The widespread deployment of IoT-enabled smart metering devices (MDs) in smart grid (SG) infrastructures has introduced serious security and privacy concerns, especially in deregulated energy markets where third-party retail energy suppliers (ESs) operate as semi-trusted entities. Since smart meter communications may disclose sensitive consumer behavior patterns, such as energy usage habits and occupancy information, privacy-preserving key sharing protocols (KSPs) are critically required. However, most existing KSPs assume fully trusted ESs, which is impractical in competitive retail environments. Also, they primarily provide anonymity against external adversaries that still allows internal traceability by retail ESs. To overcome these limitations, this paper proposes an efficient KSP specifically designed for semi-trusted retail ES scenarios. In the proposed protocol, IoT-enabled MDs can establish session key with semitrusted ESs without revealing their real identities, while honest-but-curious ESs can authenticate MDs via zero-knowledge authentication. By employing a novel signature-based construction, the protocol achieves strong conditional unlinkability, ensuring that no entity except the fully trusted registration center can trace or link MD sessions. Moreover, the proposed KSP reduces communication rounds and achieves at least 5% decrease in communication overhead with a comparable computational cost. Formal security analyses and comprehensive performance evaluations indicate the suitability of the proposed protocol for privacy-preserving communications in deregulated energy markets.

Although recent advancements in the information and communication technologies have facilitated the deployment of IoT-enabled telecare medical information systems (TMISs), security concerns have greatly hindered their acceptability. A good deal of session key generation protocols (SKGPs) has been proposed recently to bring a secure medium for medical data transfer in the TMISs. Nevertheless, careful investigation indicates that there are two critical gaps in current schemes. First and the foremost, most lightweight schemes are insecure against advanced threats and relatively-secure schemes have a delay in each shared key generation. Second, most schemes are designed using the RSA or ECC cryptosystem, making them insecure against quantum attacks. To fill these gaps, this paper proposes a low-latency high-reliability SKGP for resource-limited IoT devices using only symmetric encryption/decryption and hash functions. Since the proposed scheme is only based on the AES-256 and is free from the RSA or ECC cryptosystem, it can also resist quantum attacks. To support the security and efficiency claims, we have provided extensive formal security analyses and all-inclusive literature review. Our comparative results indicate that the proposed protocol is the best compared to even top 10 protocols considering both execution and communication costs. Our implementation results also indicate that the protocol execution on the selected IoT device only takes almost 80:μs and it is scalable enough so that servers can service thousands of devices.

The Industrial Internet of Things (IIoT) has developed into a promising technology that raises the level of productivity and automation for smart manufacturing. Due to the growing importance of cross-domain (e.g., factory) collaboration in manufacturing, it is now commonplace for IIoT devices from different domains to communicate with one another, raising significant privacy and security risks. Recent studies have utilized blockchain in their authentication scheme to build trust across multiple domains. This integration, however, resulted in substantial communication, computation, and storage overheads, as well as vulnerability against Distributed Denial-of-Service (DDoS) attacks. To overcome these issues, in this article, we propose an efficient and highly secure blockchain-assisted authentication scheme using a combined off-chain and on-chain approach. The suggested protocol just employs one domain server for authentication of both local and foreign domain devices. The distinctive security features and applicability of the proposed protocol are demonstrated by formal security proof and performance analysis as well as comparisons with leading research works.

The fog computing paradigm is a promising concept to overcome the exponential increase in data volume in Internet of Things (IoT) applications. This paradigm can support delay-sensitive IoT applications by extending cloud services to the network edge. However, fog computing faces challenges such as resource allocation for applications at the network edge due to limited resources as well as its heterogeneous and distributed nature. This is in line with the goals of microservice architecture and develops the placement of microservice-based IoT applications. The IoT service placement problem (SPP) on fog nodes is known as non-deterministic polynomial-time (NP)-hard. In this study, we introduce a meta-heuristic approach named SPP-differential evolution algorithm (DEA) to handle SPP, which originates from the DEA with a shared parallel architecture. The proposed method takes advantage of the scalable and deployable nature of microservices to minimize the resource utilization and delay as much as possible. SPP-DEA is developed based on monitoring, analysis, decision-making, and execution with knowledge bas (MADE-k) autonomous planning model with the aim of compromise between service cost, response time, resource utilization, and throughput. In order to address the computational complexity of the problem, we consider the resource consumption distribution and service deployment priority in the placement process. In order to evaluate the quality of placement in SPP-DEA, extensive experiments have been performed on a synthetic fog environment. The simulation results show that compared to the state-of-the-art approaches, SPP-DEA reduces the service cost and waiting time by 16% and 11%, respectively.

The Internet of Things (IoT) has advanced smart grid (SG) infrastructure by providing smart meters (SMs) with enhanced capabilities such as the ability to leverage the Internet platform for bidirectional information exchange. Cryptographic keys are necessary for securely exchanging sensitive information between SMs and energy providers. To manage these keys, a secure key management protocol (KMP) with little overhead and influence on the SG’s overall performance is necessary. Although various KMPs are available for IoT-enabled SG environments, exiting solutions have several flaws in terms of certificate revocation, security requirements, and overall SG performance. To address these challenges, this paper proposes a blockchain-based computationally-efficient and highly-secure KMP for IoT-enabled SG environments. We show that, compared to existing solutions, the proposed KMP has better SM side efficiency with improved security and more properties such as perfect forward secrecy, conditional anonymity, and simple SM revocation.

