Integration Of Wireless Sensor Networks Research Articles

A WSN and vision based smart, energy efficient, scalable, and reliable parking surveillance system with optical verification at edge for resource constrained IoT devices

As urbanization accelerates, the demand for efficient parking surveillance solutions has increased. However, existing solutions often face challenges related to energy consumption, scalability, and reliability. This paper introduces a smart hybrid parking surveillance system integrating wireless sensor networks (WSNs) with vision based solution at the edge for resource constrained IoT devices to address these challenges. The solution leverages WSNs for periodic readings of parking space occupancy and introduces a low power sleep mode in the network for energy efficiency, along with optical verification strategies using computer vision models like R-CNN and Faster R-CNN FPN on ResNet50 and MobileNetv2 backbones for distinguishing between true and false positives in the WSN data for a greater accuracy in parking space occupancy. The system utilizes edge for computing on edge servers resulting in increased responsiveness of the system, reduced data transmission and real time processing of data. The proposed solution is formulated in such a way that it automatically switches between WSN and vision based sensing resulting in less energy consumption and longer lifespan of the system without compromising on accuracy. Through experimental results it is observed that models trained on the MobileNetv2 backbone demonstrated at least twice faster for both processing the images and training compared to those models trained on the ResNet backbone. On the other hand, both Faster R-CNN FPN (input resolution: 1440) and R-CNN (input resolution: 128) models trained on the MobileNetv2 backbone have slightly lower accuracies than the same models trained on the ResNet50 backbone.

Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.

Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study.

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.

Wireless Sensor Networks Empowered by IOT for Air Quality Monitoring

This is a thorough method of monitoring air quality by integrating the Internet of Things (IoT) with Wireless Sensor Networks (WSNs). This approach seeks to improve air quality evaluations' accuracy and efficiency by utilizing the interconnectedness of IoT devices. The integration of WSNs, Internet of Things capabilities, and sophisticated filtering methods in this project helps to build reliable systems for monitoring air quality in real-time, which is essential for managing public health and environmental sustainability.

Using Deep Learning Technology Based Energy-Saving For Software Defined Wireless Sensor Networks (SDWSN) Framework

This paper discusses the significance of Wireless Sensor Networks (WSNs) in collecting critical data from various environments, highlighting the challenges presented by the limited resources of small, highly mobile sensors. The integration of WSNs into the Internet of Things (IoT) enables the collection and transmission of data to centralized locations. Especially in complex network topologies, efficient routing of packets is crucial for optimizing resource utilization in WSN nodes. Software-Defined Networks (SDNs), in which a centralized controller makes routing decisions based on network and packet data, are replacing traditional static routing. Nevertheless, due to the complexity of WSN topologies and cost-effectiveness concerns, Machine Learning (ML) techniques are currently being used to improve SDNWSN decision-making. This paper presents a technique that employs a neural network trained via Deep Reinforcement Learning (DRL) to extend the lifespan of WSNs by optimizing energy utilization via efficient routing. 2DCNN and 3DCNN neural networks are evaluated, with 3DCNN showing superior performance, resulting in an 18% increase in network lifespan. Additionally, the study emphasizes the significance of avoiding resource depletion in high-traffic nodes by considering alternative routing paths to guarantee the lifespan of the network.

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning.

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

Smart Driving Licensing System and Authorization of Autonomous Vehicle Integrating Wireless Sensor Networks and IoT

Smart Driving Licensing System and Authorization of Autonomous Vehicle Integrating Wireless Sensor Networks and IoT

Wireless Communication Chip Designs: analysis of the Wireless Integrated Network Sensors

With the development of wireless technology, wireless integrated network sensor is a new form of sensor network. It enables highly efficient data acquisition and transmission by connecting the sensor nodes wirelessly. The purpose of this study is to investigate the basic principles and techniques of wireless integrated network sensors, analyze their application fields, and conduct experimental studies to verify their performance. This study first introduces the basic principles of wireless integrated network sensors, including wireless communication, sensor nodes, and network topology. Then, related technologies, including energy management, routing protocols and network security, are studied to improve the performance and stability of wireless integrated network sensors. Wireless integrated network sensors have wide application prospects in environmental monitoring, intelligent transportation and agriculture. Meanwhile, the energy utilization efficiency and network stability of the sensor network can be improved by adopting the new energy management mechanism and routing protocol. This study reveals the potential and value in practical applications through the exploration and research of wireless integrated network sensors. In future studies, the energy management and routing mechanisms of sensor networks can be further optimized to improve their performance and reliability. In addition, more application scenarios suitable for wireless integrated network sensors can be explored to provide solutions for practical problems.

