In order to solve these problems, the performance optimization technique is accredited as one of the most important techniques for such a large scale wireless sensor network. This thesis focuses on the research in the following aspects. First, the optimal combination of the duty cycle, one of the most important parameters, is introduced to optimize the system performance. A duty cycle optimization model is put forward based on calculating n-times reachable matrix. Now that the parameter optimization model can be boiled down to a NP-hard problem, an improved genetic algorithm is introduced to solve the problem. The computational procedure and efficiency are discussed, and simulation study based on a practical road network is given to illustrate the validity of the proposed method. Second, the topological structure optimization problem is formulated as a graph problem, while fulfilling random node-to-node communication demands. A new optimization method, called un-detour optimization, is proposed to optimize the topological structure based on the improved genetic algorithm. In addition, the approach is evaluated quantitatively by simulating community wireless sensor networks. The comparison results demonstrate that some significant performance advantages can be achieved by this approach. / In addition, two important techniques required to build the new surveillance system are discussed in this thesis. (1) the sensors to collect traffic information; (2) the communication network to transmit information among all sensors and vehicles. / In order to detect and track the moving objects, this thesis presents a creative background updating method, which can works effectively even for some complex circumstances. The image processing results show that this method can realize the segmentation of the moving objects. Due to the simple model and fast calculation speed, the method can satisfy the requirements of detecting and tracking traffic objects in real time and at a high speed. Additionally, the thesis designs a new kind of object detection and tracking algorithm based on the attributive combination of contour and color in order to deal with the occlusion problem to some extent. Some experiments have testified to the robustness and practicability of the proposed system. / Nowadays, with the rapid development of economics and societies, transportation is playing a very important role in the balanced running of social and economic systems. However, urban traffic problems such as traffic accidents and traffic congestions are becoming more and more serious in almost all large cities in the world. / This thesis is focused on a traffic surveillance system which collects and transmits real-time traffic information in a large city, which is one of the most important steps in solving the transportation problems above. Considering the drawbacks of current traffic surveillance system, a brand-new system with a distributed architecture is proposed based on the concept of sensor networks. Then, an intelligent sensor node using an embedded ARM chip and MCU is developed and software system is built up accordingly, including Linux operating system, hardware drivers, and so on. Finally, a simulation program proves the validity of the system. / Shi, Xi. / "September 2007." / Adviser: YangShong Xu. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4946. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 120-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Telecommunications companies (telcos) and Internet Service Providers (ISPs) monitor the traffic passing through their networks for the purposes of network evaluation and planning for future growth. Most monitoring techniques currently use a form of packet sampling. However, exhaustive monitoring is a preferable solution because it ensures accurate traffic characterization and also allows encoding operations, such as compression and encryption, to be performed. To overcome the very high computational cost of exhaustive monitoring and encoding of data, this thesis suggests exploiting parallelism. By utilizing a parallel cluster in conjunction with load balancing techniques, a simulation is created to distribute the load across the parallel processors. It is shown that a very scalable system, capable of supporting a fairly high data rate can potentially be designed and implemented. A complete system is then implemented in the form of a transparent Ethernet bridge, ensuring that the system can be deployed into a network without any change to the network. The system focuses its encoding efforts on obtaining the maximum compression rate and, to that end, utilizes the concept of streams, which attempts to separate data packets into individual flows that are correlated and whose redundancy can be removed through compression. Experiments show that compression rates are favourable and confirms good throughput rates and high scalability.

Telecommunications companies (telcos) and Internet Service Providers (ISPs) monitor the traffic passing through their networks for the purposes of network evaluation and planning for future growth. Most monitoring techniques currently use a form of packet sampling. However, exhaustive monitoring is a preferable solution because it ensures accurate traffic characterization and also allows encoding operations, such as compression and encryption, to be performed. To overcome the very high computational cost of exhaustive monitoring and encoding of data, this thesis suggests exploiting parallelism. By utilizing a parallel cluster in conjunction with load balancing techniques, a simulation is created to distribute the load across the parallel processors. It is shown that a very scalable system, capable of supporting a fairly high data rate can potentially be designed and implemented. A complete system is then implemented in the form of a transparent Ethernet bridge, ensuring that the system can be deployed into a network without any change to the network. The system focuses its encoding efforts on obtaining the maximum compression rate and, to that end, utilizes the concept of streams, which attempts to separate data packets into individual flows that are correlated and whose redundancy can be removed through compression. Experiments show that compression rates are favourable and confirms good throughput rates and high scalability.

