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The Internet of Things (IoT) is a network of physical items implanted with software, sensors, etc., to link and exchange data with other devices. These devices vary in complexity from common household items to sophisticated industrial instruments. It would be challenging to choose an appropriate IoT service based on the requirements of the vast pool of accessible services with similar capabilities, given the growth of IoT-based service providers in the market. A suitable selection may be made using quality-of-service (QoS) parameters that characterize a service. IoT has several benefits over traditional communication systems. Also, it is a component of a safe and smart city system known as the Industrial Internet of Things (IIoT) which is particularly useful in the industrial field. However, it suffers from various issues such as high costs, energy consumption, and long delays. The production scheduling problem is one of the main issues in IIoT, and it is an NP-hard problem regarding cost and energy efficiency. Therefore, a meta-heuristic algorithm based on the elephant herd optimization algorithm is proposed to minimize resource costs, conversion costs, and the cost of continuous development delays. By combining the clan updating factor, separating operator, and the proposed algorithm, we created an effective and efficient method to solve the issue of production scheduling. Many experiments are performed to determine the performance of industrial environments. The outcomes demonstrate that the suggested technique can optimize planning and achieve cost reduction, efficient energy consumption, and latency decrease. [ABSTRACT FROM AUTHOR]

Clustering and routing protocols for Internet of Things (IoT) need to consider energy usage and how to reduce it. Unbalanced power usage is a common concern with current solutions to cluster-based routing problems in the IoT ecosystem. This research developed a swarm intelligence-based clustering technique to achieve a more uniform dispersion of cluster heads. The data packets across cluster heads and the sink are routed via a Jaya algorithm. Based on average remaining energy, number of active nodes, number of nodes that have failed or have been removed from the network, and overall network throughput, this combined clustering and routing method's quality has been assessed. The integrative clustering and routing protocol based on the flower pollination algorithm and Jaya algorithm described here exhibit considerable improvements over the current state-of-the-art. The network throughput and the number of the alive node are essential statistics for evaluating IoT in which battery-powered devices periodically acquire surroundings data and transmit gathered samples to a base station. The proposed strategy improved network throughput and the number of dead nodes by at least 14% and 18%, respectively. [ABSTRACT FROM AUTHOR]

With the galloping progress of the Internet of Things (IoT) and related technologies in multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of Drones (IoD), and Internet of Vehicles (IoV), carry special attention due to their providing a resilient infrastructure in which users can be sure of a secure connection among smart devices in the network. By considering particular parameters which overshadow the resiliency in distributed environments, we found several gaps in the investigated review papers that did not comprehensively touch on significantly related topics as we did. So, based on the resilient and dependable management approaches, we put forward a beneficial evaluation in this regard. As a novel taxonomy of distributed environments, we presented a well-organized classification of distributed systems. At the terminal stage, we selected 37 papers in the research process. We classified our categories into seven divisions and separately investigated each one their main ideas, advantages, challenges, and strategies, checking whether they involved security issues or not, simulation environments, datasets, and their environments to draw a cohesive taxonomy of reliable methods in terms of qualitative in distributed computing environments. This well-performed comparison enables us to evaluate all papers comprehensively and analyze their advantages and drawbacks. The SLR review indicated that security, latency, and fault tolerance are the most frequent parameters utilized in studied papers that show they play pivotal roles in the resiliency management of distributed environments. Most of the articles reviewed were published in 2020 and 2021. Besides, we proposed several future works based on existing deficiencies that can be considered for further studies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) comprises many nodes dispersed around a particular target region, and it has lately been applied in a variety of sectors such as smart cities, farming, climatology, smart metering, waste treatment, and others. Even though the IoT has tremendous potential, some difficulties must be addressed. When building the clustering and routing protocol for huge-scale IoT networks, uniform energy usage and optimization are two significant concerns. Clustering and routing are well-known NP-hard optimization challenges applied to the IoT. The ease with which chicken can be implemented has garnered much interest compared to other population-based metaheuristic algorithms in solving optimization problems in the IoT. Aiming to reduce and improve node energy consumption in the IoT network by choosing the most suitable cluster head, the current effort seeks to extend the life of a network by selecting the most appropriate cluster head. A new cost function for homogenous dispersion of cluster heads was proposed in this research, and a good balance among exploration and exploitation search skills to create a node clustering protocol based on chicken search. This procedure is a big step forward from previous state-of-the-art protocols. The number of packets received, the total power consumption, the number of active nodes, and the latency of the suggested integrated clustered routing protocol are all used to evaluate the protocol's overall performance. The proposed strategy has been demonstrated to improve power consumption by at least 16 percent. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enables intelligent and heterogeneous things to access the Internet and subsequently interact and share info. A service management methodology is required by growing IoT applications and the number of services supplied by various objects. Nevertheless, making decisions, finding, and choosing a service is complex. Therefore, numerous techniques are explored in this regard. This paper employed Flower Pollination Algorithm (FPA) for service discovery and selection in IoT. The FPA is a nature-inspired algorithm that mimics flowering plant pollination behavior. Through a hand-over probability, it is possible to adjust the balance between local and global search properly. The survival of the fittest and the optimal reproducing plants regarding numbers are parts of an optimum plant reproduction strategy. These elements are optimization-oriented and constitute the FPA's basics. The suggested methodology has an excellent performance in minimizing data access time, energy usage and optimizing cost according to simulation findings. [ABSTRACT FROM AUTHOR]

Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today's internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices' energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees’ fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. [ABSTRACT FROM AUTHOR]