Wireless sensor networks (WSN) can be used in precision agriculture to monitor various parameters like temperature, humidity, and soil conditions. The network is made up of small sensor nodes called motes that self-organize to communicate sensory data to a gateway. This allows farmers to selectively harvest crops, monitor crop health over time, and view sensor measurements on a web application for historical analysis and geostatistical modeling. However, the technology faces limitations due to the small size and limited resources of the sensor nodes.
A sensor node, also known as a mote (chiefly in North America), is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. A mote is a node but a node is not always a mote.
A sensor node, also known as a mote (chiefly in North America), is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. A mote is a node but a node is not always a mote.
With the popularity of laptops, cell phones, PDAs, GPS devices, RFID, and intelligent electronics in the post-PC era, computing devices have become cheaper, more mobile, more distributed, and more pervasive in daily life. It is now possible to construct, from commercial off-the-shelf (COTS) components, a wallet size embedded system with the equivalent capability of a 90’s PC. Such embedded systems can be supported with scaled down Windows or Linux operating systems. From this perspective, the emergence of wireless sensor networks (WSNs) is essentially the latest trend of Moore’s Law toward the miniaturization and ubiquity of computing devices. Typically, a wireless sensor node (or simply sensor node) consists of sensing, computing, communication, actuation, and power components. These components are integrated on a single or multiple boards, and packaged in a few cubic inches. With state-of-the-art, low-power circuit and networking technologies, a sensor node powered by 2 AA batteries can last for up to three years with a 1% low duty cycle working mode. A WSN usually consists of tens to thousands of such nodes that communicate through wireless channels for information sharing and cooperative processing. WSNs can be deployed on a global scale for environmental monitoring and habitat study, over a battle field for military surveillance and reconnaissance, in emergent environments for search and rescue, in factories for condition based maintenance, in buildings for infrastructure health monitoring, in homes to realize smart homes, or even in bodies for patient monitoring [60; 76; 124; 142]. After the initial deployment (typically ad hoc), sensor nodes are responsible for self-organizing an appropriate network infrastructure, often with multi-hop connections between sensor nodes. The onboard sensors then start collecting acoustic, seismic, infrared or magnetic information about the environment, using either continuous or event driven working modes. Location and positioning information can also be obtained through the global positioning system (GPS) or local positioning algorithms. This information can be gathered from across the network and appropriately processed to construct a global view of the monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission. In a typical scenario, users can retrieve information of interest
from a WSN by injecting queries and gathering results from the so-called base stations (or sink nodes), which behave as an interface between users and the network. In this way, WSNs can be considered as a distributed database. It is also envisioned that sensor networks will ultimately be connected to the Internet, through which global information sharing becomes feasible. The era of WSNs is highly anticipated in the near future. In September 1999, WSNs w
With the popularity of laptops, cell phones, PDAs, GPS devices, RFID, and intelligent electronics in the post-PC era, computing devices have become cheaper, more mobile, more distributed, and more pervasive in daily life. It is now possible to construct, from commercial off-the-shelf (COTS) components, a wallet size embedded system with the equivalent capability of a 90’s PC. Such embedded systems can be supported with scaled down Windows or Linux operating systems. From this perspective, the emergence of wireless sensor networks (WSNs) is essentially the latest trend of Moore’s Law toward the miniaturization and ubiquity of computing devices. Typically, a wireless sensor node (or simply sensor node) consists of sensing, computing, communication, actuation, and power components. These components are integrated on a single or multiple boards, and packaged in a few cubic inches. With state-of-the-art, low-power circuit and networking technologies, a sensor node powered by 2 AA batteries can last for up to three years with a 1% low duty cycle working mode. A WSN usually consists of tens to thousands of such nodes that communicate through wireless channels for information sharing and cooperative processing. WSNs can be deployed on a global scale for environmental monitoring and habitat study, over a battle field for military surveillance and reconnaissance, in emergent environments for search and rescue, in factories for condition based maintenance, in buildings for infrastructure health monitoring, in homes to realize smart homes, or even in bodies for patient monitoring [60; 76; 124; 142]. After the initial deployment (typically ad hoc), sensor nodes are responsible for self-organizing an appropriate network infrastructure, often with multi-hop connections between sensor nodes. The onboard sensors then start collecting acoustic, seismic, infrared or magnetic information about the environment, using either continuous or event driven working modes. Location and positioning information can also be obtained through the global positioning system (GPS) or local positioning algorithms. This information can be gathered from across the network and appropriately processed to construct a global view of the monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission. In a typical scenario, users can retrieve information of interest
from a WSN by injecting queries and gathering results from the so-called base stations (or sink nodes), which behave as an interface between users and the network. In this way, WSNs can be considered as a distributed database. It is also envisioned that sensor networks will ultimately be connected to the Internet, through which global information sharing becomes feasible. The era of WSNs is highly anticipated in the near future. In September 1999, WSNs w
A novel approach for high speed convolution of finite and infinite length seq...eSAT Journals
Abstract
Digital signal processing, Digital control systems, Telecommunication, Audio and Video processing are important applications in
VLSI. Design and implementation of DSP systems with advances in VLSI demands low power, efficiency in energy, portability,
reliability and miniaturization. In digital signal processing, linear-time invariant systems are important sub-class of systems and are
the heart and soul of DSP.
