DESIGN FRAMEWORK FOR INTERNET OF THINGS BASED NEXT GENERATION VIDEO SURVEILLANCE

Modern artificial intelligence and machine learning opens up new era towards video surveillance system. Next generation video surveillance in Internet of Things (IoT) environment is an emerging research area because of high bandwidth, big-data generation, resource constraint video surveillance node, high energy consumption for real time applications. In this thesis, various opportunities and functional requirements that next generation video surveillance system should achieve with the power of video analytics, artificial intelligence and machine learning are discussed. This thesis also proposes a new video surveillance system architecture introducing fog computing towards IoT based system and contributes the facilities and benefits of proposed system which can meet the forthcoming requirements of surveillance. Different challenges and issues faced for video surveillance in IoT environment and evaluate fog-cloud integrated architecture to penetrate and eliminate those issues. The focus of this thesis is to evaluate the IoT based video surveillance system. To this end, two case studies were performed to penetrate values towards energy and bandwidth efficient video surveillance system. In one case study, an IoT-based power efficient color frame transmission and generation algorithm for video surveillance application is presented. The conventional way is to transmit all R, G and B components of all frames. Using proposed technique, instead of sending all components, first one color frame is sent followed by a series of gray-scale frames. After a certain number of gray-scale frames, another color frame is sent followed by the same number of gray-scale frames. This process is repeated for video surveillance system. In the decoder, color information is formulated from the color frame and then used to colorize the gray-scale frames. In another case study, a bandwidth efficient and low complexity frame reproduction technique that is also applicable in IoT based video surveillance application is presented. Using the second technique, only the pixel intensity that differs heavily comparing to previous frame’s corresponding pixel is sent. If the pixel intensity is similar or near similar comparing to the previous frame, the information is not transferred. With this objective, the bit stream is created for every frame with a predefined protocol. In cloud side, the frame information can be reproduced by implementing the reverse protocol from the bit stream. Experimental results of the two case studies show that the IoT-based proposed approach gives better results than traditional techniques in terms of both energy efficiency and quality of the video, and therefore, can enable sensor nodes in IoT to perform more operations with energy constraints

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Contextually and identity aware 5G services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe fifth generation (5G) mobile networks aim to be ten times faster than the existing 4G connection, whilst providing low latency, and flexibility. Hence, various alterations are planned to the existing network infrastructure to be able to reach the 5G expected performance levels. The main technologies that were used, to ensure high performance, flexible network, and efficient resource allocation, are Software Defined Network and Network Function Virtualization. As these technologies are replacing the device-based architecture with, a service-based architecture. This thesis provides a design of location database interactive web interface and interactive mobile application. The implementation of real time video streaming location server, the streaming system's performance parameters demonstrated a high level of QoS (0.07ms jitter and 9.53ms delay). In regard to experimental examination, it measured the localisation coverage, accuracy measurements and a highly scalable security solution. The localisation coverage and accuracy measurements were achieved through the mmWave and VLC link transmitters. The proposed simulated annealing algorithm aimed at data optimisation for location measurements accuracy showed results of the average location error of x and y which showed significant improvement from x= 22.5 and y=21.6 to x=11.09 and y= 11.63. The proposed indoor location security solution showed significant results, as it provides a high scalability solution using the VNF. The solution showed that it was not 100% effective, as some of the fake discover packets still reached the DHCP server. This was due to the high load of traffic passing through the network. Nonetheless, 90% of the fake DHCP discover packets never reached the DHCP server because the scripts began blocking all fake discover packets after realising it was an attack. This conveys that the proposed system was able to run successfully without crashing or overloading the controller. Overall, the main challenges facing 5G have been addressed with their proposed solutions, which showed promising results. Conclusively showing that there is a lot more space for technological advancements to support the future of mobile networks.European Union’s Horizon 2020 research program - the Internet of Radio-Light (IoRL) project H2020-ICT 761992

