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Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.

Mobile cloud computing (MCC) plays a significant role in obtaining virtualized data stored on cloud servers without additional waiting time. However, the limited battery power of mobile devices limits access to ample data. To address this problem, job scheduling algorithms based on completion time constraints are introduced to reduce energy consumption. The time complexity of scheduling algorithms increases for applications not based on completion time constraints. This paper proposes a hybrid energy-efficient (HEE) paradigm to support the Internet of Things (IoT). The proposed HEE algorithm leverages the features from known pheromone termite (PT) and cuckoo search (CS) algorithms, supporting IoT-enabled users and devices. The integration of PT and CS helps determine the optimal path that leads to improved throughput and reduced energy consumption. The performance of the HEE algorithm is considerably higher than that of the PT and CS algorithms when the number of tasks increases. The proposed paradigm involves several modules (e.g., physical machine (PM) module, controller module, blockchain-enabled authentication module, and data mining-enabled mobile cloud user (MCU) authentication). The PM module helps to attain a higher energy efficiency, regardless of whether the cloud's level of utilization is at its maximum efficiency. The controller module schedules the PMs efficiently and thus effectively provides resources to MCUs. Securing the identity of MCUs is of paramount significance. Safeguarding the identity of the MCU, the blockchain-enabled authentication module is employed, which authenticates the MCU to control the malicious role of the adversary. Furthermore, the data-mining blockchain-enabled authentication module authenticates the insider and outsider MCUs based on the data contents. The legitimate MCUs are allowed, and illegitimate MCUs are restricted. The proposed HEE paradigm is tested on the CloudSim simulator. Finally, the performance of the HEE paradigm is compared with that of known algorithms in terms of energy, security, throughput, and latency. • Proposes a hybrid energy-efficient (HEE) paradigm to support the IoT. • Leverages the features from known pheromone termite (PT) and cuckoo search (CS). • The proposed algorithm has the support of a data oriented detection algorithm. • The Blockchain enabled detection module is incorporated to authenticate the MCU to control malicious adversaries. [ABSTRACT FROM AUTHOR]