WSN Devices Research Articles

Enhancing agricultural wireless sensor network security through integrated machine learning approaches

AbstractWireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.

Multi-Objective ED2MT Strategy-based Clustering Algorithm for Large Scale WSN

Wireless Sensor Networks play a crucial role in everyday life, providing a wide range of networking structures to develop real-time applications. Energy consumption is limited by WSN devices, so reducing the energy consumption of malicious nodes is crucial to improving network performance. A main concern of WSNs from an application perspective is the prolonged lifetime of the network and reduced energy usage. To overcome these challenges a novel multi-objective ED2MT (Energy, Distance, Degree, Mobility, Time) driven strategy has been proposed which consists of three phases namely dual Cluster Head Selection, data compression, and routing. In the first phase, the proposed ED2MT uses a Fuzzy C-Means (FCM) clustering algorithm to select dual Cluster Head (CH) which has been performed in two stages, namely Inter-Cluster and Intra-Cluster. In the second phase, a Neighborhood Indexing Sequence (NIS) technique is utilized to compress the number of bits in the data before it is transmitted. In the third phase, a multi-objective shortest-path routing algorithm is utilized for route selection. The performance of the ED2MT method has been evaluated using network lifetime, delay, throughput, alive nodes, energy efficiency, and packet delivery ratio to provide a better result. The proposed ED2MT framework achieves 20.55%, 18.4%, and 15.2% more residual energy than C3HA, CAFL, and DHSCA algorithms.

Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering

Abstract: Wireless Sensor Networks are generally deployed in dynamically changing environment. When compared to common wired network nodes, WSN nodes must do more work. Since WSN devices are battery powered, so power management is a challenge. Clustering is one solution that has been proposed to alleviate the issue of limited power. Clustering is the most important method stabilizes the lifetime of the network. It entails the aggregation of sensor nodes into clusters and cluster head is picked out from all the clusters. Clustering is implemented in wireless sensor networks through the Machine Learning techniques. In wireless sensor networks, machine learning algorithms have an important role in cluster head formation and maintain the stability of the nodes in the cluster. Machine Learning approaches used in wireless sensor networks can be classified as Supervised learning, Unsupervised learning, and Reinforcement learning. Among these learning techniques, unsupervised learning deals with different clustering algorithms such as k-means, K-medoids, Fuzzy C-means, hierarichalbased, and SOM. This paper evaluates the performance of the variants of k-Means (kM) and Fuzzy C-means (FCM) algorithms in terms of the clustering and accuracy. This paper imparts performance analysis of different clustering algorithms in machine learning applied for wireless sensor networks. From the analysis, the Fuzzy C-Means algorithm found to be more suitable for node clustering in WSN

A deep learning framework optimised by Harris Hawks algorithm for intelligent ECG classification in WSN-IoT environment

Nowadays, WSN-IoT may be used to remotely and in real-time monitor patients’ vital signs, enabling medical practitioners to follow their status and deliver prompt treatments. This equipment can evaluate the gathered data on-site thanks to the integration of edge computing, enabling quicker diagnostic and medical options with the need for massive data transmission to a centralized server. Making the most of the resources accessible without sacrificing monitoring efficiency is critical due to the constrained lifespan and resource availability that these intelligent devices still encounter. To make the most of the assets at hand and achieve excellent categorization performance, intelligence must be applied through a learning model. Making the most of the resources that are available without sacrificing performance monitoring is essential given the restricted lifespan and resource availability that these intelligent devices still suffer. A learning model must incorporate intelligence in order to maximize the utilization of resources while maintaining excellent classification performance. In this study, a unique Harris Hawks Optimized Long Short-Term Memory (HHO-LSTM) that categorizes Electrocardiogram (ECG) data without compromising optimum utilization of resources is proposed for Edge enabled WSN devices. We will train the model to correctly categorize various kinds of ECG readings by employing cutting-edge techniques and neural networks. Significant testing is carried out on fifty individuals utilizing real-time test chips with integrated controllers coupled to ECG sensors and NVIDIA Jetson Nano Boards as edge computing devices. To show the benefits of the suggested model, performance comparisons with various deep-learning techniques for peripheral equipment are conducted. Experiments show that in terms of classification results (98% accuracy) and processing expenses, the suggested model, which is based on Edge-enabled WSN devices, beat existing state-of-the-art learning algorithms. The ability of this technology to help medical personnel diagnose a range of heart issues would eventually enhance customer management.

