Energy In Wireless Sensor Networks Research Articles

A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.

Enhancement of Tiny Encryption Algorithm for Resource-Constrained WSNs Using Four Connected Additive Fibonacci Generators

The usage of wireless sensor networks (WSN) is widespread in industries where data security is crucial. Due to the energy and computational limits of WSN, the cryptographic protocols designed for it must be as computationally cheap as feasible. The components of these protocols, such as the random number generator, are subject to the same constraints. For such resource-constrained devices, several lightweight encryption techniques have been created. One of the most efficient lightweight ciphers is Tiny Encryption Algorithm (TEA). TEA uses a few lines of source code that are based on Feistel. It is however susceptible to attacks utilizing equivalent and related key attacks. In order to address TEA’s key vulnerabilities, a modification is suggested in this paper that focuses on key creation. Four connected Additive Fibonacci Generators (AFGs) make up the structure, which addresses the security vulnerability by using a unique key each round. Performance evaluation was assessed using three statistical tests: avalanche effect, randomness analysis, and completeness testing. Through experimental results, ATEA outperforms TEA by an average of 51.68 % to 47.51 % for the avalanche effect, and 51.95 % to 48.36 % for the completeness test, and satisfies all the NIST requirements. Results of advanced security measurements indicate that, ATEA can be used to secure WSN devices.

A Topology Control Algorithm Taking into Account Energy and Quality of Transmission for Software-Defined Wireless Sensor Network

The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for softwaredefined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the Max Power algorithm in terms of average node degree and energy expansion ratio.

An Energy-Efficient Cluster Head Selection and Secure Data Transmission in WSN using Spider Monkey Optimized Algorithm and Hybrid Cryptographic with Security

To conserve energy in wireless sensor networks, clustering is the well-known strategies. However, choosing a cluster head that is energy efficient is crucial for the best clustering. Because data packets must be transmitted between cluster members and the sink node, improper cluster head selection (CHs) uses more energy than other sensor nodes. As a result, it lowers the network's performance and lifespan. Due to the requirement that this network implement appropriate security measures to guarantee secure communication. This paper provides a novel cluster head selection technique that addresses issues of networks’ lives and energy usages using Spider Monkey Optimised Fuzzy C-Means Algorithm (SMOFCM). The CH is chosen using the Spider Monkey Optimisation method in the proposed SMOFCM approach, which builds on the Fuzzy C-means clustering framework. The hybrid cryptographic technique is appropriate for WSN for safe data transmission because it can address sensor challenges such processing power, storage capability, and energy. The Rivest-Shamir-Adleman (RSA), advanced encryption standards (AES), and the suggested algorithm are all used at various stages. Because asymmetric key cryptography makes key management simpler but symmetric key cryptography offers a high level of security. The AES algorithm has been created for phase 1. Phase 2 employed RSA, and all phases were carried out concurrently. According to the simulation results, it reduces energy use, lengthens the network's lifespan, and offers faster encryption, decryption, and execution times for secure data transmission

Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks

This research has a research object, namely the optimization of the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm in the context of wireless sensor networks. The problem in this research is the imbalance in energy consumption across clusters, which has an impact on battery life and affects network performance. Other problems include selecting a cluster head that is not focused so that it is difficult to balance network performance as well as computational limitations that require optimization. The results obtained from this research are in the form of optimizing the leaching algorithm by modifying the clustering-based leaching algorithm that will be used in wireless sensor networks. In carrying out modifications, this research uses several stages in the process of selecting sensor nodes that will become members who function as cluster heads in a cluster that will be used in a wireless sensor network. In the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm the cluster head will be selected based on the modified probability value. Modifying the algorithm by considering two factors, namely distance and remaining energy used in the Cluster Head selection process on the network and increasing network usage time must be based on the energy consumption used and then compared with the remaining energy. When modifying the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm, it is necessary to pay attention to the distance factor between the nodes on a sensor and the selected cluster so that it can result in increased network performance. Network lifetime is indicated by the average death time of the first Node in the network. This research is novel in producing a modified leaching algorithm by improving network performance and extending battery life so that it can be used for wireless sensor networks in the context of natural disaster mitigation

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks

The collection of data from the physical environment, such as temperature and humidity readings, is an essential function of Wireless Sensor Networks (WSNs). Because of its ability to communicate data wirelessly, WSNs are crucial for connecting the digital and physical worlds. Nevertheless, WSN energy efficiency optimization has become a more complicated challenge because of their battery dependency and deployment in adverse locations. The emphasis of this research is the Improved Low-Energy Adaptive Clustering Hierarchy (ILEACH) protocol, which is used as an example of a mathematics-driven strategy to improve WSN energy efficiency. To tackle important problems with energy consumption and network lifetime, mathematical modelling and equation-based analysis are used to study and enhance ILEACH's performance. Particularly in faraway places with few resources, energy efficiency is of the utmost importance when discussing WSNs. This study's mathematical models explore the equations and probability distributions that control the lifetime, energy consumption, and behaviour of sensor nodes. Our goal is to shed light on the energy optimization processes of ILEACH by means of thorough mathematical analysis and derivations. Our method incorporates both theoretical modelling and empirical assessments of ILEACH's cluster head formation, throughput, and network performance as a whole. Our comparison of the mathematical formulations of ILEACH and LEACH shows that our Improved LEACH is better in terms of data throughput, network lifetime, and energy efficiency. Using a figure of merits criterion, we compare LEACH, ILEACH, and the proposed Improved LEACH in detail to help with decision-making and protocol selection. With its solid mathematical groundwork, this study is a great tool for improving the efficiency of WSNs' energy-saving algorithms, which in turn can help networks last longer and run better.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

