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Wireless Sensor Networks for Pollution Monitoring and Control

Wireless Sensor Networks for Pollution Monitoring and Control

Guillermo Barrenetxea et al. [19] explained that wireless sensor networks have been extensively studied in the past few years, most results are of theoretical nature and were obtained outside of a practical context. This can be problematic for real applications, especially in the area of environmental monitoring where many factors, such as harsh weather conditions, can greatly influence the performance of such a network, while reliable delivery and high-quality measurements are required. SensorScope is an interdisciplinary project, elaborated by environmental and networking researchers, that aims at narrowing the gap between theory and practice. Several successful real-world deployments have already been undertaken in rugged environments. In this paper, They analyzed the particular requirements of environmental monitoring and how these requirements have been met in the SensorScope project. They also present an application example of a deployment, undertaken in a harsh mountain environment.
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On the Embedded Intelligent Remote Monitoring and Control System of Workshop Based on Wireless Sensor Networks

On the Embedded Intelligent Remote Monitoring and Control System of Workshop Based on Wireless Sensor Networks

Abstract: The embedded intelligent remote monitoring and control system of workshop based on wireless sensor networks sets the sensor technology, embedded technology, network communication technology, data processing technology, Beidou positioning technology, sensing information technology of image and weather, geographic information technology and remote sensing technology in one, forming a digital information management system that can provide a full range of electronic remote monitoring and control for the workshop. Its wireless sensor network is a self-organizing network that is constructed from a large number of sensor nodes, which sets such three technologies of sensor, micro-electromechanical system and network in one, taking the perception, collection and processing of the information of the perceive objects in the network coverage as its aim and transfering it to data processing center to provide a basis for the remote monitoring and control of the workshop. This embedded intelligent remote monitoring and control system of workshop has many advantages: high safety, low cost, intelligence, timely alarm, energy conservation, good real-time control, wide monitoring range, strong adaptability, and so on. This monitoring system can be applied to not only plant monitoring but also other fields, such as environmental monitoring, industrial control, intelligent city, intelligent home, etc, so it has important practical significance and valuable practical value for exerting network advantage and making artificial intelligence promote social progress.
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An Energy Efficient Congestion control Technique for Wireless Sensor Networks

An Energy Efficient Congestion control Technique for Wireless Sensor Networks

Wireless sensor networks (WSN) consists of wireless nodes. These nodes are scattered in a decentralized manner to monitor the environment or system. Each node in sensor network consists of three subsystems: the sensor subsystem which senses the environment, the processing subsystem which performs the local computations on the sensed data, and the communication subsystem which is responsible for message exchange with neighboring sensor nodes. Individual sensors have limited sensing region, processing power, and energy. When sensor nodes detect an event, it become active in collecting and transmitting the data, which cause congestion and results in packet drops, decrease in throughput and retransmission of data. Some of the congestion control techniques are TADR [1], DPCC [2] and IPD [3]. In this paper we evaluate a Contextual Cooperative Mode (CCM) [4] to reduce the congestion in the link level. In this mode each sensor node periodically broadcast 1-hop CAM (Cooperative Awareness Messages) through a common channel, referred as communication channel. CAM carries the information such as node energy and node position. The data exchanged through CAMs is used to reduce congestion. By transmitting the important data the congestion can be avoided and the energy level can be consumed. For instance, priority is assigned to the data [5]. We propose an energy efficient algorithm [6] to
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Congestion Control in Wireless Sensor Networks

Congestion Control in Wireless Sensor Networks

In [7] Bret Hull described an experimental evaluation of three complementary congestion control strategies for wireless sensor networks. They show that unless a sensor network operating under load has some means of controlling congestion, it will face significant degradation in efficiency and fairness. As network load increases, or when channel variations cause fluctuations in achievable bandwidth, nodes must modulate their send rates based on local congestion feedback or the network will go into congestion collapse. They evaluate three techniques for mitigating congestion both in isolation and in concert. Their results show that hop-by-hop own control with a simple queue occupancy-based congestion detection method orders substantial efficiency improvements for all types of workloads and utilization levels. This including holds because a successful wireless transmission requires both the sender and receiver to be contention-free with respect to both the wireless channel and queue space. Implementing a rate-limiting policy results in substantial improvements to fairness. Finally, MAC enhancements support the operation of hop-by-hop own control.
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Comparative Study of Routing Protocols in Pollution Monitoring System Based On Underwater Wireless Sensor Networks

