The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT application and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

Sixth Generation (6G) is envisioned to be a spawned key technology that will support the ubiquitous and seamless connection of a massive number of Internet of Things (IoT) devices. The extremely high data rate, low end-to-end delay, high mobility of IoT devices propel the desideratum of extenuating the concern of reducing the energy consumption i.e., green communication. Hence, in this paper, we address the concern of green communication in 6G-enabled massive IoT devices by following the cluster-based data dissemination in the network. We propose a novel Hybrid Whale Spotted Hyena Optimization (HWSHO) algorithm by synthesizing the Whale Optimizer Algorithm (WOA) with exploitation capabilities of Spotted Hyena Optimizer (SHO). We perform simulation experimental study that shows the supreme performance of our proposed technique over the most recent proposed energy efficient data dissemination methods. The proposed technique is an exemplary solution that could be pertinent to various hostile applications seeking green communication of 6G-enabled IoT devices.

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and `security compromise' are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and `number of packets sent', which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-à-vis CIRP.

Massive Machine Type IoT Communication (mMTIC) has the potential for high impact in anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering Routing Protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight towards the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multi-hop communication among the nodes often causes such an energy hole problem. In this paper, we develop an AI-based CRP (CIRP) framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.