Approach For Wireless Sensor Networks Research Articles

Performance Analysis of New Hybrid Distributed Energy Efficient Clustering Approach for WSNs

One of the biggest obstacles to the development of wireless sensor network (WSN) is achieving energy efficiency. Since transmission accounts for the majority of energy consumption, clustering of the network is adopted by the research community to address this issue. In clustering protocols, the prime focus is given on cluster head selection method to achieve better network lifetime. In this paper, the cluster head selection is optimized by using Firefly method where the attractiveness of the nodes is computed using Firefly which optimizes the head selection process. The protocol was simulated in MATLAB and the performance of the network was analyzed based on energy consumed in the network and number of nodes alive. The proposed protocol showed better performance than the other two indicating the achievement of better network lifetime. Keywords: Wireless sensor network (WSN), Firefly Optimization, Clustering, Energy efficiency

A comprehensive survey on several fire management approaches in wireless sensor networks

The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.

Power Optimization in Wireless Sensor Network Using VLSI Technique on FPGA Platform

Nowadays, the demand for high-performance wireless sensor networks (WSN) is increasing, and its power requirement has threatened the survival of WSN. The routing methods cannot optimize power consumption. To improve the power consumption, VLSI based power optimization technology is proposed in this article. Different elements in WSN, such as sensor nodes, modulation schemes, and package data transmission, influence energy usage. Following a WSN power study, it was discovered that lowering the energy usage of sensor networks is critical in WSN. In this manuscript, a power optimization model for wireless sensor networks (POM-WSN) is proposed. The proposed system shows how to build and execute a power-saving strategy for WSNs using a customized collaborative unit with parallel processing capabilities on FPGA (Field Programmable Gate Array) and a smart power component. The customizable cooperation unit focuses on applying specialized hardware to customize Operating System speed and transfer it to a soft intel core. This device decreases the OS (Operating System) central processing unit (CPU) overhead associated with installing processor-based IoT (Internet of Things) devices. The smart power unit controls the soft CPU’s clock and physical peripherals, putting them in the right state depending on the hardware requirements of the program (tasks) being executed. Furthermore, by taking the command signal from a collaborative custom unit, it is necessary to adjust the amplitude and current. The efficiency and energy usage of the FPGA-based energy saver approach for sensor nodes are compared to the energy usage of processor-based WSN nodes implementations. Using FPGA programmable architecture, the research seeks to build effective power-saving approaches for WSNs.

A Fuzzy Congestion Control in Wireless Sensor Networks based on Spider Monkey Optimization Algorithm

Wireless Sensor Networks (WSN) have been broadly applied in various fields, such as medicine and agriculture. A network is likely to experience information congestion when many sensors initiate sending data concurrently. This could lead to maximizing in the packet loss ratio, thereby reducing efficiency and affecting the total system performance, so congestion control is an utmost challenge. To solve this problem, a Fuzzy Congestion Control in WSN based on the Spider Monkey Optimization Algorithm (FCC-WSN-SMOA) is proposed. The proposed method combines random early detection with the fuzzy proportional integral derivative controller. The proportional integral derivative (PID) and fuzzy logic conjointly together to help control the target buffer queue. FLC regulates the transmitting rate of every node. Then, FLC input and output parameters are optimized by SMOA. The simulation is activated in MATLAB. The FCC-WSN-SMOA method attains 21.28%, 32.20%, and 17.42% lower packet loss ratio and 16.25%, 26.07%, and 23.38% lower packet loss probability compared with existing methods, such as progressive fuzzy PSO-PID congestion control approach for WSN (FCC-WSN-PSO), optimized fuzzy clustering utilizing moth-flame optimization approach in WSNs (FCC-WSN-MFOA), and time synchronization depending on Improved Wolf Colony Algorithm-Cuckoo Search Optimized Fuzzy PID Controller for Smart Grid (FCC-WSN-IWCA-CSO), respectively.

