in Dublin, Ireland, and demonstrate very good performance in balancing energy consumption among multiple nodes and reducing service delay in urban mobile scenarios.
After 3C and energy resources are properly managed, Chapter 4 concentrates on dynamic service provisioning in multi‐tier computing networks. Firstly, at the network edge, an online orchestration framework is proposed for cross‐edge service function chaining to maximize the holistic cost‐efficiency through joint optimization of resource utilization and traffic routing. By carefully combining an online optimization technique with an approximate optimization method, this framework runs on top of geographically dispersed edge/fog nodes to tackle the long‐term cost minimization problem with future uncertain information. In this way, the benefits of service function chaining are fully unleashed for configuring and providing various intelligent services in an agile, flexible, and cost‐efficient manner. Secondly, inside a computing network using renewable energy, a network slicing framework for dynamic resource allocation and service provisioning is proposed, where a regional orchestrator timely coordinates workload distribution among multiple edge/fog nodes, and provides necessary slices of energy and computing resources to support specific IoT applications with Quality of Service (QoS) guarantees. Based on game theory and the Markov decision process, an effective algorithm is developed to optimally satisfy dynamic service requirements with available energy and network resources under randomly fluctuating energy harvesting and workload arrival processes. Thirdly, across multiple networks, a multi‐operator network sharing framework is proposed to enable efficient collaborations between resource‐limited network operators in supporting a variety of IoT applications and high‐speed cellular services simultaneously. This framework is based on the Third Generation Partnership Project (3GPP) Radio Access Network (RAN) sharing architecture, and can significantly improve the utilization of network resources, thus effectively reducing the overall operational costs of multiple networks. Both the network slicing and multi‐network sharing frameworks are evaluated by using more than 200 base station (BS) location data from two mobile operators in the city of Dublin, Ireland. Numerical results show they can greatly improve the workload processing capability and almost double the total number of connected IoT devices and applications.
Chapter 5 addresses the privacy concerns in public IoT applications and services, where IoT devices are usually embedded in a user's private time and space, and the corresponding data is in general privacy sensitive. Unlike resourceful clouds that can apply powerful security and privacy mechanisms in processing massive user data for training and executing deep neural network models to solve complex problems, IoT devices and their nearby edge/fog nodes are resource‐limited, and therefore, have to adopt light‐weight algorithms for privacy protection and data analysis locally. With low computing overhead, three approaches with different privacy‐preserving features are proposed to tackle this challenge. Specifically, random and independent multiplicative projections are applied to IoT data, and the projected data is used in a stochastic gradient descent method for training a deep neural network, thus to protect the confidentiality of the original IoT data. In addition, random additive perturbations are applied to the IoT data, which can realize differential privacy for all the IoT devices while training the deep neural network. A secret shallow neural network is also applied to the IoT data, which can protect the confidentiality of the original IoT data while executing the deep neural network for inference. Extensive performance evaluations based on various standard datasets and real testbed experiments show these proposed approaches can effectively achieve high learning and inference accuracy while preserving the privacy of IoT data.
Chapter 6 considers clock synchronization and service reliability problems in wide‐area IoT networks, such as long‐distance powerlines in a state power grid. Typically, the IoT systems for such outdoor application scenarios obtain the standard global time from a Global Positioning System (GPS) or the periodic timekeeping signals from Frequency Modulation (FM) and Amplitude Modulation (AM) radios. While for indoor IoT systems and applications, the current clock synchronization protocols need reliable network connectivity for timely transmissions of synchronization packets, which cannot be guaranteed as IoT devices are often resource‐limited and their unpredictable failures cause intermittent network connections and synchronization packet losses or delays. To solve this problem, a natural timestamping approach is proposed to retrieve the global time information by analyzing the minute frequency fluctuations of powerline electromagnetic radiation. This approach can achieve sub‐second synchronization accuracy in real experiments. Further, by exploiting a pervasive periodic signal that can be sensed in most indoor electromagnetic radiation environments with service powerlines, the trade‐off relationship between synchronization accuracy and IoT hardware heterogeneity is identified, hence a new clock synchronization approach is developed for indoor IoT applications. It is then applied to body‐area IoT devices by taking into account the coupling effect between a human body and the surrounding electric field generated by the powerlines. Extensive experiments show that this proposed approach can achieve milliseconds clock synchronization accuracy.
Finally, Chapter 7 concludes this book and identifies some additional challenging problems for further investigation.
We believe all the challenges and technical solutions discussed in this book will not only encourage and enable many novel intelligent IoT applications in our daily lives but, more importantly, will deliver a series of long‐term benefits to businesses, consumers, governments, and human societies in the digital world.
Yang Yang
ShanghaiTech University and
Peng Cheng Laboratory, China
August 19, 2020
Notes
1 1 Bernard Marr, What is Digital Twin Technology and Why is it so Important?, Forbes, March 6, 2017.
2 2 Cisco, Cisco Annual Internet Report (2018–2023), February 2020.
3 3 Scott Buchholz and Bill Briggs, Tech Trends 2020, Deloitte, January 7, 2020.
4 4 Keynote by Alexandra Rehak and Steve Bell, IoT World, May 14, 2019.
5 5 Yigang Cai, 3GPP Release 16, IEEE Communications Society Technology Blog, 10 July 2020.
6 6 10 Breakthrough Technologies 2020, MIT Technology Review, February 26, 2020.
7 7 David Reinsel, John Gantz, and John Rydning, Data Age 2025, The Digitization of the World: from Edge to Core, IDC and SEAGATE, November 2018.
8 8 Worldwide Big Data and Analytics Spending Guide, IDC, April 2019.
9 9 David Reinsel, John Gantz, and John Rydning, Data Age 2025, The Digitization of the World: from Edge to Core, IDC and SEAGATE, November 2018.
10 10 N. Chen, Y. Yang, T. Zhang, M. T. Zhou, X. L. Luo, and J. Zao, “Fog as a Service Technology,”
