Wireless Sensor Network Technology Research Articles

An implentation of IoT for environmental monitoring and its analysis using K-NN algorithm

Environmental monitoring is a process for observing around with various conditions. Recently, internet of things (IoT) and wireless sensor network (WSN) technologies support to solve these problems. In this paper, we implemented a system to monitor environmental conditions using IoT and WSN technology. The data measure is Temperature, Humidity, Carbon Monoxide (CO) and Carbon Dioxide (CO2) sensors. All sensor data will be sent and stored to the cloud through the internet in real-time. We provide applications for monitoring website and mobile phone-based environmental conditions, so users can access wherever and whenever. Furthermore, we also confirm the evaluation of analyst data that used k-NN method is better than other methods with an accuracy rate of 99.0657%.

An implentation of IoT for environmental monitoring and its analysis using k-NN algorithm

Environmental monitoring is a process for observing around with various conditions. Recently, internet of things (IoT) and wireless sensor network (WSN) technologies support to solve these problems. In this paper, we implemented a system to monitor environmental conditions using IoT and WSN technology. The data measure is temperature, humidity, carbon monoxide (CO) and carbon dioxide (CO2) sensors. All sensor data will be sent and stored to the cloud through the internet in real-time. We provide applications for monitoring website and mobile phone-based environmental conditions, so users can access wherever and whenever. Furthermore, we also confirm the evaluation of analyst data that used k-NN method is better than other methods with an accuracy rate of 99.0657%.

Geometric Midpoint Algorithm for Device-Free Localization in Low-Density Wireless Sensor Networks

Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.

The Construction of Roof Greening Intelligent Management System Based on Wireless Sensor Network Technology

Roof greening can make up for the problem of urban green deficiency to some extent, add green space for people in urban life, and also alleviate urban ecological problems and heat island effect. With the development of information technology, the development level of modern automation is higher and higher. Information has penetrated into various industries. So it is very important to develop intelligent management system(IMS) of roof greening to use information technology reasonably. At present, roof greening is mainly artificial operation management, and there are many random factors and can not guarantee that roof greening is always the best state. Irrigation system is also very simple. Therefore, this paper proposes the research and construction of IMS of Roof Greening Based on wireless sensor network(WSN) technology. This paper describes the frame design of IMS of roof greening and the overall design of IMS of roof greening. The design factors of WSN are also studied. The plant survival rate of intelligent management and natural growth is studied by field detection. The effect of roof greening on reducing heat island effect is proved by data chart. The experimental results show that the survival rate of plants under the care of intelligent roof greening system is 91.27, while the natural growth survival rate is only 67.32. At the same time, the green roof plays a great role in the heat island effect. When the sun is the strongest at 12:00 noon, the temperature of the green roof is 25 degrees, while the common roof is 32 degrees.

Data detection technology of wireless sensor network based on non-stationary filtering

By optimizing the data detection performance of distributed wireless sensor networks, the data sensing and collecting ability of wireless sensor networks can be improved. Traditional methods adopt statistical characteristic parameter detection algorithm for distributed wireless sensor network data detection. Distributed wireless sensor network data has strong time-frequency coupling, so it is difficult to realize frequency domain spatial parameter clustering in frequency domain, and the detection performance is not good. A distributed wireless sensor network data detection algorithm based on non-stationary filtering and high-order statistical feature peak retrieval is proposed. The data model of distributed wireless sensor network is constructed under the interference of color noise. The weak vibration signal is subjected to time-frequency analysis and noise separation by non-stationary filtering, and the spectral peak of distributed wireless sensor network data is searched by the fourth-order cumulant slice post-operator to realize the optimal detection of signals. The simulation results show that the algorithm has a high probability of accurate detection, and has a good ability of suppressing noise and noise sidelobe information interference, which improves the probability of accurate detection of distributed wireless sensor network data under low signal-to-noise ratio.

Internet of Things and Intelligent Transportation System

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.

Greenhouse data acquisition system based on ZigBee wireless sensor network to promote the development of agricultural economy

Information technology continues to promote the in-depth progress and development of smart agriculture, while the corresponding agricultural economic development is increasingly dependent on the degree and level of agricultural informatization. At present, the development of digital and information greenhouse technology has become the focus and difficulty of agricultural development. Based on this, this paper will design a set of agricultural digital greenhouse system based on ZigBee wireless sensor network technology. At the same time, in the corresponding data acquisition and processing problems, this paper uses the PID controller under the particle filter optimization technology to optimize the error of the corresponding data acquisition system, and eliminate the corresponding noise and interference, so as to ensure the stability and effectiveness of the corresponding data acquisition system in the digital greenhouse, reduce the power consumption of the whole system, and ensure the stable transmission of data. Experimental results show that the proposed digital greenhouse technology can not only improve crop yield rapidly, but also optimize the noise control level of key algorithm by 10 points compared with the traditional filtering technology, so it has a long-term promotion significance.

