Sensor Network

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Optimal Sensor Deployment in Homogeneous &Heterogeneous Wireless Sensor Network

Optimal Sensor Deployment in Homogeneous &Heterogeneous Wireless Sensor Network

are used in WSNs. This work implements a dynamic deployment using non-uniform sensor network to achieve energy balance and longer network lifetime. Different from the previous study, the sensor placement is dynamic which can change the sensor densities according to application. A mathematical model is proposed to synthesis the expected sensor density in the monitored area. Here, first introduce the system model for both homogeneous and heterogeneous WSNs and formulate the problems of sensing and connectivity. The density requirement may also change after a network has been created due to changes in application modes or environmental conditions. Some regions may create or transmits more packets than others for some needful events still their locations are not near the sink node. For example, after an intruder is detected, the region in the location of the intruder may generate more messages. Proposed system develops a model which provides minimum distortion with Maximum Coverage. We develop node deployment strategy in Dimensional environment. By considering node deployment we can deploy maximum number of sensor nodes in a limited range that results in optimization of network lifetime. This work can derive number of homogeneous and heterogeneous sensors to be deployed along with their deployment region. Deployment system in WSN explore each sensor node sends its data to the relay node having maximum energy and then it is send to the nearest sink node of the WSN.
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INCREASE THROUGHPUT OF A NETWORK THROUGH WIRELESS SENSOR NETWORK

INCREASE THROUGHPUT OF A NETWORK THROUGH WIRELESS SENSOR NETWORK

The proposed work is implemented on Wireless Sensor network to improve the network life in case of clustered Network. The main problem with cluster network is to find the cluster head for each cluster. Here the improvement is done for Spin protocol. In this work we have include one parameter to select the cluster head and the vice cluster head. This parameter is represented by Ideal Time Analysis. The work has considered the concept of congestion parameter along with effective throughput for that instance. The node will be selected that is working with low congestion and better throughput over the network.
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Distributed Wireless Sensor Network Deployment

Distributed Wireless Sensor Network Deployment

This chapter will start the phase with the explanation on the keywords of the project which then further proceeds to discuss and study on Literature Review of the Distributed Wireless Sensor Network which explores the characteristic of Crossbow’s IRIS sensor network as well as the study of the previous research on this project as the final phase. For this project, the research will be done using the reference on books, articles, online journals about the existing systems that are available in market nowadays.

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Study of Wireless Sensor Network

Study of Wireless Sensor Network

Smart environments represent the next evolutionary development step in building, utilities, industrial, home, shipboard, and transportation systems automation. Like any sentient organism, the smart environment relies first and foremost on sensory data from the real world. Sensory data comes from multiple sensors of different moralities in distributed locations. The smart environment needs information about its surroundings as well as about its internal workings; this is captured in biological systems by the distinction between exteroceptors and proprioceptors. Wireless sensor networks are a group of specialized devices or sensors which are used to monitor different environmental conditions and to collect and organize that data at some certain central location. It detects and measures a number of physical conditions such as humidity, temperature, sound, pressure, speed and direction, chemical concentrations, vibrations, pollutant levels and many other such conditions. It has much application with microcontroller projects. Nowadays wireless sensor network is playing a important role in the whole world.
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Security in Wireless Sensor Network

Security in Wireless Sensor Network

A wireless sensor network is a composed of large number of nodes that are densely deployed either inside the phenomenon or very close to it. It is spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. Wireless Sensor Network may operate in hostile environment, so security is needed to ensure the integrity and confidentiality of sensitive information. Security is important field in WSNs, which is quite different from traditional security mechanism. This is because of two major reasons. Firstly, there are severe constraints on these devices namely their minimal energy, computational and communicational capabilities. Secondly, there is an additional risk of physical attacks such as node capture and tampering. Sensor networks also introduce severe resource constraints due to their lack of data storage and power. Both of these represent major obstacles to the implementation of traditional computer security techniques in a wireless sensor network.
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Authenticated Encryption for Wireless Sensor Network

Authenticated Encryption for Wireless Sensor Network

ABSTRACT: Wireless Sensor Networks (WSNs)are becoming popular day by day. Security issues on WSN thus draw much attention. To receive trusted information from these sensor nodes they have to send authenticated messages. Encryption is alone not enough to ensure the confidentiality, authenticity and integrity of communicated data. AES encryption algorithm is adopted by many WSN standards such as IEEE 802.15.4 and IEEE 802.15.6. AES algorithm which provides data encryption, also provides additional security services such as data authentication, integrity when it is combined with appropriate functionality. In this paper we have discussed different modes of operation of block ciphers which gives authenticated encryption. AES-CCM (Advanced Encryption Standard –Counter Mode with Cipher Block Chaining Mode) is a security standard that gives highest level of security. Software implementation of the algorithm is done in java and the obtained results show that it is a good solution for wireless sensor network Security. KEYWORDS: SECURITY, AUTHENTICATION, BLOCK CIPHERS.
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Smart Acoustic Sensor Network

