Sensor Nodes In Wireless Sensor Networks Research Articles

Analysis of ANN Routing Method on Integrated IOT with WSN

Internet of Things (IoT) in recent times offers a greater amount of flexible design during the deployment of it in the form of network. The interfacing of IoT with a wireless sensor network (WSN) requires an optimal solution to preserve the consumption of energy while the data has been transmitted via nodes. In this paper, we develop machine learning (ML)-assisted routing to route the data packets of IoT sensors from the real-time environment over WSN. In this study, we use three different planes for optimal routing of packets from the source node to the destination node. The source node is the IoT sensors that involve data collection, and the intermediate nodes are the WSN sensor nodes that route the packets. Optimal routing decisions based on the pre-trained data in an artificial neural network (ANN) stabilize the routing of data packets without congestion. The simulation is conducted to test the efficacy of the ANN routing in the integrated network, and the results show that the ANN-based routing achieves higher energy efficiency and throughput than other models.

Intelligent Trajectory for Mobile Element in WSNs with Obstacle Avoidance

In wireless sensor networks (WSNs), mobile sink-driven data acquisition can mitigate hotspot issues, which further increases WSN efficiency, such as throughput, lifetime, and energy efficiency, while reducing delay and packet loss. Recently, most mobile sink algorithms have focused on efficient paths, and few consider obstacles in the network environment. Nevertheless, constructing an obstacle-aware trajectory in a WSN is challenging. In this context, this paper proposes a bug algorithm based on an obstacle-aware intelligent trajectory (CSOBUG) for a mobile sink to acquire data from sensor nodes in WSNs efficiently with the help of cat swarm optimization (CSO). The proposed CSOBUG algorithm has two phases: selecting visiting points and constructing a trajectory. A CSO-based clustering approach is used to select visiting points, and a bug algorithm is used to select a trajectory. Comparing CSOBUG with existing techniques, it is found that CSOBUG is less computationally intensive than the existing techniques. As well as outperforming traditional methods based on multiple performance metrics, the CSOBUG achieves superior results in a variety of scenarios.

Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory Strategy-Directed MADDPG Algorithm

This paper presents an in-depth study of the application of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms with an exploratory strategy for duty cycle scheduling (DCS) in the wireless sensor networks (WSNs). The focus is on optimizing the performance of sensor nodes in terms of energy efficiency and event detection rates under varying environmental conditions. Through a series of simulations, we investigate the impact of key parameters such as the sensor specificity constant α and the Poisson rate of events on the learning and operational efficacy of sensor nodes. Our results demonstrate that the MADDPG algorithm with an exploratory strategy outperforms traditional reinforcement learning algorithms, particularly in environments characterized by high event rates and the need for precise energy management. The exploratory strategy enables a more effective balance between exploration and exploitation, leading to improved policy learning and adaptation in dynamic and uncertain environments. Furthermore, we explore the sensitivity of different algorithms to the tuning of the sensor specificity constant α, revealing that lower values generally yield better performance by reducing energy consumption without significantly compromising event detection. The study also examines the algorithms' robustness against the variability introduced by different event Poisson rates, emphasizing the importance of algorithm selection and parameter tuning in practical WSN applications. The insights gained from this research provide valuable guidelines for the deployment of sensor networks in real-world scenarios, where the trade-off between energy consumption and event detection is critical. Our findings suggest that the integration of exploratory strategies in MADDPG algorithms can significantly enhance the performance and reliability of sensor nodes in WSNs.

SVM‐SFL based malicious UAV detection in wireless sensor networks

SummaryIn the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

Energy Consumption Analysis for Continuous Phase Modulation in Smart-Grid Internet of Things of beyond 5G.

Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications. However, the energy consumption (EC) characteristic of sensor nodes is a key factor that affects the operational performance (e.g., lifetime of sensors) and the total cost of ownership of WSNs. In this paper, to find the modulation techniques suitable for WSNs, we investigate the EC characteristic of continuous phase modulation (CPM), which is an attractive modulation scheme candidate for WSNs because of its constant envelope property. We first develop an EC model for the sensor nodes of WSNs by considering the circuits and a typical communication protocol that relies on automatic repeat request (ARQ)-based retransmissions to ensure successful data delivery. Then, we use this model to analyze the EC characteristic of CPM under various configurations of modulation parameters. Furthermore, we compare the EC characteristic of CPM with that of other representative modulation schemes, such as offset quadrature phase-shift keying (OQPSK) and quadrature amplitude modulation (QAM), which are commonly used in communication protocols of WSNs. Our analysis and simulation results provide insights into the EC characteristics of multiple modulation schemes in the context of WSNs; thus, they are beneficial for designing energy-efficient SG-IoT in the beyond-5G (B5G) and the 6G era.

Decentralized fault tolerant source localization without sensor parameters in wireless sensor networks

In this paper, we study the source (event) localization problem in decentralized wireless sensor networks (WSNs) under faulty sensor nodes without knowledge of the sensor parameters. Source localization has many applications, such as localizing WiFi hotspots and mobile users. Some works in the literature localize the source by utilizing the knowledge or estimates of the fault probability of each sensor node or the region of influence of the source. However, this paper proposes two approaches: the hitting set and feature selection for estimating the source location without any knowledge of the sensor parameters under faulty sensor nodes in WSN. The proposed approaches provide better or comparable source localization performances. For the hitting set approach, we also derive a lower bound on the required number of samples. In addition, we extend the proposed methods for localizing multiple sources. Finally, we provide extensive simulations to illustrate the performances of the proposed methods against the centroid, maximum likelihood (ML), fault-tolerant ML (FTML), and subtract on negative add on positive (SNAP) estimators. The proposed approaches significantly outperform the centroid and maximum likelihood estimators for faulty sensor nodes while providing comparable or better performance to FTML or SNAP algorithm. In addition, we use real-world WiFi data set to localize the source in comparison to the support vector machine based estimator in the literature, where the proposed methods outperformed the estimator.

Analysis and Improvement on an Authentication Scheme for Wireless Sensor Networks in Internet of Things Environment

Nowadays, Internet of Everything has become a major trend, and Internet of Things (IoT) has emerged. Wireless sensor networks (WSNs) are core technologies for IoT to sense the real world. Due to the unattended and resource-constrained characteristics of WSNs, it is a great challenge to design an efficient and secure authentication scheme for communication between users and sensor nodes in WSNs. Recently, Hu et al proposed an authentication scheme for WSNs in an IoT environment. They claimed that their scheme could maximize the balance between security and computational cost as well as efficiency, and be resistant to many known attacks. However, we find that the scheme is difficult to resist stolen smart card attack and denial-of-service attack. Moreover, during the login and key negotiation phase of the scheme, Gateway (GWN) is unable to extract key values for subsequent computation based on the messages sent by the sensor nodes, which in turn leads to the inability to achieve mutual authentication and key agreement. To overcome these shortcomings, we propose an improved scheme. The proposed scheme enables real-time data exchange and transmission as well as secure communication between users and sensor nodes.

An Improved Genetic Algorithm for Scheduling Sensor Nodes in WSN

The extended lifetime of Wireless Sensor Networks (WSN) is an attractive goal for various types of research. This can be achieved if not all sensor nodes in the network consume their energy; this is because the energy consumption of every sensor node is a vital resource for a WSN. The scheduling techniques have recently enticed the interest of the researchers’ community, as they provide the ability to adjust a set of nodes in sleep mode instead of activating all sensor nodes. However, we consider the sensors that were selected to be in sleep mode, which will not affect network coverage for any target or full connectivity. In this paper, the genetic algorithm has been used to build efficient scheduling for the sensor nodes, which were based on multiple objectives in the fitness function, and we have proposed improved mutation and crossover operations. Then, we evaluate our approaches in a target tracking application compared with previous GA approaches. Our simulations show that we have an optimal chromosome that contains a minimum number of active sensor nodes for scheduling within 3-5 iterations.