Retraction Note to: Marine ecological monitoring based on wireless sensor technology and the development of traditional music education

Due to the upsurge of ocean exploration in the world, the use of wireless sensor network technology is not only limited to the insight and capture of information on land, but also slowly to a broader ocean. As the extension of traditional wireless sensor network in the underwater, marine wireless sensor network has been widely used in the fields of network detection and utilization of deep-sea mineral resources, prevention and treatment of water pollution, pre warning of tsunami and earthquake, navigation, and positioning, which has also aroused the wide attention of researchers in the industry. In addition to the detection of marine ecology, due to China’s vast territory and national prosperity, Chinese traditional culture is very rich, with a long history, profound cultural heritage, and abundant music reserves, so traditional music has a very high value of music culture education. Based on the teaching effect, this paper analyzes the development trend and favorable conditions of traditional music, shows the practical use and value of traditional music education in Colleges and universities, and then analyzes the existing problems of traditional music education in colleges and universities. In addition, in order to implement the scientific concept of development, rationally develop and utilize the ocean, divide the marine ecological red line control area, maintain marine resources, and improve the carrying capacity of marine ecosystem, this paper uses wireless sensor technology to study the educational development theory of marine ecological monitoring and traditional music.

An Integrated Signal Allocation Model with Effective Collision Resolution Model for Performance Enhancement of Wireless Sensor Networks

A Wireless Sensor Network (WSN) differs from conventional wireless or wired networks in that it interacts with the environment. Orthogonal Frequency Division Multiplexing (OFDM) was investigated as a possible interface technology for making effective use of bandwidth. Such networks have been proposed for a variety of purposes such as search and rescue, disaster assistance, and smart positioning systems. These applications often require a large number of wireless sensors that are powered by batteries and are designed for long-term, human-free deployment. Collisions between network nodes can significantly degrade performance in WSNs. Although increased bandwidth facilitates wireless access to high data frequencies, it is prohibitively expensive to increase due to spectrum limits. This necessitates making good use of the available bandwidth. OFDM has been considered as a possible interface mechanism for efficiently utilising bandwidth. While many signals available in WSN technology can be employed to mitigate collisions, multi-signal allocations may have a significant impact on the efficiency of multistage communications. Real-time multimedia flow raises the chance of sensor network failures and congestion, which reduces the efficiency of Quality of Service (QoS). The main goal of the Signal Allocation Scheme is to allocate an appropriate number of signals to any node in order to use professional bandwidth and assure QoS. Load balancing is intended to measure and prevent collisions caused by the number of available slots in the frame. Preparation is another important component in preventing collisions because it decreases delay and optimises energy utilisation. In this paper, an Integrated Signal Allocation Model with Effective Collision Resolution Model (ICAM-ECR) is used to deploy non-overlapping signals dynamically for varying application loads based on expected bandwidth estimation. The suggested model is compared to standard methods, and the findings reveal that the proposed model outperforms existing models.

POSIMNET-R: An immunologic resilient approach to position routers in Industrial Wireless Sensor Networks

Industry 4.0 has increased the interest in employing Industrial Wireless Sensor Network (IWSN) technologies in industrial automation. The advantages range from ease of installation and maintenance to reduced deployment time and infrastructure costs. However, industrial automation has critical requirements regarding network infrastructure, such as reliability and failure tolerance. Therefore, it is imperative to have an adequate placement of sensor and router nodes, to obtain a network with multiple paths, allowing the data to reach management systems within a reasonable time, even in the event of failures. The placement of router nodes has to consider latency, network lifespan, connectivity, and failure tolerance aspects in a possibly hostile environment, with classified areas and obstacles such as silos, tanks and buildings. We present a new approach, called POSIMNET-R, to place IWSN routing nodes in an industrial configuration, which circumvents forbidden areas and obstacles, based on Artificial Immunological Networks. The resulting network offers low failure rates and path redundancy criteria. The results have shown that POSIMNET-R was capable of providing a reliable network with multiple paths and resilience of the used routers equal to 81.50% in the basic case study and 73.66% in the real case scenario.