Smart Acoustic Sensor Network

inaccurate localization and navigation of packets in the underwater acoustic sensor network between the anchor nodes. The packet get transmitted to the anchor nodes using two scheme such as collision tolerant scheme (CTS) and collision free packet scheduling(CFS).We are mainly focusing on minimizing the localization time so the coverage area and throughput get increased. In the collision tolerant scheme the anchor nodes work independently .so it consume more energy but CTS provide better localization in a minimum localized time with less complexity. In this Gauss Newton algorithm is employed to the every anchor nodes for the self-localization .Cramer-Rao lower bound value is also estimated for the additional justification.
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Centralized Wireless Sensor Network Deployment

Centralized Wireless Sensor Network Deployment

Most of existing device to measure the temperature and humidity are wired type, which means wires or cables need (e.g USB cable) to be attached to the control center (computer) to obtain required sensed data. There are some problems in using wired device where it involves lots of wire to be connected with the control center if data needed for different location. Another problem is when temperature and humidity need to be measured in long range of distance. It will be tedious to connect the device to the control center. So, by using wireless sensor network we can measure the temperature and humidity for different node and retrieve the data from a long distance.
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Deploying a Sensor Network in an Extreme Environment

Deploying a Sensor Network in an Extreme Environment

We have designed a working wireless sensor network for an extreme environment, overcoming technical and logistical problems to successfully investigate subglacial processes. However, even with careful design and construc- tion we have learnt there is a difference between the pre- dicted and actual behaviour of sensor networks when they are actually deployed. A combination of unknown factors and unpredicted interactions tend to affect the system in a way which can only be studied later. We have gradually re- fined our systems to be more fault tolerant and ”smarter” and believe that the deployments have proved to be essen- tial to a better understanding of how to make real sensor networks.
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Reckoning Network Lifetime Ratio for Wireless Sensor Network

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Abstract: Advanced Technologies such as Internet of Things, Machine Networking give rise to the deployment of autonomous Wireless Sensor Nodes. They are used for various domains namely battlefield monitoring, enemy detection and monitoring the environment change. These Wireless Sensor Nodes have the properties of low cost and high battery life. NL (Network Lifetime) is an important phase of Wireless Sensor Network (WSNs), in which the nodes can maintain sensing for a more amount of time. NL can be improved by use of multiple techniques namely Opportunistic Transmission, Scheduling of Timed Data Packets, Clustering of Nodes, Energy Harvesting and Connectivity. This paper provides the energy consumption computation, life time ratio definition and the overview of NL improvement techniques. The paper also presents brief review of the Destination based and Source based routing algorithm.
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Underwater optical wireless sensor network

Underwater optical wireless sensor network

[31] [32] [33]. Cox implemented a Reed-Solomon channel coding in the underwater optical wireless communication system, and found that the power requirement can be reduced by 8dB to achieve a bit-error- rate of 10 compared to OOK return-to-zero modulation techniques [34]. Later, Simpson implemented error correction coding for a 5Mbps link, and tested the system for up to 7.7m [12]. He also built a spatial diversity system to measure the optical fading in the underwater environment [35]. An unidirectional optical wireless link capable of sending data at 320kbps up to a distance of 2.2m for underwater sensor networks was presented in [3]. To achieve this communication distance, a high powered LED array was used, and the link was only used to upload the sensor data from sensor node to an AUV. The same group advanced their research to achieve a data rate of 1.2Mbps in the communication range of 30m [11]. Recently, they reported a bi-directional communication system to achieve a communication range of 50m [8]. In this paper, a software defined radio approach was adopted to perform different encoding and decoding techniques. They used 18, high power LEDs, in an array (each LED was driven at 600mA) at the transmitter side, and also used an avalanche photodiode at the receiver side to achieve the distance of 50m. A SNR of 5.1 was determined for a distance of 50m. Anguita et al also presented a Physical and MAC layer architecture for an underwater optical wireless sensor network in [26] [15]. The main focus was to build a diffuse optical wireless communication system capable of interfacing with the current terrestrial wireless sensor network technologies. They achieved a data rate of 100kbps for a communication distance of 1.8m.
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WISENET (WIRELESS SENSOR NETWORK)

WISENET (WIRELESS SENSOR NETWORK)

Abstract: Wisenet is a wireless sensor network that monitors the environmental conditions such as light, temperature, and humidity. This network is comprised of nodes called “motes” that form an ad-hoc network to transmit this data to a computer that function as a server. The server stores the data in a database where it can later be retrieved and analyzed via a web based interface. The network works successfully with an implementation of one sensor mote.

