With the rapid advances in Internet of Things (IoT) technologies, the number of IoT connected devices at the Edges of the Internet is exponentially growing, accounting for billions of connected devices world-wide. While this is bringing unforeseen opportunities to all fields of science, engineering, businesses and everyday life, many challenges have also risen and need to be addressed by researchers, developers and engineers in the field. Among such challenges, we can distinguish the architectural challenge and the data deluge challenge. As for the former, the exponential growth of computing devices at the Edges of the Internet has created an architectural bottleneck due to a very large number of IoT devices connected to a rather small number of servers in Cloud data centers. The latter is due to data generation by IoT devices, which constitute a main source for data sensing and data streaming leading to Big Data and Big Data streams. Other challenges at the Edges of Internet include leveraging small data centers and data storage capabilities, security, privacy, etc.

Edge computing aims to alleviate the computational burden of the IoT for the Cloud by pushing part of the computations and logics of processing from the Cloud to the Edges of the Internet. At this layer, it is now commonplace to allocate tasks and applications such as data filtering, classification, semantic enrichment and data aggregation, among others. In order to support the pre-processing of data generated from the IoT layer at high rates, the Edge computing layer needs to efficiently implement massively parallel and distributed algorithms, otherwise this new layer would become another bottleneck for the whole computing stack from IoT to the Cloud. While already known parallel, scheduling and allocation algorithms are also implementable for Edge computing, the limited computing capacity as well as the variety and heterogeneity of computing resources at the Edges of Internet require deeper understanding and innovative solutions. Likewise, issues arising from integration and communication of this new wide layer with IoT and the Cloud at large scale need further investigation.

Researchers and developers are currently seeking to achieve a twofold view of Edge Computing, namely, that of computing fabric and data fabric. Altogether, the objective is to create pools of computing, network, communications and storage resources configurable into a scalable fabric that can process, analyse and store massive amounts of data, both offline and online. Indeed, the view of computing fabric is suitable for the Edges of Internet to maximize the usage of Edge computing resources, close to the end- users, for accommodating allocation of tasks of varying sizes and complexity. Therefore, IoT systems can benefit from this computing fabric flexibility whereby applications can be allocated to the suitable pool of resources they need to for their execution and completion. Likewise, the data fabric view can support IoT data stream processing and applications at the Edges of Internet by faster access to data, Edge cache for caching of Internet-based content in support to real time applications of virtual reality, critical decision making, etc., requiring low latency.

The vision of computing and data fabric of Edges of Internet is supported by 5G technologies, Mobile Edge Computing (MEC) servers, micro-data centers, which can improve substantially Quality of Service (QoS), reduce significantly latency (when compared to Cloud latency), provide support for mobility, scalability, real time applications, etc.

This book brings to readers a number of contributions on Edge Computing from a multi-disciplinary perspective. Fundamental research as well as applications from various domains such as smart homes, eHealth, robotics and smart cities are presented. Issues and challenges in respective domains are also identified and discussed towards envisioning efficient and scalable solutions at the Edges of Internet.

The book comprises 11 chapters, which are arranged and summarized as follows.

The first chapter “The Vision of Edges of Internet as a Computing Fabric” by Xhafa discusses the main concepts behind Edge computing as a compute fabric to support real-time applications, high performance computing, big data and big data stream processing at large scale. Besides main concepts, IoT data stream processing, semantic data enrichment, task and application allocation, semantic resource categorization and clustering at the Edges of Internet and introduced and analyzed. The challenges of achieving compute and data fabric vision due to a number of complexities are also discussed.

Pisani et al. in the second chapter “Fog Computing on Constrained Devices: Paving the Way for the Future IoT” present a survey of the concepts of constrained devices, IoT, and fog and mist computing. A classification of applications according to the amount of resources they require (e.g., processing power and memory) is also provided. The authors also discuss what can be expected in a future, where constrained devices and fog computing are used to push the IoT to new limits as well as some challenges and opportunities that these technologies may bring.

The third chapter by Confais et al. “A Fog storage software architecture for the Internet of Things” motivate the need for an operational Fog architecture that supports low latency, mobility (of users and possibly infrastructure) and network partitioning. The authors discuss and analyze in detail such an architecture that consists in coupling an object storage system and a scale-out Network Attached Storage allowing both scalability and performance. Additionally, a new protocol inspired from the Domain Name System to manage replicas in a context of mobility is presented. Finally, the authors discuss the conceptual proximity between Fog storage, Content Delivery Networks and Name Data Networking. A complete experimental evaluation is done over the French cluster Grid’5000 to support the findings with empirical results.

In the fourth chapter, “Towards an Architecture for Information-Centric Network with Content Replication in Edge Computing”, Nascimiento et al., present and analyze the advantages of Information-Centric Networks (ICN) as a new internet architecture and a content distribution model for the Edges of Internet. A discussion towards an architecture using cache replication for ICN networks through Software-Defined Networking (SDN) in Edge computing future scenarios is also included.

