Wireless Sensor Networks Network Research Articles

An effective metaheuristic based optimized type‐II fuzzy inference system for fault node identification in wireless sensor network

SummaryDeploying a wireless sensor network (WSN) for accessing the network of distant environments is more crucial. Node defect detection is a core technology of WSN and is essential for most WSN networks. The defective node reduces the overall service quality (OSQ) of the global WSN network system. Therefore, a type II fuzzy inference system (T2_FIS) is proposed for detecting defective nodes in the WSN network. The adaptive genetic algorithm (AGA) and proposed method are introduced for tuning the parameters in the T2_FIS system. The proposed T2_FIS method effectively detects the broken node in the WSN network using the fuzzy rules. In addition, the improved Mud Ring (IMR) optimization algorithm is proposed to replace the faulty node with the neighborhood node in the network. The defective nodes are identified and replaced with the closest nodes based on the membership function (MF) generated by the T2_FIS. Furthermore, the lifetime and throughput of the WSN network are increased by minimizing energy consumption. The overall performance is evaluated using the MATLAB tool, and the implementation results are compared with the existing methods. The total energy consumption for the proposed method is 50.451, with a throughput and lifetime of 153.657 and 22979.25. Furthermore, the performance metrics for the existing and proposed strategies are analyzed, and the accuracy of the proposed method is proven to be 99.3827%, the false alarm rate (FAR) is 0.0008, and the false positive rate (FPR) is 0.0617. The results show the effectiveness and performance of the proposed method.

Localization and Detection of Sinkhole Attacks in Wireless Sensor Networks Based on Denial of Service (DoS) Attacks

A wireless sensor network (WSN) uses uniformly deployed and positioned sensors to regularly send sensed data to a centralized station. Sinkhole attacks, in which a malicious node draws packets from other legitimate sensor devices and drops them, are the main danger to the WSN network layer and continue to be a difficult problem on wireless sensor networks. Security becomes a major issue since these networks have limited assets including less memory, less energy, and less transmission capacity. Additionally, given the dynamic context in which they are deployed, they are vulnerable to a variety of Denial of Service (DoS) assaults, including wormhole, sinkhole, and black hole attacks. The most dangerous routing attack at the network layer is known as a "sinkhole assault," which directs all network traffic onto a fake path by sending false information under the impression that it is the quickest one to the server. We explore sinkhole attacks to prevent them, and this work proposes a method of detection based on the redundancy process. Messages are transmitted across multiple paths to the suspicious nodes. Upon a thorough evaluation of the response, the attacked nodes are ultimately verified. Finally, a simulation is run to evaluate the method's efficacy. Furthermore, the simulation indicates that the strategy might be somewhat successful. Finally, it draws attention to difficulties and offers a prospective viewpoint for identifying sinkhole attacks.

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

Design of routing protocol for enhancing quality of service in wireless ad hoc and sensor netw ork: LEQA

<p>Wireless sensor networks (WSNs) are now adopting mobile sensors due to their increased popularity in research and industry. To enhance WSNs' performance, mobility can be utilized to gather data. But, if the collector's route is fixed and movement is not manageable, current quality of service (QoS) strategies and protocols are ineffective in achieving timely data delivery while maintaining energy efficiency. In the real world, WSN networks use both actuator - actuator and sensor - actuator coordination. To conserve energy in communicati on tasks with heavy traffic and high volume, sensors/actuators can be relocated to desired locations. This study introduces a routing protocol that optimizes delivery latency and energy conservation in WSNs. The proposed latency, energy, and quality of ser vice aware (LEQA) protocol uses a cooperative approach to track the sink and coordinate communication between sensors and actuators. Each sensor schedules its time division multiple access (TDMA) to improve QoS metrics such as low energy consumption, low l atency, or packet loss. It also addresses sensor - actuator coordination and proposes a data communication protocol for efficient and fast communication with actuator nodes. This reduces energy consumption and minimizes latency.</p>

A Balanced Intrusion Detection System for Wireless Sensor Networks in a Big Data Environment Using CNN-SVM Model

