Energy Consumption In Wireless Sensor Networks Research Articles

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

Node depth Representation-based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method.

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).

Firebug Optimized Modified Bee Colony Algorithm for Trusted WSN Routing

Wireless sensor networks (WSNs) have become a crucial technology because they are used in several contexts, such as public safety, environmental monitoring, healthcare, military operations, and surveillance systems. The applications of WSNs have been increased because they can be deployed in areas where cable and power supply are difficult to use. However, a variety of security assaults can easily penetrate these networks. To solve this problem, a Firebug Optimized Modified Bee Colony algorithm (FOMBC) has been proposed, which achieves the maximum network security goal with lower energy consumption in WSN. The proposed method is carried out into two phases namely Secure node selection and Orthogonal Routing (OR). In the first phase, secure node among the WSN’s initial nodes is selected based on characteristics such as trust, delay, and Quality of Services. In the second phase, Firebug Optimized Modified Bee Colony (FOMBC) Optimization technique is used to find optimal route based on factors like trust, distance, latency, and quality of service. The effectiveness of the proposed FOMBC approach is evaluated against existing techniques like Trust-Aware Routing Framework (TARF), SOAR (Simple Opportunistic Adaptive Routing Protocol), Particle Swarm Optimization, and Whale Optimization Algorithm for Secure Routing (PSWOSR). According to the experimental findings, the proposed FOMBC technique achieves high detection, maximum throughput, least latency, and minimum range. The proposed method achieves network lifetime of 4.7%, 5.8%, and 9.87% than the existing TARF, SOAR, and PSWOSR, respectively.

Low-power wireless sensor design for LoRa-based distributed energy harvesting system

With the development of electronic power technology, distributed clean energy harvesting is widely concerned, in the process of distributed energy harvesting, the process of monitoring energy harvesting equipment does not need to transmit a large amount of data to the host device, a small amount of status information can meet the purpose of monitoring, so in the wireless sensor network energy consumption is an important issue to be considered. It is very energy efficient, can transmit wireless data over long distances, and supports mesh networking. In the process of distributed energy harvesting, the wireless sensor network composed of LoRa can effectively monitor the environmental information and wireless energy harvesting system, and the battery requirement is very low, and the data can be distributed with very little power consumption, which can be used in harsh environment and remote mountainous areas. In this paper, the composition of the distributed energy harvesting system is discussed first, then the energy situation of LoRa in low-power wireless energy transmission is analyzed, and finally the wireless low-power sensor network is tested.

Comparison of the new version of DEEC protocol to extend WSN lifetime

Wireless sensor networks (WSNs) are used in many different fields. One of the most recent and rapidly expanding applications is the Internet of Things (IoT), which enables the connectivity of numerous objects or devices over the Internet. However, WSNs have a bigger concern about battery power than mobile ad hoc networks, which shortens the network's lifetime. As a result, extensive research has been done on lowering WSNs' energy usage. Designing a hierarchical clustering algorithm is one of many methods for lowering the energy consumption of WSNs. Given the restricted hardware capabilities of the sensor nodes, modeling and building energy-efficient routing solutions to enhance the overall network lifetime has emerged as one of the most crucial approaches in wireless sensor networks. Cluster-based heterogeneous routing protocols, a typical feature of routing technology, have demonstrated efficacy in managing topology, energy consumption, data collection or fusion, dependability, or stability in distributed sensor networks. The existing Distributed Energy-Efficient Clustering (DEEC) clustering protocol is changed in this study by switching the power level between the nodes and adding a threshold limit for cluster head selection. The number of packets sent to a base station increases by 843% when comparing the proposed improved DEEC protocol to the existing DEEC protocol, increasing the number of living nodes to more than 10,000 rounds, or over 30,000 rounds, which can be used to extend the WSN lifetime. The suggested algorithm outperforms alternative energy-efficient protocols in terms of stability period and network lifetime under various scenarios of area, energy, and node density.

Nonlinear Energy Optimization in the Wireless Sensor Network through NN-LEACH

Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. Mote clustering is one of the competent topological control approaches to boost the networks’ energy efficiency, scalability, and performance. Energy is dissipated during the cluster creation, cluster head selection, routing from the head and base stations of the cluster, and data aggregation—clustering and routing emphasis on the stability and the longevity of the network. This research work provides the optimization technique for the wireless sensor network to optimize the energy through NN-LEACH. The main goal is to extend network life and reduce power consumption by clustering and routing sensor nodes using the two-step NN-LEACH protocol, which is suggested. An additional goal might be to establish the appropriate course of action for the suggested approach for this network.

