This paper presents a traffic-aware position-based routing protocol for vehicular ad hoc networks (VANETs) suitable for city environments. The protocol is an enhanced version of the geographical source routing (GSR) protocol. The proposed protocol, named efficient GSR, uses an ant-based algorithm to find a route that has optimum network connectivity. It is assumed that every vehicle has a digital map of the streets comprised of junctions and street segments. Using information included in small control packets called ants, the vehicles calculate a weight for every street segment proportional to the network connectivity of that segment. Ant packets are launched by the vehicles in junction areas. In order to find the optimal route between a source and a destination, the source vehicle determines the path on a street map with the minimum total weight for the complete route. The correct functionality of the proposed protocol has been verified, and its performance has been evaluated in a simulation environment. The simulation results show that the packet delivery ratio is improved by more than 10% for speeds up to 70 km/h compared with the VANET routing protocol based on ant colony optimization (VACO) that also uses an ant-based algorithm. In addition, the routing control overhead and end-to-end delay are also reduced.

Advances in the development of low powered sensors has meant they can now provide solutions to IoT networks that suffer from restricted power supply and a lack of resource facilities. In this paper, a hybrid TDMA-CSMA/CA MAC protocol has been proposed that efficiently utilises the energy of the nodes and dynamically adapts the sleep/wake-up periods according to the variance in the network loads. This hybrid protocol first schedules the TDMA time slots (TDMAslots), and then allocates each slot to a group of devices that compete for the medium using the CSMA/CA. This case is different from the traditional CSMA/CA-TDMA hybrid protocol, in which all the devices compete to access the channel, following which, each successful device is allocated an individual time slot. At the commencement of each superframe, the base station broadcasts a scheduler table, which includes network grouping information that is used by the IoT devices to categorise themselves into wake-up and sleep groups. To reduce the number of collisions or channel access failures, this information permits only one group to compete for each TDMAslot. A three-dimensional Markov model is used to develop a per user stochastic behaviour for the proposed hybrid MAC protocol-based adaptable sleep mode. The simulation results demonstrate the effectiveness of the proposed protocol, which improves the network throughput and enhances energy conservation by 40% -60% more than the IEEE 802.15.4-based MAC protocol.

Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors’ data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks’ trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks’ sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

This paper proposes: (i) a traffic flow management policy, which allocates and organises Machine Type Communication (MTC) traffic flows network resources sharing within Evolved Packet System (EPS), (ii) an access element as a Wireless Sensor Network (WSN) gateway for providing an overlaying access channel between the Machine Type Devices (MTDs) and EPS and (iii) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices and the related Quality of Service (QoS) issues among them. This work overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in Packet Loss Ratio (PLR), a decrease of 99.89% in packet End To End (E2E) delay, a decrease of 99.21% in Packet Delay Variation (PDV). Furthermore, it retains the perceived Quality of Experience (QoE) of the real time application users within high satisfaction levels, such as the VoLTE service possessing a Mean Opinion Score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardised values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV.

Digital Twin (DT) technology in healthcare is relatively new and faces several challenges, e.g., real-time data processing, secure system integration, and robust cybersecurity. Despite the growing demand for real-time monitoring frameworks, further improvements remain possible. In this study, an architecture has been introduced that utilises cloud computing to create a DT ecosystem. A group of 20 participants has been monitored continuously using high-speed technology to track key physiological parameters, i.e., diabetes risk factors, heart rate (HR), oxygen saturation (SpO2) levels, and body temperature (BT). To strengthen the study and enhance diversity, the dataset was supplemented with 1177 anonymized medical records from the publicly available MIMIC-III Public Health Dataset. The DT model functions as a tool, storing both real-time sensor data and historical records, to effectively identify health risks and anomalies. An MLP model was combined with XGBoost, resulting in a 25% reduction in training time and a 33% reduction in testing time. The model demonstrated reliability with an accuracy of 98.9% and achieved real-time accuracy of 95.4%, alongside an F1 score of 0.984. Meticulous attention has been paid to cybersecurity measures, ensuring system integrity through end-to-end encryption and compliance with health data regulations. The incorporation of DT and AI within the healthcare sector is seen as having the potential to overcome existing limitations in monitoring systems, while workloads are relieved and data-driven diagnostics and decision-making processes are improved, e.g., through enhanced real-time patient monitoring and predictive analysis.