An optimized multi-hop routing protocol for wireless sensor network using improved honey badger optimization algorithm for efficient and secure QoS

A wireless sensor network (WSN) is a significant technology that might contribute in the industrial revolution. Sensor Nodes (SNs) in WSNs are equipped with a battery as a power source. Energy is the most critical resource for WSNs since the battery cannot be replaced or refilled. Several solutions have been devised and implemented over the years to preserve WSNs, which are a scarce resource. This study suggested a model to solves the energy issue as well as security issue and presents an optimized multi-hop routing in WSNs based on improved Honey Badger Algorithm (I-HBA). This method for multi-hop routing involves two steps: selecting a Cluster Head (CH) and routing the data packets. The I-HBA is used to pick energy-efficient CHs for efficient data transfer. The SNs then send their data through the chosen CH, which forwards it to the base station (BS) using the most efficient number of hops. The suggested I-HBA is used to select the finest hops. The proposed model employs the multi-objective fitness function including eight parameters for effective routing in the WSNs. Furthermore, security conscious multihop routing is carried out by employing a trust model that includes direct and indirect trust, data, integrity and forwarding rate factor. Additionally, the proposed I-HBA based routing protocol compared with the existing protocol such as LEACH, HEED, and PSO; and the proposed protocol outperforms the existing models. The findings show that for the same number of iterations, the whole quantity of data packets receives by the WSN using the proposed I-HBA based multihop routing protocol is substantially larger than for the PSO, LEACH, and HEED-based models.

An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning

Wireless Sensor Networks (WSNs) play a crucial role in Precision Agriculture by providing real-time data on various environmental parameters like temperature, humidity, soil moisture, etc. However, the efficient utilization of energy in the sensor nodes of WSNs is a major challenge that needs to be addressed. To address this issue, a new multi-objective clustering approach is introduced in this work for grouping the sensor nodes of WSNs. Moreover, a multi-objective hybrid optimisation technique called Election based Aquila Optimizer (EAO) which is the combination of Aquila Optimizer (AO) and the Election-Based Optimisation Algorithm (EBOA) is proposed in this work to make sure that the Cluster Head (CH) selection process in WSNs to identify the best CH. In addition, the proposed method incorporates the newly developed optimization technique with convolutional neural network (CNN) as an Optimized CNN (O-CNN) to improve the clustering algorithm's precision and also enhance the training accuracy and testing accuracy. The proposed approach is evaluated through experiments and proved as better than other approaches by obtaining 99.23% as classification accuracy, 76.92% as throughput, 99% as packet delivery ratio, 98.24% as network lifetime and 50% as maximum energy consumption and it resolves a significant difficulty in precision agriculture.

New scheme of WSN routing to ensure data communication between sensor nodes based on energy warning

Among the different challenges related to the improvement of wireless sensor network (WSN) performance, the optimization of energy consumption takes an important place. Basically, sensor nodes in WSN are powered by limited battery. These batteries can allow sensor nodes to be functional for a limited period of time then sensor nodes turn out to be dead. Enhancing energy consumption and finding new manners to save sensor nodes' energy is becoming essential and more challenging. Improving energy consumption for each sensor nodes leads to enhance the lifetime of WSN. Clustering is among the outstanding strategies to reach this goal. In this paper, a new scheme of routing protocol in WSN will be presented in order to minimize the energy consumption and improve WSN lifetime. The new scheme is proposed in order to find the best selection of cluster head (CH) by taking in consideration the energy at each sensor node. It has been proposed based on Firefly Algorithm. We have compared the proposed scheme with LEACH, EAMMH, SEP, E-SEP, BRE, NEAHC and WEB protocol. Experimental results show that the proposed scheme yields better performance than the compared routing protocols in terms of better energy consumption and packet delivery between sensor nodes and base station. Results obviously prove that the proposed scheme could improve the WSN lifetime.

Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

AbstractThe protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Certificateless Public Auditing Scheme With Data Privacy and Dynamics in Group User Model of Cloud-Assisted Medical WSNs.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Optimization Network Lifetime Through Residual Energy-Based Cluster Head Selection in IoT-Enabled WSNs

Wireless Sensor Networks (WSNs) have gained an emerging importance in different application domains especially in event tracking and monitoring. The sensor nodes in WSNs are observed to have shorter lifetime due to the continuous sensing and processing operations that result in quicker energy depletion. Small, inexpensive, low-power, multipurpose nodes that are connected to one another form the basis of WSNs. Efficiently gather & communicate data to a washbasin. Cluster Heads (CHs) are used in cluster-based approaches to effectively arrange WSNs for data collection and energy conservation. A CH collects data from cluster nodes and aggregates/compresses it before sending it to a sink. The node's greater responsibility does, however, result in a higher energy drain, which leads to uneven network deterioration. This is made up for by LEACH (Low Energy Adaptive Clustering Hierarchy), which probabilistically alternates CH roles among nodes with energy over a set threshold. CH selection in WSN is NP-Hard because optimal data aggregation with effective energy savings cannot be done in polynomial time. To improve system performance, the synchronous firefly approach, a modified firefly heuristic, is introduced in this paper. A thorough simulation shows that the suggested method performs better than LEACH and energy-efficient hierarchical clustering. In today's world of intelligent networks, the internet of things (IoT) and industrial IoT (IIoT) are extremely important, and they fundamentally use a wireless sensor network (WSN) as a perception layer to collect the necessary data. The difficulty here is the usage of minimal energy for processing and communication. This data is processed as information and sent to cloud servers through a base station. The lifespan of WSNs is increased by the dynamic generation of cluster heads and energy-conscious clustering strategies.

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

Cluster based energy efficient routing protocol for heterogeneous wireless sensor networks

SummaryWith the advancement in recent technologies, wireless sensor networks (WSNs) have emerged to become one of the key research areas. Sensor nodes in WSN are deployed randomly to sense the events that occur in an area. Clustering of sensor nodes in a wireless sensor network will result in better energy efficiency and topology management. In many scenarios, wireless sensor networks are deployed in remote areas and in hostile environments where batteries cannot be recharged or replaceable. In real world applications, heterogeneous sensor nodes with different initial energies in a WSN are more preferable to prolong stability period, network lifetime, and throughput. The main objective of this article is to design and develop a cluster‐based energy efficient routing protocol for heterogeneous wireless sensor networks (CEER). The proposed CEER routing protocol is based on the residual energy of all the sensor nodes in each round, distance of each sensor node from the base station, weighted election probabilities and reliability of a sensor node and the network. It is found from the results that, the proposed CEER routing protocol provides better stability, network lifetime, and throughput as compared with the existing routing protocols of heterogeneous wireless sensor networks.

Cryptanalysis and improvement of an authentication protocol for wireless sensor networks

SummaryThe Wireless Sensor Network (WSN) is a network which is composed of many sensor nodes. The sensor nodes in WSN are widespread and lightweight which are deployed for environmental or system monitoring. These sensor nodes within the WSNs communicate with one another using wireless lines. WSNs have practical applications in numerous fields such as smart city, medicine, military, agriculture, and so forth. It is easy for an adversary to intercept the network and steal the valuable information. Consequently, these sensor nodes need enhanced security. Recently, Kwon et al have proposed an authentication scheme for WSNs. However, in this paper, we analyze the security of their scheme and discover that their protocol fails to provide perfect forward security and user untraceability. Also their protocol fails to withstand against session specific random number leakage attack, privileged insider attack and password guessing attack. Their protocol uses only two‐factor authentication scheme. In order to overcome the weaknesses of their protocol, we develop a secure three‐factor authentication protocol which is based on symmetric key cryptography. We use ProVerif tool to perform its formal security verification and use random oracle model for its formal security analysis. We also show the correctness of mutual authentication of the proposed scheme using BAN logic. Its informal security analysis indicates that the designed scheme can withstand against various known attacks. In the performance analysis, we show that the proposed scheme offers more security features than the other related schemes and has less communication cost as compared to Kwon et al protocol.

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.