Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime

A Review on Swarm Intelligence Based Routing Approaches

The principles of bio-inspired or swarm intelligence algorithms can be effectively used to achieve optimal solutions in routing for complex and dynamic wireless sensor networks or body area networks. As the name indicates, it is a field that is inspired by natural living beings like ants, bees, fishes, etc. Studies have proved that the routing protocols based on such bio-inspired methods perform better in terms of energy efficiency, reliability, adaptivity, scalability, and robustness. The general classification of routing protocols is classical-based and swarm-based routing protocols, where both the protocols were specifically categorized as data-centric, location-aware, hierarchical and network flow, and QoS aware protocols. In this paper, an evocative taxonomy and comparison of various swarm-based routing algorithms are presented. A brief discussion about the sensor network design and the major factors that influence the routing is also discussed. The comparative analysis of the selected swarm-based protocols is also done with respect to routing characteristics like query based, route selection, energy efficiency, and path selection. From the review, it is observed that the selection of a routing protocol is application dependent. This paper will be helpful to the researchers as a reference on bio-inspired algorithms for new protocol designs and also for the proper selection of routing protocols according to the type of applications

A Review on Swarm Intelligence Based Routing Approaches

The principles of bio-inspired or swarm intelligence algorithms can be effectively used to achieve optimal solutions in routing for complex and dynamic wireless sensor networks or body area networks. As the name indicates, it is a field that is inspired by natural living beings like ants, bees, fishes, etc. Studies have proved that the routing protocols based on such bio-inspired methods perform better in terms of energy efficiency, reliability, adaptivity, scalability, and robustness. The general classification of routing protocols is classical-based and swarm-based routing protocols, where both the protocols were specifically categorized as data-centric, location-aware, hierarchical and network flow, and QoS aware protocols. In this paper, an evocative taxonomy and comparison of various swarm-based routing algorithms are presented. A brief discussion about the sensor network design and the major factors that influence the routing is also discussed. The comparative analysis of the selected swarm-based protocols is also done with respect to routing characteristics like query based, route selection, energy efficiency, and path selection. From the review, it is observed that the selection of a routing protocol is application dependent. This paper will be helpful to the researchers as a reference on bio-inspired algorithms for new protocol designs and also for the proper selection of routing protocols according to the type of applications

Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR

Systems and algorithms for wireless sensor networks based on animal and natural behavior

In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. 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