Dynamic load balancing in IoT-enabled WSNs using fuzzy logic-based control

The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks (WSN) gather information and sense information samples in a certain region and communicate these readings to a base station (BS). Energy efficiency is considered a major design issue in the WSNs, and can be addressed using clustering and routing techniques. Information is sent from the source to the BS via routing procedures. However, these routing protocols must ensure that packets are delivered securely, guar- anteeing that neither adversaries nor unauthentic individuals have access to the sent information. Secure data transfer is intended to protect the data from illegal access, damage, or disruption. Thus, in the proposed model, secure data transmission is developed in an energy-effective manner. A low-energy adaptive clustering hierarchy (LEACH) is developed to efficiently transfer the data. For the intrusion detection systems (IDS), Fuzzy logic and artificial neural networks (ANNs) are proposed. Initially, the nodes were randomly placed in the network and initialized to gather information. To ensure fair energy dissipation between the nodes, LEACH randomly chooses cluster heads (CHs) and allocates this role to the various nodes based on a round-robin management mechanism. The intrusion-detection procedure was then utilized to determine whether intruders were present in the network. Within the WSN, a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes. Subsequently, an ANN was employed to distinguish the harmful nodes from suspicious nodes. The effectiveness of the proposed approach was validated using metrics that attained 97% accuracy, 97% specificity, and 97% sensitivity of 95%. Thus, it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

Distributed data analytics for wireless sensor networks (WSNs) using fuzzy logic-based machine learning

Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis.

?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks

Sensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives.

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Fingerprint-Based Localization Approach for WSN Using Machine Learning Models

The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

TEEECH: Three-Tier Extended Energy Efficient Clustering Hierarchy Protocol for Heterogeneous Wireless Sensor Network

The energy efficiency in wireless sensor networks (WSNs) is a significant challenge. A clustering-based approach is an energy-saving approach in WSN. The practical clustering approach divides the network into various clusters that directly affect the total energy consumption and reduce the network lifetime of the WSN. As a result, this paper introduces novel energy-efficient clustering approaches for cluster head (CH) selection and cluster formulation. The cluster head (CH) selection is designed based on the threshold-based advanced LEACH (ADV-LEACH2) approach. The cluster formulation (sensor node distribution) among the cluster heads is done with modified fuzzy c-mean (MFCM) approaches. The clusters are formed using the MFCM approach, and cluster heads (CHs) are selected using ADV-LEACH2. The cluster formulation and head selection are selected at the beginning of each round. The MFCM approach forms balanced clusters and balances sensor node distribution among the cluster heads. The proposed approach maintains the cluster head energy. The experimental simulation outcome justifies that the proposed approach, TEEECH surpasses the existing clustering approach by an average of 13%. The comparison with DEC, SEP-E, EBCS, MEDDEEC, and EDDEEC presents the TEEECH approach to maintain the CH energy and prolong the network lifetime. The proposed approach, TEEECH, also significantly improves the network stability and lifetime based on Half node dead (HND).

Complete outlier detection and classification framework for WSNs based on OPTICS

Wireless Sensor Networks (WSNs) consist of multi-functional sensors with limited resources that collect information in various applications (medical, manufacturing, militarily, etc.). However, data gathered by sensors are susceptible to outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. This paper proposes a centralised outlier detection and classification approach for WSNs, which can distinguish between errors due to a faulty sensor and those due to an event. Also, it considers the spatial–temporal correlation between neighbouring sensor nodes and sensors’ data values. Our approach, titled OPTICS-K, combines the benefits of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm for clustering, the Inter-Cluster Distance (ICD) method and the K-Nearest Neighbours (KNN) algorithm for outlier detection. Furthermore, OPTICS-K uses a new method based on Kriging interpolation to classify the outliers. For evaluation, we conduct a comparison study between OPTICS-K and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the OPTICS-K outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy rate (ACC), F1_score and Area Under the Curve (AUC).

An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

AbstractMaintaining high residual energy is one of the major challenges of wireless sensor networks (WSNs), where nodes are moving at a random speed. In addition, data availability is also a major concern that must be maintained constantly during the packet transmission phase. In this research work, a Residual‐Energy‐Based Data Availability Approach (REDAA) for WSNs is developed to increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. During the first phase of the work, the network model, and assumptions for route creations are adopted to effectively initialize the data transmission phase. During the second phase, a cluster was formed, and two categories of cluster heads were created based on quadrature low energy adaptive clustering hierarchy (Q‐LEACH) and multi‐hop low energy adaptive clustering (MH‐LEACH) algorithms to formalize the route and to make data available whenever requested. During the third phase, energy conservation routes are initialized, and data gathering is improved through slot‐based code division multiple access schemes. Simulation results demonstrate that the proposed REDAA approach can achieve an improvement in terms of throughput and energy consumption by 37% and 70% respectively when compared to MH‐LEACH approach, whereas in comparison to Q‐LEACH approach, these improvements are found to be 30% and 73% respectively.

