There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Human opinion cannot be restricted to yes or no as depicted by conventional fuzzy set (FS) and intuitionistic fuzzy set (IFS) but it can be yes, abstain, no and refusal as explained by picture fuzzy set (PFS). In this article, the concept of spherical fuzzy set (SFS) and T-spherical fuzzy set (T-SFS) is introduced as a generalization of FS, IFS and PFS. The novelty of SFS and T-SFS is shown by examples and graphical comparison with early established concepts. Some operations of SFSs and T-SFSs along with spherical fuzzy relations are defined, and related results are conferred. Medical diagnostics and decision-making problem are discussed in the environment of SFSs and T-SFSs as practical applications.

An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets

Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaningless if IoT objects are not equipped with cognitive radio capability. Equipping IoT objects with CR capability has lead to a new research dimension of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We highlight potential applications of CR-based IoT systems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues, research challenges, and future direction for these CR-based IoT networks.

Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of VANETs over the past few years. VANET consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of Intelligent Transport Systems (ITS). Hence, government, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interested field of wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well the way forward to achieving the long awaited ITS.

Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS.

/pdf/advances-in-vehicular-ad-hoc-networks-vanets-challenges-and-2nao6sy0gu.pdf

Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

The recent advancements in technology and the availability of the Internet make it possible to connect various devices that can communicate with each other and share data. The Internet of Things (IoT) is a new concept that allows users to connect various sensors and smart devices to collect real-time data from the environment. However, it has been observed that a comprehensive platform is still missing in the e-Health and m-Health architectures to use smartphone sensors to sense and transmit important data related to a patient's health. In this paper, our contribution is twofold. Firstly, we critically evaluate the existing literature, which discusses the effective ways to deploy IoT in the field of medical and smart health care. Secondly, we propose a new semantic model for patients' e-Health. The proposed model named as ‘k-Healthcare’ makes use of 4 layers; the sensor layer, the network layer, the Internet layer and the services layer. All layers cooperate with each other effectively and efficiently to provide a platform for accessing patients' health data using smart phones.

Effective ways to use Internet of Things in the field of medical and smart health care

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements

/pdf/towards-5g-a-reinforcement-learning-based-scheduling-36pteu409g.pdf

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Cognitive Radio (CR) has emerged as an intelligent technology to address the spectrum scarcity issues. CR aims to use the unoccupied spectrum band when it is not used by the licensed user. An extensive research has been carried out since the inception of this technology in 1999 where different challenges like spectrum sensing, cooperation amongst CR users and applicability of CR networks have been widely explored. In this paper, we provide new applications of CR technology for Internet of Things (IoT) and propose appropriate solutions to the real challenges in CR technology that will make IoT more affordable and applicable.

Sijing Zhang

Papers

Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward

Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

Effective ways to use Internet of Things in the field of medical and smart health care

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Cognitive radio networks for Internet of Things: Applications, challenges and future