Sensor Cloud Frameworks: State-of-the-Art, Taxonomy, and Research Issues

In recent times, Wireless Sensor Network (WSN) technology has received significant attention owing to its numerous applications in various mission critical services, such as healthcare, military monitoring, public safety systems, and forest monitoring. However, sensor node’s low computational power and inadequate advancements in battery design hinder resource-rigorous applications from fully exploiting WSN capabilities. The trend of shifting computations and storage to remote clouds offers the opportunity to integrate WSNs into the cloud to mitigate their limitations. The Sensor Cloud augments WSN capabilities of remote sensing and monitoring while exploiting resource-rich cloud infrastructure. To date, numerous solutions have been proposed to handle the integration concerns of WSNs with the cloud. However, a comprehensive study that covers various Sensor Cloud aspects in terms of architecture, network dynamics, heterogeneity, communication patterns, data management, and security, is still lacking. To fill this gap, state-of-the-art sensor cloud integration frameworks are analyzed in this article. Moreover, a detailed thematic taxonomy is presented to classify existing sensor cloud frameworks while focusing in service oriented and client server architectures. Related features and critical aspects of existing frameworks are inspected through a qualitative analysis based on the parameters selected from the literature and extracted from the taxonomy. Several research opportunities in this research domain are suggested to assist researchers in identifying and designing optimal resource sensor cloud integration frameworks to host emerging applications.

Design of Sports Training System and Motion Monitoring and Recognition under Wireless Sensor Network

In conventional sports training, coaches record and observe athletes' sports data and judge whether it is reasonable based on their own experience. This qualitative analysis method is highly subjective, has large errors, and is susceptible to interference. To solve the above problems, the design of the sports training system under the wireless sensor network and the research of movement monitoring and recognition become very important. This article aims to study the design of sports training system and the monitoring and recognition of actions under the wireless sensor network technology. This paper simulates the implementation of the proposed data collection protocol and the two basic protocols, the direct transfer algorithm and the flooding algorithm, and compares the protocol proposed in this paper with the other two algorithms in terms of average information transmission success rate and average network overhead. Among them, the average information transmission success rate represents the ratio of the number of messages successfully arriving at the base station to the total amount of information generated by all nodes, and the average network overhead represents the average number of messages sent by each node. Experimental results show that the data collection protocol proposed in this paper can dynamically provide different transmission qualities for information of different importance levels, effectively reducing network overhead, and the reduced overhead is 11% of the original.

Utilization of Wireless Sensor Network to Monitor Citarum River Quality for Milkfish Cultivation in Karawang Regency

The purpose of this research (study) is to demonstrate the superiority of the wireless sensor network (WSN) technology method over the laboratory method in monitoring the water quality of the Citarum River in Karawang. To monitor the Citarum river water quality, government of Karawang regency conducted a test by taking the sample water to the laboratory. It seems to be such a conventional method. With the results of the BOD value obtained from the DLHK data, which is 47.93 mg/L, of course it will be a problem for the development of milkfish. However, an advanced method using Wireless Sensor Network (WSN) technology seems to be an alternative. Basically, this technology can monitor the water quality in real-time using multiple parameters and help cultivators to monitor the water used in milkfish cultivation. Of course WSN technology is more efficient than waiting for laboratory results. Therefore, if this technology is applied, the data will be obtained in real time. Of course the data can be processed and monitored directly to determine the quality of the water used. In terms of cost, this technology can be built at a moderate cost compared to laboratory work.

Design of Remote Real-Time Positioning Method for Long-Distance Running under Wireless Network Technology

Aiming at the problems of low precision, long time, and poor effect of current remote real-time motion location methods, a long-distance real-time motion location method based on wireless network technology was proposed. Through the analysis of wireless network technology, the basic structure and positioning technology of wireless sensor networks are studied. In addition, this paper adopts the core matrix Isomap node location algorithm based on partial least squares (PLS). The distance of geodesic between long-distance running nodes is used to characterize the dissimilarity between nodes, and the sample contribution rate of the partial least squares method is used to find and eliminate the phenomenon of short circuits in long-distance running fields. High-dimensional feature interval is mapped by centroid transformation and kernel transformation. The experimental results show that the method can improve the positioning accuracy and shorten the positioning time effectively.

Quantum-based wireless sensor networks: A review and open questions

Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.

Symmetric Connectivity of Underwater Acoustic Sensor Networks Based on Multi-Modal Directional Transducer.

