Various Applications Of Wireless Sensor Networks Research Articles

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

Hybrid Energy Regulated Constant Gain Kalman-Filter for Optimized Target Detection and Tracking in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are one of the most researched areas worldwide as the wide-scale networks possess low cost, are small in size, consume low power, and can be deployed in various environments. Among various applications of WSNs, target tracking is a highly demanding and broadly investigated application of wireless sensor networks. The parameter of accurate tracking is restricted because of the limited resources present in the wireless sensor networks, noise of the network, environmental factors, and faulty sensor nodes. Our work aims to enhance the accuracy of the tracking process as well as energy utilization by combing the mechanism of clustering with the prediction. Here, we present a hybrid energy-regulated constant gain Kalman filter-based target detection and tracking method, which is an algorithm to make the best use of energy and enhance precision in tracking. Our proposed algorithm is compared with the existing approaches where it is observed that the proposed technique possesses efficient energy utilization by decreasing the transference of unimportant data within the sensor network, achieving accurate results.

Machine Learning for Advanced Wireless Sensor Networks: A Review

Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures.

Naked Mole-Rat Algorithm with Improved Exploration and Exploitation Capabilities to Determine 2D and 3D Coordinates of Sensor Nodes in WSNs

Wireless sensor network (WSN) consists of sensor nodes deployed with limited battery power to observe and report certain events taking place in the sensor field. Location determination is a critical task, but an important parameter in various applications of WSNs. It is an important task to locate all the sensor nodes efficiently to get the accurate and precise location of an occurring event. Numerous applications require the actual determination of their exact location that includes surveillance, animal tracking, tracking of moving objects, military applications, and forest fire detection. There are various localization algorithms available in the literature to deal with the localization problem in WSNs. A single anchor node and six virtual anchors are placed and assumed to locate all unknown nodes deployed in 2D and 3D environments using various meta-heuristic algorithms available in the literature. In this paper, three versions of the Naked Mole-Rat Algorithm (NMRA) are proposed to enhance its exploration and exploitation capabilities. To check the effectiveness of NMRA variants, CEC 2019 benchmark functions are used for comparison with basic NMRA. The results show that proposed NMRA variants converge fast and provide an optimum solution for most of the benchmark functions. Also, the proposed NMRA variant outperforms well in terms of localization error in comparison to existing localization solutions for both 2D and 3D environments.

Wireless Sensor Networks: Technologies and Applications

Wireless Sensor Networks (WSNs) have emerged as a key technology in various fields due to their ability to monitor and collect data in real-time. This paper provides a comprehensive review of WSNs, covering their fundamentals, technologies, applications, challenges, and future trends. The paper begins with an introduction to WSNs, discussing their basic architecture and components, communication protocols, and network topologies. It then explores the various applications of WSNs, including environmental monitoring, industrial automation, and smart agriculture, highlighting their importance and relevance in each field. The paper also addresses the challenges faced by WSNs, such as security, energy efficiency, and integration with Internet of Things (IoT) technologies and discusses future trends and advancements in the field. Overall, this paper aims to provide a thorough understanding of WSNs and their potential impact on society.

Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

Analysis of QoS for PAT and EnOPATr System in WSN

In the situations of natural disasters, Wireless Sensor Networks (WSN) is extensively used. Quality of Service (QoS) is an important parameter for ensuring effectiveness and robustness of service provided. QoS gets degraded when being applied in various applications of WSN. Qos expected in WSN applications gets reduced further. In this case, efficient use of the scarce resources is important to ensure continuous data transmission is ensured by proficient use of resources which are limited resources. There are many WSN protocols which focus on broadcasting of important information. But there are many parameters which get neglected by these protocols. For instance, normal data traffic gets neglected by these protocols. Hence, this research proposes EnOPATr system which is independent and distributed. The essential and normal data is handled at the same time in EnOPATr which achieves dependable and congestion free data. In a sensor node, power consumption can be reduced by decreasing the rate of packet retransmission. This is because of congestion in network. The simulation of the proposed system is done using NS-2 software which shows that the proposed system is able to reduce network congestion thereby improving the performance..

A PSO Based Improved Localization Algorithm for Wireless Sensor Network

Wireless sensor network (WSN) is consisted of enormous amount of miniature devices recognized as sensor nodes empowered with sensing, processing, and computing abilities. The main role of WSN is sensing, gathering, and transmission of data to the destination. In several applications, the location of collected data is of much significance. Such type of information is acquired by a localization technique. So, localization is a vital issue in various applications of WSNs. On account of range estimates, the localization techniques can be divided into range based and range free. The range based schemes require added device, thus costly to implement in practice. Range free schemes reduce the expenditure because they do not need additional ranging device. Therefore, the range free methods are practiced as worthwhile alternatives to range based schemes. However, these schemes comprised more localization error as compared to range based schemes. Hence, to overcome the existing drawback of conventional DV-Hop, a PSO based improved localization algorithm is offered in this paper. Simulation results confirm that our proposed algorithm reduces the error and error variance of localization whereas improves the accuracy in comparison with the existing localization algorithms.

A Localization and k-Connectivity Detection System for TinyOS based Wireless Sensor Networks

Localization and connectivity detection are two important topics in Wireless Sensor Networks (WSNs). In localization problem the aim is finding the position of mobile or static nodes with unknown coordinates. The purpose of the k -connectivity problem is to find the number of nodes whose removal destroys the connectivity of the network. In this paper, we have designed and implemented a localization and k -connectivity detection system. The proposed system uses trilateration approach to find the up to date locations of nodes where the trilateration method uses the messages from anchor nodes. In our system, the RSSI (Received Signal Strength Indicator) values of transmitted messages are used to estimate the distance between nodes. Using the proposed system, nodes can estimate their locations after receiving messages from at least 3 anchor nodes. The exchanged message and detected positions are snooped by the sink node, transferred to a Java program and presented to the user on GUIs. To find the latest connectivity statuses of a network, a k -connectivity testing algorithm periodically is run on the current detected topology from incoming messages. The proposed system has been tested with MEMSIC-IRIS motes and supports all WSNs which use TinyOS based motes. This system can be used as an infrastructure for various applications in WSNs.

Soft computing approach towards localisation in wireless sensor networks: a survey

A wireless sensor network (WSN) is playing a vital role in monitoring a wide variety of real-world phenomenon. Capability of WSNs is further enhanced by the efficient localisation algorithms. Location information is essential for integrating the various applications of WSNs. Localisation is the process by which a sensor node determines its own location after deployment. Soft computing techniques are gaining popularity in evolving new localisation algorithms that are capable of optimising various parameters of WSNs. In this paper, a comprehensive review is presented to understand the role of fuzzy logic, genetic algorithm, and neural network in localisation of nodes in WSNs.