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains.

Emerging Routing Method Using Path Arbitrator in Web Sensor Networks

Sophisticated Routing has a big impact on wireless sensor network performance and data delivery. Because nodes join and leave the network on a whim, routing in WSN is not as simple a task as it is throughout sensor networks that are wireless. The fact that the most of WSN devices are resource constrained is another restriction on how routing is implemented in WSN. The WSN uses a variety of routing protocols. However, the primary goal of this research is to determine the best route from the source to the destination using wireless sensor networks and machine learning techniques Which is Particle Swarm Optimization. In this study, an innovative and intelligent machine dubbed the Path Arbitrator or selector, which will store all sensor data and use machine learning methods, is used to develop a new routing mechanism.

Comparative Analysis of Security Issues and Trends in IoT and WSN

Recent advancements in the field of wireless sensor networks and the internet of things market give rise to security concerns. While using the application of these technologies in the real world, many security issues have emerged. There are security risks involved in using IoT and WSN devices in areas such as financial, health, and business, as well as in industry. Security requirements are exponentially increasing with the demand for IoT devices in the market around the world. The trends have shown that the WSN field is slowly merging into the IoT ecosystem. Another factor that encourages the integration of the WSN field in IoT is advancements in addressing and the internet of everything. If we study both technologies in minute detail, we will find that although both technologies are interrelated, there is a huge difference in application areas, connectivity issues, routing issues, security issues, etc. WSN gives great importance to the efficient management of constrained resources. On the other hand, security, scalability, quality of service, and heterogeneity are the main concerns in IoT systems. In this paper, there is a detailed analysis of several factors, requirements, and trends in the advancement of both technologies.

PENGIRIMAN DATA NRF24L01+ DENGAN KONDISI LINE OF SIGHT DAN NON LINE OF SIGHT

Wireless Sensor Network is a wireless network technology that includes sensor nodes and embedded systems. WSN has several advantages: it is cheaper for large-scale applications, can withstand extreme environments, and data transmission is relatively more stable. One of the WSN devices is nRF24L01+. Within the specifications given, the maximum communication distance is 1.1 km. However, the most effective distance for transmitting data in line of sight and non-line of sight is still unknown. Therefore, testing and analysis are needed so that the nRF24L01+ device can be used optimally for communication and data transmission. Through testing analysis on nRF24L01+ line of sight, Kuta beach location in Bali and non-line of sight on the STMIK STIKOM Indonesia campus. The effective communication distance of the nRF24L01+ module in line of sight is between 1 and 1000 meters. The distance of 1000 meters is the limit of the effective distance for sending data, and the packet loss rate is less than 15% which is included in the medium category. Meanwhile, in the non-line of sight, the effective distance of the nRF24L01+ communication module is 20 meters, and the packet loss is close to 15%, which is a moderate level limit. With the analysis module, nRF24L01+ can be a reference in determining the effective distance on WSN nRF24L01+ in determining remote control equipment data communication.

A Radical Study of Energy Efficient Hierarchical Cluster-Based Routing Protocols for WSN

For many decades, researchers and vendors are continually developing and designing sensors and wireless network devices for countless applications. These low power wireless sensor network devices have designed to gather and propagate data for applications such as environment, industry, habitat, patient monitoring, and many more to excel humankind— however, these devices also inherent many challenges and drawbacks due to the default hardware design. Subsequently, to mitigate limitations and enhance the capability, authors and researchers have investigated and conferred that minor optimization in modeling or routing techniques gradually elevates the performance of WSN. One of the primary concerns which remain on top of the Domain for discussion is energy conservation in WSN devices. Our primary goal is to analyze and design a cluster-based routing protocol for WSN, An efficient way to elevate the network performance. Finally, the emanate results showcase that the performance of the proposed protocol is much more optimized and favorable when combined with soft-computing tactics when compared to the conventional paradigm.