Comparative Study of Routing Protocols in Pollution Monitoring System Based On Underwater Wireless Sensor Networks

for this reason named as hop-by-hop vector based forwarding (HH-VBF). Upon a reception calculates the vector from its sender to the sink, it compute its distance to that vector. If the distance is less than the predefined threshold, it is drop the packet. Time period is representing a node carries the packet before forwarding it. The self-adaption algorithm in HH-VBF is different from that in the original VBF. Due to effective suppression strategy package approved in VBF, can select just a few paths to route packets. That can cause problems in dispersed networks. To improve the delivery ratio in dispersed networks, introduced some repetition control in the self-adaption procedure for HH-VBF. In HH-VBF 10 when a node receives a packet, it first
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Water Quality Monitoring and Control Using Wireless Sensor Networks

Water Quality Monitoring and Control Using Wireless Sensor Networks

efficient monitoring and control of water quality in lakes, rivers and sea tends to keeping the human resources healthy and sustainable, and to increase population growth and urbanization. Due to climate changes and variability so many huge impacts are caused by the water system to the natural environment. Incredible methods are used by collecting water samples, testing and analyses in water laboratories alone. However, It is not always easy to be captured, analyses and fast dissemination of information to relevant users for making timely and well-versed decisions. In this paper, Water Sensor Network (WSN) system prototype is developed for water quality monitoring in Lakes is presented. These kind of growth was introduced by the assessment of widespread atmosphere that including accessibility of cellular network coverage at the site of process. The system consists of an PIC microcontroller, water quality sensors, and wireless network connection module. It detects water temperature, ph (Potential of Hydrogen), and electrical conductivity in real-time and disseminates the information and these are sent to relevant stakeholders through a web-based portal and can be gain via mobile phone platforms.These Information are specified with the location of the lakes where it has been placed. With the view to complementing the experimental results that shows the system has immense vision and can be used to run in real world environment for monitoring and optimum control of water resources by providing relevant and timely information to stakeholders for facilitate quick action taking and protect themselves.
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Wireless Sensor Networks for Water Quality Monitoring and Control within Lake Victoria Basin: Prototype Development

Wireless Sensor Networks for Water Quality Monitoring and Control within Lake Victoria Basin: Prototype Development

The WSN system gateway is one of the most important and unique block in the proposed system. It collects all the information received from multiple sensor nodes. The developed gateway is equipped with microcontroller unit, General Packet Radio Service (GPRS) module, RF transceiver, memory card and power supply. Arduino Uno microcontroller is used to acquire and process received sensor data from WSN sensor nodes through the RF transceiver. The same XBee module as used in WSN sensor nodes is used to implement WSN gateway RF tran- sceiver module. The GPRS module residing on top of the gateway node is used to communicate with the cellular network to forward the Short Message Services (SMS) data to a WaGoSy system. To implement the GPRS module, Arduino GPRS shield from Seed Studio was selected. This is quad-band low power consumption Glob- al System for Mobile Communications (GSM)/GPRS module SIM900 with a compact PCB antenna and con- sumes 1.5 mA in sleep mode. A 1 GB compact memory card is used to store measurements data from WSN sensor nodes when the GSM connectivity is not available. Figure 4 shows the architecture of the proposed ga- teway node.
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RFID Based Air Pollution Monitoring and Control Using IoT

RFID Based Air Pollution Monitoring and Control Using IoT

ABSTRACT: In this project we have designed new thing of the monitoring the pollution level in vehicles to be monitored continuously and updated the every value in IOT (internet of things) based cloud server monitor page. In recent years most of the incident happening in traffic. Because of the most of the vehicles are queue in traffic area and traffic signal. So that we have designed this project to help and monitor the air pollution and automatically data’s are updated in cloud based system. The co 2 sensor measures the co 2 level of the vehicle. If the value higher than the
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Automatic Water Pollution Control and Filtration System for Rivers Using IOT