Recent growth of the Internet in both scale and complexity has imposed a number of difficult challenges on existing measurement techniques and approaches, which are essential for both network management and many ongoing research projects. For any measurement algorithm, achieving both accuracy and scalability is very challenging given hard resource constraints (e.g., bandwidth, delay, physical memory, and CPU speed). My dissertation research tackles this problem by first proposing a novel mechanism called residual sampling, which intentionally introduces a predetermined amount of bias into the measurement process. We show that such biased sampling can be extremely scalable; moreover, we develop residual estimation algorithms that can unbiasedly recover the original information from the sampled data. Utilizing these results, we further develop two versions of the residual sampling mechanism: a continuous version for characterizing the user lifetime distribution in large-scale peer-to-peer networks and a discrete version for monitoring flow statistics (including per-flow counts and the flow size distribution) in high-speed Internet routers. For the former application in P2P networks, this work presents two methods: ResIDual-based Estimator (RIDE), which takes single-point snapshots of the system and assumes systems with stationary arrivals, and Uniform RIDE (U-RIDE), which takes multiple snapshots and adapts to systems with arbitrary (including non-stationary) arrival processes. For the latter application in traffic monitoring, we introduce Discrete RIDE (D-RIDE), which allows one to sample each flow with a geometric random variable. Our numerous simulations and experiments with P2P networks and real Internet traces confirm that these algorithms are able to make accurate estimation about the monitored metrics and simultaneously meet the requirements of hard resource constraints. These results show that residual sampling indeed provides an ideal solution to balancing between accuracy and scalability.

This research contributes to improved risk analysis of performance measure forecasts in road safety engineering by designing and applying a method to characterize uncertainty associated with forecast input data in cases where input uncertainty is not known. The research applies this method to quantify uncertainty in three categories of inputs used in risk analysis of performance measure forecasts in road safety engineering: (1) estimates of pedestrian exposure to collision risk; (2) estimates of vehicular exposure to collision risk; and (3) estimates of engineering economics parameters that assign valuations to mortality risk reductions based on individual willingness to pay. The common methods used in each of these categories are repeated comparisons of input ground truth to input estimations, the use of simulation approaches (e.g. the simulation of short-term counts by sampling permanent count data), and the use of non-parametric techniques to characterize input uncertainty. Some highlights of quantified input uncertainty levels include: (1) when obtaining pedestrian risk exposure estimates at a site in Winnipeg, MB by expanding two-hour short-term counts using the National Bicycle and Pedestrian Documentation Project method, 90% of errors are between 62% and 170%; (2) when obtaining estimates of vehicle exposure to collision risk by expanding two 48-hour counts using the individual permanent counter method for Manitoba highways, 92 % of errors are between 9.5% and 10.8%; and (3) when applying an income-disaggregated transfer function to estimate value of a statistical life for road safety in developing countries, 90% of errors are between 53% and 54%. The results provide further detail on the structure of these input uncertainties. Analytic and computational capabilities in forecasting and risk analysis have advanced beyond our understanding of corresponding input uncertainty levels; this research closes some of this gap and enables better risk analysis of performance measure forecasts in road safety engineering.

This study examined the drivers' perception of the SAHER (means "watchful" in Arabic) system in Jeddah, Saudi Arabia. The purpose of this study was to analyze the perception of the SAHER system on impacting the overall traffic conditions in Jeddah, Saudi Arabia including its effectiveness and flaws. A survey was conducted and distributed to 70 drivers and residents of Jeddah. Drivers were divided into two groups based on their age. Five hypotheses were tested in this study. Hypotheses one through four were tested using the averages of related questions. Hypothesis five was tested statistically using a z-test for differences between the means. The overall conclusion of drivers' perception of SAHER on increasing road safety and reducing loss of life was generally positive. The conclusion for hypothesis 1, 2, and 3 was positive. The conclusion for hypothesis four was inconclusive. The conclusion for hypothesis five was retained to the null hypothesis with a 95% confidence level. A key recommendation from the study is that to measure the overall effectiveness of the system it will be prudent to observe how the system is perceived in other major cities of Saudi Arabia apart from Jeddah.

[pt] Sistemas computadorizados de vigilância de veículos têm despertado grande interesse devido à demanda para automatizar tarefas que atualmente são realizadas por operadores humanos. Porém, para realizar estas tarefas é preciso resolver alguns problemas clássicos de visão computacional como sombras, oclusões e variação de iluminação. Este trabalho propõe algoritmos em tempo real para máquinas de baixo custo com o objetivo de rastrear, classificar e determinar a velocidade de cada veículo de uma rodovia. / [en] Vehicle surveillance computerized systems have grown great interest due to the automatizing duties demand, which recently executed by computer vision like shadows, occlusion and light variation have to be solved. The present work proposes real time algorithms for low cost machines focused on tracking, classifying and determining each vehicle`s speed on a highway.