Software tools and techniques for fog and edge computing

The Internet of Things (IoT) paradigm promises to make “things” such as physical objects with sensing capabilities and/or attached with tags, mobile objects such as smartphones and vehicles, consumer electronic devices, and home appliances such as fridge, television, health care devices, as part of the Internet environment. In cloud-centric IoT applications, the sensor data from these “things” is extracted, accumulated, and processed at the public/private clouds, leading to significant latencies. To satisfy the ever increasing demand for cloud computing resources from emerging applications such as IoT, academics and industry experts are now advocating for going from large-centralized cloud computing infrastructures to micro data centers located at the edge of the network. These micro data centers are often closer to a user (geographically and in access latency) compared to the centralized cloud data center. The aim of utilizing such edge resources is to off load computation that would have “traditionally” been carried out at the cloud data center to a resource that is closer to a user or edge devices. This vision also acknowledges the variation in network latency from an end-user to cloud data center. While the network around a data center is often high capacity and speed, that near the user device may have variable properties (in terms of resilience, bandwidth, latency, etc.). Referred to as “fog/edge computing,” this paradigm is expected to improve the agility of cloud service deployments in addition to bringing computing resources closer to end-users. The emergence of computing paradigms such as edge and fog computing supports the data analysis near the data sources for a wide range of applications. Edge computing is the middle layer between users and cloud data centers, and it plays an important role in the IoT use cases where applications required near real-time actions. The intermediate edge layer provides limited computing and storage resources, which consists of network gateways and micro data centers. Edge computing application orchestration is capable of big data processing and can be installed in heterogeneous hardware configurations. Due to the large-scale deployment and device heterogeneity, edge computing infrastructure designing and implementation are challenging, including model analysis, system integration, protocol designing, energy, and security modeling. In addition, since edge data centers are installed in network gateways with open network configuration, they are prone to several network threats, less trustworthy, and easy to compromise. On the one hand, the development of fog and edge clouds includes dedicated facilities, operating system, network, and middleware techniques to build and operate such micro data centers that host virtualized computing resources. On the other hand, the use of fog and edge clouds requires extension to current programming models and proposes new abstractions that will allow developers to design new applications that take benefit from such massively distributed systems. The use of this approach also opens up other challenges in security and privacy (as a user now needs to “trust” every micro data center they interact with), support for resource management for mobile users who transfer session from one micro data center to another, and support for “embedding” such micro data centers into devices (eg, cars, buildings, etc). The objective of this special issue is to disseminate original contributions and research findings concerning the challenges and changes (both evolutionary and disruptive) in edge and fog computing. It provides cutting-edge research from both academia and industry, with emphasis on current developments and future directions in security and privacy issues of emerging fog computing. The call for special issues received a number of submissions. Each paper was reviewed by at least three reviewers and went through at least two rounds of reviews. After a two-phase peer review process, we have accepted 14 high-quality papers related to the aforementioned areas of interest. The accepted papers focus on recent solutions by developing novel research ideas around edge and fog computing for several applications, such as health care, smart city, urban pollution monitoring, etc. The brief contributions of these papers are discussed in the following section. The first paper titled “Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance” by Xu et al proposes an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places. It utilizes an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. This approach has thoroughly experimented, and the experimental results demonstrate the efficiency of the proposed approach by achieving better abnormal event detection performance for a crowded scene of urban public places with an edge environment.