A Learning based Secure Routing Approach using Deep Reinforcement Learning in IoT Integrated Wireless Sensor Network

The usage of Wireless Sensor Network (WSN) is ubiquitous in nature. With the emergence of Internet of Things (IoT) technology and its unprecedented use cases, the role of sensor networks as part of IoT application became crucial. WSN became backbone of IoT to realize integration between physical and digital worlds and connectivity to Internet. However, IoT devices are resource constrained with limited computational capabilities. The entire network is distributed in nature and has increased complexity. Routing in such WSN integrated IoT network plays an important role in achieving meaningful communication among objects. In this context, it is indispensable to have more energy efficient routing method. Since the IoT integrated sensor network is highly complicated, it is very dynamic in nature. Thus routing decisions are also dynamic leading to much importance to routing in such use cases. With the emergence of Artificial Intelligence (AI), it became possible to solve complex real world problems through learning based approach which acquires desired intelligence prior to making decisions. In this paper we proposed a deep reinforcement learning based routing mechanism for energy efficient routing in WSN-IoT integrated application. We proposed novel algorithms for network setup, formation of clusters and routing. Our method adapts to network changes due to energy levels, mobility and makes learning based routing decisions. We enhanced the method further with security to ensure its Qualityof Service (QoS) in presence of attacks. Our simulation study using MATLAB has revealed that the proposed secure routing approach outperforms existing protocols.

Ultralow Thermal Conductivity and Improved Thermoelectric Properties of Al‐Doped ZnO by In Situ O2 Plasma Treatment

The thriving of Internet‐of‐Things and integrated wireless sensor networks has brought an unprecedented demand for sustainable micro‐Watt‐scale power supplies. Development of high‐performing micro‐thermoelectric generator (μ‐TEG) that can convert waste thermal energy into electricity and provide sustainable micro‐Watt‐scale power is therefore extremely timely and important. Herein, a significant advance in the development of earth‐abundant, nontoxic thermoelectric materials of aluminum‐doped zinc oxide (AZO) is presented. Through nanostructure engineering using a novel in situ O2 plasma treatment, AZO films are demonstrated with ultralow thermal conductivity of 0.16 W m−1 K−1 which is the lowest reported in the literature. This nanostructured film yields a power factor of 294 μW m−1 K−2 at 563 K and has resulted in a state‐of‐the‐art ZT of 0.11 at room temperature and 0.72 at 563 K for AZO thin films. Furthermore, the fabrication and testing of a prototype lateral μ‐TEG are reported based on the AZO thin film which achieves a power output of 1.08 nW with an applied temperature difference of 16.9 °C.

Comprehensive analysis of radiative cooling enabled thermoelectric energy harvesting

The market for Internet-of-things (IoT) with integrated wireless sensor networks is expanding at a rate never seen before. The thriving of IoT also brings an unprecedented demand for sustainable micro-Watt-scale power supplies. Radiative cooling (RC) can provide a continuous temperature difference which can be converted by a thermoelectric generator (TEG) into electrical power. This novel combination of RC with TEG expands the category of sustainable energy sources for energy harvesting. However, the further application of RC-TEG requires a holistic investigation of its RC-TEG performance which is dependent on many different parameters. Using 3D finite element method simulation, this works provides a comprehensive analysis of the concept of RC-TEG by investigating the impact of radiative cooler properties, TEG parameters, and environmental conditions, to provide a full picture of the performance of RC-TEG devices. The capability of RC-TEG to provide continuous power supply is tested using real-time environmental data from both Singapore and London on two different days of the year, demonstrating continuous power supply sufficient for a wide range of physical devices.

Optimized Energy-Efficient Routing Protocol for Wireless Sensor Network Integrated with IoT: An Approach Based on Deep Convolutional Neural Network and Metaheuristic Algorithms