Automatic Water Pollution Control and Filtration System for Rivers Using IOT

Internet of Things the technology of connecting everything using wireless technology. We can control or monitor anything using the sensors designed for different objects. IoT is the collection of objects, devices, houses and other items embedded with sensors, and network connectivity to enable them to communicate with each other and to send data to the best stations. Internet of Things (IoT) technology is rapidly evolving with the latest innovation which is used in Wireless technology and an embedded technologies. Using Micro controllers working on low powers it defined that are best solution for virtually deployed Internet of Things systems to connect peoples and works for years without any maintenance has made the IoT not only for luxury functions but also for needful data aggregation as for defense systems. The devices participating in IoT are designed to be interoperable with different vendors of embedded controllers as well as with different wireless technologies. IoT is progressing with millions of things connecting each day to generate large amount of information resulting in useful future actions. Wireless sensor networks have limited power resources. To extent network life time switching mode has been assigned to each sensor node .where each node become sleep mode as soon as it transmit the data packet from one end to another end. Most common task of an IoT based applications are transmitting huge sensed data to a specific node. To acquire, process and transmit the sensed data efficiently is a key challenge in the IoT . Wireless sensor networks and RFID (Radio frequency identification) technologies are achieving excellent infrastructure in manufacturing industry to sense as well distribution of data in decentralized environment.
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Monitoring real time urban Sulphur Dioxide 
		and Ammonia emissions using the Wireless Sensor Networks

Monitoring real time urban Sulphur Dioxide and Ammonia emissions using the Wireless Sensor Networks

wireless link and also a low-cost ZigBee module. Wireless sensor network formed with ZigBee links can be scaled up using the GSM connectivity to interface with the external world. We proposed a static Wireless Sensor Network to monitor air pollution through the use of WSN. A prototype version of the platform is realized and tested. Experimentation carried out using the developed wireless air pollution monitoring system under different physical conditions show that the system collects reliable source of real time fine-grain pollution data.
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Standards-based wireless sensor networks for power system condition monitoring

Standards-based wireless sensor networks for power system condition monitoring

Security is paramount in any industrial system to mitigate against intrusions and to maintain data confidentiality and integrity. Security presents an additional challenge in WSNs as the network uses a wireless broadcast medium. While monitoring data may not immediately present itself as being confidential in nature, the communications channel between the sensor and the control room must still be secure; for instance, to stop injection of spurious measurements which would misrepresent the condition of a unit, which in turn could potentially require a site visit to investigate. Akyol et al. [12] have specified that, for this reason, industrial wireless sensor networks must use data encryption in multiple network layers to validate data and mitigate against eavesdropping, and 2-way, mutual authentication of nodes to mitigate against malicious nodes joining and participating in the network. In addition, application-level security and validation in addition to network and data-level encryption schemes is recommended.
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IoT based Industrial Pollution Monitoring

IoT based Industrial Pollution Monitoring

Quality Monitoring System”. This paper describes to ensure the safe supply of drinking water the quality should be monitored in real time for that purpose new approach IOT (Internet of Things) based water quality monitoring has been proposed. In this paper, the design of IOT based water quality monitoring system that monitors the quality of water in real time. This system consists of some sensors which measure the water quality parameter such as pH, turbidity, conductivity, dissolved oxygen, temperature. The measured values from the sensors are processed by the micro- controller and these processed values are transmitted remotely to the core controller that is raspberry pi using Zigbee protocol. Finally, sensors data can view on internet browser application using cloud computing. [2]
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Air Pollution Monitering Using Arduino and Iot

Air Pollution Monitering Using Arduino and Iot

So it becomes more and more important to monitor and control air pollution. It will become easy to control it by monitoring the concentration air pollutant parameters in air. Using laboratory analysis, conventional air automatic monitoring system has relatively complex equipment technology, large bulk, unstable operation and high cost. This system can only be installed in key monitoring locations of some key enterprises, thus system data is unavailable to predict overall pollution situation. Using empirical analysis, conventional air automatic monitoring system has high precision, but large bulk, high cost make it impossible for large-scale installation. Nowadays, air pollution is monitored by static air quality measurement stations which are highly reliable and can measure the pollutants in air to a high level of accuracy and precision using analytical instruments, such as mass spectrometers, operated by official authorities. However, extensive cost of acquiring and operating such stations limits the number of installations. To monitor air quality, wireless sensor networks (WSNs) might be a great tool, because they can automatically collect air quality data. It will also help us to keep a working staff away from danger and a high security can be achieve and it will also help the Government authorities to monitor the air pollution.
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Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

