The application strategy for intelligent wireless sensor network in flight test

SummaryWith the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network.

The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.

Wireless sensor networks (WSNs) receive considerable attention as one of the key and enabling technologies of cyber-physical systems (CPSs) that, in turn, present a new computing paradigm now and into the future. WSNs are open to a wide range of potential applications, such as environmental monitoring, human health monitoring, target detection and tracking, and industrial process control. To be smart and highly efficient services are the present and future demand of WSNs. In this chapter, we aim to introduce the fundamentals of an intelligent wireless sensor network (iWSN) and its requisites, characteristics, and applications. Also, we distinguish between the traditional WSN and the iWSN. First, the definition and properties of an intelligent system are described. Then we explain how a WSN can be deployed in a cyber-physical system and provide the facility for communications among different components of the system. In the next sections, various characteristics of an iWSN, its applications, and comparison to WSN are highlighted. Two applications of WSN, comprising smart grid and smart field monitoring system for pest control, will be introduced and compared in terms of the features of intelligence they require.

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

A kind of intelligent wireless sensor networks (IWSN) is set up from the point of view of multi-agent systems. IWSN is composed of some sensor node agents, some cluster head agents, a base agent and a command agent. According to the characteristics of these agents, intelligent wireless sensor networks model (IWSNM) based on Petri nets is proposed, which can accurately and unambiguously model the overall and individual characteristics of the networks. Moreover, IWSNM can be analyzed, verified and validated by the supporting tools of Petri nets. Consequently, the defects in early design stage can be detected, and the security and reliability can be improved. Finally, IWSNM are employed to develop the target tracking systems.

Considering emerging technologies of the intelligent space and wireless sensor network, a business architecture, system architecture and technology architecture are proposed for the mission planning system of a space sensor network. The business architecture of a mission planning system is constructed from application flows, such as those of task modelling, planning and scheduling. The system architecture is designed from the software platform, resource configuration and node structure. The technology architecture is proposed according to applied key technologies. Key technologies are also proposed for the mission planning system of a space sensor network. Through the strengthening of top-level planning and the innovation of the conception design, the exploration of the architecture and key technologies of the mission planning system of a space sensor network will promote technological innovation and provide comprehensive benefits.

The main objective of this research is to control the traffic issues in developing countries like India. As per the survey in metropolitan cities like Bangalore per day, there will be 4000 four wheeler vehicles are getting sold out. Just imagine the increase in traffic, which made the vehicle owners to wait at signals more than the driving time to reach their destination. And another major issue because of this is providing path to the emergency service vehicles like ambulance and fire engine. The best solution for this problem is automatic creation of Green Corridor whenever it is needed. To provide a better solution for these two problems especially in any metropolitan city like Bangalore, this system will be proposed based on the latest technology called Internet of Things (IOT). This system will give solution for waiting time at signals by using the technique called intelligent camera and wireless sensor networks. And coming to the Green Corridor it is purely a sensor-based application or hardware which is going to be fixed at signals to solve this issue. The entire sample data to carry out this process is collected from Bangalore city in order to create data sets. And the sample data sets will be treated as an input for the application which is developed to reduce waiting time at signals, whereas with respect to Green Corridor it is going to be live data, the storage of information or data not really required. In this project, the concept of big data is used especially to process live data for the Green Corridor.

The advancement of smart technologies has significantly transformed traditional agricultural practices into datadriven and efficient systems. The integration of Internet of Things (IoT) and Wireless Sensor Networks (WSNs) enables realtime monitoring of critical agricultural parameters such as soil moisture, temperature, humidity, and soil health. However, existing systems often face challenges related to data reliability, scalability, and efficient processing of large volumes of sensor data. This paper presents an intelligent IoT-based wireless sensor network system for precision agricultural monitoring. The proposed system utilizes distributed sensor nodes for continuous data acquisition, a wireless communication framework for reliable data transmission, and a centralized processing mechanism for structured data handling and analysis. The system is designed to support scalable deployment in agricultural environments while ensuring efficient resource utilization and minimal manual intervention. The proposed approach focuses on improving monitoring accuracy, enabling timely decision-making, and enhancing agricultural productivity through data-driven insights. The system architecture provides a flexible and efficient framework that can be adapted to various agricultural scenarios, supporting sustainable and intelligent farming practices.

