The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

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

Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge. *Corresponding author at: Department of Electronics and Communication Engineering, Tenaga Nasional Universiti, P13-B-07-06, Sri Cempaka, Jalan Sepakat Indah 2/2 Taman Sep, Kajang 43000 Selangor, Malaysia. Tel.: +60 183262643.

A review of residential demand response of smart grid

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

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Efficient Energy Management for the Internet of Things in Smart Cities

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Smart grids (SG) represent the next step in modernizing the current electric grid. In this structure, a communications network is combined with the power grid in order to gather information that can be used to increase the efficiency of the grid, reduce power consumption, and improve the reliability of services, among other numerous advantages. SG communication networks are unique in their large scale and the limited capabilities of nodes which present several challenges in the design of efficient routing protocols. This paper provides a comprehensive survey of the main networking challenges present in the design of SG communication networks, and some of the important routing protocols proposed to address those challenges. Various technologies and architectures proposed for routing in SGs are discussed. A detailed comparison of the protocols considered in this paper is also given, and key areas that require further investigation are highlighted.

A Survey of Networking Challenges and Routing Protocols in Smart Grids

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Reconfigurable Wireless Networks

The Internet-of-Things (IoT) envisions a world where billions of everyday objects and mobile devices communicate using a large number of interconnected wired and wireless networks. Maximizing the utilization of this paradigm requires fine-grained QoS support for differentiated application requirements, context-aware semantic information retrieval, and quick and easy deployment of resources, among many other objectives. These objectives can only be achieved if components of the IoT can be dynamically managed end-to-end across heterogeneous objects, transmission technologies, and networking architectures. Software-defined Networking (SDN) is a new paradigm that provides powerful tools for addressing some of these challenges. Using a software-based control plane, SDNs introduce significant flexibility for resource management and adaptation of network functions. In this article, we study some promising solutions for the IoT based on SDN architectures. Particularly, we analyze the application of SDN in managing resources of different types of networks such as Wireless Sensor Networks (WSN) and mobile networks, the utilization of SDN for information-centric networking, and how SDN can leverage Sensing-as-a-Service (SaaS) as a key cloud application in the IoT.

Software-defined wireless network architectures for the Internet-of-Things

The Internet of Things (IoT) has enabled unprecedented interactions with our physical world, with the aim to deliver a wide range of customizable services in many domains. With recent advancements in IoT technology, users are increasingly expecting these services to be intelligent and context aware. Nevertheless, there is still no framework capable of delivering personalized IoT services on a large scale. For such a framework to be conceived, it is likely that technologies from many domains have to be utilized. This paper examines the readiness of the leading state-of-the-art technologies in several key fields for realizing the goal of a truly scalable and personalized IoT experience. We discuss the important requirements and challenges for realizing this goal. Then, we identify the major approaches that can contribute to this goal and categorize them into: technologies for adaptive personalized sensing, scalable solutions for user-centric networking, and intelligence techniques that leverage context awareness and adaptability at the application and system levels. In the first category, our discussion centers around virtualization and reprogrammability at the sensing layer. In the second category, we investigate the readiness of Fog computing and information-centric networking to develop scalable personalized IoT infrastructures. These approaches were chosen for their combined ability to match dynamic user requirements with available system resources, while guaranteeing overall efficient utilization. Finally, in the third category, we examine context awareness, reasoning, and machine learning techniques as well as semantic technologies for realizing proactive and adaptive intelligent IoT systems and applications. This paper offers a focused discussion of the key topics that drive the research in the important and timely topic of scalable and personalized IoT networks.

Scalable Personalized IoT Networks

The Internet of Things is expected to foster billions of heterogeneous sensors and actuators that support a wide range of applications. Maximizing the utilization of these sensors is hinged upon the ability to discover their capabilities (ex: measured attributes, location, accuracy, etc.). This process is known as Service Discovery (SD), and can have a local scope (SD within a bounded area), or a global scope. Traditionally, SD is performed using a dedicated gateway that is always active and connected to the Internet. This gateway stores the attributes of sensors within its area. However, this approach may be too expensive in the presence of billions of sensors. In this paper, a novel protocol for local SD is proposed that eliminates the need for a dedicated gateway. The protocol utilizes a multi-tier network architecture in order to achieve two main objectives: energy efficiency and high success rate in satisfying service requests. Energy efficiency is achieved by limiting the number of hops that an SD request has to traverse before being satisfied, while a high success rate is guaranteed using a hierarchical structure of Distributed Hash Tables (DHTs), where information regarding sensor capabilities is stored. Extensive computer simulations are used to evaluate the performance of the proposed protocol in comparison to single level architectures and gateway-based solutions. The results show that the proposed protocol achieves energy efficiency without sacrificing success rate of serving requests.

Amr El-Mougy

Papers

A Survey of Networking Challenges and Routing Protocols in Smart Grids

Reconfigurable Wireless Networks

Software-defined wireless network architectures for the Internet-of-Things

Scalable Personalized IoT Networks

An energy-efficient Service Discovery protocol for the IoT based on a multi-tier WSN architecture