According to the optimization object, node deploy- ment can be divided into coverage-based deployment, network connectivity-based deployment, and energy efficiency-based deployment. The performance of the whole WSN depends on the network coverage. In the node deployment in WSNs, it is highly important to im- prove the network performance and coverage. Many scholars at home and abroad have done a lot of research on how to improve the network coverage. Hou Y et al. [8] proposed a node optimization algorithm by trans- forming the node deployment problem into a computa- tional geometry problem]. Fan Zhigang [9] proposed a sensor node deployment algorithm based on cellular grid, which can accurately deploy the monitoring area, not only can achieve complete coverage but also can accurately deploy some redundant nodes to extend net- work life cycle and can be applied to areas with stringent control for the density of wireless sensor nodes. Fadi M. Al-Turjman et al. (2013) [10] proposed a general method to evaluate the average connectivity based on grid deploy- ment strategy by analyzing the practical problems in de- ployment. Achieving complete coverage while guaranteeing connectivity is of practical significance in ap- plication. S.M. Nazrul Alam et al. (2015) [11] used Voro- noi elements to divide the deployment space into several polyhedrons and calculated the furthest distance between any two points in the polyhedron so as to ensure that the wireless sensors communicate with each other and trans- mit information to the base station. After simulation, the deployment method proposed by S. M. Nazrul Alam et al. was proved to have good connectivity.
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IOT Based Air Pollution Monitoring System Using Arduino and ESP8266 Nilima Bhure, Yamini Lonare, Minal Madavi, Prof. I. P. Nikose

IOT Based Air Pollution Monitoring System Using Arduino and ESP8266 Nilima Bhure, Yamini Lonare, Minal Madavi, Prof. I. P. Nikose

So it becomes more and more important to monitor and control air pollution. It will become easy to control it by monitoring the concentration air pollutant parameters in air. Using laboratory analysis, conventional air automatic monitoring system has relatively complex equipment technology, large bulk, unstable operation and high cost. This system can only be installed in key monitoring locations of some key enterprises, thus system data is unavailable to predict overall pollution situation. Using empirical analysis, conventional air automatic monitoring system has high precision, but large bulk, high cost make it impossible for large-scale installation. Nowadays, air pollution is monitored by static air quality measurement stations which are highly reliable and can measure the pollutants in air to a high level of accuracy and precision using analytical instruments, such as mass spectrometers, operated by official authorities. However, extensive cost of acquiring and operating such stations limits the number of installations. To monitor air quality, wireless sensor networks (WSNs) might be a great tool, because they can automatically collect air quality data. It will also help us to keep a working staff away from danger and a high security can be achieve and it will also help the Government authorities to monitor the air pollution.
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Environmental Monitoring and Greenhouse Control by Distributed Sensor Network

Environmental Monitoring and Greenhouse Control by Distributed Sensor Network

VI. CONCLUSION AND FUTURE WORK A model of agricultural application using sensor networks for greenhouses monitoring and control was presented. The wireless sensor network technology, although under development, seems to be promising mainly because it allows real time data acquisition. However, for such agricultural application to be developed, some technological challenges should be resolved. A greenhouse is a controlled environment and does not require a lot of climatic parameters to be controlled. The use of this technology in large scale seems to be something for the near future. In this application, the great number of climatic parameters can be monitored using the sensors available. As a greenhouse is a relatively small and controlled environment, and energy is a limited resource, the possibility of replacing batteries or even resorting to a steady energy source adaptation is a constructive aspect. This paper reveals an idea of environmental monitoring and greenhouse control using a sensor network. The hardware implementation shows periodic monitoring and control of greenhouse gases in an enhanced manner. Future work is concentrated in application of the same mechanism using wireless sensor network. This technology can also be applied in breeding of confined animals in precision zoo, where the sensor nodes should send information about animal temperature, pressure and other vital signals to guarantee a healthy environment to animals. In order to attain better energy efficiency, this mechanism will be implemented in real-world wireless sensor network, with a well-known energy efficient distributed clustering mechanism (HEED).
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Pollution Monitoring System Using Gas Sensor Based On Wireless Sensor Network

