Network Lifespan Research Articles

Effective proactive routing protocol using smart nodes system

Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires.

An efficient multi-objective gorilla troops optimizer for minimizing energy consumption of large-scale wireless sensor networks

The multi-objective gorilla troops optimizer (MOGTO) is a new version of the gorilla troops optimizer (GTO) proposed in this paper to address multi-objective optimization issues. The Pareto optimum solutions acquired by the GTO are saved in an external archive. In the multi-objective search region, the archive was used to mimic the gorilla groups’ collective behavior. The suggested approach is evaluated statistically and qualitatively in solving various multi-objective issues using the congress on evolutionary computation (CEC) 2020 test bed. In large-scale wireless sensor networks, the proposed algorithm is also utilized to discover the minimal number of sink nodes with the lowest localization error, which will cap the whole network and increase the network lifespan. Meanwhile, the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective grey wolf optimizer (MOGWO), multi-objective whale optimization algorithm (MOWOA), multi-objective sine–cosine algorithm (MOSCA), multi-objective slime mould algorithm (MOSMA), multi-objective particle swarm optimization with ring topology and special crowding distance (MO_Ring_PSO_SCD), hybrid NSGAII-MOPSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and improved multi-objective manta ray foraging optimization (IMOMRFO) are the ten familiar and strong optimization models, which are compared with the proposed algorithm. Simulation results in CEC’2020 test functions indicated that the proposed MOGTO can provide remarkable results than other optimization models in terms of Pareto set proximity (PSP), inverted generational distance in decision space (IGDX), and hyper volume (HV) indicators. Additionally, simulation results in large-scale wireless sensor networks show that the proposed algorithm can discover the smallest number of sink nodes and diminish the network’s energy usage.

LEACH based hybrid energy efficient routing algorithm for dynamic cognitive radio networks

Detection of high energy using spectrum sensing step especially when using a collaborative method, is one of the key difficulties in cognitive radio which has the potential to improve energy efficiency. The Energy-Efficient Scalable Routing Algorithm(EESRA) is used in this study which combines Low Energy Adaptive Clustering Hierarchy (LEACH) with an Energy-Efficient clustering and hierarchical routing algorithm. A novel machine learning approach is used in this paper to consider identical channels in Cognitive Radio Networks (CRN). When compared to LEACH, the EESRA algorithm extended the lifespan of network, resulting in the incorrect rate of .9895 and an error rate of 0.0105.

A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.

A Secure and Efficient Method to Protect Communications and Energy Consumption in IoT Wireless Sensor Networks

The rapid growth of technology has resulted in the deployment of a large number of interconnected devices, resulting in a wide range of new societal services. Wireless sensor networks (WSNs) are a promising technology which is faced with the challenges of operating a large number of sensor nodes, information gathering, data transmission, and providing a means to act in different scenarios such as monitoring, surveillance, forest fire detection, and many others from the civil to military spectrum. The deployment scenario, the nature of the sensor-equipped nodes, and their communication methods make this architecture extremely vulnerable to attacks, tampering, and manipulation than conventional networks. Therefore, an optimal solution to ensure security in such networks which captures the major constraints of the network in terms of energy utilization, secured data transmission, bandwidth, and memory fingerprint to process data is required. This work proposes a fast, reliable, and secure method of key distribution and management that can be used to ensure the integrity of wireless sensor networks’ communications. Moreover, with regards to efficient energy utilization, an improvement of the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm (a cluster routing protocol that is mainly used in WSN) has been proposed to enhance the networks’ energy efficiency, simplicity, and load-balancing features. Therefore, in this paper, we propose a combination of a distributed key exchange and management methods based on elliptic curve cryptography to ensure security of node communication and an improved routing protocol based on the LEACH protocol to demonstrate better performance in parameters such as network lifespan, dead nodes, and energy consumption.

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

The battery power limits the energy consumption of wireless sensor networks (WSN). As a result, its network performance suffered significantly. Therefore, this paper proposes an opportunistic energy-efficient routing protocol (OEERP) algorithm for reducing network energy consumption. It provides accurate target location detection, energy efficiency, and network lifespan extension. It is intended to schedule idle nodes into a sleep state, thereby optimising network energy consumption. Sleep is dynamically adjusted based on the network’s residual energy (RE) and flow rate (FR). It saves energy for a longer period. The sleep nodes are triggered to wake up after a certain time interval. The simulation results show that the proposed OEERP algorithm outperforms existing state-of-the-art algorithms in terms of accuracy, energy efficiency, and network lifetime extension.

