Lifetime Of Wireless Sensor Networks Research Articles

A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks

• A CH selection optimized based on GA (NCOGA) for the heterogeneous WSN is proposed. • A comprehensive discussion on related work is presented which highlights the research gaps. • The proposed algorithm is discussed in a step-by-step manner. • Rigorous simulations and analysis are presented. • Results are validated against the most recent state-of-the-art algorithms. In this paper, an optimized cluster head (CH) selection method based on genetic algorithm (NCOGA) is proposed which uses the adaptive crossover and binary tournament selection methods to prolong the lifetime of a heterogeneous wireless sensor network (WSN). The novelty of the proposed algorithms is the integration of multiple parameters for the CH selection in a heterogeneous WSN. NCOGA formulates fitness parameters by integrating multiple parameters like the residual energy, initial energy, distance to the sink, number of neighbors surrounded by a node, load balancing factor, and communicating mode decider (CMD). The parameters for load balancing and CMD are utilized to discover out the best candidate to be selected as a relay CH and for deciding the mode of communication (single or multi-hop) of CH. Further, these parameters are useful in avoiding hot-spot problem in the network. The working of the NCOGA starts based on the criteria “consider only those nodes which have energy higher than the pre-defined threshold energy”. This criterion of nodes selection makes the NCOGA more efficient and quickly convergent. Extensive computer simulations are conducted to determine the effectiveness of the NCOGA. Simulation results reveal that the proposed NCOGA outperforms the state-of-the-art optimization algorithms based on GA in terms of several performance metrics, specifically, stability period, residual energy, network lifetime, and throughput.

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

<p>Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.</p>

On the Global Maximization of Network Lifetime in Wireless Rechargeable Sensor Networks

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review.

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined.

Problem Characteristics and Dynamic Search Balance-Based Artificial Bee Colony for the Optimization of Two-Tiered WSN Lifetime with Relay Nodes Deployment.

Lifetime optimization is one of the key issues among the many challenges of wireless sensor networks. The introduction of a small number of high-performance relay nodes can effectively improve the quality of the network services. However, how to deploy these nodes reasonably to fully enhance the network lifetime becomes a very difficult problem. In this study, a modified and enhanced Artificial Bee Colony is proposed to maximize the lifetime of a two-tiered wireless sensor network by optimal deployment of relay nodes. First, the dimension of the problem is introduced into the candidate search equation and the local search is adjusted according to the fitness of the problem and number of iterations, which helps to balance the exploration and exploitation ability of the algorithm. Second, in order to prevent the algorithm from falling into local convergence, a dynamic search balance strategy is proposed instead of the scout bee phase in the original Artificial Bee Colony. Then, a feasible solution formation method is proposed to ensure that the relay nodes can form the upper-layer backbone of the network. Finally, we employ this algorithm on a test dataset obtained from the literature. The simulation results show that the proposed algorithm for two-tiered wireless sensor network lifetime optimization can obtain higher and stable average network lifetime and more reasonable relay node deployment compared to other classical and state-of-the-art algorithms, verifying the competitive performance of the proposed algorithm.

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

AbstractWireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is “energy consumption.” However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy‐efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

SUMMARYEstablishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

A Secure LEACH-PRO Protocol Based on Blockchain.

Wireless Sensor Networks (WSNs) are becoming more popular for many applications due to their convenient services. However, sensor nodes may suffer from significant security flaws, leading researchers to propose authentication schemes to protect WSNs. Although these authentication protocols significantly fulfill the required protection, security enhancement with less energy consumption is essential to preserve the availability of resources and secure better performance. In 2020, Youssef et al. suggested a scheme called Enhanced Probabilistic Cluster Head Selection (LEACH-PRO) to extend the sensors' lifetime in WSNs. This paper introduces a new variant of the LEACH-PRO protocol by adopting the blockchain security technique to protect WSNs. The proposed protocol (SLEACH-PRO) performs a decentralized authentication mechanism by applying a blockchain to multiple base stations to avoid system and performance degradation in the event of a station failure. The security analysis of the SLEACH-PRO is performed using Burrows-Abadi-Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, the SLEACH-PRO is evaluated and compared to related protocols in terms of computational cost and security level based on its resistance against several attacks. The comparison results showed that the SLEACH-PRO protocol is more secure and requires less computational cost compared to other related protocols.