In many application areas, linear and circular convolution are fundamental computations. Convolution with very long sequences is
often required. Discrete linear convolution of two finite-length and infinite length sequences using circular convolution on for
Overlap-Add and Overlap-Save methods can be computed. In real-time signal processing, circular convolution is much more
effective than linear convolution. Circular convolution is simpler to compute and produces less output samples compared to linear
convolution. Also linear convolution can be computed from circular convolution. In this paper, both linear, circular convolutions are
performed using vedic multiplier architecture based on vertical and cross wise algorithm of Urdhva-Tiryabhyam. The implementation
uses hierarchical design approach which leads to improvement in computational speed, power reduction, minimization in hardware
resources and area. Coding is done using Verilog HDL. Simulation and synthesis are performed using Xilinx FPGA.
Keywords: Linear and Circular convolution, Urdhva - Tiryagbhyam, carry save multiplier, Overlap –Add/ Save Verilog
HDL.
It tells about the connection of different wireless sensors so that data can be shared between them.the information provided by this environment is accessed by the user through internet.It has various topologies and protocols which you can see in this ppt.
A Survey on the Specification of the Physical Environment of Wireless Sensor...Ivano Malavolta
28th August 2014. My presentation at SEAA 2014 (http://esd.scienze.univr.it/dsd-seaa-2014) about our survey on
the specification of the physical environment of Wireless Sensor Networks (WSNs).
Accompanying paper: TO APPEAR
Abstract:
A wireless Sensor Network (WSN) consists of spatially distributed sensor nodes that cooperate in order to accomplish a specific task. What really sets WSNs apart from all the other kinds of distributed systems is the limited processing capabilities of the nodes, contingent energy restrictions, and their strict dependence to physical phenomena like attenuation, reflection, etc. Under this perspective, the physical environment in which WSN nodes are deployed strongly affects the overall quality of the system. Under this perspective, how WSN engineers currently specify the physical environment and how they would like to do it? This paper presents a survey we run by interviewing WSN engineers with a special focus on their practical needs and activities.
By analyzing the collected data, we can conclude that: a) a good number of practitioners describing the physical environment do it by GIS software or informally, b) practitioners not specifying the physical environment do not see a clear return on investment on doing it, c) practitioners rate as (definitely) useful a potential tool for deploying WSN nodes on a virtually specified physical environment.
The paper presents an Arduino-based wireless sensor network to monitor parking lots using a non-standard low-power energy-balanced system. The event-driven routing protocol follows the hierarchical clustering philosophy. Energy is saved by minimising the number of transmissions needed to forward information to the base station. The smart sensor platform is build using the popular Arduino development platform, Sharp IR distance sensors and nRF24 low-power radio modules. Our practical results show that this platform is easy to use, but not the most appropriate platform to develop low-power wireless sensor network applications.
One of the most important aspect of Wireless Sensor Networks is Monitoring the content of moisture in the soil, temperature and humidity with the help of Zigbee.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
UNIT IV WIRELESS SENSOR NETWORKS (WSNS) AND MAC PROTOCOLS 9 Single node architecture: hardware and software components of a sensor node - WSN Network architecture: typical network architectures-data relaying and aggregation strategies -MAC layer protocols: self-organizing, Hybrid TDMA/FDMA and CSMA based MAC- IEEE 802.15.4.
