Development Of Wireless Sensor Networks Research Articles

Resource allocation design method based on WSN multi-dimension soil inspection

Abstract. With the industrialization of agriculture and the sharp rise of food prices, in order to promote the intensive development of natural resources and achieve economies of scale agriculture in production, people put forward the innovative strategy of "precision agriculture" to maintain the sustainable development of agriculture. To protect the environment and farmer communities and increase productivity. However, in the special context of precision agriculture, the problems related to energy consumption and electromagnetic wave propagation in complex environments have a significant impact on precision agriculture. In the case of no monitoring and harsh environment, wireless sensor networks (WSN) can complete the tasks of real-time monitoring and information collection. In the past, the understanding of WSN in agriculture was limited to unilateral detection, which led to the underutilization of the soil. This paper introduces an innovative solution for precision agriculture based on wireless sensor networks, which mainly focuses on the observation and impact assessment of soil water volume,PH value, temperature and pressure in agricultural production process. It aims to reduce the waste of natural resources and the use of fertilizer substances, while maintaining or improving the yield of farming, in order to better promote the development of WSN in the agricultural field.

Wireless sensor networks protocols, applications, and network-on-chip communications

A wireless sensor network (WSN) is a network consisting of self-governing sensors that are deployed in space and communicate with each other using wireless technology to monitor physical or environmental variables. These networks generally include compact, inexpensive sensor nodes equipped with sensing, processing, and communication functionalities. WSNs are specifically engineered to gather data from their immediate environment, do local data processing, and subsequently communicate pertinent information either to a central hub or to other interconnected nodes within the network. Continuous research in the domain of WSNs is devoted to advancing security concerns, developing novel sensing technologies, and optimizing communication protocols. The advancements in these domains enhance the ongoing development and efficiency of WSNs. The WSNs are very important for getting information from the real world in many situations. WSNs are flexible tools for keeping an eye on and controlling different environments because they have sensor nodes, wireless communication, and distributed processing. WSNs use network-on-chip (NoC) communication architecture to connect sensor nodes. The article explains the introduction to WSN, the background of wireless communication, motivation, ZigBee protocol, and WSN applications.

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

LoRa Based IoT Enabled Sensor Networks for Plantations

The integration of Wireless Sensor Network (WSN) technology with the Internet of Things (IoT) in agriculture plays a pivotal role in addressing a range of challenges and constraints faced by the sector, encompassing issues such as labour shortages, suboptimal farm management practises, and unpredictable weather conditions. In response to these pressing concerns, this study focuses on the development of WSN with IoT for agriculture, employing a spread spectrum modulation technique named the Long Range (LoRa) module. By leveraging LoRa devices and wireless radio frequencies, this technology serves as a versatile platform for delivering wireless, long-range, and energy-efficient communication to support small and medium-sized agricultural operations. These operations often lack adequate technological assistance due to factors such as limited expertise and the high costs associated with cutting-edge agricultural technology. The research undertakes a comprehensive exploration, involving LoRa parameter testing, Line-of-Sight evaluations, Non-Line-Of-Sight (NLOS) simulations, and sensor calibration, to assess the efficacy of LoRa-based IoT-enabled sensor networks within plantation environments. The overarching objective of this research endeavour is to provide valuable insights that contribute to the optimisation of agricultural practises through streamlined IoT solutions. By implementing practical and cost-effective strategies, the local agricultural sector stands poised to achieve seamless strides in both sustainable and efficient food production.

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

A Modular Architecture for Energy Efficient Wireless Sensor Networks Nodes

The current evolution of Wireless Sensor Networks (WSN) has led us towards real world problems such as the power supply of network nodes in an efficient and sustained way. The actual implementation of these networks calls for perpetually powered devices, since the constant changing of the batteries is not a realistic scenario. That is the main motivation behind the proposal of this new architecture, having the low power consumption as a key objective and an intelligent interaction with power supply modules. The energy efficiency of this platform comes from three different factors: the use of a last generation ultra low-power microcontroller, the use of different communication modes and the intelligent interaction with a harvesting module able to perpetually supply energy to the network nodes. Hence, the modularity of this platform, allowing for independent development of its components, is a very important characteristic. We present and discuss the major features of the proposed architecture, such as the used processor, how the modularity is achieved and how we intend to implement the energy solutions.