Wireless Sensor Networks (WSNs) were exposed to several distinct safety issues and attacks regarding gathering and sending data. In this scenario, one of the most prevalent WSN assaults that may target any tier of the protocol stack is the Denial of Service (DoS) attack. The current research suggested various strategies to find the attack in the network. However, it has classification challenges. An effective ensemble deep learning-based intrusion detection system to identify the assault in the WSN network was, therefore, suggested in this research to address this issue. The data pre-processing involves converting qualitative data into numeric data using the One-Hot Encoding technique. Following that, Normalization Process was carried out. Then Manta-Ray Foraging Optimization is suggested to choose the best subset of features. Then Synthetic Minority Oversampling Technique (SMOTE) oversampling creates a new minority sample to balance the processed dataset. Finally, CNN–SVM classifier is proposed to classify the attack kinds. The Accuracy, F-Measure, Precision, and Recall metrics were used to assess the outcomes of 99.75%, 99.21%, 100%, and 99.6%, respectively. Compared to existing approaches, the proposed method has shown to be extremely effective in detecting DoS attacks in WSNs.

Energy-efficient routing protocol for wireless sensor networks based on progressive and concentric clusters

Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In comparison to alternative parking lot management protocols already in use, the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.

Sinkhole detection and prediction using watermarking (SNDW)

Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique.

Energy efficiency enhancement in wireless sensor network: ant colony optimization

<p><span>Wireless sensor networks (WSNs) have received significant scholarly attentionwithin the scientific community lately. The use of small and energy efficient devices with restricted computing capabilities has gained traction in various fields of application, including environmental monitoring, target tracing, and medical health observation. Node localization is a fundamental system parameter in numerous applications. Categorizing hybrid solutions as either range-based or range-free poses a challenge in general. In this article, we aim to simplify the classification process by examining range-based and range-free schemes. There are two distinct classifications of schemes, namely fully schemes and hybrid schemes. Furthermore, we conduct a comparative analysis of the predominant energy efficiency measures in WSN networks and deliberate on prospective avenues for research in WSNs deploying ant colony algorithm.</span></p>

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.

Development of a wireless sensor network simulator using energy-efficient traffic routing methods

This document represents possible algorithms and methods for the analysis, optimization, and control of wireless sensor networks. The main goal of the work is to find the optimal implementation of the particle swarm optimization method for solving the problem of improving the energy consumption of such networks. The result of the work is a software package for simulating WSN (Wireless sensor network) networks using all the methods of routing and optimization of WSN networks presented in the work. The usage of a combination of routing schemes and different systems of device state management raises the wireless sensor network lifetime and reduces the power consumption of the network. The developed and presented in the work network simulator allows any developer to design and test different network topologies with various network parameters. Such software will make it easy to design such networks both for large enterprises and independent developers.

Analysis of Life Time Improvement Energy Efficient Risk-Free Protocol (ERP) for Data Transmission in Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have come to be seen as the fundamental architecture that paved the way for the development of the Internet of Things (IoT). However, a challenging problem develops when WSNs are connected to the IoT because of their nodes' high energy consumption and low network lifetime. As a result, the core concepts of WSNs include energy limits in sensor nodes, sensor data exchange, and routing protocols. The enhanced smart-energy-efficient routing protocol (ESEERP) presented in this study addresses the aforementioned shortcomings by extending the network's lifespan and enhancing connectivity. Since the Internet of Things (IoT) demands a significant amount of energy for sensing, processing, and data transport, the IoT application in wireless sensor networks (WSNs) poses substantial issues in maintaining network durability. As a result, multiple traditional algorithms incorporate different optimization techniques to improve the performance of WSN networks. We utilize IPv4 in wireless sensor networks to transmit data (packets), however, this strategy prevents users from accessing big amounts of data over long distances since it may need a lot of power to transmit large amounts of data using IPv4. The Internet of Things (IoT) can be used to solve this problem because it connects small battery-operated devices like sensors as well as consumer electronics and home appliances. One of the most often utilised wireless methods for sensor network connectivity is the IEEE 802.11 standard.

Quality of Service (QoS) Performance Analysis in a Traffic Engineering Model for Next-Generation Wireless Sensor Networks