Wireless sensor networks (WSNs) have emerged as a significant architecture for data collection in various applications. However, the integration of WSNs with IoT poses energy-related challenges due to limited sensor node energy, increased energy consumption for wireless data sharing, and the necessity of energy-efficient routing protocols for reliable transmission and reduced energy consumption. This paper proposes an optimized energy-efficient routing protocol for wireless sensor networks integrated with the Internet of Things. The protocol aims to improve network lifetime and secure data transmission by identifying the optimal Cluster Heads (CHs) in the network, selected using a Tree Hierarchical Deep Convolutional Neural Network. To achieve this, the paper introduces a fitness function that takes into account cluster density, traffic rate, energy, collision, delay throughput, and distance from the capacity node. Additionally, the paper considers three factors, including trust, connectivity, and QoS, to determine the best course of action. The paper also presents a novel optimization approach, using the hybrid Marine Predators Algorithm (MPA) and Woodpecker Mating Algorithm (WMA), to optimize trust, connectivity, and QoS parameters for optimal path selection with minimal delay. The simulation process is implemented in MATLAB, and the developed method’s efficiency is evaluated using several performance metrics. The results of the simulation demonstrate the effectiveness of the proposed method, which achieved significantly lower delay (99.67%, 98.38%, 89.34%, and 97.45%), higher delivery ratio (89.34%, 89.34%, 83.12%, and 88.96%), and lower packet drop (93.15%, 91.25%, 79.90%, and 92.88%) in comparison to existing methods. These outcomes indicate the potential of the optimized energy-efficient routing protocol to improve network lifetime and ensure secure data transmission in WSNs integrated with IoT.

WIRELESS SENSOR NETWORK FOR CURRENT AND FUTURE TRENDS: A REVIEW

Wireless Sensor Networks (WSNs) are an emerging technology that has gained immense popularity due to its ability to provide real-time data from remote locations. In recent years, WSNs [1] have been widely adopted in various applications, including environmental monitoring, healthcare, agriculture, smart homes, and smart cities. This paper presents an abstract on the current and future trends of WSNs. One of the significant trends in WSNs is the development of energy-efficient protocols and algorithms. Researchers are exploring new techniques to minimize energy consumption by sensors, which directly impacts the network's lifetime. Another trend is the integration of WSNs with other technologies, such as the Internet of Things (IoT), cloud computing, and big data analytics, to enable more advanced applications. Another trend is the use of Machine Learning (ML) and Artificial Intelligence (AI) techniques to analyze the data collected by WSNs. These techniques can be used to identify patterns and anomalies in the data, which can be used to improve the efficiency and accuracy of the system. The future of WSNs looks promising, with new technologies emerging that will enhance their capabilities. One of the upcoming trends is the development of Low-Power Wide Area Networks (LPWANs), which will enable long-range communication with low power consumption. This technology will be particularly useful in smart cities and industrial applications. The development of autonomous WSNs is another future trend. These networks will be capable of self-organization, self-configuration, and self-management, reducing the need for human intervention. This will result in more efficient and reliable networks. WSNs are an essential technology that has the potential to revolutionize various industries. As the technology continues to evolve, it is essential to keep up with the latest trends to ensure that WSNs remain at the forefront of innovation.

A Novel Algorithm for Redundant Data Filtering in WSN and RFID Integrated Networks

Wireless sensor networks (WSN) and radio frequency identification (RFID) are base technologies employed in decentralised dynamic environments. In the hybrid network formed by integrating RFID and WSN, RFID data can be used applying WSN protocols for multi-hop communications. However, RFID data contain excessive duplication, which increases time delay and energy consumption, resulting in wastage of different resources. There exist four popular RFID–WSN integration architectures: hierarchical RFID-sensor topology, network RFID-sensor topology, reader-sensor nodes topology, and mixed topology. In this paper, we propose a new plan for a WSN–RFID integrated network. The entire network is divided into clusters, and the clustering hierarchical routing algorithm is employed to send data from the head nodes to the base station. Further, we propose two algorithms to overcome redundant data on the hybrid network. Our simulation results demonstrate that the proposed method reduces redundant data and processing time compared with existing methods.

Data transmission reduction using prediction and aggregation techniques in IoT-based wireless sensor networks

Currently, broad ranges of economic sectors exploit the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) technology generating problems to be managing, processing and organizing the big data streams. Given the inherent sensor constraints (battery-dependent life, lack of memory space, limited processor power), there is a need for systems capable of reducing data transmission flux of the distributed IoT-based WSNs applications to mitigate the continuously increasing network traffic. Prediction and data aggregation techniques are promising solutions to meet this requirement. These techniques are particularly powerful solutions in forecasting processes where a huge data to be collected, transmitted and recorded. This is right even if we consider the integration of IoT-based WSNs with cloud computing technology. This paper proposes the combination of the recently developed prediction scheme EADPS (Extended Adaptive Dual Prediction Scheme) and the temporal correlation based data aggregation technique as a power tool to address the problem of communication reduction in connected IoT-based WSNs. First, we studied separately the impact of each technique before considering their combination. To achieve this goal, we consider the multi-hop ring model of the IoT-based WSNs. The results show that the aggregation technique is powerful compared to the DPS prediction scheme but loses its superiority for small WSNs size (with lesser than five rings) when the EADPS schema is applied. In addition, the data correlation has a feeble impact on reduction of transmission rates. According to the imposed tolerance thresholds, we show that the proposed scheme reduced the average transmission rates in the range 85%–96% for the entire network. The obtained results are presented and discussed.