The Evolution of Wireless Sensor Networks through Smart Radios for Energy Efficiency

This article investigates the significant influence of smart radios on the energy efficiency of Wireless Sensor Networks (WSNs). It explores the incorporation of intelligent radios as a crucial element to enhance WSNs’ sustainability, providing a novel approach to tackle energy-related challenges. Smart radios are essential for prolonging the lifespan of sensor devices and reducing the environmental effect of WSNs by autonomously adjusting communication protocols and optimizing energy consumption. This paper also highlights the differences between 5G and 6G technologies within the WSNs framework. While 5G brought improvements in data speed and connectivity, 6G represents a significant progress by prioritizing energy efficiency as a fundamental goal. This shift represents a fundamental change, with a primary focus on achieving extremely low energy consumption and ensuring the sustainable operation of WSNs. The paper examines crucial technological factors enabling 6G to outperform its predecessor, establishing it as a revolutionary force in the field of wireless communication for sensor networks. This research illuminates the crucial function performed by smart radios in connecting the desire for energy efficiency with the changing environment of wireless sensor networks.

RETRACTED ARTICLE: Application of wireless sensors and deep learning algorithms in comfort analysis of wooden structures

In many buildings with wooden structures, the service life and comfort of the floor will be affected by the walking load of the human body. The vibration effect generated by the human walking on the floor will affect the comfort that the floor brings to people. This article primarily focuses on the study of the correlation between floor vibration and human walking load in wooden buildings. It provides an analysis and summary of research findings from both domestic and international sources. Furthermore, the data collected is utilized to establish a model for estimating human walking load. The advent of information technology has witnessed significant transformative changes spurred by shifts in societal needs. Notably, the establishment of wireless sensor networks stands as a significant milestone within the field of information technology, highlighting advancements in science and technology. The evolution of wireless sensor networks has proven instrumental in various sectors within China, including but not limited to the military, environmental monitoring, biological research and medical systems, disaster monitoring, and energy allocation. This paper uses deep learning algorithms to analyze the node deployment problems of wireless sensor networks, summarizes a reasonable node deployment plan through specific research and practice, and uses collaborative deep learning methods to improve the operating efficiency of the network. In addition, this article also tested the vibration effect of wood structure buildings, analyzed different influencing factors, and provided a good reference point for the formulation of wood structure comfort standards.

Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are widely used in various fields such as military, industrial, and transportation for real-time monitoring, sensing, and data collection of different environments or objects. However, the development of WSNs is hindered by several limitations, including energy, storage space, computing power, and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefore, we propose an energy harvesting system and a low-energy-consumption mechanism for WSNs. Firstly, we delved into the energy harvesting technology of WSNs, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieved a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatiotemporal correlation, resulting in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.

Advancements in Energy-Harvesting Techniques for Agricultural Applications: A Review

Abstract: Over time, there has been rapid advancement in wireless sensor network (WSN) technologies. These networks have proven to be efficient tools in enhancing agricultural productivity. One significant development in WSNs is the integration of energy harvesting capabilities, allowing them to extract energy from the surrounding environment and utilize it to power wireless Internet of Things (IoT) devices. Energy harvesting involves converting ambient energy from various sources such as solar power, wind, and mechanical vibrations into usable power for devices. This review focuses on exploring the different techniques of energy harvesting for WSNs that can be effectively employed in agricultural monitoring systems. Additionally, it addresses the power consumption issues prevalent in agriculture and aims to identify the most effective strategies for resolving these problems.

An optimized approach to enhance the network lifetime through integrated data aggregation and data dissemination in wireless sensor network

<div class="WordSection1"><p>Wireless sensor network (WSN) is an integral part of internet of things (IoT), it comprises multiple sensor nodes to sense that data for various applications; also, sensor nodes have limited energy. Hence, several researchers to improvise the network lifetime and reduce energy through machine learning approaches like clustering have used data aggregation; considering the WSN architecture and development of novel use cases and dynamic behavior, data aggregation cannot solve the problem of efficiency solely. Hence integrating data aggregation and data dissemination can provide a research scope to achieve optimal efficiency. This research work introduces an integrated-data aggregation and data dissemination (DADD) to develop an efficient WSN-based model for lifetime enhancement. Integrated-DADD follows two sub-mechanisms; the first part of the mechanism introduces an optimal clustering technique to perform the clustering and optimal parameter tuning is formulated and efficient data aggregation takes place. The second part of the integrated-DADD introduces optimal data dissemination through optimal path selection, which helps in finding the suitable path for data dissemination. Integrated-DADD is evaluated considering the parameters like energy consumption, network lifetime in terms of rounds; active node participation, and communication overhead, comparative analysis indicates that integrated-DADD outperforms the existing model.</p></div>