An improved approach for energy consumption minimizing in WSN using Harris hawks optimization

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations.

Adaptive Parallel Seeker Optimization-based Route Planning for clustered WSN in smart cities

Smart city is a phenonmenon that integrates physical and social infrastructures with Information Technology to keep a city's cooperative intelligence under control. Smart cities primarily rely on Wireless Sensor Networks (WSN) to manage and maintain its service offerings. In literature, clustering and multihop routing techniques have been proposed, validated and implemented to reduce the consumption of energy in the network. With this motivation, the current study develops Adaptive Parallel Seeker Optimization-based Energy Aware Route Planning Technique (APSO-EARPT) for clustered WSN in smart cities. The presented APSO-EARPT technique concentrates on appropriate selection of Cluster Heads (CHs) and optimal routes in WSN. To accomplish this, APSO-EARPT model encompasses Weight-Based Clustering Scheme (WBCS) for effective selection of CHs. Then, routing process is performed with the help of APSO algorithm. The proposed APSO-EARPT technique computes a Fitness Function (FF) that comprises of three variables such as Residual Energy (RE), distance to Base Station (BS), and node degree. This fitness function helps in optimal selection of routes in WSN. In order to validate the supremacy of the proposed APSO-EARPT model in terms of network lifespan and energy efficiency, simulations were conducted and the results confirmed the excellent performance of the proposed model.

Hierarchical stable election protocol for WSN‐based IoT inhabitant and environmental monitoring applications

SummaryWireless sensor networks (WSNs) play an essential role in IoT applications, especially for long‐term data gathering and monitoring applications. The deployment field and the limited energy sources are two critical constraints directly influencing WSN lifespan, and both must be considered while designing a routing protocol for WSN‐based IoT applications. Routing the data consumes a significant amount of energy in such an extensive network. Several hierarchical clustering protocols have been developed in recent years to address this purpose. We propose a new hierarchical stable election protocol (HISEP), an improved version of the Stable Election Protocol (SEP) equipped with the Dual CH election algorithm using a new enhanced threshold formula. Accepting nodes' heterogeneity and incorporating Voronoi‐based cluster formation ensures that the 2D/3D WSN network stays alive for an extended period and the HISEP algorithm will be suitable for habitat tracking and environmental monitoring applications. The simulation results show that the proposed algorithm outperforms other protocols in terms of routing efficiency and network lifespan.

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

SummaryDuring the past few years, wireless sensor networks (WSNs) have created a substantial interest because of their capabilities namely, self‐healing and self‐organizing under restricted network resources. Numerous WSN‐based routing algorithms are implemented to perform well, but the energy consumption of every data packet and information loss is more. Due to the power restriction in the sensor nodes, energy consumption is regarded as the major issue in networks that need to be minimized to improve the network lifetime. Hence, to conquer these consequences, this paper aims to propose an adaptive spider monkey optimization (ASMO)‐based effective optimal routing path from various sensor nodes with enhanced network lifetime. Here an ASMO is employed in selecting an optimal routing path. In addition to this, the proposed ASMO algorithm is capable of selecting energy‐aware routes by energy consumption. It also chooses the routes with minimum end‐to‐end delay using the bare least hop count criteria, thus enhancing the network life span. The results of the proposed method are compared with other methods such as DMEERP, EGSMRP, EEIABC, and RACO. The results reveal that the proposed approach consumed less amount of energy; hence, network lifetime is more when compared with other approaches.

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks

In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.