FTOR-Mod PSO: A fault tolerance and an optimal relay node selection algorithm for wireless sensor networks using modified PSO

The wireless sensor network (WSN) is deployed with enormous sensors responsible for data collection. The most critical issue in WSN is higher energy utilization which degrades network lifetime and communication ability. Hence, several methods are applied in WSNs to expand network lifetime. Clustering is a proven technique for reducing energy consumption in WSNs. CH collects data from different sensor nodes (SN) in every cluster and then forwards it to the sink node. On the other hand, fault occurrence in CH collapses entire data communication. Hence, this paper proposes a fault-tolerant cluster-based routing technique to avoid such difficulties. The proposed scheme is fault tolerance and optimal relay node with modified Particle Swarm Optimization FTOR-Mod PSO. Here, fault tolerance is achieved by selecting the backup cluster head (BKCH) for every cluster through the battle royale optimization (BRO) algorithm. Moreover, an aggregator node (AG) is adopted for inter-cluster data transmission, and modified PSO (Mod PSO) identifies CH and AG’s best relay. Results demonstrate that the proposed method reduces delay to 0.15 s by reducing the routing overhead to 2.4%, thereby improving the network lifetime to 92.714%.

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.

Optimal topology control of monitoring sensor network based on physical layer security for smart photovoltaic power system

In order to realize the intelligent management of photovoltaic power generation system, wireless sensor network (WSN) is considered as a promised solution. However, the energy of sensor nodes in WSN is limited and the security is not guaranteed. In order to prolong the service life of WSN and to improve the security of monitoring, this paper comprehensively considers the factors those affect the security and efficiency of WSN, such as transmission power, sensor association and the choice of Cluster Head (CH). Therefore, this paper proposes a joint sensor secure rate and energy efficiency optimization algorithm to obtain the optimal topology and transmission power design. Then, an effective iterative algorithm based on Block Coordinate Descent (BCD) technology is proposed to obtain the optimal solution to minimize the energy consumption of WSN and maximize the secure rate of sensor networks, thus ensuring the security and reliability of monitoring. Sensor association is modeled as a 0–1 multi-knapsack optimization problem. Different complexity methods are implemented to solve this NP-hard problem, and their performance are compared with simulations.

A Centralized Routing for Lifetime and Energy Optimization in WSNs Using Genetic Algorithm and Least-Square Policy Iteration

Q-learning has been primarily used as one of the reinforcement learning (RL) techniques to find the optimal routing path in wireless sensor networks (WSNs). However, for the centralized RL-based routing protocols with a large state space and action space, the baseline Q-learning used to implement these protocols suffers from degradation in the convergence speed, network lifetime, and network energy consumption due to the large number of learning episodes required to learn the optimal routing path. To overcome these limitations, an efficient model-free RL-based technique called Least-Square Policy Iteration (LSPI) is proposed to optimize the network lifetime and energy consumption in WSNs. The resulting designed protocol is a Centralized Routing Protocol for Lifetime and Energy Optimization with a Genetic Algorithm (GA) and LSPI (CRPLEOGALSPI). Simulation results show that the CRPLEOGALSPI has improved performance in network lifetime and energy consumption compared to an existing Centralized Routing Protocol for Lifetime Optimization with GA and Q-learning (CRPLOGARL). This is because the CRPLEOGALSPI chooses a routing path in a given state considering all the possible routing paths, and it is not sensitive to the learning rate. Moreover, while the CRPLOGARL evaluates the optimal policy from the Q-values, the CRPLEOGALSPI updates the Q-values based on the most updated information regarding the network dynamics using weighted functions.

Clustering based Dynamic Routing Algorithm for Efficient Energy Utilization in Wireless Sensor Networks (WSNs)

The significance of proper and efficient energy utilization is quite high in Wireless Sensor Networks (WSNs). As a result, network lifetime can be massively enhanced. Therefore, Graph enabled Energy Optimized Routing (GEOR) algorithm is adopted to extend the lifetime of the sensor network and minimize energy consumption in WSNs. An efficient solution for lifetime enhancement of sensor network and energy minimization are key objectives of the proposed GEOR algorithm. The proposed GEOR algorithm provides improved performance than the traditional Low Energy Adaptive Clustering Hierarchy (LEACH) approach. Here, the proposed GEOR algorithm enhances cluster size and the CH selection process is performed to elect CHs for multiple clusters present in the sensor network. A detailed solution for the optimization problem in WSNs is also presented. The mobility aspect of the sink node has a massive impact on energy consumption in WSNs. Experimental results are evaluated in terms of sensor network lifetime enhancement (number of alive nodes and number of dead nodes). Cluster formation and CH selection are performed based on the mobility cost function. Throughput and network lifetime results are obtained in terms of residual energy using the proposed GEOR algorithm. The result is compared against the traditional LEACH approach and the proposed GEOR algorithm shows superior performance.

Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

The purpose of sensing the environment and geographical positions, device monitoring, and information gathering are accomplished using Wireless Sensor Network (WSN), which is a non-dependent device consisting of a distinct collection of Sensor Node (SN). Thus, a clustering based on Energy Efficient (EE), one of the most crucial processes performed in WSN with distinct environments, is utilized. In order to efficiently manage energy allocation during sensing and communication, the present research on managing energy efficiency is performed on the basis of distributed algorithm. Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption (EC). The three stages of performance are proposed in this research work. At the outset, during routing and Route Searching Time (RST) with fluctuating node density and PKTs, EC is reduced by the Hybrid Energy-based Multi-User Routing (HEMUR) model proposed in this work. Energy efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this model. By utilizing the Approximation Algorithm (AA), the Bregman Tensor Approximation Clustering (BTAC) is applied to improve the Route Path Selection (RPS) efficiency for Data Packet Transmission (DPT) at the Sink Node (SkN). The enhanced Network Throughput Rate (NTR) and low DPT Delay are provided by BTAC. To MAX the Clustering Efficiency (CE) and minimize the EC, the Energy Effective Distributed Multi-hop Clustering (GISEDC) method based on Generalized Iterative Scaling is implemented. The Multi-User Routing (MUR) is used by the HEMUR model to enhance the EC by 20% during routing. When compared with other advanced techniques, the Average Energy Per Packet (AEPP) is enhanced by 39% with the application of proportional fairness with Boltzmann Distribution (BD). The Gaussian Fast Linear Combinations (GFLC) with AA are applied by BTAC method with an enhanced Communication Overhead (COH) for an increase in performance by 19% and minimize the DPT delay by 23%. When compared with the rest of the advanced techniques, CE is enhanced by 8% and EC by 27% with the application of GISEDC method.

Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs’ network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs’ nodes.

An Enhanced Energy Efficient Routing Protocol over Wireless Sensor Networks

Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further enhance the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs.

Software-Defined Network-Based Energy-Aware Routing Method for Wireless Sensor Networks in Industry 4.0

Recent technological developments have led to the emergence of the next generation of industry—Industry 4.0. The Industrial Internet of Things (IIoT) is a key enabler of this new manufacturing paradigm where millions of interconnected smart devices, such as sensors and robots, manage massive amounts of data. Wireless sensor networks (WSNs), which allow the integration, flexibility, and scalability of the production line, thus avoiding the need for complex and expensive wired networks, are essential for IIoT. Nevertheless, the nonstop improvements of the smart industry have increased the amount of data transmitted by WSNs, making their nodes, which rely on small batteries, prone to exhaustion. In this scenario, where the transmission could be abruptly interrupted, losing time, information, and money, the development of energy-based management strategies for reducing the energy consumption of WSNs is urgent. In this paper, a software-defined network (SDN)-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. The SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the widely used shortest-path criterion. Experimental results, obtained via a Mininet-Wifi simulation, show that the proposed approach prevents WSNs’ nodes from draining their batteries and abruptly interrupting the data transmission. Hence, valuable retransmission time is saved, potential information loss is prevented, the need for replacing the node’s battery is avoided, and the transmission lifetime is prolonged. In addition, the baseline shortest-path routing method is outperformed in terms of energy consumption and node failure, doubling its transmission time.

SMLMAC-HEAP: Slotted Multi-Layer MAC Protocol for Wireless Sensor Networks Powered by Ambient Energy Harvesting

Objectives: To study the lifetime of Wireless Sensor Network (WSN) as well as to propose and simulate a suitable approach so that the Quality-of-Service (QoS) of the network in terms of throughput improved when compared with the previous methods. Methods: The lifetime of WSN depends on the MAC layer because energy consumption is more due to the sharing access of media. Our approach is designed not only to adopt the division of the MAC layer into a number of slots by keeping both listen and sleep mode but also using the solar harvested energy; hence, the network’s lifetime is improved with the compared protocol. Our approach with compared protocol is simulated through MATLAB simulator on EZ430-RF2500-SHE” (Datasheet of Texas Instruments’). Findings: In WSN, we found that energy consumption is more at the MAC layer, so network performance degrades in terms of lifetime. To solve the issue of energy consumption in WSN, researchers focused on and designed several techniques to improve the MAC layer. To bring out the practicality of the proposed protocol, we present a case study on the variation of layers with slots in terms of throughput metrics. Finally, the simulation result shows that the proposed protocol performs better than MLMAC-HEAP, Probabilistic Polling and Optimal Polling from layer 7 with slot 7. Novelty: The proposed approach merges the two concepts; one is by using the ambient solar energy, and the other, by adopting slot concepts for each layer of MAC. Keywords: MAC; MLMAC; MLMAC-HEAP; WSN

Performance analysis of energy efficient improved LEACH protocol in IoT networks

AbstractThe Internet of Things incorporates embedded technologies, wireless sensor networks, control and automation technologies, and wearable devices to enable integrated buildings, enterprises, intelligent homes, and wearable devices. Because sensor nodes have limited power, the energy consumption of the Internet of Things network is critical. Low‐energy adaptive clustering hierarchy is a green protocol that divides the network into clusters of a fixed size. Cluster heads are assigned to each cluster so that information can be relayed to the base station. This protocol is a dynamic clustering protocol that changes cluster heads after each round within the community. The cluster heads are chosen in the network entirely based on the energy of each node and the distance between the sensor node and the base station. When sensor nodes aggregate data to the cluster head, energy is also consumed. The work proposed in this paper reduces wireless sensor network energy consumption by improving proposed protocol. The cache nodes combine data from the cluster heads node and send the extra data to the base station. The results show that the proposed work reduces the number of dead nodes in the network by 83 percent and increases the node energy level by 1.66 times.

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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.