☆Improving IEEE 802.15.4 channel access performance for IoT and WSN devices

IEEE 802.15.4 is a well-known protocol for low power, low rate wireless personal area networks that determines the specifications for the physical layer and the media access control sub-layer. However, IEEE 802.15.4 suffers from several limitations that play a role in reducing its performance such as using a default maximum backoff (macMaxCsmaBackoffs) value for channel access. In terms of energy efficiency, complexity, delay, using a default macMaxCsmaBackoffs does not provide optimum performance in every channel condition. For this reason, we built an adaptive channel access model, ACAMRO that maximizes channel utilization via adjusting macMaxCsmaBackoffs according to channel conditions. ACAMRO provides higher throughput which yields up to a 120% performance gain compared to the default IEEE 802.15.4 setup. It provides an implementation requiring low memory space that is less than 20 kBytes. Measured results show that ACAMRO achieves significant throughput increase, and a low complexity solution for Internet of Things devices.

Applications of Nano-Rkin Internet of Things (Iot)

Internet of Things (IoT) is notoriously described as World wide web of objectives as ‘Things’ is a global and interconnected communicate for whatever easily interchange information through Internet sensing systems. The tiny wsn devices are actually having qualities like low energy capability, less weight minimal battery support managed through Real-time operating systems. [6]. Nano-RK is open source and having to work on real-time tasks. The Nano-RK according reserve reservations tasks known other name as resource kernel. A source little bit uses reservations on specifically just how typically device information could be consumed as well as handled. For example, using resource reservations concept, create Central Processing Unit resource appointments present day innovation, a task might just be actually enabled to carry out 10 ms every 150 ms. Likewise one more case through making use of Nano-RK by using System source reservations innovation a node could merely be enabled to transfer 10 network packages every moment. The utilizing these bookings to secure electric battery lifestyle of a node along with likewise protecting a fallen short node from making too much messages in system internet network scenario. According this, the energy capability of a battery life of a node are going to definitely be actually reduced. The Nano-RK is open resource as well as additionally support in programming language like C and C++. Nano-RK can operates on the Atmel-based FireFly platform. The target of this paper is an intro importance of features and applications an intro of Nano-RK. Depending on to running device capacities as well as additionally qualities as well as an open resource of ecological community facilities, the Nano-RK is the a suited operating system os for tightened as well as minimized electrical power sensing unit resources for World wide web of connected things. By particular study, the research study of one of the most crucial functions of IOT enable Nano-Rk located Reservations Booking, Deep-in sleep mode environment, Error administration and likewise pointing characteristics of Nano-RK os. [3].

Trust Based Security Model for IoT and Fog based Applications.

Wireless sensor networks (WSN’s) are becoming increasingly popular in the current Era. WSN and Smart Devices are key enablers for the Internet of things (IoT) and Fog Computing based applications and it brings IoT applications with enhanced capabilities for sensing and actuation. IOT is a very growing area and having wide scope for research. Integration of WSNs into IoT is today’s need. Adoptions to cloud computing, increasing use of automation of tools, Integration of internet into most activities of human had reached to the stage where Internet of Things based applications are becoming more popular today. Many new applications are now being developed in the field of IoT. The broad areas of application are Home automation, Smart cities, Retail, Agriculture, Manufacturing, Healthcare, Habitat Monitoring, Security and Surveillance etc. With this wide scope the applications are prone to many security threats. Providing Security for IoT and Fog computing-based applications is a Research Challenge. This paper tries to discuss with the common security attacks, Reasons, their counter measures and research directions. We are also proposing a Trust based Security model for implementing application layer level security in IoT and fog.

A hybrid protection approaches for denial of service (DoS) attacks in wireless sensor networks

ABSTRACTWireless sensor network (WSN) contains the distributed autonomous devices with the sensing capability of physical and environmental conditions. During the clustering operation, the consumption of more energy causes the draining in battery power that leads to minimum network lifetime. Hence, the WSN devices are initially operated on low-power sleep mode to maximise the lifetime. But, the attacks arrival cause the disruption in low-power operating called denial of service (DoS) attacks. The conventional intrusion detection (ID) approaches such as rule-based and anomaly-based methods effectively detect the DoS attacks. But, the energy consumption and false detection rate are more. The absence of attack information and broadcast of its impact to the other cluster head (CH) leads to easy DoS attacks arrival. This article combines the isolation and routing tables to detect the attack in the specific cluster and broadcasts the information to other CH. The intercommunication between the CHs prevents the DoS attacks effectively. In addition, the swarm-based defence approach is proposed to migrate the fault channel to normal operating channel through frequency hop approaches. The comparative analysis between the proposed table-based intrusion detection systems (IDSs) and swarm-based defence approaches with the traditional IDS regarding the parameters of transmission overhead/efficiency, energy consumption, and false positive/negative rates proves the capability of DoS prediction/prevention in WSN.

