Routing Protocol For Wireless Sensor Networks Research Articles

Enhanced Clustering Routing Protocol for Power-Efficient Gathering in Wireless Sensor Network

Wireless sensor network (WSN) is a new and fast advancing technology, which is opening up many opportunities in the field of remote sensing and data monitoring. In spite of the numerous applications of WSN, issues related to determining a suitable and accurate radio model that will foster energy conservation in the network limit the performance of WSN routing protocols. A number of radio models have been proposed to address this issue. However, the underlying assumptions and inaccurate configuration of these radio models make them impractical and often lead to mismanagement of scarce energy and computational resources. This paper addresses this problem by proposing an enhanced radio model that adapts to the frequent changes in the location of the sensor nodes and is robust enough to report reliable data to the base station despite fluctuations due to interference. The impact of incorporating stepwise energy level and specialized data transmission schemes in the proposed radio model is also investigated in this paper. The performance of the proposed radio model is evaluated using OMNET++ and MATLAB and the results obtained is benchmarked against PEGASIS. It is shown by simulation that the novel LEACH-IMP performs better with respect to energy consumption, number of links faults, number of packets received, signal attenuation, and network lifetime.

An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

PurposeThe paper aims to introduce an efficient routing algorithm for wireless sensor networks (WSNs). It proposes an improved evaporation rate water cycle (improved ER-WC) algorithm and outlining the systems performance in improving the energy efficiency of WSNs. The proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are performed.Design/methodology/approachThis proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head (CH) energy, CH location and CH density in improved ER-WCA. The proposed study will solve the energy efficiency and improve network throughput in WSNs.FindingsThis proposed work provides optimal clustering method for Fuzzy C-means (FCM) where efficiency is improved in WSNs. Empirical evaluations are conducted to find network lifespan, network throughput, total network residual energy and network stabilization.Research limitations/implicationsThe proposed improved ER-WC algorithm has some implications when different energy levels of node are used in WSNs.Practical implicationsThis research work analyzes the nodes’ energy and throughput by selecting correct CHs in intra-cluster communication. It can possibly analyze the factors such as CH location, network CH energy and CH density.Originality/valueThis proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

Adaptive coding clustered routing protocol for energy efficient and reliable WSN

In Wireless Sensor Networks (WSNs), the sensor nodes are expected to remain autonomous for a long time and the communication must be successful. So, energy efficiency and communication reliability are the main challenges for WSN. Routing protocols and, in particular, clustering is the key to energy constraints in WSNs. In a loss milieu, data shared between nodes is susceptible to corruption by errors caused by signal fading, random noise, and some other forces. For this reason, channel coding is necessary to assure efficient and reliable transmission. Therefore, energy efficiency and reliability of communications are the most crucial factors in the design of an efficient cluster-based routing protocol. In this context, to raise the reliability of the communication link and minimize the necessary transmission energy, a new adaptive coding routing protocol is proposed, which implements RS and LDPC codes to a routing protocol. Simulation results show that our proposed protocol improves energy consumption, network lifetime, stability, and throughput compared to LEACH and BRE-LEACH. In addition, it reaches a good BER performance and coding gain by using the LDPC code.

A REVIEW ON DESCENDANTS OF LEACH PROTOCOL - A PRAGMATIC APPROACH

A Wireless Sensor Networks (WSN) is a Wireless Networks comprising of spatially disseminated independent sensors to screen physical or natural circumstances. The battery power in these sensor nodes assumes a significant part in expanding the life expectancy of the nodes. Hierarchical routing protocols are the most popular protocols to limit the energy utilization when compared to Flat Routing. Low-Energy Adaptive Clustering Hierarchy (LEACH) is a traditional Cluster based routing protocol for WSNs having great execution, energy productivity, and successful in elongating the lifetime of the network life time by consuming a little level of the complete disseminated energy in the framework. This paper reviews the condition of craft of various progressed, enhanced routing protocols that have been created from the LEACH. This paper features a portion of the downsides and issues of LEACH how these issues are vanquished by the relatives of LEACH. Appraisal of descendants of LEACH routing protocols in advised with regards to versatility, self-association, and conveyance of nodes, network control, Hop count, energy proficiency, and utilization of data.