Study of Wireless Sensor Networks Topological Protocol and Its Applications

In this paper we studied about the wireless sensor networks and topology protocols and applications of wireless sensor networks (WSN). Many applications of RFID and Wireless Sensors Networks (WSN) in diverse areas including intelligent transport vehicles, defense, environmental monitoring and forecasting such as air pollution monitoring, structural health monitoring, intelligent home, and warehouse management were offered [1-4]. The integration infrastructure of WSN and RFID system with a network was dedicated. Furthermore, the architecture framework of Electronic Production Code (EPC) global sensor network was proposed aiming to establish the infrastructure of global network which is able to aggregate various data [6]. However, RFID and WSN integration was developed by [7]. This research strives to elaborate accurate traceability of different objects by means of RFID which are not conveniently identifiable by applying ordinary sensors. Notwithstanding the fact that the condition of items cannot be monitored by RFID, the environmental condition as well as condition of items can be achieved by sensor nodes. To address this issue, both RFID system and WSN are applied together to overcome some difficulties in industrial environment.

Applications of wireless sensor networks to improve occupational safety and health in underground mines

Introduction: The very complex and hazardous environment of underground mines may significantly contribute to occupational fatalities and injuries. Deploying wireless sensor network (WSN) technology has the potential to improve safety and health monitoring of miners and operators. However, the application of WSN in the industry is not fully understood and current research themes in this area are fragmented. Thus, there is a need for a comprehensive review that directly explores the contribution of WSNs to occupational safety and health (OSH) in underground mines. Method: This study aims to conduct a systematic literature review on the existing applications of WSNs for improving OSH in the underground mining industry to pinpoint innovative research themes and their main achievements, reveal gaps and shortcomings in the literature, recommend avenues for future scholarly works, and propose potential safety interventions. The major contribution of this review is to provide researchers and practitioners with a holistic understanding of the integration of WSN applications into underground mine safety and health management. Results: The review results have been categorized and discussed under three predominant categories including location monitoring and tracking, physiological and body kinematics monitoring, and environmental monitoring. Finally, seven major directions for future research and practical interventions have been identified based on the existing research gaps including: (1) further applications of WSNs for underground mining OSH management; (2) application of WSNs from research to real-world practice; (3) big data analytics and management; (4) deploying multiple WSNs-based monitoring systems; (5) integration of WSNs with other communication systems; (6) adapting WSNs to the Internet of Things (IoT) infrastructure; and (7) autonomous WSNs.

Analysis of the benefits, challenges and risks for the integrated use of BIM, RFID and WSN: a mixed method research

PurposeThe purpose of this study is to identify, classify and prioritize the benefits, challenges and risks for the integrated use of building information modeling (BIM), radio frequency identification (RFID) and wireless sensor network (WSN) in the architecture, engineering, construction and operation (AECO) industry.Design/methodology/approachThis study relies on the mixed method approach which consists of systematic literature review, semistructured interviews and Delphi technique. A systematic literature review was performed and face-to-face semistructured interviews with seven subject matter experts (SMEs) were conducted for identification and classification purposes. Delphi method was applied in two structured rounds with eleven SMEs for prioritization purpose. These three research techniques were chosen to reach the most accurate data by combining different perspectives on the subject matter. Data gathered by these three methods was triangulated to increase the validity and reliability of this research.FindingsThirteen benefits, ten challenges and four risks for the integrated use of BIM, RFID and WSN were identified. The results could aid the practitioners and researchers comprehend the pros and cons of this integration by representing SMEs’ valuable insights and perspectives about the current and future status, trends, limitations and requirements of the AECO industry. The identified risks and challenges show the requirements for future studies while the benefits demonstrate the capabilities and the potential contributions of this hybrid integration to the AECO industry.Originality/valueThe integration of BIM, RFID and WSN is still not commonly implemented in the AECO industry. Some studies focused on this topic; however, none of them reveals the benefits, risks and challenges for integrating BIM, RFID and WSN in a holistic manner. This research makes a significant contribution to the AECO literature and industry by uncovering the benefits, challenges and risks for the integrated use of BIM, RFID and WSN that could increase industry applications.

Integrating Wireless Sensor Network with Li-Fi Wireless Communication Technology based on NOMA Technique: A Survey

Integrating Wireless Sensor Network with Li-Fi Wireless Communication Technology based on NOMA Technique: A Survey

An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization.

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.