Federated learning (FL) has emerged as a pivotal technology for the Internet of Things (IoT) that models distributed client data without compromising privacy. The IoT-based wearable generates data and FL running on a private edge performing human activity recognition (HAR). In this article, we proposed a novel technique to protect sensitive data during the training process and ensure the confidentiality of model updates before transmission to the edge server. The proposed technique integrates the El-Gamal encryption technique for data protection, and the FL process is rigorously optimized using pruning, quantization, and network slicing. Pruning removes redundant connections, which reduces model complexity and communication delays. On the other hand, quantization decreases the bit precision of model parameters, and network slicing strategically allocates resources solely for FL resulting in low latency and optimal bandwidth utilization. The results are evaluated in terms of accuracy and communication overhead, which is highly required in real-world applications. Furthermore, the HAR system within PEC shows better results by achieving an accuracy of 99% at 300 epochs that outperformed existing machine learning (ML) algorithms.

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.

The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of long short-term memory (LSTM) networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service in the cloud and Software as a Service in the fog. The proposed method is evaluated on the telemetry datasets of IoT and IIoT sensors (ToN-IoT) dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%.

The Internet of Things (IoT) is considered a key enabler for a wide range of smart applications. In IoT, a large number of heterogeneous devices form anad hocconnection with each other. Thead hocinfrastructure is considered an integral part of IoT-empowered applications because of its efficient, cost-effective, and dynamic nature. These networks need to ensure the quality of service using their limited resources, particularly in multihop communication. Because multihop communication can be an easy target of attackers, it needs a secure and reliable data transmission scheme. In this article, we propose a secured and reliable continuous transmission scheme for cognitive hybrid automatic repeat request (HARQ)-aided IoT (SRCT-HARQ) capable of maintaining high throughput and lower delay. The SRCT-HARQ scheme is analytically modeled using a probability-based approach. The mathematical formulas are derived for delay and throughput using a probability-based analysis, and the results are verified using the Monte Carlo simulations. The performance results elaborate that the network throughput and delay are improved, mainly due to the proposed authentication scheme. Using our experimental results, we evaluated the optimal time for data transmission to protect the legal rights of primary users that resulted in improved performance.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.

In this paper, we propose a cognitive Go-Back-N Hybrid Automatic Repeat reQuest (CGBN-HARQ) scheme for a cognitive radio (CR) system to opportunistically transmit data over a primary radio (PR) channel. We model the activity of PR users (PRUs) occupying the PR channel as a Markov chain with two states: `ON' and `OFF'. In order to use the PR channel, the CR system first senses the availability/unavailability of the PR channel. Once it finds that the PR channel is free, the CR system transmits data packets over the PR channel's spectrum, whilst relying on the principles of GBN-HARQ. In this paper, we investigate both the throughput and delay of CGBN-HARQ, with a special emphasis on the impact of various system parameters involved in the scenarios of both perfect and imperfect spectrum sensing. Our studies demonstrate that the activity of PRUs, the transmission reliability of the CR system as well as the number of packets transmitted per time-slot may have a substantial impact on both the throughput and the delay of the CR system.

Detecting spectrum holes and efficiently accessing them are the two basic functions that enable a cognitive radio (CR) to make use of the licensed spectrums of a primary radio (PR). In this paper, we consider a CR scheme, which opportunistically accesses a PR channel for communication between a pair of nodes based on the stop-and-wait hybrid automatic repeat request (SW-HARQ). Hence, it is referred to as the cognitive SW-HARQ (CSW-HARQ) arrangement. In our CSW-HARQ system, the PR channel is modelled as a two-state Markov chain having `On' and `Off' states. The CR may only access the PR channel in its `Off' state. In this paper, we analyze both the throughput and delay performance of the CSW-HARQ system, for which a range of closed-form formulas are derived that are also validated by simulation results. Our performance results show that both the activities of PR users and the reliability of the CR channel have a substantial impact on the achievable performance of the CR system.