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks (VANETs). By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme (CLASC) is proposed by using a fog computing framework that supports mobility, low latency, and location awareness. It is combined with online/offline encryption (OOE) technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and nondeniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie-Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. The simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system but also achieve higher efficiency in computing and communication.

In order to monitor the water quality of rivers and lakes in real time in indoors, and protect the green hills and blue waters, a remote monitoring system of unattended surface water quality based on WSN (wireless sensor network) is designed and studied. The structure of the on-line monitoring system of water quality is studied, and the network and communication of the monitoring network are realized. Designs the pH monitoring sensor node, the intelligent wireless sensor network and the real-time information processing system are designed, and the information acquisition, processing, production, broadcasting and other functions of the whole system are finally completed. The experimental results show that the data transmitted by the method is accurate and reliable, can monitor the water quality of the river and lake basin in real time, and provide reference basis for the decision-making department, and provide real-time information for the good condition of the surface water quality.

The vehicle-road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multisource traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS. Nonetheless, the complexity of communication environment and the massive transmission of information have made VRCS optimization an increasingly difficult task, involving the intricate processes of coverage and routing optimization within IWSNs. This challenge essentially transforms into a very complicated many-objective optimization problem, where multiple conflicting objectives and resource constraints need to be tackled simultaneously. Traditional optimization methods struggle to offer satisfactory solutions. Our study introduces a many-objective evolutionary algorithm that concurrently manages coverage and routing optimization in IWSNs. Initially, we present a 3-D sensing model to delineate the perception range of an individual sensor. Subsequently, we propose a comprehensive four-objective optimization model, encompassing coverage, connectivity, energy consumption, and deployment cost, to accurately depict the performance of IWSNs. Especially this integrated model includes a swift connectivity evaluation method and a specialized routing forwarding strategy. Furthermore, we suggest a node sleeping strategy to minimize energy consumption further. To find a solution to the resultant many-objective optimization problem, which refers to numerous objectives and constraints, we developed a swarm optimization algorithm based on rapid fitness evaluation strategy and differential co-evolution, aimed at boosting convergence speed and accuracy of the proposed approach. Comparative experiments indicate that our model and algorithm outperform other algorithms in terms of efficiency and effectiveness.

Forest fires pose significant threats to both natural ecosystems and human communities due to their unpredictable nature and capacity for widespread destruction. Identifying and mitigating fires in the trunk, ground, and canopy of forests is crucial for reducing their adverse effects on the ecosystem and climate. The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. Our method seeks to reduce fire detection time and improve the reliability of the detection process. This is achieved by employing environmental indicators and moisture levels to swiftly identify fires. The intelligent WSN functions by partitioning the forest into suitable clusters and intelligently positioning sensor nodes to guarantee extensive coverage and effective data transmission to the sink. The proposed UTM system’s core component is the computation of trust ratings for every sensor node. These ratings consider communication, energy, and data trust factors to evaluate the reliability of the data being delivered. This integrated trust model enhances the robustness and accuracy of fire detection, especially under difficult environmental conditions. Furthermore, a machine learning regression model is deployed at the base station to augment the precision of fire detection. This is accomplished by examining essential attributes such as temperature, humidity, and CO2 concentrations. We have conducted thorough experiments using actual datasets consisting of 7200 samples to confirm the efficacy of our proposed UTM in detecting forest fires at an early stage. The results suggest that our system obtains a high rate of data processing and a reduced time delay in comparison to existing systems. This renders it a promising solution for the imperative need to promptly detect and prevent forest fires. Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.

Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems. The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring. The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities. The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.

Wireless Sensor Networks (WSNs) have emerged with many useful applications but require a number of conflicting desirable properties such as: scalability, self-configuration, self-organization, deployability and simplicity. Most existing WSN proposals meet one or more of these requirements, but not all of them. Besides prior to the deployed of a network, it is challenging to alter the underlying technologies, such as: localization, routing, etc. This paper attempts to solve the complexity of these systems by proposing a dynamic structured framework using agents to facilitate different demands of WSN applications. For proof-of-concept, an initial prototype system was developed on the Solarium SunSPOT emulator.