Components Of Wireless Sensor Networks Research Articles

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

SummaryA fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.

Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

A new method based on swarm intelligence with encrypted data in wireless sensor networks

Wireless sensor networks (WSNs) technology is one of the most essential Internet of things technologies. It is utilized efficiently in a variety of real-world applications, including healthcare, environmental monitoring, tracking, etc. WSNs are composed of sensor nodes with restricted resources. However, the communication between WSN components is not secure. Therefore, it is necessary to build efficient and lightweight cryptographic algorithms to secure shared data. Our paper comprises proposes a secure protocol called grasshopper optimization algorithm routing protocol (GOARP) with a lightweight encryption method in each sensor called rivest cipher 5 (RC5) to enhance network efficiency and simulation in terms of power consumption, required memory space, and computational time. Subsequently, the network lifetime result achieved in the proposed method is about (70%) more than in GOA elliptic curve cryptographic and Diffie Hellman (GOA-ECCDH).

Security In Wireless Sensor Networks Based On Lightweight Algorithms : An Effective Survey

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance penalty. Due to the massive calculations required by conventional public-key and secret encryption methods, information security in this limited context calls for light encryption techniques. In many applications involving sensor networks, security is a crucial concern. On the basis of traditional cryptography, a number of security procedures are created for wireless sensor networks. Some symmetric-key encryption techniques used in sensor network setups include AES, RC5, SkipJack, and XXTEA. These algorithms do, however, have several flaws of their own, including being susceptible to chosen-plaintext assault, brute force attack, and computational complexity.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).

ILSM: Incorporated Lightweight Security Model for Improving QOS in WSN

In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, Tiny OS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the quality of service. But due to the high cost of the network, WSN components have limited resources to manage the network. There is a need to design a lightweight solution that ensures the quality of service in WSN. In this given manner, this study provides the quality of services in a wireless sensor network with a security mechanism. An incorporated hybrid lightweight security model is designed in which random waypoint mobility (RWM) model and grey wolf optimization (GWO) is used to enhance service quality and maintain security with efficient routing. MATLAB version 16 and Network Stimulator 2.35 (NS2.35) are used in this research to evaluate the results. The overall cost factor is reduced at 60% without the optimization technique and 90.90% reduced by using the optimization technique, which is assessed by calculating the signal-to-noise ratio, overall energy nodes, and communication overhead.

A Comprehensive Study on Security in Wireless Sensor Networks (WSNs).

Wireless Sensor Networks(WSNs) are an important component of today's ubiquitous and pervasive computing. Without WSNs, the applications aren't as clever as they could be. Almost every scenario involving WSNs necessitates a quick and precise localization process. Existing frameworks and algorithms, on the other hand suffer from a significant disadvantage when it comes to beacon node trust, which is a critical component in Wireless Sensor Network. For localization this has to be ensured.. This issue is addressed in our current solution. In the harsh environment of WSN operations, malicious nodes are inescapable. As a consequence, A technique has been proposed to find out the problem while simultaneously offering a safe trust based localization system. It focuses on the algorithm for assessing trust and the creation of blockchains. Every beacon node’s truth value(trust value) are determined using various trust criteria with the corresponding weights being dynamically changed during localization process. After that the most reliable beacon nodes are chosen for mining. This two-step process ensures that the blockchain is kept up to current, and that beacon nodes have consistent Tvalues(Trust values). We conducted a series of simulations to test the suggested algorithm’s performance and effectiveness. The accuracy of localization, harmful activity detection, the confusion matrixes are used to compare results.

Grey Hole Attack Detection and Prevention Methods in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) gained wide attention in the past decade, thanks to its attractive features like flexibility, monitoring capability, and scalability. It overcomes the crucial problems experienced in network management and facilitates the development of diverse network architectures. The existence of dynamic and adaptive routing features facilitate the quick formation of such networks. But flexible architecture also makes it highly vulnerable to different sorts of attacks, for instance, Denial of Service (DoS). Grey Hole Attack (GHA) is the most crucial attack types since it creates a heavy impact upon the components of WSN and eventually degrades the performance of network. In current study, a simple attack detection, prevention and reduction approach is proposed. This is to secure the WSN from GHA and other such attacks by warning and blocking the malicious suspensions and by examining the storage table. Instead of blocking the entire host, the presented approach specifically eliminates the malicious nodes. Further, in case of no malicious traffic detection, the host gets unblocked. In current study, the researchers simulated the model under MATLAB environment and the outcomes showed an enhanced performance and increased utilization of Central Processing Unit (CPU) and packet delivery ratio.

From the Wireless Sensor Networks (WSNs) to the Web of Things (WoT): An Overview

In the last two decades, Wireless Sensor Networks (WSNs) are gaining more popularity, where the concept of WSN always exists when everything connects. Almost of WSN applications cover wide area and large spaces for assessing and monitoring certain phenomenon. Moreover, WSN components have been integrated in daily life objects or things (object, place, and person), so that they could be monitored and controlled. As a result, a new paradigm called the Internet of Things (IoT) connects WSN components to the Internet to be globally monitored and controlled representing the surrounding environmental events and conditions. The future IoT is called the Web of Things (WoT), which visualizes the IoT data (sensory data) using current web tools and services (HTTP, RESTful services). This paper presents an overview of the WSNs, the IoT and its future paradigm (WoT) discussing key elements, main layers, main challenges, and annotation formats.

Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.

Numerous Clustering Techniques with Application and Limitations in Wireless Sensor Network

Wireless Sensor Network (WSN) is a very common area to work with different technology which is very useful for real time application. There are various application works with the help of WSN .some of internet of things, machine learning, big data etc. in WSN there are four basic component of WSN that is sensing unit, transceiver unit, processing unit, and storage unit. Energy is the important critical design issue in WSN. By applying clustering approach we can save the energy and attain load balancing of a network. Sensor nodes are viewed as homogeneous since the looks into in the field of WSNs have been advanced, however, a few nodes might be of various energy levels and it is draw out the lifetime of a WSN and its unwavering quality. Clustering methods are required so that sensor networks can work long time with different area. In this paper, we discussed the various clustering techniques those are helpful to maximize the lifespan of WSN.

A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.

IoT Based Real-time River Water Quality Monitoring System

Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

A novel SMP-based survivability evaluation metric and approach in wireless sensor network

In Industrial Internet of Things (IIoT) device and network, wireless sensor network (WSN) is an important component. Routing protocol is the critical component of WSN. As the WSN may be attacked by all kinds of intruders, the survivability of WSN is important to IIoT security. To precisely evaluate the systematic survivability ability under external attack and internal security mechanism, a novel survivability entropy-based quantitative evaluation metric is proposed to calculate the systematic survivability ability of WSN routing protocol. Numerical analysis and simulation experiments are combined to precisely calculate the survivability entropy metric. To validate the evaluation approach, NS2 (Network simulator) is used to simulate the DoS attack and security mechanism in WSN. Experimental results show that the novel survivability evaluation metric and method can precisely evaluate the systematic survivability ability of WSN.

An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture

In this paper the usage of Wireless Sensor Networks (WSN) in smart agriculture applications was analyzed. The main focus of the paper is on the power consumption of the various WSN components, on the both levels, physical and functional. The analysis, from the energy efficiency aspect, includes a comparative review and discussion of the most commonly used protocols on the physical, data link and network layers. The analysis outcome provides a precise identification of the main power consumers, the magnitude of their consumption, and a deep understanding of the key mechanisms that should be applied in order to improve the energy efficiency in a WSN. The analysis also includes simulation of a WSN operation in a smart agriculture application. The simulation scenario and the measured values of the average power consumption and the average time of activity of the radio component of each network node provide a confirmation of the key points of the previously performed analysis and detailed insights into the possible directions of the strategy for energy efficiency improvement. Additionally, the simulation results reveal the magnitude of the energy savings that can be accomplished by deploying the duty cycle mechanisms within the WSNs. Finally, the paper includes a discussion about various factors and the way they impact the level of energy efficiency, which have to be addressed within the requirements gathering, comprehensive analysis and the design phases of a WSN life cycle implementation.

Web Browser Based Data Visualization Scheme for XBee Wireless Sensor Network

Wireless sensor network (WSN) plays an important role in the infrastructure of Internet of Things (IoT). Data visualization is an essential component in WSN to facilitate data scientists to interpret information clearly and efficiently. In this paper, we conduct a study of XBee based WSN which integrates the DASH data visualization scheme for building a web-browser based application without using HTML or JavaScript. The data collected from wireless sensors in a WSN were displayed in a web browser with interactive functions. The proposed visualization scheme is real-time, cross platform, and hardware independent. Thus, it could be easily employed on any operating system. Experimental results demonstrated that our WSN data visualization scheme using XBee Python package and Plotly's DASH is feasible for IoT applications like smart buildings, environment monitoring, as well as other WSN applications.

RMAC - A Lightweight Authentication Protocol for Highly Constrained IoT Devices

Nowadays, highly constrained IoT devices have earned an important place in our everyday lives. These devices mainly comprise RFID (Radio-Frequency Identification) or WSN (Wireless Sensor Networks) components. Their adoption is growing in areas where data security or privacy or both must be guaranteed. Therefore, it is necessary to develop appropriate security solutions for these systems. Many papers have proposed solutions for encryption or authentication. But it turns out that sometimes the proposal has security flaw or is ill-suited for the constrained IoT devices (which has very limited processing and storage capacities).In this paper, we introduce a new authentication protocol inspired by Mirror-Mac (MM) which is a generic construction of authentication protocol proposed by Mol et al. Our proposal named RMAC is well suited for highly constrained IoT devices since its implementation uses simple and lightweight algorithms. We also prove that RMAC is at least as secure as the MM protocol and thus secure against man-in-the-middle attacks.

무선센서네트워크에서 센서 데이터 손실과 오류 감소를 위한 데이터 압축 방법

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/ minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

ProTru: a provenance‐based trust architecture for wireless sensor networks

SummaryTrust is an important component of wireless sensor networks for believability of the produced data, and trust history is a crucial asset in deciding trust of the data. In this paper, we show how provenance can be used for registering previous trust records and other information such as node type, data type, and node location. Our aim is to design a distributed trust‐enhancing architecture using only local provenance during sensor fusion with a low communication overhead. Our network is cognitive in the sense that our system reacts automatically upon detecting low trust. Copyright © 2016 John Wiley & Sons, Ltd.