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Localization of wireless sensor network

Localization of wireless sensor network

The massive advances of microelectromechanical systems (MEMS), computing and communication technology have fomented the emergence of massively distributed, wireless sensor networks consisting of hundreds and thousands of nodes. Each node is able to sense the environment, perform simple computations and communicate with its other sensors or to the central unit. One way of deploying the sensor networks is to scatter the nodes throughout some region of interest. This makes the network topology random. Since there is no a priori communication protocol, the network is ad hoc. These networks are tremendously being implemented to perform a number of tasks, ranging from environmental and natural habitat monitoring to home networking, medical applications and smart battlefields. Sensor network can signal a machine malfunction to the control center in a factory
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A Survey of Security in Wireless Sensor Network

A Survey of Security in Wireless Sensor Network

The world population lives in cities more than 50%and this percentage can be increase 70% by 2050.City infrastructure have many problem that can be solved by communication technology (ICT) and information security. Now days, communication technologies, information security and smart management is important part of smart city development [1].smart city concept include water supply, sanitation, transport system, waste management ,mobility and energy system[2]. Smart city concept includes new communication technology and services to improve the quality of life. Smart city acquired some components such as smart phones, networks, sensors to connect the p eople with mobiles, computers etc. Information communication technology used in the public place, home, work place, city. Wireless sensor network is a collection of large number of heterogeneous sensor devices that are scattered in large area, connected by wireless media. These sensors monitors physical and environmental conditions such as sound, temperature,
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Wireless Sensor Network Deployment

Wireless Sensor Network Deployment

In our work, seven different optimization algorithms are developed, including three distributed algorithms and three centralized algorithms for sensor relocation and one distributed algorithm for sensing range adjustment. All of the algorithms focus on both optimizing coverage with a limited number of sensors and prolonging the lifetime of the entire sensor network. However, this focus is considered differently in various applications and conditions, such as, if a central node exists or whether the sensors can self-locate. These algorithms should be capable of working with both indoor and outdoor monitoring applications, in harsh environments and disaster areas, and in real-time. Simulation results show that in all the different conditions described above, the corresponding algorithms optimize coverage of the sensor network within a reasonable cost of energy.
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Modeling Link Qualities in a Sensor Network

Modeling Link Qualities in a Sensor Network

Power efficiency plays a central role in sensor networks. A lot of work has been done on estimating link quality, most of which focus on modeling reception rate over time. In [12] the authors derived analytical expressions for expected link lifetimes, rate of new link arrivals, and probability distributions for the above quantities, both of which are crucial for the understanding the underlying communication structure. Authors in [13] discuss different experiments to measure packet delivery performance. This work also models the spatial correlation between packet loss among individual receivers. A generic nonparametric statistical procedure for establishing a mapping between two characteristic properties of a sensor network is discussed in [14]. For example in this paper the authors model a probability density function of the reception rate and the distance between the two sensors, demonstrating the spatial correlation aspect of link qualities.
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Wireless Sensor Network: An Overview

Wireless Sensor Network: An Overview

Wireless Sensor network are dense wireless networks of small, low-cost sensors, which collect disseminate environment data. Wireless sensors networks facilitate monitoring and controlling of physical environments from remote locations with better accuracy. They have applications in a variety of fields such as environmental monitoring , military purpose and gathering sensing information inhospitable locations. Sensor nodes have various energy and computational constraints because of their inexpensive nature and ad-hoc method of deployment. Considerable research has been focused at overcoming these deficiencies through more energy efficient routing, localization algorithms and system design. Our survey attempts to provide an overview of these issues as well as the solutions proposed in recent research literature.
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Power Wireless Sensor Network (ZIGBEE)

Power Wireless Sensor Network (ZIGBEE)

This chapter is focusing in explaining about all the research doing in related scope of this project thesis. Under this subtitle, the sources of literature review are from the article and pass thesis. The literature included in this section is about the SCADA system, WSN (Wireless A Sensor Network), Zigbee, and the Visual Basic 6.0 used for GUI.

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IRRIGATION BY WIRELESS SENSOR NETWORK

IRRIGATION BY WIRELESS SENSOR NETWORK

The system presents an conceptual innovative, simple and affordable, hard- and software based; a wireless and low-power sensor network(maintenance); an expert system for irrigation planning in view of less water availability and an irrigation scheduler for water sources. This involvement already ensures that the results will be implemented in a short time into adequate and appropriate products for the end-user irrigation market. Also the power consumption of the final products leads to the battery long life ,will also be fine-tuned to the economic and physical conditions of markets, where the largest growth for irrigation equipment is expected.
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Airborne Sensor Network for Atmospheric Profiling

Airborne Sensor Network for Atmospheric Profiling

DOI: 10.4236/wsn.2018.104005 94 Wireless Sensor Network in-situ data has a distinct advantage over these systems in this regard. However, the technologies used to gather in-situ data are fairly limited. These technologies include weather balloons [7], dropsondes [8], sporadically located ground sta- tions [9], and aircraft communications addressing and report system (ACARS) data [10]. Unfortunately, these technologies are not able to gather in-situ data with high spatial and temporal resolution and are especially limited for collect- ing data on storms and other significant atmospheric phenomena [11].
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