SDBMND: Secure Density-Based Unsupervised Learning Method with Malicious Node Detection to Improve the Network Lifespan in Densely Deployed WSN

Random deployment, the absence of central authority, and the autonomous nature of the network make wireless sensor networks (WSNs) prone to security threats. Security, bandwidth, poor connectivity, intrusion, energy constraints, and other challenges are critical and could affect the performance of the WSN while considering the energy-efficient and secure routing protocols in WSNs. Security threats to WSNs are gradually being expanded. Thus, to improve the network’s performance, detection of anomalies (malicious and suspicious nodes, redundant data, bad connections, etc.) is important. This paper is aimed at introducing the malicious node detection algorithm based on the DBSCAN algorithm, which is a density-based unsupervised learning method for enabling wireless sensor networks to be much more secure and reliable. The prime objective of this algorithm is to develop a routing algorithm capable of detecting malicious nodes and having a prolonged network lifespan and higher stability period. Clustering and classification are two well-known methods in the field of machine learning that can be successfully used in various domains. Density-based clustering is a popular and extensively used approach in various domains. The DBSCAN is the utmost popular and best-known density-based clustering algorithm and is capable of determining arbitrary-shaped clusters. This paper addresses the two anomalies in the WSN, namely, spatial redundancy and malicious node identification. In this article, an algorithm has been suggested to reduce redundant data transmission along with the identification of suspicious nodes to conserve energy and to avoid falsification of data through malicious nodes. The analysis of simulation results and comparison of other algorithms that are in the same class shows that the SDBMND performs significantly better than EAMMH, TEEN, IC-ACO, and LEACH in dense networks.

Energy-Efficient and Secure Opportunistic Routing Protocol for WSN: Performance Analysis with Nature-Inspired Algorithms and Its Application in Biomedical Applications.

Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature-inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust-based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO-based and PSO-based routing algorithms. It is observed that the TBSIOP outperforms the NIO-based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end-to-end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes' test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy-efficient services and keeping the network alive for a longer period.

Galactic swarm optimized convolute network and cluster head elected energy-efficient routing protocol in WSN

Nodes in a Wireless Sensor Network (WSN) monitor and collect data in different environments. Data can be sent from one location to another using the network's spontaneous connectivity. The networks' nodes have sensing capabilities that make sending data to the sink easier. They can. Routers are used to determine the most efficient method of transmitting information between points of origin and destinations. The traditional methods focus on energy and routing to get to the sink. Nevertheless, securing systems and anticipating malicious activity are major research issues. These issues can be overcome by using an energy-efficient routing algorithm in a convolution neural network. Security and energy efficiency are considered when selecting the cluster head, in this case, Asteroids from Outer Space. To eliminate malicious activity, an optimized convolute network is used to predict whether a node is malicious or trustworthy. The network uses optimized training patterns to reduce the malicious node prediction-error rate. As a result, trusted nodes are used to determine who should be in charge of leading each cluster. This procedure ensures the network's long-term security and improves overall data transmission.

F2SO: An Energy Efficient Cluster Based Routing Protocol Using Fuzzy Firebug Swarm Optimization Algorithm in WSN

AbstractThe most important design concern associated with wireless sensor networks is energy efficiency that mostly deals with routing and clustering techniques. The major aim of this study is to provide energy efficient cluster routing protocols for wireless sensor network. Initially, we used a Honey Badger Algorithm for cluster heads selection. Honey Badger Algorithm is used to choose the distance to the base station, residual energy, distance to its neighbors, node degree node and centrality with the optimal cluster head among all sensors. The routing between base stations and cluster heads is performed through Fuzzy Firebug Swarm Optimization algorithm. Hence, the fuzzy decision module automatically adjusts the search behavior of firebug swarm optimization. Fuzzy rules are used with the node degree, residual energy and distance to the base station to address the firebug swarm optimization algorithm’s constraints during routing. Finally, cluster maintenance is done which is the major step to eliminate the failure nodes. This process assists to improve the network lifetime during data transmission by eliminating the faulty nodes and establish an efficient path between the source node and the base station. Wide experimentation is accomplished to assess the efficiency of the proposed protocol using different evaluation measures. The proposed methodology offers improved end-to-end delay, increased packet delivery ratio, higher throughput, low packet drop ratio and minimizes energy consumption when compared with the existing wireless sensor network routing protocols.

Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol

The nodes of wireless sensor networks (WSN) are severely restricted in terms of computing capabilities, limited communications, and limited power supplies, as it is difficult or impossible to replace or recharge the sensor battery. Consequently, the energy of nodes is one of the most important resources to consider when designing of WSNs. So, most of the routing protocols in WSNs are to assure the saving of energy as a significant aim for improvement. Nevertheless, just providing power is not sufficient to extend the lifetime of WSN. Where unbalance energy depletion in WSNs is a challenging issue often leading to splits the network and reduces its lifetime, also retrogression of its performance. This paper, therefore, uses a powerful routing protocol named spider monkey optimization routing protocol (SMORP) to generate an optimal data routing of the pathway for heterogeneous WSNs (HWSNs). SMORP, here, can compute the best way from a sensor to the sink through the cluster head, inside the intra-cluster, and the inter-cluster respectively. For this purpose and the organization of heterogeneous nodes, this paper uses the clustering partition. The simulation results revealed that SMORP significantly improves in terms of data latency reduction, stabilizing depletion of energy, and maximizing the network lifetime for HWSNs.