Quality of Service (QoS) refers to techniques that function on a network to dependably execute high-priority applications and traffic reliably run high-priority applications and traffic even when the network’s capacity is limited. It is expected that data transmission over next-generation WSNs (Wireless Sensor Networks) 5G (5th generation) and beyond will increase significantly, especially for multimedia content such as video. Installing multiple IoT (Internet of Things refers to the network of devices that are all connected to each other) nodes on top of 5G networks makes the design more challenging. Maintaining a minimal level of service quality becomes more challenging as data volume and network density rise. QoS is critical in modern networks because it ensures critical performance metrics and improves end-user experience. Every client attempts to fulfill QoS access needs by selecting the optimal access device(s). Controllers will then identify optimum routes to meet clients’ core QoS needs in their core network. QoS-aware delivery is one of the most important aspects of wireless communications. Various models are proposed in the literature; however, an adaptive buffer size according to service type, priority, and incoming communication requests is required to ensure QoS-aware wireless communication. This article offers a hybrid end-to-end QoS delivery method involving customers and controllers and proposes a QoS-aware service delivery model for various types of communication with an adaptive buffer size according to the priority of the incoming service requests. For this purpose, this paper evaluates various QoS delivery models devised for service delivery in real time over IP networks. Multiple vulnerabilities are outlined that weaken QoS delivery in different models. Performance optimization is needed to ensure QoS delivery in next-generation WSN networks. This paper addresses the shortcomings of the existing service delivery models for real-time communication. An efficient queuing mechanism is adopted that assigns priorities based on input data type and queue length. This queuing mechanism ensures QoS efficiency in limited bandwidth networks and real-time traffic. The model reduces the over-provisioning of resources, delay, and packet loss ratio. The paper contributes a symmetrically-designed traffic engineering model for QoS-ensured service delivery for next-generation WSNs. A dynamic queuing mechanism that assigns priorities based on input data type and queue length is proposed to ensure QoS for wireless next-generation networks. The proposed queuing mechanism discusses topological symmetry to ensure QoS efficiency in limited bandwidth networks with real-time communication. The experimental results describe that the proposed model reduces the over-provisioning of resources, delay, and packet loss ratio.

A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

The Wireless Sensor Network (WSN) is a network of Sensor Nodes (SN) which adopt radio signals for communication amongst themselves. There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Data security, detection of faults, management of energy, collection and distribution of data, network protocol, network coverage, mobility of nodes, and network heterogeneity are some of the issues confronted by WSNs. There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data. Towards the goal of boosting the mobility-based WSNs’ network performance and energy, data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy (LEACH-M) and Energy Efficient Heterogeneous Clustered (EEHC) scheme have been examined in this work. A novel Artificial Bee Colony (ABC) algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station (BS) with least energy utilization. There is avoidance of the local optima problem at the time of solution space search in this proposed technique. Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

Energy‐efficient animal tracking with multi‐unmanned aerial vehicle path planning using reinforcement learning and wireless sensor networks

AbstractIn recent years, the integration of wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) has been a popular research domain in animal tracking due to animal habitats in harsh environments. A well‐designed WSN network provides a robust mechanism to detect the animal appearances which can be then collected by UAVs. In this paper, we first design a WSN‐based network model that eliminates the deficiencies of traditional WSNs by intelligently building a WSN topology. Second, we propose a multi‐agent (UAVs) Q‐learning‐based trajectory planning algorithm, which enables UAVs to acquire the detection information of the wild animal on time. Q‐learning algorithm coordinates the UAVs to visit WSN nodes containing fresh animal detection data in a timely manner according to past experience. The experimental results denotes that the proposed animal tracking system delivers significantly higher timely animal detection messages of 45% than the existing algorithms and the single agent Q‐learning approaches. The results also show that since the Zebras' movement contains some randomness, relatively high epsilon value (0.2) in the epsilon‐greedy mechanism of the Q‐learning provides highest rewards (timely delivered messages) for agents (UAVs). In the light of the results, it is clearly seen that intelligently designed WSN and UAV swarm combination could enable tracking of the wild animal in near real time.

Wireless sensor and wireless body area network assisted biosensor network for effective monitoring and prevention of non-ventilator hospital-acquired pneumonia