Application placement with shared monitoring points in multi-purpose IoT wireless sensor networks

The main function of a wireless sensor network (WSN) is to gather data from a certain region and transfer the data to a center or remote locations for further processing. The collected data can be of interest for many applications. Therefore, a physical WSN owned by a single provider can be utilized by many customer applications. Additionally, the data of a particular point or sub-region can satisfy the need of multiple applications. Hence, sensing the data only once in such cases is beneficial to reduce the energy consumption, network traffic and acceptance ratio of the applications. We call this as monitoring point based shared data approach. In this paper, we focus on the placement of applications each of which requires several points to be monitored in an area a WSN covers. We first propose such a monitoring point based shared data approach for WSNs that will serve multiple dynamic applications. We also propose two methods for application placement over a shared physical WSN: one greedy method and one genetic algorithm based method called GABAP. We did extensive simulation experiments to evaluate our algorithms. The results show the effectiveness of our methods in fast and close-to-optimum placement of applications over a single network.

Enhanced efficient outlier detection and classification approach for WSNs

Wireless Sensor Networks (WSNs) are one of the main components of the Internet of things (IoT) for gathering information and monitoring the environment in a variety of applications (medical, agricultural, manufacturing, militarily, etc.). However, data collected by sensors and sent to the base station are susceptible to have outliers. These outliers can occur due to sensor nodes themselves or to the harsh environment where they are deployed. Thus, the WSNs have to detect the outliers and take actions to ensure network quality of service (in terms of reliability, latency, etc.) and to avoid further degradation of the application efficiency. In this paper, we propose an enhanced approach of our previous work to achieve better performance for outlier issues in WSNs. The enhancement tackles the clustering phase and the outlier detection phase. The classification phase remains the same as in the EODCA work by using the Inverse Distance Weighting (IDW) method. The enhancement is titled EEODCA for Enhanced Efficient Outlier Detection and Classification Algorithm. For evaluation, we conduct a comparison study between EEODCA, EODCA and another work from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets showed that the EEODCA outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy Rate (ACC), F1_score and Area Under the Curve (AUC).

Energy consumption in cluster communication using mcsbch approach in WSN

Internet of Things (IoT) proposed a new digital computing paradigm enabling interaction between devices and machines. It deliberately creates connectivity between the internet, electronics, and other forms of hardware. A novel modern cluster supervisor-based cluster head selection algorithm (MCSBCH) is proposed for the Wireless Sensor Network (WSN). The proposed cluster supervisor mechanism is responsible for controlling and monitoring the network effectively. In this approach, the cluster supervisor is the heart of the network, and the whole mechanism work under its supervision. The Cluster supervisor (CS) monitors the node’s energy level and allocates CH (cluster head) node. Each node’s energy level is considered for electing the CH. Obviously when the cluster head energy level gets drained, then it allocates the next higher energy node as cluster head. The assigned CH is the next node with the highest energy level known as the backup node. This cluster supervisor (CS) is supported by the cluster head (CH) and other backup nodes in the network. The proposed MCSBCH is boosted with an enhanced clustering routing protocol. An experimental result is based on the aspect of a lifetime, energy consumption, and throughput, to test the proposed mechanism performance.

CFDDR: A Centralized Faulty Data Detection and Recovery Approach for WSN With Faults Identification

One of the most challenging problems in wireless sensor networks (WSNs) is detecting and recovering faulty data. Due to resource limitations, WSNs are subject to many failures, including hardware (permanent faults), software, and communication failures. To ensure data reliability, the data collected by WSNs must be clean of faults. This article presents a centralized faulty data detection and recovery approach, which can detect, recover, and recognize different types of faults (offset, gain, stuck-at, out of bound, and random faults). It operates in two phases: Faulty data detection and recovery and fault type identification (FTI). After detecting the fault, the faulty data will be discarded and replaced by an estimated value based on the Kalman filter. FTI provides a unique solution by determining the types of faults and report them to the end-user for an application-specific decision-making process. The effectiveness of the proposed approach is demonstrated through experimental results, with comparison to state-of-the-art techniques of the same application.