An enhanced routing algorithm based on a re-position particle swarm optimization (RA-RPSO) for wireless sensor network

Energy saving is a major requirement in the design of routing algorithms to maximize the throughput and lifetime of wireless sensor networks. To address this requirement, this work proposes a routing algorithm based on a novel Reposition Particle Swarm Optimization (RPSO) algorithm and a new fitness function. The role of the RPSO algorithm is to salvage particles that may have fallen into local minima, if any. The algorithm is evaluated using three types of benchmark functions, namely unimodal, multimodal, and rotated multimodal. The evaluation results demonstrate that RPSO outperforms native Particle Swarm Optimization and Particle Swarm Optimization with Levy Flight in terms of convergence speed and global optimum identification. The proposed RPSO is then employed to develop an efficient routing algorithm. The routing algorithm is tested in terms network lifetime, number of dead sensor nodes, energy consumption, and number of packets delivered to base station, against four competitive Meta-Heuristic algorithms. The test results clearly indicate that the routing algorithm outperforms all the competitive algorithms for all the performance metrics. In particular, it achieves 14% to 29% higher network lifetime, 52% to 58% less dead sensor nodes, 61% to 70% less energy consumption, and 13% to 36% more throughput.

Energy efficient with prolonging lifetime in homogeneous wireless sensor networks

In this artical, we suggest a new clustering model called the Fuzzy Low Energy Adaptive Clustering Hierarchy Protocol (FLEACH). It has the ability to go around obstacles and fix the issue of uneven energy consumption in homogenous wireless sensor networks (WSNs). In order to determine the best routing path for the homogeneous WSNs, we also suggest the Ant Colony Routing Method (ACORM), which is an energy-efficient routing technique. ACORM aims to investigate issues relating to balancing energy consumption and maximizing network life. To illustrate FLEACH-ACORM's effectiveness in managing energy consumption and optimizing homogeneous network life, we compare our system with two approaches: Power-Efficient Sensor Information Systems (PEGASIS) and LEACH. Results of the simulation indicate that the network lifetime achieved by FLEACH-ACORM could be increased by almost 28.5 % and 19.8 % more than that obtained by LEACHPEGASIS, LEACH clustering protocols, respectively.

Modulated relay based stable election protocol for large‐scale wireless sensor networks

SummaryInternet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.

AN ENERGY- AND DELAY-EFFICIENT TRANSMISSION MECHANISM USING REINFORCEMENT LEARNING IN WIRELESS BODY SENSOR NETWORKS

Clustering mechanism saves energy consumption and prolongs the lifetime of wireless sensor network (WSN). In this paper, we propose an energy and delay efficient transmission mechanism using reinforcement learning in wireless body sensor networks (WBSNs). In the WBSN network, in addition to the efficient energy consumption of the sensor node, when vital information is measured, additional information measured from other sensors must be transmitted to the medical staffs within a limited time so that the medical staff can make a correct decision. The proposed transmission mechanism in this paper considers two novel techniques: a novel CH selection process using reinforcement learning approach and super-frame extension to enhance energy efficiency and network lifetime. Additionally, when a vital signal is measured, it is also proposed to quickly collect relevant information and deliver it to the medical staffs so that the medical staff can make a correct decision. Because of the simulation, the proposed mechanism increased the average network lifetime by 12% and decreased the packet transmission delay by 29.4% compared to the method without reinforcement learning.