Air pollution, climate change, and chemical exposure constitute the world's most significant environmental health concern, resulting in the early deaths of 6. 5 million people annually. Reducing child mortality from preventable causes, primarily pneumonia and other respiratory illnesses, would have contributed to the united nation's sustainable development goals (SDG). Some significant goals are sustainable cities, industry innovation, green and resilient infrastructure, good health, and well-being. Non-ventilator hospital-acquired pneumonia (NV-HAP) is a severe but preventable cause of morbidity and mortality in hospitalized patients. Despite being the most frequent and fatal hospital-acquired infection (HAI), NV-HAP is not tracked, documented, or avoided in most hospitals. The success of NV-HAP prevention and monitoring initiatives relies on reliable, up-to-date surveillance data. Surveillance offers the information needed to target, analyze, and quantify the efficacy of preventative activities by identifying patients at the highest risk for NV-HAP. However, pneumonia monitoring is complex due to the clinical criteria's subjective, imprecise, inconsistently recorded, and labor-intensive nature. Non-ventilator hospital-acquired pneumonia must be monitored and standardized, which demands cutting-edge technologies and the deployment of advanced sensors. In the framework of this research, initially, a wireless body area networks (WBANs) architecture has built by making use of wearable biosensors, and then real-time sensor data were uploaded to a cloud platform. Researchers have devised a wireless sensor network (WSN) to track volatile organic compounds (VOC) and other atmospheric characteristics in real time to curb the spread of NV-HAP. The ESP32 Internet of Things (IoT) and Raspberry Pi 4B graphical processing unit platforms host the finalized WBAN and WSN network. To reduce the mortality rate of NV-HAP, this research aims to investigate clinics' and hospitals' indoor and outdoor air quality. The developed biosensor-assisted IoT enabled framework is used in hospitals to keep tabs on the conditions of individual patient rooms, treatment areas, and critical care units in real time. The research found the suggested technique achieves better results than existing state-of-the-art methods regarding computing cost, communication overhead, storage cost, and energy utilization.

Impact of Evaporation on the Performance of Ant Colony Optimization-Based Routing Protocols on the Wireless Sensor Network

Wireless Sensor Network (WSN) has the characteristics limited computing, memory and energy, so a routing protocol that supports WSN network performance is needed. Routing protocols based Ant Colony Optimization (ACO) are very suitable for use in WSN for routing data packets. In this report, we will try out several kinds of parameter values that bear on the pheromone evaporation (ρ) on the ACO algorithms applied to the WSN routing protocol with grid topology on the number of nodes variables in the WSN network. Where this can increase the number of delivery packets on a busy WSN network and each node transmits a packet continuously. From the results, the value of ρ 0.75 makes WSN performance on the grid topology with the number of nodes 12 and 30 very good in terms of the delivery packet, but with the number of nodes 70 the value of ρ 0.70 makes WSN performance better than the delivery packet side.

Proficient and Safe Token with Secret and Public Keys Sharing Algorithm for Preventing Cryptographic Key Leakage in Wireless Sensor Network

Sensor devices in the Wireless Sensor Network (WSN) are commonly subjected to various forms of attacks, such as flood attacks, eavesdropping attacks, etc. When an attacker compromises a sensor device, the sensor device's data contents become non-confidential and are grabbed by the attacker, putting the entire network at risk. As a result, to prevent key leaks in WSN networks, this paper proposes a Token with Secret and Public Keys Sharing (TSP-KS) algorithm. In the existence of attackers, cryptography is used to provide secure communication. A traditional public-key cryptosystem is appropriate in cryptography since it does not need the sender and receiver to supply the same secret to communicate without risk. However, they frequently rely on complex mathematical calculations, making them far less capable than equivalent symmetric-key cryptosystems. The high cost of encrypting long messages with public-key cryptography could be problematic in a wide range of applications. A hybrid system deals with it using a combination of the two. In WSN, Admin creates a token, a secret key, a public key, and a private key. Here, the token is used for access control in sensor devices and the administrator, the secret and public keys are utilized for packet encryption in sensor devices and the base station, and the private key is utilized for decryption in the administrator. Admin shares token with secret and public key for sensor devices and base station for encryption purposes. As a result, the TSP-KS algorithm was utilized to securely share these token with secret and public keys for sensor devices and base station over a distributed way. Experimental results demonstrate that the TSP-KS algorithm securely shares a token with a secret and public key.

An improved approach for energy consumption minimizing in WSN using Harris hawks optimization

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations.

IDENTIFIKASI KEUTUHAN DAN PENANGAN KEAMANAN DATA PADA WIRELESS SENSOR NETWORK

Wireless Sensor Networks (WSN) is becoming a trend of technology along with the development of data transmission and the human need for the Internet of Things (IoT). IoT is a technology term created to connect one object to be able to send data through a transmission medium (internet) without the help of computers and humans. WSN is one of the technological elements in IoT that helps create connectivity. Currently WSN is widely used to assist tasks related to monitoring, evaluating, tracking, controlling things, and others. WSN consists of a collection of small sensors that will collect data and then transmit it. WSN with wireless system is very vulnerable to security holes. Here we will focus on examining the security vulnerabilities of WSN, how to avoid, and protect the WSN network.