AI Based Energy Efficient Routing Protocol for Intelligent Transportation System

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy-efficient, static scenario-oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.

Quantum Firefly Secure Routing for Fog Based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) become an integral part of Internet of Things (IoT) and finds their applicability in several domains. As classical WSN faces several issues in service-based IoT applications, fog computing has been introduced in real-time, enabling local data processing and avoid raw data transmission to cloud servers. The Fog-based WSN generally involves advanced nodes, normal nodes, and some Fog Nodes (FN). Though the Fog-based WSN offers several benefits, there is a need to develop an effective trust-based secure routing protocol for data transmission among Cluster Heads (CHs) and FNs. In this view, this paper presents a Quantum Firefly Optimization based Multi-Objective Secure Routing (QFO-MOSR) protocol for Fog-based WSN. The main intention of the QFO-MOSR technique is to derive an optimal selection of routes between CHs and FNs in the network. The QFO-MOSR technique has incorporated the concepts of quantum computing and Firefly (FF) optimization algorithm inspired by the flashing behaviour of FFs. In addition, a multi-objective fitness function is derived by the QFO-MOSR technique using seven objectives: distance, inter-cluster distance, energy, delay, intra-cluster distance, link lifetime, and trust. The proposed routing technique derives a fitness function including trust factor from ensuring security. The design of the QFO-MOSR technique with a multi-objective fitness function shows the novelty of the work. To validate the performance of the QFO-MOSR technique, a series of experiments were carried out, and the results are investigated in terms of different measures. The experimental analysis ensured that the QFO-MOSR technique is superior to other methods in terms of different measures.

Chaotic Krill Herd with Fuzzy Based Routing Protocol for Wireless Networks

Energy is considered a valuable source in wireless sensor networks (WSN) for effectively improving the survivability of the network. The non-uniform dispersion of load in the network causes unbalanced energy dissipation which can result in network interruption. The route selection process can be considered as an optimization problem and is solved by utilize of artificial intelligence (AI) techniques. This study introduces an energy efficient chaotic krill herd algorithm with adaptive neuro fuzzy inference system based routing (EECKHA-ANFIS) protocol for WSN. The goal of the EECKHA-ANFIS method is for deriving a better set of routes to destination in such a way as to improve the survivability in the wireless networks. Primarily, the ANFIS model utilizes the models of fuzzy logic and neural networks (NN) to effectively select the relay nodes for energy efficient communication. Besides, a group of fuzzy rules with membership functions (MF) are designed for selecting the next hop node in wireless networks based on distinct input parameters. Moreover, the optimal selection of MF takes place by the use of chaotic krill herd algorithm (CKHA). In order to showcase the improved performance of the EECKHA-ANFIS approach, a series of simulations are implemented and outcomes are inspected under several aspects. The extensive result analysis demonstrates the betterment of the EECKHA-ANFIS technique over the existing techniques interms of different measures.

Trust Aware Manta Ray Foraging Optimization based Secure Routing Protocol for Wireless Sensor Networks

In Wireless sensor network (WSN) is a different type of ad-hoc network has of battery powered minimum cost sensor node with restricted computation and communication abilities used densely from a target region. The routing in WSN roles is an essential part due to their inherent energy storing ability and is suitable to extremely scalable networks. The trust in WSN roles an essential play in creating the network and addition or deletion of sensor nodes in a network very smooth and translucent. The trust was formalized as a computational method. This study develops a novel Trust Aware Manta Ray Foraging Optimization based Secure Routing (TAMRFO-SR) protocol for WSN. The proposed TAMRFO-SR model accomplishes optimal and secure routes for WSN. In addition, the TAMRFO-SR model is mainly stimulated by the fascinating characteristics of manta rays. Besides, the TAMRFO-SR model derives a fitness function with multiple parameters for secure route selection process. Moreover, the inclusion of trust in the route selection process assists in accomplishing enhanced security. The design of TAMRFO-SR model for secure data transmission depicts the novelty of the study. The experimental validation of the TAMRFO-SR model demonstrates better performance over the other approaches.

Lifetime Enhancement Of Wireless Sensor Networks Using Energy Centric Clustering Algorithm

The major issue in Wireless Sensor Networks (WSN) is energy consumption. Over the past years, various protocols have been proposed to prolong the lifetime and minimize the energy consumption of the sensor node. The main limitations in WSN are limited battery capacity, communication range and storage capacity. A suitable routing protocol is required to improve the network lifetime and minimize energy consumption. In this paper, we propose an Energy Centric Clustering Routing Protocol for WSNs (EC2RP). In EC2RP, node residual energy and distance parameters are considered. Further, the data aggregation rate is proposed which allows the cluster heads (CH) nodes to aggregate the data before it sending to the next CH node or base station node. The simulation results show that EC2RP reduces energy consumption and extends the network lifetime.