Singh et al. in the fifth chapter, “A Quality-Assuring, Combinatorial Auction Based Mechanism for IoT-Based Crowdsourcing” address some research issues from IoT-based crowdsourcing in a strategic setting. The authors have considered the scenario in the IoT-based crowdsourcing, where there are multiple task requesters and multiple IoT devices as task executors with tasks having start and finish times. The objective is to allocate the subset of IoT devices to the tasks, distributed into different slots, in a non-conflicting manner while maximizing the social welfare. The concept of peer grading and the truthful mechanism design are used to that end.

The sixth chapter “Edge Intelligence and the Industrial Internet of Things”, the authors Hill and Al-Aqrabi examine the Edge intelligence in the context of the Industrial Internet of Things as a key enabler of a new set of capabilities in the manufacturing industry. Edge intelligence is seen as a consequence of the desire to “push” computation and storage to the limits of network infrastructure, in a way that supports intelligent visualization and interaction with data within the context that it is generated. Pertinent topics are analysed for potential consumers of the Industrial Internet of Things (IIoT), and by way of a case study in the manufacturing domain, the limits and potential of edge intelligence are explored.

Tinamas and Natwichai in the seventh chapter “Issues in Privacy Preservation for Republishable Data” are concerned with privacy issues related to re-publishing and changing of data in a volatile environment such as edge computing model with distributed data sources. The authors start by presenting the background of privacy preservation environment and then formulate he re-publishable dataset problem. An analysis of possible privacy breach is illustrated. Anonymization techniques including k-anonymity and l-diversity and m-distinct are analysed and discussed.

In the eighth chapter “A study on various cryptographic techniques towards fog environment”, Mahendran et al., various existing security mechanisms in fog computing such as Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Black-Box Traceable CP-ABE, Lightweight Fine-Grained ciphertext search as well as the security mechanism based on evolutionary game theory, the hybrid scheme for fine-grained search and access authorization, variant of password-authenticated key exchange protocol, fog computing through the blockchain and quantum-based steganography protocol for fog are discussed and compared. Likewise, techniques for trust and authentication-based attacks with the future research challenges are analysed. The chapter concludes with a discussion on the advantages and limitations of other security techniques and a summary of the existing research challenges in fog security.

The ninth chapter “iHome: Secure Smart Home Management in the Cloud and the Fog” by Petrakis and Myrizakis present iHome, a smart home management service that runs in the Cloud, implemented based on the principles of Service-Oriented Architecture (SOA) design as a composition of RESTful services. The service addresses the need of users to monitor and control their homes remotely provided that the home devices are “smart” themselves. To mitigate concerns of data protection, response time and communication delays in delivering large amounts of data to the cloud, all services for the home are realized within a fog node installed at home. Data publication and subscription services, an innovative feature of iHome, is a rule-based event management service which forwards alerts to subscribed users for responding to critical events such as incidents of fire, malfunctioning appliances at home, etc.

Goli at al. in the tenth chapter “Hybrid Neural Network and Improved Cuckoo Optimization Algorithm for Forecasting Thermal Comfort Index at Urban Open Spaces”, study the thermal comfort of urban open spaces as a relevant and complex topic in the field of edge and fog computing. The prediction of thermal comfort is significant in order to enable planning of the usage time of urban open spaces. The chapter develops an Improved Cuckoo Search algorithm for forecasting physiological equivalent temperature (PET) values one hour ahead. A proper strategy for tuning the parameters is presented. Moreover, the generation of laid eggs is done by implementing the cross-over operator of a Genetic Algorithm (GA). Then, it is employed to train feed forward neural networks for PET prediction. Finally, the performance of the proposed algorithm is compared to the state-of-the-art; i.e., traditional Cuckoo Optimization Algorithm (COA) and Genetic Algorithm (GA). The reported simulation results demonstrate the effectiveness of the proposed algorithm.

The last chapter “A Cloud Robotic Solution to Assist Medical Application” by Harinee Shan and Anand Mahendran deal with performance and cost issues of conventional robots used in the medical field needing high memory and computation power for decision making and task execution. The authors propose a new architecture which makes use of Commercial Off-The-Shelf (COTS) for cloud robotics for assisting patients who need first aid at times of emergency (based on the data from COTS product). COTS help in tracking the health of individuals and these reports are stored either in the devices or on storage spaces. The authors discuss how to address the memory requirement for the robot as well as the cost reduction on the memory of the robot and of its size.

The Book Editors

Prof. Dr. Fatos Xhafa

Visiting Professor

Department of Computer Science, University of Surrey, UK

On Leave from Universitat Politècnica de Catalunya, Barcelona, Spain

Dr. Arun Kumar Sangaiah

Vellore Institute of Technology, Vellore, India