A QoS and Network Lifetime Aware Reliable Multicast Routing Protocol Using a Chaos Integrated Cuckoo Search Rider Optimization Algorithm

In MANET, the multicast routing is considered as a non-deterministic polynomial (NP) complexity, it contains assorted objectives and restrictions. In the multicast issue of MANET, the Quality of services (QoS) based upon cost, delay, jitter, bandwidth, these are constantly deemed as multi-objective for intending multiple cast routing protocols. Conversely, mobile node has finite battery energy and the lifetime of network depending on its mobile node battery energy. If the mobile node has high battery energy consumption, it automatically reduces the network’s life time because of their route breaks. Alternatively, node’s battery energy has to be consumed to ensure higher level quality of services in multicast routing for transmitting the accurate data anywhere and anytime. Therefore, the network lifespan is deemed as multi-objective in the multicast routing (MR) problem. To overcome these issues, QoS and Dr. Valli Kumari Vatsavayi2 2Professor, Department of Electronic Communication and Engineering, Andhra University, visakhapatnam,Andhra Pradesh ,India. 2Email:vallikumariv001@yahoo.com Network Lifetime Aware Reliable Multicast Routing Protocol are proposed by applying Chaos integrated Cuckoo Search Rider Optimization Algorithm for effectual data transmission in MANET (QOS-MRP-CSROA-MANET). The proposed method is the joint execution of both the Cuckoo Search Algorithm with chaos theory (Chaotic-CSA) and Rider Optimization (ROA) algorithm and hence it is called as Chaotic-CSA-ROA, which is utilized to solve the MR problem of MANET. Here, the MR problem of MANET have five objectives, viz cost, delay, jitter, bandwidth, network lifetime are optimized with the help of Chaotic-CSA-ROA. Then the proposed method is simulated in NS2 simulator for validating the performance of the proposed QOS-MRP- CSROA-MANET system. Here, evaluation metrics, via delay, delivery ratio, drop, Network lifespan, overhead and throughput are analyzed with node, rate and speed. The proposed QOS-MRP-CSROA-MANET provide higher throughput in node as 32.9496% and 65.5839%, higher throughput in rate as 16.6049% and 30.4654%, higherthroughput in speed as 10.1298% and 7.0825%, low packet drop in node as 63.7313% and 52.2255%, low packet drop in rate as 51.5528% and 25.6220%, low packet drop in speed as 18.0857% and 24.5953% compared with existing methods, like QOS aware of multicast routing protocol using particle swarm optimization algorithm in MANET (QOS-MRP-PSOA-MANET) and QOS aware of multicast routing protocol using genetic algorithm in MANET (QOS-MRP-GA-MANET) respectively.

Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network

The wireless sensor networks (WSNs) provides an advance way for connection of various applications. Most of the applications use the wireless sensor network to transmit the information from the device to the main base station. The main operation of the wireless sensor network is to first sense the data using the sensor, then collect the data and finally transmit to the required base station. The data is transmitted in a timely manner such that the other wireless sensor network transmitting the information should not have any problem. During the transmission of the data the main objective of the wireless sensor network device is to provide reliability to the information which is being sent with less latency and to reduce the energy consumption in order to increase the lifespan of the wireless sensor network. Therefore, this paper presents an Energy Efficient Routing for the Target Tracking (EER-TT) in the wireless sensor network which provides reliability, less latency and reduces the energy consumption during the transmission of the information to the base station. Proposed model also provides a cluster selection method for the routing of the wireless sensor network devices. The results attained show that our model EER-TT shows better results when compared with the existing routing-based models.

Optimal Relay Angle-Based Clustering Routing Protocol for Wireless Sensor Networks

In this paper, a novel optimal relay angle-based clustering routing protocol called RACR is proposed to balance nodes’ energy consumption and prolong the network lifespan of wireless sensor networks (WSNs). In RACR, each node considers the parameters residual energy, number of neighbors, and average distance with neighbors as a criteria to determine whether to become a cluster head (CH) which cooperates with its neighbors to form a cluster. Afterward, the optimal relay angle is calculated for each CH to find its best relay node according to a target function considering the least energy consumption, which is applied to reduce the search range for finding routing paths. Consequently, all the CHs can find their best next-hop nodes within the determined field according to their residual energy, distance to the next-hop CH, and loads. Iteratively, the CHs obtain their best routing paths to the BS in the end. Simulation results demonstrate its effectiveness of RACR in terms of energy consumption, standard deviation of residual energy, data communication delay, network throughput, and lifespan.

