Routing In Sensor Networks Research Articles

A review of routing algorithms for underwater wireless sensor networks

With the rise of the upsurge of exploiting marine resources and the rapid development of the research of terrestrial wireless sensor networks, the research of underwater wireless sensor networks (UWSNs,) has become a new research hotspot. In UWSNs, routing protocol plays a very important role as one of the important components. This paper introduces several classical routing algorithms from the following categories: location, sector, reinforcement learning, and intelligent optimization algorithms. Finally, the paper also summarizes and prospects the research direction of underwater wireless sensor network routing algorithms.

A Hybrid Cross Layer with Harris-Hawk-Optimization-Based Efficient Routing for Wireless Sensor Networks

Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them.

IATLR: Improved ACO and TOPSIS-Based Layering Routing Protocol for Underwater Acoustic Networks

Aiming at the challenges of underwater acoustic networks (UANs) such as low bandwidth, long propagation delay, dynamic network topology, and energy limitation, an improved ant colony optimization (IACO) and technique for order preference by similarity to an ideal solution (TOPSIS)-based layering routing (IATLR) protocol for UANs is proposed. In the IATLR protocol, the transfer probability of the ant colony optimization (ACO) is optimized by comprehensively considering multiple factors such as energy consumption, propagation distance, and forwarding angle. Moreover, the ACO is improved and the optimized ACO is combined with the TOPSIS method by using the pheromone concentration of ACO as the decision basis of TOPSIS. Furthermore, the multifactor decision ranking of the TOPSIS method is optimized by the transfer probability of the ACO. Consequently, the best next-hop node is determined. In addition, the energy consumption is balanced through the whole network by the node silence mechanism in the IATLR protocol. Simulation results show that the IATLR protocol has significant advantages compared with the depth-based routing for underwater sensor networks (DBR), layer-based energy-efficient routing (LEER), and depth and distance based layering routing protocol for underwater sensor networks (DDLR) routing protocols in terms of packet delivery rate (PDR), energy consumption, and end-to-end delay (EED). The PDR increases by 5%–30%, the EED decreases by about 1–7 s, and the average energy consumption (AEC) reduces by 0.5–6 J.

Multi‐objective‐derived energy efficient routing in wireless sensor networks using hybrid African vultures‐cuckoo search optimization

SummaryWireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy‐efficient routing in WSNs utilizing the hybrid meta‐heuristic algorithm with the help of Hybrid African Vultures‐Cuckoo Search Optimization (HAV‐CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV‐CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi‐objective function in terms of distance, intra‐cluster distance, delay, inter‐cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C‐SSA, 25.45% better than BSO‐MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.

Environment-Aware Energy Efficient and Reliable Routing in Real-Time Multi-Sink Wireless Sensor Networks for Smart Cities Applications

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

Capacity trust assessment for multi-hop routing in wireless sensor networks

This paper proposed a newIntrusion Detection mechanism based on Multiple Trust Attributes in Wireless Sensor Networks (WSNs). Mainly this work concentrated to assess the trust in ems of capacities of the sensor nodes. The capacity of a node is formulated based on two trusts namely Fault Tolerance Trust and Stability Trust. Every SN checks the trustworthiness of its neighbour SNs based on the Capacity Trust and confirms their trustworthiness. If any node is discovered as malicious, such type of node is called as intrusion or outlier and isolated from network.Extensive Simulations are conducted over the proposed intrusion detection mechanism and the performance is evaluated through Malicious Detection Rate, and False Positive Rate.

Compatibility issues of wireless sensor network routing in internet of things applications

Compatibility issues of wireless sensor network routing in internet of things applications

Compatibility issues of wireless sensor network routing in internet of things applications

Compatibility issues of wireless sensor network routing in internet of things applications

Comparison and Analysis of Wireless Sensor Networks Routing Protocols

Comparison and Analysis of Wireless Sensor Networks Routing Protocols

A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks

<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>

Optimizing Wireless Sensor Network Routing Through Memetic Algorithms: Enhancing Energy Efficiency and Data Reliability

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods.