Technological advances in the internet of things (IoT) allowed a low-cost, yet small sensor device to operate with limited power in a dynamic harsh environment where human intervention is impossible. The wireless sensor network (WSN) is an example of the IoT in which physical devices’ software and sensors can interconnect to provide application services. It is important that such applications be dependable to meet the required quality of service (QoS) and function as expected. Consequently, the multi-objective optimization (MOO) problem in WSNs aims to address the trade-off among coverage, connectivity, and network lifetime requirements. Node scheduling is one approach of many used to optimize energy in WSNs. The contribution of this work is the proposal of a self-organizing feature map (SOFM) to enhance the node scheduling in WSNs. The proposed SOFM node-scheduling algorithm aims to spatially explore the state space domain and obtain an optimal solution. In our experiment, the proposed SOFM node-scheduling algorithm is evaluated against a comparable algorithm, namely the BAT node-scheduling algorithm, via MATLAB simulator. The results showed that the SOFM node-scheduling algorithm outperformed the latter by 27% and 28% for the maximum and minimum coverage, respectively, with similar performance of 99% of connectivity and network lifetime.

Cluster Head Selection and Secured Routing Using Glowworm Swarm Algorithm and Hybrid Security Algorithm for Over IoT-WSNs

WSNs (Wireless Sensor Networks) have received a lot of attention in a number of industries due to its self-configurability, simplicity in maintenance, and scaling capacities. WSN is configured with extra nodes in order to transport data around the network. The usual characteristics of sensor networks are low bandwidth, low energy, constrained power supply, and little memory space, which makes security particularly difficult in these networks. CH (Cluster heads) in a cluster experience heavier traffic loads than other cluster nodes. This results in the hotspot problems. Therefore, in a cluster-oriented routing scheme, picking the right CH is crucial. The two-level security is recommended in this research to safeguard data transmission via the WSN. A novel CH selection methodology is presented in order to increase the network's lifespan and energy effectiveness. This work also combines the fruit fly algorithm (FGF) with the GSA (firefly swarm algorithm) based on fitness to choose the optimal CH. To defend the network from assaults, it employs a hybrid security method that combines the Diffie-Hellman key exchange mechanism and the ECC (elliptic curve cryptography) algorithm. The suggested GSA-HSA protocol consistently produces good performance results in terms of MSR (Message Success Rate), E2E (End-to-End) Latency, NT (Network Throughput), and network throughput. average AEE (energy efficiency). The suggested approach outperforms all prior research by giving the wireless sensor network combination security, optimised coverage, and energy savings.

ETXRE: Energy and delay efficient routing metric for RPL protocol and wireless sensor networks

AbstractInternet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low‐power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low‐cost communications. A routing protocol for low‐power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link‐based routing metric (i.e. ETX) and node‐based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end‐to‐end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

Performance Analysis of MIMO Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSN) are widely used in remote applications related to defence and healthcare. A network with nodes having different capabilities like sensing, various computational capabilities, power-efficient communication, and a varied sensing range is called a heterogeneous wireless sensor network. Heterogeneous wireless sensor networks using MIMO wireless channels are more useful for energy-efficient multi-channel communication. MIMO applications in wireless sensor networks have the potential to enhance throughput, reduce End-to-End Delay, improve packet delivery ratios, and conserve energy in wireless sensor networks. Its implementation needs to be carefully considered in light of the specific deployment conditions and resource constraints of the network, considering proper antenna design, synchronisation mechanisms, and energy-efficient algorithms. This paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO for various Quality of Service parameters like Packet Delivery Ratio, End to End Delay, Throughput and Residual energy. The research work shows that the application of MIMO in Wireless Sensor Networks enables sensor nodes to collaborate effectively, leading to improved reliability and coverage, and also increases the network's lifetime by conserving energy in resource-constrained sensor nodes through the preservation of Residual Energy.