Topology control is one of the most essential technologies in wireless sensor networks (WSNs); it constructs networks with certain characteristics through the usage of some approaches, such as power control and channel assignment, thereby reducing the inter-nodes interference and the energy consumption of the network. It is closely related to the efficiency of upper layer protocols, especially MAC and routing protocols, which are the same as underwater acoustic sensor networks (UASNs). Directional antenna technology (directional transducer in UASNs) has great advantages in minimizing interference and conserving energy by restraining the beamforming range. It enables nodes to communicate with only intended neighbors; nevertheless, additional problems emerge, such as how to guarantee the connectivity of the network. This paper focuses on the connectivity problem of UASNs equipped with tri-modal directional transducers, where the orientation of a transducer is stabilized after the network is set up. To efficiently minimize the total network energy consumption under constraint of connectivity, the problem is formulated to a minimum network cost transducer orientation (MNCTO) problem and is provided a reduction from the Hamiltonian path problem in hexagonal grid graphs (HPHGG), which is proved to be NP-complete. Furthermore, a heuristic greedy algorithm is proposed for MNCTO. The simulation evaluation results in a contrast with its omni-mode peer, showing that the proposed algorithm greatly reduces the network energy consumption by up to nearly half on the premise of satisfying connectivity.

Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks

The development of the wireless sensor networks technology commonly named WSNs has been gaining a significantly increased amount of attention from researchers over the last few decades. Its large number of sensor nodes is one of the features that makes it beneficial to the technology. The sensors can communicate with each other to form a network. These sensor nodes are generally used for diverse applications, such as pressure monitoring, fire detection, target tracking, and health monitoring, etc. However, the downside is that WSNs are often deployed in hostile, critical environments where they do not restrain physical access. This reality makes them incredibly vulnerable to clone node attacks or node replication attacks. The adversary can capture the legitimate sensor nodes, extract them and then collect some sensitive information, such as node ID, keys and perform a replication attack. This possibility will afterward facilitate the attacker to be able to take control of the whole network and execute the same functions as that of the authorized nodes. Based on this vulnerability, it is of great importance for researchers to invent a detection protocol for the clone attacks as well as a mitigation method. From all of the researches that have been published, a lot of them proposed some techniques to detect the clone node attacks and also to mitigate the attacks. However, almost none of them semantically focused on the security layer establishment. Based on this fact, we proposed an ontology-based approach Ontology for Replication Attacks in Static Wireless Sensor Networks “ORASWSN”, which can semantically be used for the detection and mitigation of the attacks by taking into consideration the importance of using security layers.

Energy-efficient routing sensing technology of wireless sensor networks based on Internet of Things

This research was conducted to enhance the energy performance of wireless sensor networks (WSN) and improve the performance of end-to-end delay and packet receiving rate of network operation. In this study, the low-energy data collection routing algorithm and adaptive environment sensing method in WSN were mainly examined. The node centrality, energy surplus, and node temperature were calculated for cluster head selection to reduce the energy consumption of nodes and improve the reliability of network data. The research results have shown that the parameter setting guided by the theoretical analysis makes each node selfishly achieve the maximum expected benefit while the whole network runs reliably, and the energy consumption is reduced by the selfishness of the node. As a result, the proposed algorithm can effectively reduce the network energy consumption and increase the network life cycle of wireless sensor networks. It can be seen that the machine learning methods such as support vector machine are used to model and analyze the state of the sensing node, and to obtain more accurate wireless channel availability judgment based on the historical state data, thereby adaptively adjusting the working duty ratio and reducing the invalidity data sent.

Research on improved localization algorithms RSSI-based in wireless sensor networks

The localization problem is a key supporting technology in wireless sensor network (WSN). The performance of commonly used positioning algorithms in WSN in terms of node density, beacon node ratio and different terrain is firstly analyzed and compared in this paper. The analysis shows that the magnitude of the positioning error of the trilateration method based on the received signal strength indicator (RSSI) ranging technology is 10−15, which is far lower than other positioning algorithms, but the node proportion that cannot be located is 20%–50% or so, which is much higher than other algorithms. Therefore, an improved trilateration positioning method based on RSSI ranging (ITRSS) is proposed, that is, the unknown nodes that have been located is used as new beacon nodes to locate other unknown nodes. MATLAB simulation shows that the positioning error of the proposed ITRSS method is only reduced by an order of magnitude, moreover, the proportion of nodes that cannot be located is reduced to about 1%, which is only some remote individual node out of the communication range in the area. The Zigbee network established by the CC2530 chip is used for preliminary experimental verification. The experimental results show that the nodes that could not be located at first can be located, and the positioning error does not exceed the set threshold of 3 m. Thus shows the proposed ITRSS method is of feasibility and effectiveness, which help to improve the positioning capability of the entire WSN network.

5G-Oriented IoT Big Data Analysis Method System

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.