Linear optimization model for mobile cluster heads to improve the lifetime of sensor network

Mobile elements are used in wireless sensor network to increase the longevity of network by minimizing the overloading of one hop sensors. Mobility has proven very useful in reducing the formation of energy holes by balancing the load among all the sensor nodes. The paper proposes exploiting mobile cluster heads to move along a predetermined trajectory to collect data. As autonomous moving strategies impose heavy overhead and data latency issues, predetermined trajectories have proven successful in improvising the lifespan of network. The contributions of the paper are twofold. First we have proposed a predetermined trajectory model along with a cluster formation procedure for the cluster heads to involve in data collection. The cluster heads stop for a fixed time duration “t” at each rendezvous point for data collection. Second, we have proposed a mathematical formulation to optimize the sojourn time duration of thecluster head based on the one hop residual energy of the sensornodes.

6G wireless communication assisted security management using cloud edge computing

AbstractSecurity management is the process of identifying a company's assets (such as people, buildings, equipment, systems, and information assets) and then developing, documenting, and implementing policies and procedures to secure those assets. Meanwhile, artificial intelligence (AI) applications are flourishing thanks to advances in deep learning and numerous hardware architecture improvements based on cloud edge computing (CEC) issues are associated with the Internet of Things (IoT), including inadequate security measures, user ignorance, and the dreaded active monitoring. Therefore, a 6G wireless communication‐assisted security management using artificial intelligence (WC‐SM‐AI) has been introduced to enhance security. The energy‐efficient 6G real‐time communication framework and the enhanced deep neural network security module are key components of this architecture. The first module optimizes network lifespan and spectral efficiency while reducing energy consumption and latency. Another module offers a more secure network connection while increasing privacy, data integrity, and access. For this reason, this article discusses how AI can strengthen the security of 6G networks while promoting strategy problems and solutions.

An optimized joint medium access control and routing protocol with iterative contention window adjustment mechanism for a guaranteed lifetime in Internet of Things‐based wireless sensor networks

SummaryThe problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J‐RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT‐based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k‐covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi‐objective seagull optimization algorithm (MO‐SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end‐to‐end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW‐AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay.

An Energy Efficient Clustering Protocol Using CM-YSGA Optimization Approach in WSN

Abstract: While dealing with the wireless sensor networks (WSNs), one of the greatest factors that must be taken into consideration is energy consumption of nodes. One of the most effective common way of preserving energy in sensor nodes is clustering technique in which CH selection is of great importance. In this manuscript, an improved energy efficient clustering protocol is proposed in which chaotic mapping algorithm is clubbed along with the advanced variant of Yellow Saddle Goatfish Algorithm (YSGA). The main objective of the proposed model is to reduce the energy consumption of nodes which ultimately prolongs the lifespan of wireless network. Initially, cluster are formed in the network by selecting the nodes randomly. After this, four important parameters which included, residual energy, distance to sink, average distance to CH neighbor node and delay are taken into consideration and on the basis of these factors’ fitness value is calculated by the chaotic-YSGA technique. The node whose fitness value came out to be highest of all other nodes, is selected as the CH in that particular cluster. In addition, the Huffman data compression technique is used in the proposed system to reduce energy consumption in nodes. Finally, the performance of the proposed Chaotic-YSGA model is analyzed and compared with conventional energy efficient models in MATLAB software in terms of alive node, dead nodes, throughput, residual energy, FND, HND and LND. Keywords: WSN, Energy Efficiency, CH selection, Optimization Algorithms.

Novel dynamic route aware clustering (DRAC) protocol for enhancing energy efficiency by clustering concept in WSN

"Wireless Sensor Networks (WSNs)" have started to recognize clustering as among the best effective ways of saving energy. The Cluster-Heads (CHs) probably have spent higher energy than typical nodes in a WSN based on clustering, resulting in higher consumption of energy and a shorter network lifespan. To lower the consumption of energy of CHs, many energy-efficient routing techniques had already been developed and demonstrated. It is possible to reduce the consumption of energy of CHs by increasing the number of CHs nearby. Earlier research has not taken into account these conditions. Poorly balanced CH consumption of energy is also a major design concern, resulting in increased misspent energy and initial network downfall. These situations need re-creation of the shortest path route if the previously found routed path is faulty or interrupted. Extra node consumption of energy may exhaust the sensor's energy supply as a result of this reconfiguration. Researchers in the last several years also aimed to enhance the overall balance of energy through the use of huge and costly energy harvesting equipment but nothing had solved this issue.