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Due to the extremely important role of data in the Industrial Internet of Things (IIoT) network, trust and security of data are among the major concerns. In this study, we develop a cloud-integrated 5G-IIoT network architecture enabled by a three-party Authenticated Key Exchange (AKE) protocol with privacy-preserving to secure data exchanged via wireless communication, cope with unauthorized entities and ensure data integrity. Moreover, we develop a trust model based on the Dempster-Shafer theory to check the trustworthiness of data collected by smart devices/sensor nodes. Security analysis performed on our scheme demonstrates that it can withstand different well-known attacks in the IIoT environment. We also analyzed the validity of our scheme by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Additionally, the performance evaluation and experimental results prove the effectiveness of the proposed scheme compared to the existing works in terms of accuracy, delay, trust, and throughput.

Security and privacy of data-in-transit are critical issues in Industry 4.0 which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this work, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. Additionally, we conduct a security analysis that proves that the scheme resists several attacks in the industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

Although modern Maritime Transportation Systems (MTS) have been extensively benefited from Internet of Things (IoT) technology, but still the risks and challenges in safety and reliability have increased substantially. The involvement of different maritime parties in the marine transportation flow scheduling and management further escalates these challenges. Thus, we need an IoT-based collaborative processing system that unifies the modular structure and integrates multiple modules involved in MTS. Moreover, the need for a shared and controlled access mechanism that cannot be manipulated or tampered by unauthorized parties is also essential requirement in MTS. Blockchain, as an emerging technology, has become a key tool in data security protection because of its non-tampering and non-forgery characteristics. Keeping in view of this aspect, in this paper, an IoT-based collaborative processing system based on blockchain is proposed for marine transportation flow scheduling and management. In addition, we propose a novel consensus mechanism based on Verifiable Random Function (VRF) and reputation voting to reduce the communication cost in blockchain consensus communication process. The proposed scheme has been validated in a simulated environment and the results illustrate that the scheme has obvious effect in resisting replay attack and camouflage attack. Furthermore, the optimized consensus mechanism improves the security by 8% and the transaction processing speed by 6% on the premise that the communication cost is basically unchanged.

Academia community started the research beyond 5G (B5G) while 5G systems and networks are still being landed for large-scale commercial applications. In order to enhance the agility and flexibility attributes of B5G networks, network function virtualization (NFV) and network slicing (NS) are attracting extensive research attention. Meanwhile, vehicles are promised to connect to the B5G networks so as to expand the service coverage and reach the ‘last one mile’. In this paper, we research the virtual resource allocation of slices in vehicles-assisted B5G networks. We aim at saving total energy cost of deployed slices while ensuring high slice acceptance ratio. We firstly present the system model of vehicles-assisted B5G networks, supporting both virtualization and slicing schemes. Then, we present the energy cost of vehicles-assisted B5G networks. Afterwards, we propose one energy efficient algorithm, abbreviated as Ener-Eff-Slice, to solve the virtual resource allocation of slices in vehicles-assisted B5G networks. Numerical results are recorded, plotted and discussed, which prove the efficacy of our scheme. Finally, we do the conclusion marks and discuss the next-step work.

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors.

With increase in the use of contactless deliveries during the times such as COVID-19 pandemic, the emphasis was to minimize the human presence to reduce the spread of virus. In this regard, drones are one promising alternative to be used as delivery agents. However, security and Quality of Service (QoS) are major concerns while making use of drones for deliveries. In order to secure the drone communication system, we propose, CovaDel: a blockchain-based scheme to secure data transactions for the drone delivery use case that works in a phased manner. The proposed scheme make use of decoupled blockchain architecture to overcome the limited resources capabilities of drones to perform blockchain-based computations. Further, to ensure the QoS adherence, we also propose a QoS-aware communication approach that handles collisions and congestion on the basis of firefly algorithm’s attractiveness parameter (light intensity) received by the drones. Results obtained using a simulated environment verify the efficacy of the proposed scheme on the basis of gas consumed, transaction time, average network throughput, and delay.