Fuzzy Rule-Based Clustering Algorithm for Energy Efficiency in WSN

Abstract: Wireless Sensor Networks (WSNs) have gained much attention in public and research communities due to their incredible capabilities and ever-growing range of applications. The WSN is equipped with a specialized transducer that adds sensing services to IoT. This equipment is limited to battery and resource capacity, which introduces many challenges to academia and industry. Many recent wireless sensor networks (WSN) routing protocols are enhancements to address specific issues with the low-energy adaptive clustering hierarchy (LEACH) protocol. Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm compensates for the energy flow in CH by probabilistically rotating the position between nodes with energy above the specified threshold. The selection of CH in WSN is NP-Hard as the optimal data aggregation with efficient energy efficiency cannot be resolved in polynomial time. Since the performance of LEACH deteriorates sharply with increasing network size, the challenge for new WSN protocols is to extend the network lifespan while maintaining high scalability. This paper introduces a fuzzy logic-based energy-efficient scalable routing algorithm (F-EESRA). The goal of the proposed algorithm is to extend the network lifespan despite an increase in network size. This paper compares F-EESRA against EESRA WSN routing protocol in terms of network performance. EESRA uses multi-hop transmissions for intra-cluster communications to implement the proposed work. The simulation results show that F-EESRA outperforms the fuzzy protocol in terms of energy efficiency on large-scale WSNs.

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

AbstractAn enhanced Elman spike neural network (EESNN) optimized with hybrid wild horse optimization and chameleon swarm algorithm is proposed in this manuscript for multi‐objective cluster head selection and energy aware routing in wireless sensor network (CH‐EESNN‐Hyb‐WH‐CSOA‐WSN). Initially, EESNN is used to intelligent selection of cluster head. Then, the optimal cluster head utilized the selected data transferring process with the consideration of multi‐objective fitness function based EESNN. Here, some of the multi‐objective fitness function factors are considered, like energy, delay, throughput, distance between the nodes, traffic rate and cluster density. The hybrid wild horse optimization and chameleon swarm algorithm (Hyb‐WH‐CSOA) is taken into account for the optimal route path selection with minimal delay. The proposed CH‐EESNN‐Hyb‐WH‐CSOA‐WSN method is activated in network simulator 3 (NS‐3) tool. The performance of the proposed method is examined under certain performance metrics, like count of alive nodes, drop, network lifetime, delay, throughput, energy consumption, and packet delivery ratio. Finally, the proposed method attains 98.78%, 97.21%, 99.61% lower delay, 98.78%, 99.21%, 96.78% higher delivery ratio, and 99.57%, 98.67%, 98.88% lower packet drop compared with the existing methods, like optimal secure cluster head placement through source coding techniques in wireless sensor networks (CHP‐HMC‐WSN), optimal placement of single cluster head in wireless sensor networks via clustering (CHP‐K‐Means C‐PSO‐WSN) and hybrid firefly approach along particle swarm optimization for energy efficient optimum cluster head selection in wireless sensor networks (CHP‐HFAPSO‐WSN) respectively.

An improved blockchain based encryption scheme for secure routing in wireless sensor network using machine learning technique

AbstractA secure routing scheme plays an integral role in ensuring the secure routing and efficiency of wireless sensor networks (WSNs). Recently, many studies have been undertaken to improve the data aggregation process, data security, and routing security. However, these approaches are highly suffered due to major limitations like time complexity, malicious attacks, and data insecurity. This research aims to develop an improved blockchain based encryption scheme for secure routing in wireless sensor networks using machine learning techniques. An improved artificial quantum beetle swarm neural network approach is proposed to perform the data aggregation among the sensor nodes. The cosine equivalent function and packet density correlation degree approach is emphasized to perform clustering in sensor nodes. The cluster head (CH) is selected based on the similarity of each sensor node. The Kalman filtering technique is introduced to filter out the data from unwanted malicious. This technique helps to filter the data before it is sent to the CH. Blockchain‐based encryption scheme is emphasized for data security by preventing malicious attacks during data transmission. The enhanced differential evaluation based firefly routing protocol is presented to maintain secure routing by choosing the optimal routing for the protected data transmission. Performance metrics such as latency, throughput, routing performance, energy consumption, FPR, blockchain (BC) computational performance, dead sensor count and average hop count are analyzed and compared with existing techniques. In an experimental scenario, the proposed approach achieves a maximum packet delay of 500 slots, throughput of 581 times/s, a latency of 0.31 ms, a computation complexity of 0.6, and a routing complexity of 2081 ms with a BC computation time of 95 ms. The performance of the proposed approach shows a better result than other existing approaches.