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

TinySurveillance: A Low-power Event-based Surveillance Method for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have always been faced with power management challenges. Managing power consumption becomes critical, especially in surveillance applications where the longer flight time results in wider coverage and a cheaper solution. While most current studies focus on utilizing new models for improving event detection without considering the power constraints, our design's first priority is our platform's power efficiency. Implementing an algorithm on a portable device with minimal access to power supply sources requires special hardware and software considerations. An improved algorithm may need more powerful hardware, which can surge power consumption. Therefore, we aim to propose a method to be suitable for such devices with power consumption constraints. In this work, we propose an event-driven surveillance method with an efficient video transmission algorithm that reduces power consumption while preserving image quality. The surveillance will start automatically once the low-power AI-based onboard processor detects the desired event. The drone repeatedly solves a classification problem by employing a lightweight deep learning algorithm. When the UAV detects the defined event, a sample image is sent to the server for validation. Afterwards, if the server validates the drone decision, the drone, which can be a UAV, starts sending a colored image accompanied by a group of N grayscale images. Then, in the server, the grayscale images will be colorized using a convolution neural network trained by the colored images. By adopting this method, the sent data rate decreases and the server's computation load increases. The former part results in a drop in the UAV's power consumption, which is our aim. In this work, an application of wildfire detection and surveillance has been implemented to show the proof of concept of the TinySurveillance method. Using four videos of similar scenarios with different spatial and temporal information that a UAV may face, with various spatial and temporal characteristics, we show the effectiveness of our method. Our results show that the power consumption of the onboard processing unit in detection mode will be reduced by at least 4 times, reaching a detection accuracy of 85%, while in surveillance mode, we can decrease the data transmission rate by almost 66% while achieving a competent image quality with PSNR_Avg of 41.35 dB, PSNR of 30.94 dB, and output frame rate of 5.2. Also, the reproduced images show the outstanding performance of the algorithm by generating colorized images identical to the original scenes. There are main features that affect our method's power consumption and output quality, like the number of grayscale images, sent video bitrate, learning rate, and video characteristics that are discussed comprehensively

Performance Evaluation of Wireless Medium Access Control Protocols for Internet of Things

The Internet of Things makes the residents in Smart Cities enjoy a more efficient and high-quality lifestyle by wirelessly interconnecting the physical and visual world. However, the performance of wireless networks is challenged by the ever-growing wireless traffic data, the complexity of the network structures, and various requirements of Quality of Service (QoS), especially on the Internet of Vehicle and wireless sensor networks. Consequently, the IEEE 802.11p and 802.11ah standards were designed to support effective inter-vehicle communications and large-scale sensor networks, respectively. Although their Medium Access Control protocols have attracted much research interest, they have yet to fully consider the influences of channel errors and buffer sizes on the performance evaluation of these Medium Access Control (MAC) protocols. Therefore, this thesis first proposed a new analytical model based on a Markov chain and Queuing analysis to evaluate the performance of IEEE 802.11p under imperfect channels with both saturated and unsaturated traffic. All influential factors of the Enhanced Distributed Channel Access (EDCA) mechanism in IEEE 802.11p are considered, including the backoff counter freezing, Arbitration Inter-Frame Spacing (AIFS) defers, the internal collision, and finite MAC buffer sizes. Furthermore, this proposed model considers more common and actual conditions with the influence of channel errors and finite MAC buffer sizes. The effectiveness and accuracy of the developed model have been validated through extensive ns-3 simulation experiments. Second, this thesis proposes a developed analytical model based on Advanced Queuing Analysis and the Gilbert-Elliot model to analyse the performance of IEEE 802.11p with burst error transmissions. This proposed analytical model simultaneously describes transmission queues for all four Access Categories (AC) queues with the influence of burst errors. Similarly, this presented model can analyse QoS performance, including throughputs and end-to-end delays with the unsaturated or saturated load traffics. Furthermore, this model operates under more actual bursty error channels in vehicular environments. In addition, a series of simulation experiments with a natural urban environment is designed to validate the efficiency and accuracy of the presented model. The simulation results reflect the reliability and effectiveness of the presented model in terms of throughput and end-to-end delays under various channel conditions. Third, this thesis designed and implemented a simulation experiment to analyse the performance of IEEE 802.11ah. These simulation experiments are based on ns-3 and an extension. These simulation experiments' results indicate the Restricted Access Window (RAW) mechanism's influence on the throughputs, end-to-end delays, and packet loss rates. Furthermore, the influences of channel errors and bursty errors are considered in the simulations. The results also show the strong impact of channel errors on the performance of IEEE 802.11ah due to urban environments. Finally, the potential future work based on the proposed models and simulations is analysed in this thesis. The proposed models of IEEE 802.11p can be an excellent fundamental to optimise the QoS due to the precise evaluation of the influence of factors on the performance of IEEE 802.11p. Moreover, it is possible to migrate the analytical models of IEEE 802.11p to evaluate the performance of IEEE 802.11ah

Direction of the Play: The Fantasticks

This project entailed the selection, background research and documentation, musical analysis, casting, direction, vocal coaching, and post-production analysis of Ridgefield High School\u27s production of Tom Jones and Harvey Schmidt\u27s The Fantasticks. Documentation includes research and analysis of the play, its music, and an evaluation of the musical as a production vehicle for the Department of Theatre Arts at Central Washinton University. The analysis also includes a discussion as to the non-traditional directorial vision of this production