Abstract Edge computing environment provides processing capability and computing at the network edge and close to users. Edge equipment includes small data centers that locally perform process and content delivery. Therefore, edge equipment management has received much attention due to the rapid growth of information and resources limitations. The content caching and proactive caching techniques are management methods of edge equipment resources. We recently witnessed the development of proactive caching mechanisms that have a crucial enabler in improving heavy traffic, energy, and bandwidth. Also, it has huge potential to increase the quick response time to users' requests that today, these services are demanded by many users and applications. This article prepares a systematic literature review content caching approach in the edge computing environment. The purpose of this study is to survey the research done on the proactive caching strategies in the edge computing environment to identify subjects that must be emphasized more in current and future research paths. This research has studied 71 articles divided into three classes: model‐based, machine‐learning‐based, and heuristic‐based. Next, we discuss content caching approaches based on critical factors such as performance metrics, case studies, utilized techniques, assessment tools, advantages, and disadvantages. Finally, open issues and challenges are presented, and the survey is concluded.

Abstract Over the past few years, Internet of Things (IoT) applications have grown rapidly. The data‐intensive IoT applications that take advantage of cloud servers for computations and data storage will result in higher latency and other network traffic in the Internet core. IoT applications are characterized by their sensitivity to latency. As an example, delays will result in irreparable damage in the medical and healthcare industries. Cloud servers are no longer necessary because cloud computing utilizes fog nodes that are closer to users. Nodes with different hardware capabilities pose a significant challenge since they differ significantly in latency and traffic reduction. This article presented a metaheuristic‐based method using the non‐dominated sorting genetic algorithm II for data‐intensive IoT applications in fog infrastructure. Besides, we provide a new automatic method for managing data replica transmissions, including deploying them in a fog cloud environment. The proposed solution was evaluated in the iFogSim simulator and compared with two other data replica placement methods in different scenarios. The results showed a decrease in latency and cost for data access and an increase in data availability.

With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the challenging tasks in resource management scope to be considered. In this work, we propose a learning-based resource provisioning approach for managing time-varying workloads of IoT applications in the fog network. Our proposed approach utilises the nonlinear autoregressive (NAR) neural network as prediction method and hidden Markov model (HMM) as a decision-maker to identify scaling decisions to provision the fog resources for serving of workloads of IoT applications. The effectiveness of our proposed solution is evaluated using extension experiments under real-world datasets, and the obtained results from iFogSim toolkit demonstrated that it yields a reduction of the delay and cost and improves resource energy consumption compared with existing baseline mechanisms.

Abstract Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

Fog computing (FC) with a distributed architecture plays an essential role in Internet‐of‐Things (IoT). This paradigm utilises the processing abilities of Fog devices (FDs) and decreases latency. The large volume of data and its process in IoT can cause network failures. Researchers tend to consider communication reliability to reduce fault effects and achieve high performance. Fault tolerance becomes a necessary matter to enhance the reliability of the Fog. Notably, fault tolerance studies have been performed mostly on the Cloud system. To counter this issue, the authors propose a novel fault‐tolerant scheduling algorithm of modules in FC and optimise it. The main idea of this approach is a classification method for different modules alongside of computing the energy consumption of all FDs and finding minimal FDs' energy consumption. To distribute modules between FDs, they present an energy‐efficient checkpointing and load balancing technique based on the Bayesian classification and call it by ECLB. The performance of the proposed method is evaluated by comparing it with the state‐of‐the‐art algorithms in terms of delay, energy consumption, execution cost, network usage, and total executed modules. Analysis and simulation results indicate that the authors' methods are efficient and superior to others.

Summary In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

Summary In recent years, novel mobile applications such as augmented reality, virtual reality, and three‐dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource‐intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)‐based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT‐based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state‐of‐the‐art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid‐base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.

ABSTRACT This paper proposes a technique of quantum code generation using optical tweezers. This technique uses a microring resonator made of nonlinear fibre optics to generate the desired results, which are applicable to Internet security and quantum network cryptography. A modified add/drop interferometer system called PANDA is proposed, which consists of a centred ring resonator connected to smaller ring resonators on the left side. To form the multifunction operations of the PANDA system—for instance, to control, tune and amplify—an additional Gaussian pulse is introduced into the add port of the system. The optical tweezers generated by the dark soliton propagating inside the PANDA ring resonator system are in the form of potential wells. Potential well output can be connected to the quantum signal processing system, which consists of a transmitter and a receiver. The transmitter is used to generate high‐capacity quantum codes within the system, whereas the receiver detects encoded signals known as quantum bits. Therefore, an entangled photon pair can be generated and propagated via an optical communication link such as a time division multiple access system. Here, narrower potential wells with a full‐width half‐maximum of 3.58 and 9.57 nm are generated at the through and drop ports of the PANDA ring resonator system, respectively, where the amplification of the signals occurs during propagation inside the system. Copyright © 2013 John Wiley & Sons, Ltd.

This study investigates the non-linear behaviours of light known as bifurcation and chaos during the propagation of light inside a non-linear silicon microring resonator (SMRR). The aim of the research is to use the non-linear behaviour of light to control the bifurcation and chaos of SMRR, which are used in engineering, biological and security systems. Bifurcation and chaos control deals with the modification of bifurcation characteristics of a parameterised non-linear system by a designed control input. The parameters of the SMRR cause bifurcation to happen in smaller round-trips among the total round-trip of 20 000 or input power. Effective parameters such as the refractive indices of a silicon waveguide, coupling coefficients (κ) and the radius of the ring (R) can be selected properly to control the non-linear behaviour. Simulated results show that rising non-linear refractive indices, coupling coefficients and radii of the SMRR lead to descending input power and round-trips when bifurcation occurs. Therefore bifurcation behaviour can be seen at a lower input power of 44 W, where the non-linear refractive index is n2=3.2×10−20 m2/W. The smallest round-trips of 4770 and 5720 can be seen for the R=40 µm and κ=0.1, respectively.