Predictive Model Techniques with Energy Efficiency for IoT-Based Data Transmission in Wireless Sensor Networks

Wireless sensor networks are limited by the vast majority of goods with limited resources. Power consumption, network longevity, throughput, routing, and network security are only a few of the research issues that have not yet been addressed in sensor networks based on the Internet of Things. Prior to becoming widely deployed, sensor networks built on the Internet of Things must overcome a variety of technological obstacles as well as general and specific hazards. In order to address the aforementioned problems, this research sought to improve rogue node detection, reduce packet latency/packet loss, increase throughput, and lengthen network lifetime. Wireless energy harvesting is suggested in the proposed three-layer cluster-based wireless sensor network routing protocol to extend the energy lifespan of the network. For the purpose of recognising and blacklisting risky sensor node behaviour, a three-tier clustering architecture with an integrated security mechanism is suggested. This clustering approach is cost-based, and the sink node selects the cluster and grid heads based on the cost function’s value. With its seemingly endless potential across a wide range of industries, including intelligent transportation, the Internet of Things (IoT) has gained prominence recently. To analyse the nodes and clustering strategies in IoT, the suggested method PSO is applied. A plethora of new services, programmes, electrical devices with integrated sensors, and protocols have been produced as a result of the Internet of Things’ explosive growth in popularity.

Evolutionary gravitational neocognitron neural network based block chain technology for a secured dynamic optimal routing in wireless sensor networks

ABSTRACT In this manuscript, Evolutionary Gravitational Neocognitron Neural-Network-based Block chain Technology is proposed for a Secured Dynamic Optimal Routing in Wireless Sensor Networks (BT-SDOR-WSN-EGNNN). The purpose of block chain (BC) technology is to distribute the distributed routing information management depending on secure BC token transactions. The Proof-of-Authority (PoA)-based consensus process is selected for effectual transaction in BC wireless sensor networks. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN) is considered to select salient nodes denote the features of node-based validators. The Evolutionary Gravitational Neocognitron Neural Network model enhances the collection of validators by the attributes corresponding to every node. Then, Trust-Based Secure Intelligent Opportunistic Routing Protocol (TBSIOP) is employed to assist routing nodes and makes more informed routing decisions and choose the dependable routing links. The proposed approach is simulated in Network Simulator (NS-2) tool. The performance metrics, such as average latency, average energy consumption, and throughput of blockchain token transactions, is examined. From the simulation, the proposed BT-SDOR-WSN-EGNNN method attains 76.26%, 65.57%, 48.99%, 42.9% lesser delay during 25% malicious routing environment, 73.06%, 63.82%, 59.25%, 38.84% lesser delay during 50% malicious routing environment analysed to the existing BT-SDOR-WSN-DCNN, BT-SDOR-WSN-MDP, BT-SDOR-WSN-RL and BT-SDOR-WSN-WL-DCNN methods.

An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

AbstractMaintaining high residual energy is one of the major challenges of wireless sensor networks (WSNs), where nodes are moving at a random speed. In addition, data availability is also a major concern that must be maintained constantly during the packet transmission phase. In this research work, a Residual‐Energy‐Based Data Availability Approach (REDAA) for WSNs is developed to increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. During the first phase of the work, the network model, and assumptions for route creations are adopted to effectively initialize the data transmission phase. During the second phase, a cluster was formed, and two categories of cluster heads were created based on quadrature low energy adaptive clustering hierarchy (Q‐LEACH) and multi‐hop low energy adaptive clustering (MH‐LEACH) algorithms to formalize the route and to make data available whenever requested. During the third phase, energy conservation routes are initialized, and data gathering is improved through slot‐based code division multiple access schemes. Simulation results demonstrate that the proposed REDAA approach can achieve an improvement in terms of throughput and energy consumption by 37% and 70% respectively when compared to MH‐LEACH approach, whereas in comparison to Q‐LEACH approach, these improvements are found to be 30% and 73% respectively.

MCEN: Maximum cooperative equilibrium WSN based on greedy prediction to reduce opposite transmission

In wireless sensor network (WSN) routing model design, energy efficiency is a key to WSN performance. Existing energy efficiency routing approaches transmit fused data packets through cluster head (CH) to save energy. However, some of these approaches do not make full use of WSNs’ energy because some sensor node transmitting paths are opposite to their transmission destination in these WSNs. Therefore, this paper proposes an approach named maximum cooperative equilibrium WSN (MCEN) to improve WSN energy utilization based on greedy policies and nodes energy consumption prediction. MCEN reconstructs the cluster structure to help nodes minimize their transmitting paths on which signals travel in the opposite direction to their destination. The energy-consumption prediction strategy in MCEN is to find the best cooperative partner for a node by predicting the energy consumption of the cooperative data transmission between the node and base station. CHs are voted to form clusters based on the maximum energy consumption prediction results. The approach can be embedded in other WSN clustering approaches, thereby further extending the overall lifetime of those WSNs. The simulation results show that the proposed method can greatly extend the lifetime of the entire network by more than 50%.

Reinforcement learning movement path for multiple mobile sinks in wireless sensor networks

SummaryMobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end‐to‐end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time. Thereupon, WSN routing can improve very much in terms of end‐to‐end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS‐3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark.