In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key. 

Communication networks are becoming increasingly important in military operations, with task fields like time-critical targeting, covert special operations, command and control, training, and logistics, all relying heavily on the communication network and its services. On the other hand, commercial communication has dramatically transformed our society and the way we communicate. The newest network mode at present, 5G and beyond (5GB), is characterized by high speed, low latency, high reliability, and high communication density. Although the use of 5GB commercial networks for defense agencies can offer greater flexibility and efficiency, they also face a new challenge that requires high standards of network protection and harsh working conditions and environments. In this paper, we discuss the significance of communication networks in several potential military applications, particularly for warfare, training/drilling, logistics, and special mission-specific stations. We present the communication trends adopted in military applications. Then, we open up various 5GB key performance indexes and their use cases for the military communication systems. We also elaborate on unique challenges of the military communication networks that are unlikely to be resolved via commercial 5GB research. The various 5GB enabling technologies for military communication systems are discussed. Lastly, we present and analyze 5GB new radio for the private military communication under C-band.

The IoT comprises billions of intelligent devices that interact, gather, and share data via sensors and actuators. The Industrial IoT (IIoT), specifically used in industry and production, is used in automation and rapid production of goods based on machine learning techniques. Similarly, Cyber-Physical System (CPS) plays a vital role in the next-generation industry. The CPSs are intelligent systems that interconnect the physical world through embedded systems, sensors, actuators with the cyberworld. We require a communication backbone for interconnecting and information processing, which 6G networks can fulfill. The 6G has a higher capacity and improved characteristics than previous cellular networks, accelerating the applications and deployments of 6G-based IIoT networks in industry platforms. This chapter discusses how the 6G networks can help interconnect the CPS and IIoT through smart connection, digital twinning, and immersive technology.

Urban cities have congested with vehicles, resulting in traffic jams, and a lot of time and energy will be wasted while traveling. In the near future, low-altitude aerial vehicles are expected to be implemented for air traffic as a resolution to overcome these issues. Due to the threats to commercial aircraft as well as danger to the public and objects on the ground, these low-altitude aerial vehicles should exhibit an equivalent level of safety similar to commercial aircraft. We propose a new safety level for low-altitude electric propeller-based compact aerial vehicles. The safety level of Aerial Vehicles is based on the concept of civil aviation safety levels and automotive safety levels, and we incorporate the appropriate safety characteristics from both automotive safety integrity levels and aviation safety levels. We also discussed adjusting the aerial vehicle safety levels with NASA’s Technical Capability Levels (TCL), which helps design an Electrical and Electronics (E/E) architecture for the aerial vehicles. We presented a new conceptual E/E architecture for the aerial vehicles based on adjusted technical capabilities levels and aerial vehicle safety levels to provide functional safety for the aerial vehicle. We also discuss the adaptive software platform based on virtualization, which partitions the time-critical operating system of aerial vehicles to host several applications of different software levels on the same hardware. 

AI accelerator is a specialized hardware processing unit that provides high throughput, lower latency, and higher energy efficiency compared to existing server-based processors available in the market. Some AI accelerators are NPU, GPU, FPGA, and ASIC. As compared to other accelerators, ASICs are much more efficient technology as they consume very low power and can be readily customized for specific activities. The AI accelerators can be used in cloud servers as well as at the edge devices. Nowadays, the cloud provides an ideal environment for Machine Learning as it gathers a massive amount of data from various sources. At the same time, edge computing or in-device computing is the ideal option for inference that requires quick output. AI accelerator architecture is necessary for advanced data centers to address the ever-increasing demands of processing and handling massive datasets workloads such as machine vision, deep learning, AI, etc. Moreover, it is necessary to consider the servers’ power consumed and the data center’s power budget while designing the AI accelerators. This chapter discusses various AI accelerators in the cloud, data centers, servers, and edge computing. © The Editor(s) (if applicable) and The Author(s),

ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL’s long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.

New radio in unlicensed spectrum (NR-U) is an evolutionary extension of the existing unlicensed spectrum technologies, which allows New radio (NR) to operate in the shared and unlicensed frequency bands. However, in such bands, NR-U should coexist with other radio access technologies (RATs) in a commonly shared spectrum. As various RATs possess dissimilar physical and link-layer configurations, NR-U should comply with the requirements for harmonious coexistence with them. For this reason, the majority of the existing studies on NR-U are focused on fair coexistence. In contrast, the efforts on attaining efficiency of the spectrum and fairness concurrently have gained comparatively few interests as they exhibit an adverse feature. Motivated by this limitation, we propose an algorithm called Thompson's sampling-based online gradient ascent (TS-OGA), which jointly considers the fairness between NR-U and incumbents and, at the same time, the efficiency via pertinent idle period adjustment of the incumbents in the operating channel. Because NR-U deals with the two conflicting and competing objectives (i.e., fairness and efficiency), we model it as a multi-objective multi-armed bandit problem using the Generalized Gini Index aggregation function (GGAF). In the proposed scheme, TS-OGA, a Thompson's sampling (TS) policy is employed together with the online gradient ascent to address the multi-objective optimization problem. Through simulation results, we show that TS-OGA can significantly enhance overall channel throughput, while maintaining fairness. Further, TS-OGA provides the best performance compared to three different baseline algorithms such as greedy, upper confidence bound, and pure TS. 

A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency. © 2023 by the authors.

Context:. Drowsiness affects the driver’s cognitive abilities, which are all important for safe driving. Fatigue detection is a critical technique to avoid traffic accidents. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. To tackle these challenges, we propose a Federated Learning (FL) approach for fatigue-driving behavior monitoring. However, in the FL system, the privacy information of the drivers might be leaked. In this paper, we propose to combine the concept of differential privacy (DP) with Federated Learning for the fatigue detection application, in which artificial noise is added to parameters at the drivers’ side before aggregating. This approach will ensure the privacy of drivers’ data and the convergence of the federated learning algorithms. In this paper, the privacy level in the system is determined in order to achieve a balance between the noise scale and the model’s accuracy. In addition, we have evaluated our models resistance against a model inversion attack. The effectiveness of the attack is measured by the Mean Squared Error (MSE) between the reconstructed data point and the training data. The proposed approach, compared to the non-DP case, has a 6% accuracy loss while decreasing the effectiveness of the attacks by increasing the MSE from 5.0 to 7.0, so a balance between accuracy and noise scale is achieved.

The neutral host network (NHN) is a new self-contained network envisioned by fifth generation (5G) of cellular networks, which offers wireless connection to its subscribers from a variety of service providers, including both conventional mobile network operators and non-conventional service providers. The NHN infrastructure, which is operated and maintained by a third neutral party, is rented or leased to network operators looking to scale up their network capacities and coverage in a cost-effective way. This paper highlights NHN as an emerging communication technology for private networks and discuss its opportunities and challenges in realizing multi-tenanted space such as factory, hospitals, stadiums, and universities. The paper also investigates the current state of the art in NHN and elaborates on the underlying enabling technologies for the NHN. Lastly, an efficient radio access network (RAN) slicing scheme based on the multi-arm bandit approach has been proposed to allocate radio resources to various slices, which maximizes resource utilization while guaranteeing the availability of resources to meet the capacity needs of each multi-tenanted operator. The simulation results show that the proposed Thompson's sampling (TS)-based approach performs best in finding the optimal RAN slice for all the operators.