The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Internet of Things (IoT) is a connection of people and things at any time, in any place, with anyone and anything, using any network and any service. Thus, IoT is a huge dynamic global network infrastructure of Internet-enabled entities with web services. One of the most important applications of IoT is the Smart Grid (SG). SG is a data communications network which is integrated with the power grid to collect and analyze data that are acquired from transmission lines, distribution substations, and consumers. In this paper, we talk about IoT and SG and their relationship. Some IoT architectures in SG, requirements for using IoT in SG, IoT applications and services in SG, and challenges and future work are discussed.

https://www.mdpi.com/2411-5134/4/1/22/pdf

Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN) By providing computational and storage resources at the edge, MEC can reduce latency for end users Hence, this article investigates MEC for 5G and IoT comprehensively It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

In the key management-based security scheme of Advanced Metering Infrastructure (AMI), it is assumed that the established third party and the links between smart meter and the third party are fully trusted. But in wired/wireless communication, man-in-the middle can interfere, and the monitors and controls in the system can expose its vulnerability. Because of this, we propose a security scheme based on two independent and partially trusted but simple servers, which includes two-level encryption without increasing packet overhead. One server (master) manages the data encryption between the meter to ES (energy supplier) and the other server manages randomized data transfer. We also propose a simple node-to-node authentication method based on electromagnetic signal strength.

A key management-based two-level encryption method for AMI

The surge of mobile devices such as smartphone and tablets requires additional capacity. To achieve ubiquitous and high data rate Internet connectivity, effective spectrum sharing and utilization of the wireless spectrum carry critical importance. In this paper, we consider the use of unlicensed LTE (LTE-U) technology in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band and develop a multiarmed bandit (MAB) based spectrum sharing technique for a smooth coexistence with WiFi. In particular, we consider LTE-U to operate as a General Authorized Access (GAA) user; hereby MAB is used to adaptively optimize the transmission duty cycle of LTE-U transmissions. Additionally, we incorporate downlink power control which yields a high energy efficiency and interference suppression. Simulation results demonstrate a significant improvement in the aggregate capacity (approximately 33%) and cell-edge throughput of coexisting LTE-U and WiFi networks for different base station densities and user densities.

/pdf/cbrs-spectrum-sharing-between-lte-u-and-wifi-a-multiarmed-8jehffb7b6.pdf

CBRS Spectrum Sharing between LTE-U and WiFi: A Multiarmed Bandit Approach

This chapter provides a high-level description and summary of smart grid IoT entity’s three logical modules responsible for its intelligence: wide, medium, and local area sensor networks. For each module, the architecture, key applications, and potential challenges to future research are discussed using a widely recognized system as an example. In particular, the interdependency between these IoT entity modules, underlying power system and overlying operational entity, is presented, with a special emphasis on big data, communication, and security challenges. This chapter will serve as a starting point for researchers entering the field of smart grid IoT big data analytics, communication, and security.

Trends and Future Directions of Research for Smart Grid IoT Sensor Networks

In smart cities, Advanced Metering Infrastructure (AMI) is an integral part of utility service which allows automated metering, control and monitoring. Since AMI employs a wireless network for communication, data privacy becomes a burning issue. The attacker can decode the smart meter consumption data, various control commands of components of the smart grid, infuse false command and may even take over the system. Since a smart meter has limited memory and computational capability, we need a light but robust security scheme. In this paper, we propose a location based encryption method where the source node encrypts the data with a key that is associated with its own coordinate points, the destination node receives this data and decrypts it with the key. Since GPS doesn't work inside the multi-stored and in some geographical location like forests, hilly place etc., we propose the localization of source nodes (meters) by Received Signal Strength (RSS) using Maximum Likelihood Estimator (MLE). Finally the strength of security of a data packet is analyzed.

A Location Based Key Management System for Advanced Metering Infrastructure of Smart Grid

In the smart grid, a consumer can choose either to expend energy from the grid or vend energy back to the grid. On this principle, for profit maximization based on the current day's electricity selling price, a smart home with a rooftop photo voltaic (PV) system can determine whether the yielded energy during the day should be expended by the consumer at night or stored in a storage cell for the purpose of selling on the following days. For this, the smart home system needs to predict the electricity generation of the PV system for making a better decision. In this paper, we propose a Multi-layer Perceptron based PV forecasting method for the PV systems. The system is trained with the historical data of irradiance, temperature, solar zenith angle, wind speed, and relative humidity. The performance is evaluated by the cross validation and testing with the help of real world PV data. Being light weight and high accuracy, our proposed algorithm can be a potential method for the roof top PV forecasting in the smart home applications.

Imtiaz Parvez

Papers

A key management-based two-level encryption method for AMI

CBRS Spectrum Sharing between LTE-U and WiFi: A Multiarmed Bandit Approach

Trends and Future Directions of Research for Smart Grid IoT Sensor Networks

A Location Based Key Management System for Advanced Metering Infrastructure of Smart Grid

Multi-layer Perceptron based Photovoltaic Forecasting for Rooftop PV Applications in Smart Grid