The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the healthcare sector where the change in data values from sensors can change the course of diagnosis which can cause severe health issues. Therefore, in order to prevent the data tempering and preserve the privacy of patients, we present a decoupled blockchain-based approach in the edge-envisioned ecosystem. This approach leverages the nearby edge devices to create the decoupled blocks in blockchain so as to securely transmit the healthcare data from sensors to the edge nodes. The edge nodes then transmit and store the data at the cloud using the incremental tensor-based scheme. This helps to reduce the data duplication of the huge amount of data transmitted in the large IoT healthcare network. The results show the effectiveness of the proposed approach in terms of the block preparation time, header generation time, tensor reduction ratio, and approximation error.

The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids. Smart grids are envisioned to be the next generation innovative power systems, guaranteeing resilience, reliability and sustainability and to facilitate energy production, distribution and management. Nonetheless, the development of such systems entails challenges covering a broad spectrum ranging from operational management up to data-driven power accounting and network security. Given the highly distributed properties of the modern grid, energy theft can now be observed at various transmission and distribution levels. Apart from the financial gain for a malicious actor, energy theft can also affect critical grid processes with a direct impact on its overall resilience and safety. This survey reviews recent energy theft strategies as well as detection methods from a data-driven perspective. By considering various operational and functional layers within modern smart grids we critically assess how energy theft can be formulated. Moreover, we provide an overview of the grid demand, supply and control chain with a focus on energy theft and associated security flaws that currently exist in the smart grid ecosystem. Different attack detection models for theft detection in the smart grid are categorized. Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field.

The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users.

With an increased usage of information and communication technologies (ICT) in the smart cities, the data generated from different smart devices has increased manifolds. This data is heterogeneous in nature which varies with respect to time and exhibits the properties of all essential V’s for big data. Therefore, to handle such an enormous amount of data, various big data processing techniques are required. To cope up with these issues, this paper presents a tensor-based big data management technique to reduce the dimensionality of data gathered from the Internet-of-Energy (IoE) environment in a smart city. The core data is extracted out of the gathered data by using tensor operations such as-matricization, vectorization and tensorization with the help of higher-order singular value decomposition. This core data is then stored on the cloud in the reduced form. After reducing the dimensionality of data, it is used for providing many services in smart cities; and its application to provide demand response (DR) services has been discussed in this paper. For this purpose, support vector machine (SVM)-based classifier is used to classify the end-users (residential and commercial) into normal, overloaded and underloaded categories from the core data. Once such users are identified to take part in DR mechanism, utilities then generate commands to handle their DR in order to alter load requirements so that the overall load is optimized. Results obtained on Open Energy Information and PJM dataset clearly indicate the supremacy of the proposed tensor-based scheme over the traditional scheme for DR management.

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities, such as load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this article, GUARDIAN , a blockchain-based secure demand response management scheme is presented so as to take energy trading decisions securely for managing the overall load of residential, commercial, and industrial sectors. In GUARDIAN , the miner nodes, which are block verifiers, are selected using their power consumption and processing power. These nodes are responsible for authenticating the energy transactions in SG. The energy transaction is initialized by an end user which creates the block of transaction to trade the energy. The miner nodes then validate these blocks and adds these in the blockchain. The successful energy trade occurs only for the blocks which are in the blockchain. The proposed scheme is lightweight in terms of communication and computation costs. Moreover, the results obtained demonstrate the effectiveness of proposed scheme for secure demand response management in the SG.

The ever-growing demand for electricity in the residential sector results in creating a severe burden on electric grids. However, with the emergence of smart homes (SHs) and smart grids (SGs), this burden can be reduced to some extent. To address this issue, we propose an energy management system in this paper which manages the power requirements of SHs automatically according to the utility constraints and user priorities. The proposed system is based on a heuristic technique, which considers the user's priority and power available from the grid as well as distributed energy resources for scheduling of appliances. It works by dividing the appliance scheduling problem in an SH into subproblems for different time slots. Then, a heuristic solution is designed for each subproblem. The instantaneous load demands are handled in real time to comply with the available power from the grid/utility. The data from different SHs is gathered to test the performance of the proposed scheme in real time. Results show that the proposed scheme efficiently manages the load demand of the SH with respect to power available from the utility, battery energy storage system, and user preferences.