A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy. These attributes will be input for the group head selection based on machine learning, The entire DSD’s inside a group are classified into HIGH, MEDIUM and LOW. The first priority will be given to HIGH followed by others for the primary group head selection. LEACH is a classical method used for transmission of chunks to the control center in a DSDN network. The selection of head DSD by LEACH will happen by making use of the random selection of DSD in each group using random probability selection mechanism. During the data chunk deliver the scanning process will happen from the initiator DSD to head DSD and from there the link is established with the base station (BS), the BS will then scan each group until the destination DSD is reached. The selection of head DSD by LEACH causes more holes in the DSDN because there are chances that the non-performer DSD can become a head DSD. Secondly for the transmission of chunks there is lot of back-and-forth propagation between the BS and the normal DSDs which reduces the battery level of the DSD by a large amount. The Energy based LEACH is modified on top of LEACH by measuring the energy of the DSDs and then selecting the group heads but suffers from multiple group head maintenance as well as more number of links. The proposed method will improve this by reducing the links used for end-to-end communication. In the proposed system the communication will happen based on initiator DSD, primary DSDs in different groups and then destination DSD which will avoid overhead compared to existing methods namely E-LEACH and LEACH. The proposed method is compared with LEACH and E-LEACH with respect to time taken, link count, energy consumption, residual energy measure, lifetime and overhead.

Security and data encryption effect of high ciphertext based on improved RC6 algorithm for WSN

Along with the continuous growth and improvement of computer and communication technologies, wireless sensors are needed in more and more places to fulfil specific tasks. A lightweight data encryption mechanism is designed for the limited processing power, storage space and energy of wireless sensor networks. The mechanism improves the RC6 algorithm and adds the “symmetric layer” operation. In order to further improve the security of the ciphertext and the strength of the data encryption, the AES-128 encryption algorithm is used in the data link layer, the network layer, and the application layer, respectively. The experiment is based on the STM32L series of ultra-low-power ARM processors, and the operating frequency of the STM32L controller is tuned to 64 MHz to obtain faster efficiency; three groups of experiments on the encryption mechanism are carried out by using the original RC6 algorithm and the improved algorithm, and the lengths of the keys used in the experiments are 16 bytes, 24 bytes, and 32 bytes, respectively, and the data-receiving and transmitting functions are tested separately. This avoids the delay of transceiver mode switching when using a single module for communication, and improves the quality and stability of multi-machine bidirectional communication.

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.

Energy Efficient Three-Factor Authentication in Wireless Sensor Networks With Resisting Insider Attacks

Wireless Sensor Networks (WSNs) play an important role in many fields such as industry, medical treatment, and national defense. Due to the limited node resources, there has always been an intractable contradiction between WSNs security and energy consumption. This contradiction needs urgent attention, especially with the increasing capability of attackers. User authentication is a key technology to ensure the security of WSNs. Unfortunately, recent insider attacks have shown vulnerabilities in the registration and authentication processes of traditional protocols. This paper analyzes the common types of insider attacks and focuses on explaining the logic of the offline guess attack launched by insiders. In order to uphold the concept of green communication, this paper proposes a new energy efficient protocol based on the widely used three-factor authentication. For the first time, the protocol combines lightweight data hiding technology to protect against offline guess and insider attacks. Experiments and analyses show that the proposed scheme not only solves the insider attack problem, but also reduces the energy consumption compared with the current typical schemes.

Fuzzy enhanced location aware secure multicast routing protocol for balancing energy and security in wireless sensor network

Fuzzy enhanced location aware secure multicast routing protocol for balancing energy and security in wireless sensor network

Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks.

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.

Self‐biased magnetoelectric composite for energy harvesting

AbstractThe wireless sensor network energy supply technology for the Internet of things has progressed substantially, but attempts to provide sustainable and environmentally friendly energy for sensor networks remain limited and considerably cumbersome for practical application. Energy harvesting devices based on the magnetoelectric (ME) coupling effect have promising prospects in the field of self‐powered devices due to their advantages of small size, fast response, and low power consumption. Driven by application requirements, the development of composite with a self‐biased magnetoelectric (SME) coupling effect provides effective strategies for the miniaturized and high‐precision design of energy harvesting devices. This review summarizes the work mechanism, research status, characteristics, and structures of SME composites, with emphasis on the application and development of SME devices for vibration and magnetic energy harvesting. The main challenges and future development directions for the design and implementation of energy harvesting devices based on the SME effect are presented.

Enhanced DV-Hop Algorithm for Efficiency Energy and Network Quality in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are wireless networks with multiple sensor nodes covering a relatively large area. One of the weaknesses of WSNs is the relatively high energy consumption, which affects the network quality of service. Although WSN network routing using the DV-Hop algorithm is widely used because of its simplicity, improvements need to be made to improve energy efficiency, which impacts network lifetime. This article proposes a method to enhance energy efficiency by comparing the original DV-Hop algorithm with the enhanced DV-Hop algorithm. There are three approaches to enhancing the DV-Hop algorithm. Firstly, the CH node selection is based on the distance to the Base Station so that the selected CH node does not have a far distance from the base station. Secondly, the CH node selection must have a minimum number of sensor nodes as neighbors. Lastly, each selected CH node calculates the minimum distance to the previously selected CH node to ensure that the selected CH node is not in a nearby position. The proposed approach obtains better data packet transfer rate, energy efficiency, and network lifetime results using Matlab software simulation that compares the enhanced DV-Hop algorithm with the original DV-Hop algorithm and three other routing algorithms.