Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

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

Partitioning and Offloading for IoT and Video Streaming Applications that Utilize Computing Resources at the Network Edge

The Internet of Things (IoT) is a concept in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. IoT applications connect physical objects for the purpose of decision making by sensing and analysing generated data from the embedded sensors in physical objects. IoT applications are growing rapidly as sensors become less expensive. Sensors generate large amounts of data that may meaningless unless the data is used to derive knowledge with in a certain period of time. Stream processing paradigm is used by IoT to provide requirements of IoT applications. In a stream processing paradigm, unlike traditional data bases, data is not stored but rather processed as it is generated. To transfer generated data from distributed data sources to a processing center such as cloud may not allow for real-time processing due to the network delay. Another high-demand application is live streaming of video. The performance of live video stream systems is inferior when there is a sudden large demand in the number of users. This thesis addresses some of the limitations of current architectures for video streaming systems and IoT applications based on the use of nearby computing resources (e.g., cloudlet, fog). First, we addressed the degrading performance in video stream systems when a flash crowd occurs. The performance of video streaming systems is affected by flash crowd and degrade the quality of service for subscribers to the content delivery system. A flash crowd happens when there is a sudden large increase in the number of users. Therefore, flash crowds increase network traffic for any particular server. The main challenge is to make sure that the video streaming system has sufficient capacity to handle the occurrence of flash crowds. Second, we address the limitation of current architectures for running mobile applications by introducing a dynamic partitioning and offloading of a mobile application. Mobile devices have limited resources including short battery life, storage capacity and processor performance. This limits the applications that can run on it. Mobile applications can be partitioned so that some of the application runs on a cloud. This works well for applications with relatively little data to be transferred and that do not have a high level of interaction with the user. Challenges with applications that have large amounts of data to be transferred and have a high level interactiveness is the high latency incurred by the network and packet loss of the wireless network. A mobile application can be partitioned so that part of it runs on a nearby computing resource e.g., fog node or cloudlet. This thesis presents a framework that introduces fine-grained offloading approach and support for runtime and dynamic partitioning of an application. Third, we present a solution for placement of stream operators over distributed fog nodes for live processing of data streams from geographically distributed data sources. This placement of stream operators takes place in such a way that it supports applications with a high volume of data that require real-time (or near real-time) analysis To this end, this thesis proposed a set of algorithms for placement of stream operators among fog nodes