In this paper, we introduce DriveShield: a decentralized framework based on Federated Learning (FL) for real time jamming attack detection in VANETs under 6G communications. The local Feedforward Neural Network (FNN) trained on each client is aggregated by the popular Federated Averaging (FedAvg) approach and achieves a global accuracy around 83%, and reaches a peak of 87% across the training rounds. In order to preserve data privacy and integrity, the integration of blockchain and the InterPlanetary File System (IPFS) is used to make tamper resistant, secure model weight transfers. DriveShield is distinguished from such static dataset reliant approaches in the fact of their reliance on static datasets and real time data simulation of network conditions enhancing adaptability to dynamic network conditions. Performance evaluation shows that 6G-THz communication has better latency performance than 5g uRLLC, as well as better transaction scalability using IPFs. Robust detection capabilities with precision = 0.832-0.879 and F1-score = 0.822-0.880, over centralized baselines, are confirmed via metrics.

In smart communities, Electric vehicles (EVs) have grown in popularity as a key component of the energy ecosystem where the focus has turned to the generation of clean, sustainable energy. The integration of EVs, charging stations (CS), and smart grids (SG), however, poses significant challenges in terms of energy trading (ET) optimization and profit maximization. Next, trust is another challenge in the ET ecosystem among the communicating entities (EVs, CS, and SG) to buy and sell energy. Recent studies have overlooked the fact of ET among CS and SG, and mostly have focused on ET by EVs. However, at peak loads, SG may experience bottlenecks in energy dissipation, and thus excess energy collected by CS from EVs might be traded to SG to manage loads during peak times. So, we propose a framework, StackGrid, that leverages the capabilities of Vehicle-to-Grid (V2G) systems over a blockchain network. We design a Stackelberg game between CS and SG for profit maximization of both parties and to obtain optimal payoff equilibria. The framework is powered over the 5G ultra-reliable low latency communications (uRLLC) service for real-time ET response and data exchange. To address blockchain scalability concerns, we incorporate Interplanetary File Systems (IPFS) as local off-chain ledgers, where only meta-information is stored on-chain to handle blockchain scaling issue. The framework is evaluated on metrics like 5G service latency, optimal payoff scenario, attack probability, and node throughput. The obtained results indicate StackGrid viability in real ET setups, with benefits for sustainable and efficient energy management.

Summary In recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.

Smart grid (SG) systems necessitate secure demand response management (DRM) schemes for real-time decisions making to increase the effectiveness and stability of SG systems along with data security. Motivated from the aforementioned discussion, in this article, we propose Q-SDRM, a secure DRM scheme for home energy management (HEM) using reinforcement learning (RL) and ethereum blockchain (EBC) to facilitate energy consumption reduction and decrease energy costs. In cooperation with RL,$Q$-learning is adopted to make optimal price decisions using Markov decision process (MDP) to reduce energy consumption, which benefits both consumers and utility providers. Then, Q-SDRM uses ethereum smart-contract (ESC) to deal with data security issues and incorporate with off-chain storage interplanetary file system (IPFS) that handles data storage costs issue. Experimental results reveal the effectiveness of the proposed Q-SDRM scheme, which significantly reduces energy consumption and energy cost. The proposed scheme also provides secure access to energy data in real time compared with state-of-the-art approaches regarding different evaluation metrics, such as scalability, overall energy cost, and data storage cost.

The coronavirus (COVID-19) outbreak has a significant impact on people’s lives, occupations, businesses, and economies globally. The world economic market is experiencing a big shift and the share market has observed crashes day-by-day. Even, the Indian economy has witnessed a slowdown in the current pandemic, and recovery of it is quite difficult. The restrictions and restrain strategies (e.g., lockdown and social distancing) introduced by the government leave many professions and facilities in a dormant state, catalyzing economy downfall. It necessitates to improve economy along with control strategies of COVID-19, which is a challenging task. To handle the above-mentioned issues, this article proposes a novel economy-boosting scheme, i.e.,$\xi $boost, which is a fusion of artificial intelligence (AI) and big data analytics (BDA) integrated with the Internet-of-Things (IoT)-based data communication. Here, a bidirectional long short-term memory (LSTM) model is anticipated for early prediction of total positive cases as well as the economy. Then, it calculates an optimal subsegment of days, in which trade and commerce related restrictions could be reduced to control a sharp decline in the economy. Next, a spark-based pre and post unlock (PPU) analytics is carried out on the rise of COVID-19 cases to validate the intensity of testing in the country and deciding economy-boosting activities. Then, the$\xi $boostscheme is evaluated based on various factors such as prediction accuracy and others while comparing to existing approaches. It facilitates healthy and profitable smart cities by the means to control pandemic with subsequent economy rise.

The next-generation energy system, i.e., Smart Grid (SG), empowers the real-time transfer of information using advanced metering infrastructure (AMI) and smart meter (SM) between end-consumers and grid. It accelerates various services such as automatic meter reading, time-of-use (TOU) pricing, demand-response management, and many more. Though it has growing security and privacy concerns and the detection of malicious activity is a critical security task that sacrifices the overall Quality-of-Service (QoS) of SG and Quality-of-Experience (QoE) for customers. To address the aforementioned issues, we propose a data analytics Scheme ArMor for malicious activity detection on the blockchain (BC)-based SG system. The ArMor detects data integrity issues in real-time like false data injection attack and SM failure. Here, we proposed a unique ARIMA-based malicious activity detection model and classified the customer. Then, we proposed a Smart Contract (SC)-based incentive mechanism for utility providers handling the malicious activity at their end. It prevents the entry of malicious data into the SG system as transactional data once stored in BC, it is secured using SC. The obtained results are compared against parameters like prediction accuracy, latency, and data storage cost compared to the state-of-the-art approaches to designate the efficacy of the proposed scheme.