Pollution Monitoring System Using Gas Sensor Based On Wireless Sensor Network

A number of researches have been conducted related to pollution monitoring. Yang et al. [4] have developed remote online monitoring system for geological carbon dioxide (CO2) leakage which consists of monitoring equipment, server, and clients. The system gets localization and time information through GPS, and then saved into SD cards storage module. The general packet radio service (GPRS) wireless transmission module will send the collected data wirelessly to the data centre server. Sivaraman et al. [5] developed HazeWatch project which uses several low-cost mobile sensor unit attached to vehicles to measure air pollution concentrations such as CO, nitrogen dioxide (NO2), and ozone (O3). The user can tag and upload the data in real time using their mobile phone. The spatial granularity of obtained data was collected to create of pollution maps which viewable in real-time over the web. The mobile personalized apps show the individual exposure history and route planning which less pollution. Peng et al. [6] implemented a total volatile organic compound (VOC) air pollution monitoring system to report value of temperature and humidity at indoor environment with the consideration of the cost, development complexity, and the operation convenience. Kadri et al. [7] developed an ambient real-time air quality monitoring system based on utilizing multi-gas (MG) monitoring stations that communicate with a platform by means of machine-to-machine (M2M) communications. Each MG station contains O3, CO, NO2, and H2S sensors. Jelicic et al. [8] developed flexible wireless system able to detect polluted air in a large environment. The system involves three levels: sensor level, node level and network level which contains metal oxide semiconductor (MOS) gas and a pyroelectric infrared (PIR) sensors. The system adopted duty-cycling of the gas sensor activity to extend the lifetime of node compared to the continuously driven gas sensor. Dian et al. [9] developed a CO2 sequestration monitoring and gas sensing by combining techniques of optical fiber sensing technology and wireless communication and analyzed the power consuming task such as sensing unit, processing unit, and transceiver unit. The result research recommended that renewable energy system such as solar cell maintain long-term and stable work. Pau et al. [10] developed a fuzzy logic system in wireless sensor network for environmental monitoring according to the battery level and to the throughput to reducing power consumption. The experiments result shows that fuzzy-based approach dynamically changes the sleeping time in order to reduce power consumption and increase the lifetime of sensor nodes.
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Implementation of SCADA Like Architecture on Web Application

Implementation of SCADA Like Architecture on Web Application

However, in order to take advantage of WSAN, traditional SCADA acquisition processes need to change in order to accommodate this new technology. For example, secure ways of delivering information from the sensors to the centralized SCADA must be implemented. Furthermore, SCADAs normally integrate proprietary protocols, which make the integration with a general purpose WSAN non- straightforward, requiring the use of gateways. Also, quality of service (QoS) for real-time communications and management support in a WSAN are challenges that have to be considered. To tackle all these issues and to propose a generic solution to the integration problem between WSANs and SCADA was, in fact, one of the goals of the European project WSAN4CIP [8], in the context of which the solution presented in this paper has been developed. The WSAN4CIP project globally aimed to protect CIs by means of a secure WSAN system architecture. In this paper, we present the solution developed for SCADAWSAN integration. The solution is based on the use of open protocols that work over conventional networks, such as the Internet. More specifically, the system uses Internet protocol (IP) and Web services together with an open-source and Web-based SCADA called Mango. Two proof-of-concept demonstrators were deployed in WSAN4CIP, one for monitoring drinking water distribution pipelines and the other for monitoring an electrical power grid. This paper only focuses on the approach followed to integrate SCADA and WSAN and the way it has been applied to the latter demonstrator, henceforth designated the Energies de Portugal (EDP) demonstrator, which reflects the name (EDP) of the electricity distribution company. A detailed description of the WSAN4CIP overall architecture and of the WSAN wireless modules and protocols can be found in.
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Comprehensive Review on Wireless Sensor Networks

Comprehensive Review on Wireless Sensor Networks

Wireless sensor network (WSN) is widely considered as one of the most important technology in the past decades. It has received tremendous attention from all over the world [1]. A WSN typically consists of a large number of low- cost, low-power, and multifunctional wireless sensor nodes, with sensing, wireless communications and computational capabilities. These sensor nodes communicate over short distance via a wireless medium and collaborate to accomplish a common task. They are finding their usages in habitat monitoring, manufacturing and logistics, environmental observation and forecast systems, military applications, health, home and office applications and a variety of intelligent and smart systems.
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Concealed Integrity Monitoring for Wireless Sensor Networks

Concealed Integrity Monitoring for Wireless Sensor Networks

of a bidirectional protocol and reduces requirements that must be met by an underlying communication platform. However, loss of messages can be detected by the re- ceiver if both, sender and receiver, use a consistent seg- ment (sequence) numbering scheme (Pa4.2). The initial sequence number for a new association is when the Sen- sor Transport Entity processes a Send Up Information primitive. The algorithm to determine the initial se- quence number from the node identifier and other suit- able characteristics known by the sensor as well as the monitor is implementation dependent and may be based on a shared secret, which acts as a seed. A non-trivial assignment of initial sequence numbers enables the mon- itor to authenticate the process of creating an association, as the result of this check is included in the Received Up Information primitive.
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