Energy efficient quality routing protocol for WSNs

Timely emerging research technology increases the need for real-time applications in Wireless Sensor Networks (WSN), which has made quality-based communication protocols an interesting and hot research topic. More specifically, network protocols must cope with power constraints while providing accurate quality assurance. In many of these applications, network traffic consists of delay-sensitive traffic and delay-tolerant traffic. Therefore, Quality of Service (QoS) routing becomes an important issue. The main purpose of this idea is to develop a way to improve the quality of the network and further improve throughput while at the same time creating an energy-saving method with excellent quality of service. This paper proposes to model an Energy Efficient Qualitative Routing Protocol (EEQRP) method based on an energy-efficient protocol that can be used to develop a fast, miniaturized, more energetic, and efficient way than existing routing protocols. It evaluates and compares the performance of the routing protocol (EEQRP). The Network Simulator-2 (NS2) is used to run and test the proposed system, which provides lower average latency, greater power savings, and a higher packet delivery rate than the current protocol.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

SOA-EACR: Seagull optimization algorithm based energy aware cluster routing protocol for wireless sensor networks in the livestock industry

The primary goal of Wireless Sensor Networks (WSNs) in the livestock industry is to monitor the health conditions of various animals and their activities. Energy conservation in WSN is one of the most challenging tasks since the devices are connected to limited resource devices. So, clustering is the best choice to save energy in WSN. The sensor nodes are connected to the animal and move from one location to another because of the dynamic nature. In recent years, several clustering protocols have been proposed using an optimization algorithm to conserve energy in the network. However, it takes more time to converge in selecting the best cluster head in the network. The energy is therefore drained early in the network. To overcome the limitation, this paper proposes a Seagull Optimization Algorithm based Energy Aware Cluster Routing Protocol (SOA-EACR) in WSN to prolong the network's lifespan and reduce the end to end delay. The SOA-EACR balances the exploration and exploitation during the CH selection. Thus, it prolongs the lifespan of the network. The simulation is performed using MATLAB 2019a. The efficacy of SOA-EACR is compared with CLPSO, CACONET and GWO. The proposed SOA-EACR enhances the network life by 20 % and increases the packet delivery ratio by 2–5 %.

Enhancing Wireless Sensor Networks Routing Protocols based on Cross Layer Interaction

Wireless sensor networks aim to develop a smart city based on sensing environment. The routing protocols of wireless sensor networks is important to transfer the data in smart cities since sensor nodes have limited power and transmission range. The aim of this research is to enhance wireless sensor networks routing protocols based on proposed cross-layer interaction between physical layer and network layer also a proposed routing table information of wireless sensor nodes is developed to consider the transmission power of neighbor’s nodes to determine the next hop. Cross-layer interaction provides a useful information and effective adaptation for WSN routing protocols. As a result, the proposed routing protocol shows an improvement in network performance when number of intermediate nodes are minimized.

A Systematical Roadmap on Various Security Vulnerabilities and Countermeasures in Routing Algorithms upon WSNs

Wireless Sensor Networks (WSN) is now an evolving technology and has a broad range of applications, such as battlefield surveillance, traffic surveillance, detection of forest fires, detection of floods, etc. The communication nature of the wireless sensor network is unprotected and dangerous due to deployment in hostile environments, restricted resources, an automatic nature, and untrusted media for broadcast transmission. For wireless sensor networks, several routing protocols have been suggested, but none of them have been developed with protection as a target. The majority function in routing algorithms currently in place for sensor networks optimize a restricted capacities in sensor nodes and the application based design of WSNs. A WSNs, however, are exposed to a number of possible threats that impede the network's regular activity. Thus, there is a strong need to provide the routing protocols of the OSI structure layer with a safe mechanism to prevent an attacker from obstructing it.
 
 The well-known attacks against all layers are discussed in this systematic roadmap, and debilitating attacks against routing protocols are analyzed and defined in particular. Several suggested attack countermeasures, design considerations and paper contributions are also included in the routing protocols. The assertion of the study is that WSN routing protocols must be built with protection in mind, and this is the only efficient solution in WSNs for safe routing. The aim of this paper is also to provide problems, attacks and countermeasures related to protection. Finally, it is hoped that this roadmap would inspire potential researchers to come up with smarter and better protection measures and make their network safer.
 The first such research analysis of secure routing protocols in WSNs is this roadmap study.