Transmission schedule for jointly optimizing remote state estimation and wireless sensor network lifetime

This paper studies scheduling multiple sensors for jointly optimizing remote state estimation and wireless sensor network lifetime. Each sensor in the network sends its packet to the remote center for further estimation over wireless communication channels. Sensors in the network will inevitably run out of battery power due to transmission consumption and then reach the end of sensor network lifetime. This article focuses on the problem how to schedule multiple sensors minimizing the total remote estimation errors and simultaneously prolonging the sensor network lifetime. The problem is formulated as a special class of Markov decision process (MDP), stochastic shortest path (SSP) problem. Under this framework, we first derive a set of conditions to guarantee the optimality of structural policies in SSP. Subsequently, threshold structure of the optimal scheduling scheme is obtained by verifying the conditions. Moreover, two Value-of-Information-based multi-sensor scheduling algorithms and a linear-architecture-based learning algorithm are designed to tackle the curse of dimensionality and unknown channel statistics in large-scale sensor networks. Numerical simulations are given to validate effectiveness of our results.

QoS enhanced energy efficient cluster based routing protocol realized using stochastic modeling to increase lifetime of green wireless sensor network

QoS enhanced energy efficient cluster based routing protocol realized using stochastic modeling to increase lifetime of green wireless sensor network

An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

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

Algorithm for Increasing Network Lifetime in Wireless Sensor Networks Using Jumping and Mobile Sensor Nodes

Sensor networks’ network connectivity must be restored as part of any solution. This strategy’s goal was to come up with a concept. Many approaches to restoring connections after a network outage can be implemented by relying on these factors: low mobility, minimal field coverage drop and a reduction in the overall number of messages sent. All of the following objectives can be met with this solution. Based on detailed simulations and a comparison with the PACR and SNR methods, it can be concluded that the proposed methodology is effective. The sensor nodes’ batteries slowly depleted over time due to power restrictions. Network nodes fail as a result; data transmission stops, and the network’s lifespan is shortened because of it. As a result, one of the most difficult challenges in wireless sensor networks is to minimize energy consumption while also maximizing the network’s lifespan. In this study, the network lifetime of a wireless sensor network is extended through the use of special jumping nodes. Instead of using wheels or other means of transportation, these nodes leap into the network, and they are used to recharge other nodes in the network that are dying upon request. Results show that the proposed technique works more efficiently with figures of 83.76%, 84.84% and 87.3% for SNR, PACR and the proposed technique, respectively, with 250 nodes. This significantly increases the network’s lifetime. The simulation results suggest that the proposed technique outperforms other strategies that have been used in the literature.

Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks

Wireless Sensor Network (WSN) is represented by a group of sensor nodes with limited battery capacity deployed in a target region for data collection purposes. Energy is treated as a major issue from the design of WSN as it mainly affects the overall functioning of the network. A commonly employed energy-efficient mechanism is clustering which groups the nodes into several groups of clusters. Earlier works reported that clustering is treated as an NP hard issue which can be addressed by soft computing techniques. In this way, this article focuses on the design of Quantum Tunicate Swarm Algorithm Based Energy Aware Clustering (QTSA-EAC) scheme for WSN. The presented QTSA-EAC technique mainly attempts to effectively manage the available energy among the nodes in WSN. In addition, the QTSA is derived by the incorporation of quantum computing concepts as to the traditional TSA. Moreover, the QTSA-EAC technique derives a fitness function with the minimization of total energy consumption in the networks. The inclusion of multiple parameters in the cluster head (CH) selection process helps to accomplish energy efficiency and maximum lifetime of WSN. The performance assessment of the QTSA-EAC technique is performed and the results reported that the presented model is effective, improves lifetime, reduce energy exploitation, and delay over the other methods.

NEMOCED: New Energy Model and Optimal Cluster Estimation Based on Density to Increase Lifetime in Wireless Sensor Networks

NEMOCED: New Energy Model and Optimal Cluster Estimation Based on Density to Increase Lifetime in Wireless Sensor Networks

Increase of the Lifetime of Wireless Sensor Network using Clustering Algorithm and Optimal Path Selection Method

Increase of the Lifetime of Wireless Sensor Network using Clustering Algorithm and Optimal Path Selection Method