A Novel Approach For Elect A Zone Head On The Basis Of Energy and Distance of Base Station In WSN

Performance estimation is a most important study part in WSN and a prosperity of literature exists in this area. IN WSN, DOS attacks are known as threats that is an extremely serious threat due to the resources feature. Applications of the WSNs are transitioning to real-world, where they face attacks already skilled through the WAN and Internet. DOS is one form of attacks, which we will believe only become large common as accessible and sensor networks. WSN devices include limitations of the inherent resource, they are most receptive to the consumption and laying waste of these weak resources. In this paper on the basis of energy and distance of base station elect a zone head. The simulation of the proposed work concludes that the results of the approach are good it provides better results in terms of throughput and packet delivery ratio.

A SURVEY ON ENERGY CONSERVATION TECHNOLOGIES IN WIRELESS SENSOR NETWORK

Conservation of energy using heat, temperature and other techniques is explored in this survey, to utilize in WSN applications. In this paper, various techniques are studied to improve the energy efficiency in some applications that are dealing with heat conversion and transfer processing. Many countries with higher temperature can transfer the heat energy to a different form which is applicable to their practical problems such charging mobile devices. It is very simple because many mobile devices are charged wirelessly and stored for next few hours which may be extended with appropriate materials. The topology of existing WSN and three dimensional WSN have been studied for energy enhancement analysis. Waste heat from WSN devices can be reused or employed to solve some routing problems such as identifying path and optimization. Heat energy reduces the cost of energy as well as protects the internal components of the devices used in the WSN.

On Location Estimation Technique Based of the Time of Flight in Low-power Wireless Systems

Abstract This study deals with the distance estimation issue in low-power wireless systems being usually used for sensor networking and interconnecting the Internet of Things. There is an effort to locate or track these sensor entities for different needs the radio signal time of flight principle from the theoretical and practical side of application research is evaluated. Since these sensor devices are mainly targeted for low power consumption appliances, there is always need for optimization of any aspects needed for regular sensor operation. For the distance estimation we benefit from IEEE 802.15.4a technology, which offers the precise ranging capabilities. There is no need for additional hardware to be used for the ranging task and all fundamental measurements are acquired within the 15.4a standard compliant hardware in the real environment. The proposed work examines the problems and the solutions for implementation of distance estimation algorithms for WSN devices. The main contribution of the article is seen in this real testbed evaluation of the ranging technology.

Refrigerated fruit storage monitoring combining two different wireless sensing technologies: RFID and WSN.

Every day, millions of tons of temperature-sensitive goods are produced, transported, stored or distributed worldwide, thus making their temperature and humidity control essential. Quality control and monitoring of goods during the cold chain is an increasing concern for producers, suppliers, logistic decision makers and consumers. In this paper we present the results of a combination of RFID and WSN devices in a set of studies performed in three commercial wholesale chambers of 1848 m3 with different set points and products. Up to 90 semi-passive RFID temperature loggers were installed simultaneously together with seven motes, during one week in each chamber. 3D temperature mapping charts were obtained and also the psychrometric data model from ASABE was implemented for the calculation of enthalpy changes and the absolute water content of air. Thus thank to the feedback of data, between RFID and WSN it is possible to estimate energy consumption in the cold room, water loss from the products and detect any condensation over the stored commodities.

RSS-Based Indoor Localization Algorithm for Wireless Sensor Network Using Generalized Regression Neural Network

Traditional received signal strength (RSS)-based localizations are often erroneous for the low-cost WSN devices. The reason is that the wireless channel is vulnerable to so many factors that deriving the appropriate propagation loss model for the WSN device is difficult. We propose a flexible location estimation algorithm using generalized regression neural network (GRNN) and weighted centroid localization. In the first phase of the proposed scheme, two GRNNs are trained separately for x and y coordinates, using RSS data gathered at the access points from the reference nodes. The networks are then used to estimate the approximate location of the target node and its close neighbors. In the second phase, the target node position is determined by calculating the weighted centroid of the Nc-closer neighbors. Performance of the proposed algorithm is compared with some existing RSS based techniques. Simulation and experimental results indicate that the location accuracy is satisfactory. The system performance is remarkably good in comparison with its simplicity and requiring no additional hardware.