Computer vision is widely used to detect anomalies in video processing systems for public safety. Applying Deep Neural Networks (i.e., DNNs) in computer vision can achieve a high detection accuracy but it requires a huge amount of computing power, storage space, and video data. Thus, DNNs-based video analytics is mostly deployed in the cloud with video data steaming from a set of stationary cameras. There are mainly three issues in this setting. First, steaming a huge amount of video data from cameras to cloud leads to high bandwidth consumption and latency. Second, when DNNs are deployed on resource-limited devices like edge nodes to reduce communication costs, it is hard to achieve a high detection accuracy. Third, stationary cameras can only collect a limited amount of video data that covers a small area, so it barely satisfies the needs of the real-time analytics in applications like public safety. We propose a mobile edge computing-based video stream processing platform, mVideo, which conducts video analytics making full use of resources at the collaborative edge and cloud nodes. On the mVideo, a mechanism is designed to partition a video analysis task based on available resources on the mobile edge node. Then, the edge nodes pre-process video data using a lightweight DNN model and upload the results to cloud nodes for further analysis. Thus mVideo not only collects video data that covers a large area, but also reduces the communication costs. To validate the proposed platform, a face recognition application is deployed on the mVideo prototype. Experimental results reveal that compared with the existing cloud computing model, mVideo reduces video data volume transmitted to the cloud nodes and power consumption by up to 99.5% and 96.2%, respectively. mVideo also improves the execution time by 90.0% to optimize mobile video analytics performance.

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the “golden record” that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality.

Cloud computing and edge computing models are popularly applied in emerging applications such as smart homes, smart parks, and connected autonomous vehicles for large-scale live video analytics. Cloud computing-based models transfer all data to the cloud for video analytics, which burdens network bandwidth and increases the data transmission overhead. Edge computing mode enables video data to be processed at the edge node, thereby reducing the bandwidth overhead. Existing edge computing-based models optimize the performance, but they still have defects in three perspectives, (1) enabling end-users to control video content in a real-time format, (2) efficiently locating and transferring the user region of interest (ROI) video data in the video stream, and (3) adapting to various network conditions. To tackle these challenges, we proposed an intelligent elastic edge framework for live video analytics, known as ElasticEdge. ElasticEdge enables the interaction between the end-user and the edge node. Elasticity is reflected in two perspectives, the dynamic changes of user requirements and the dynamic changes in network conditions. In addition, ElasticEdge transmits the video stream to the end-users based on the trade-off between the amount of video data and users’ ROI to meet various network conditions. To validate ElasticEdge, we conducted experiments to study its performance in comparison to RTFace. Experimental results show that ElasticEdge has a significant edge over RTFace in terms of data transmission. Using 1/16 reserved images, ElasticEdge saves 75% bandwidth and reduces latency by approximately 10% compared with RTFace. We also find that ElasticEdge adapts to various network conditions when streaming videos, i.e., it can reliably obtain essential information with low latency even when the network condition is poor.

Researchers have designed UAV-enabled edge servers (UES) to assist smart cameras (ECs) and optimize video analytics performance. However, most prior research neglects the impact of ECs' battery constraint on the system performance, especially in disaster scenarios. In this study, we introduce a novel time-slot-based UES-assisted system that aims to conserve energy and extend the overall lifetime of EC networks. Our proposed system operates in discrete time slots, wherein the UES alternates between hovering and serving ECs, or flying to a new position within its coverage area to provide better assistance. To minimize the computational overhead of ECs during hovering and serving within a time slot, we present a new task offloading scheme based on the differential evolutionary algorithm. For optimizing the UES's flying trajectory, we formulate the UES movement as a Markov decision process, taking into account system state changes. Subsequently, we design an efficient UES trajectory planning algorithm using double deep Q-learning. This algorithm optimizes energy consumption, feedback reward, and the overall system overhead, resulting in a doubling of the system's lifetime. Simulation results demonstrate that our proposed task offloading algorithm exhibits high accuracy and fast convergence compared to four other state-of-the-art strategies. Moreover, the UES trajectory planning algorithm doubles the system's lifetime while reducing energy consumption and total system overhead

Bandwidth-intensive applications transmit large-scale video data in the network. It causes backhaul bottlenecks and affects user experience. Deploying edge cache on an access point (AP) is a popular method to bring content files closer to end-users, but it faces significant challenges, especially in efficiently predicting and satisfying different users’ future content requests with limited cache capacity. In this article, we propose an intelligent gateway assisted edge cache deployment strategy (GACD), which jointly considers traffic usage patterns in multiple APs and the impact of new content on the cache performance. In GACD, The cache content placement problem is formulated as a many-to-one bidirectional matching problem with a dynamic quota allocation, aiming to improve cache resource utilization and minimize the average delivery latency. To address this problem, we design a heterogeneous information networks based prediction algorithm to predict end-users’ potential preference of new content files. Then, we adapt the seasonal autoregressive integrated moving average model for traffic usage prediction, and propose a many-to-one matching algorithm to achieve dynamic matching quota adjustment and efficient cache content placement. We conduct extensive real-world trace-based experiments to validate the performance of GACD. Compared with six alternative cache strategies, GACD improves the hit rate by 23.9% on average, reduces the average content delivery delay by 19.02%, and increases the accuracy by 31.02% on average.