Oxygen Level System Development in WSN and IoT-Based Factory

The health of workers is essential to factory productivity. The lack of oxygen experienced by factory workers for a prolonged duration can disrupt the brain system. One solution to this problem is to build manufacturing facilities with well-maintained airflow, especially oxygen. The system can flow air from outside the factory into the factory based on the measurement of the oxygen level. In this research, an airflow system using the internet of things (IoT) and wireless sensor network (WSN) technology was developed to ensure no oxygen shortage in the factory space. The system comprises three main parts: an oxygen level sensor, a fan controller circuit, and a cloud-based communication system. The oxygen level sensor can measure the volume of oxygen in the factory room and is also connected to the fan controller to control the airflow to the radio-frequency (RF) communication factory room. Oxygen level monitoring data are also sent to the cloud server so that the condition of the factory space can be monitored remotely using internet computers and mobile devices. Performance tests that have been carried out show that the system can increase the oxygen level by 82% from its pre-installed condition. The system built is easy-to-install, low-power, and reliable, with a data loss value of only 1.67%. WSN implementation at the factory does not require a lot of wiring, thus making the system cheaper.

Innovative DMHS Algorithm Application in Wireless Sensor Networks for Efficient Routing in High-Risk Environments.

Efficient routing is essential for the proper functioning of wireless sensor networks (WSNs). Recent research has focused on optimizing energy and delay for these networks. Nevertheless, there is a dearth of studies that have examined the effects of volatile settings, such as chemical plants, coal mines, nuclear power plants, and battlefields, where connectivity is inconsistent. In such contexts, sensor networks may face security incidents, and environmental factors such as node movement and death can result in dynamic changes to the network topology. A novel design algorithm grounded on Dynamic Minimum Hop Selection (DMHS) was introduced in this paper. The key principle behind DMHS is to use a probabilistic forwarding decision-making process through a distributed route discovery strategy that utilizes dynamically adjusted minimum hop counts of nodes. Simulation results indicate that the life cycle of the DMHS algorithm increases by more than 12% over 700 nodes when compared to the traditional energy-saving algorithm. Furthermore, our algorithm performs better in the average delivery rate of node, and has a 10% to 21% improvement compared to the other algorithms. Overall, the DMHS algorithm represents an important contribution to the development of WSNs that can function robustly in high-risk and unstable environments.

Analytical Study for Optimization Techniques to Prolong WSNs Life

Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life. Index Term— Cluster Head selection, Clustering, PSO, Routing, Wireless Sensor Networks.

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean’s environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.

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.

Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement.

Nowadays, the rapid deployment of Wireless Sensor Networks (WSNs) and the integration of Internet of Things (IoT) technology has enabled their application to grow in various industrial fields in our country. Various factors influence the success of WSN development, particularly improvements in Medium Access Control (MAC) protocols, for which WSNs-IoT are deemed vital. Several aspects should be considered, such as energy consumption reduction, performance, scalability for a large deployment of nodes, and clustering intelligence. However, many protocols address this aspect in a constrained view of handling the medium access. This work presents a state-of-the-art review of recently proposed WSN MAC protocols. Different methods and approaches are proposed to enhance the main performance factors. Various performance issue factors are considered to be the main attribute that the MAC protocol should support. A comparison table is given to provide further details about using these approaches and algorithms to improve performance issues, such as network throughput, end-to-end delay, and packet drop, translated into energy consumption.

An anonymous authentication and key agreement protocol in smart living

Wireless Sensor Networks (WSNs) play an indispensable role in the application of smart homes, smart healthcare, and precision agriculture. However, WSNs confront privacy risks that hinder its practical applications. The leakage of privacy is one of the key factors to restrict the development of WSNs. Hence, in this paper, we propose an Anonymous Authentication and Key Agreement protocol (AAKA) to accomplish identity authentication and privacy protection. Based on the dynamic sequence number, the shared secret value, and the dynamic random number, the AAKA protocol implements a two-way authentication and keys negotiation among users, gateway, and sensors, which achieves the secure access control of legitimate users to WSNs and ensures the confidential transmission of data over the public channel. We perform the security proof with BAN logic for security evaluation. The performance analysis demonstrates that compared with other WSNs authentication schemes, the AAKA protocol obtained better security features, smaller storage, and more efficient communication. Therefore, it is more suitable for applications in smart living.