Remote Monitoring of Crop Field Using Wireless Sensor NetworkIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Precision Agriculture Based on Wireless Sensor NetworkIJLT EMAS
Satellite farming or precision agriculture is a concept
based on measurement, observations and response to the inter
and intra farm variations in the crops. The growth and
advancements in wireless sensor network (WSN) technology has
directed agriculture sector into a new trend of smart agriculture.
WSN technology provides processing of real time data from field.
This is obtained through the sensors which are physically
deployed into the fields. These smart agriculture approaches by
the help of WSN reduces wastage of resources in farming unlike
the conventional practice, and contribute in effectively utilizing
the necessary resources resulting in increased crop yields. In this
paper wireless agriculture and environment sensing system for
crop monitoring is presented. The system test is implemented
using the real time agricultural data and from the historical data.
The system precisely acquires data and the information from the
environment.
Wireless sensor networks (WSNs) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location.
WSN Based Temperature Monitoring System for Multiple Locations in Industryijtsrd
Wireless sensor network technology has demonstrated a great potential for industrial, commercial, and consumer applications. Speci cally, in process monitoring and control, process data such as pressure, humidity, temperature, ow, level, viscosity, density and vibration intensity measurements can be collected through sensing units and transferred wirelessly to a control system for operation and management. Adopting WSNs for process monitoring and control provides great advantages over traditional wired system. In today's world we are facing with many di erent types of emergencies in the indoor environment. Response to such emergencies is critical in order to protect resources including human life and also we can save property from damage. This wireless sensor network for Temperature monitoring System which can report the emergency to the users in various forms, such as pop ups on a Computer screen, SMS on their cell phones and so on. Due to this exibility of reporting low cost wireless sensor network prepared for emergency response system of future. We are going to develop three wireless sensor nodes and we have to place in di erent position in the building using arduino board and we have to inform to the master node or monitoring node about the temperature available at each sensor node. While sending data to each and every sensor is very costly. Hence nodes are connected to WSN and their is only one node called 'Gateway' which collects the data from all other nodes and sends it to the cloud. Aditya Jogdand | Amit Chaudhari | Niranjan Kadu | Udaykumar Shroff ""WSN Based Temperature Monitoring System for Multiple Locations in Industry"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23124.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23124/wsn-based-temperature-monitoring-system-for-multiple-locations-in-industry/aditya-jogdand
A Border security Using Wireless Integrated Network Sensors (WINS)Saurabh Giratkar
Wireless Integrated Network Sensors (WINS) now provide a new monitoring and control capability for monitoring the borders of the country. Using this concept we can easily identify a stranger or some terrorists entering the border. The border area is divided into number of nodes. Each node is in contact with each other and with the main node. The noise produced by the foot-steps of the stranger are collected using the sensor. This sensed signal is then converted into power spectral density and the compared with reference value of our convenience. Accordingly the compared value is processed using a microprocessor, which sends appropriate signals to the main node. Thus the stranger is identified at the main node. A series of interface, signal processing, and communication systems have been implemented in micro power CMOS circuits. A micro power spectrum analyzer has been developed to enable low power operation of the entire WINS system.
it has a small description about how wireless sensor system network can be applied in various field. A application of leaksge detection is discussed in detail.
1. Aplication of Wireless Sensor
Networks (WSN)
in Agriculture
Precision Agriculture
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2. WSN in Agriculture
Introduction:
WSN is an acronym for Wireless Sensor
Network
Each node of the network is called MOTA
They communicate among themselves and
with a gateway
This gateway can be reached from Internet
Manufacturers:
CrossBow: http://www.xbow.com
Libelium: http://www.libelium.com/
http://www.tinynode.com
Mesh networks: http://www.tranzeo.com
http://www.sensicast.com
3. WSN in Agriculture
Aplications:
Deployment of such distributed
sensor networks offers several
advantages:
● Diversity, scalability and higher
density as compared to spatial
centralized
● Self-organizing robust networks
4. WSN in Agriculture
Components:
The Battery currently have an
estimated life of up to 1 year. They
are usually Nickel Cadmium
batteries.
Microprocessor: the speed is often
of several megahertz and
has a fairly low RAM
(about 256 Kbytes to
256 Kbytes RAM
and others to EEPROM).
The Battery currently have an
estimated life of up to 1 year.
They are usually Nickel
Cadmium batteries.
Also used solar panels embedded
with specks
5. WSN in Agriculture
Components (cont.)
● The radio: IEEE 802.15.4 and
Zigbee (future de facto standard
that combines IEEE 802.15.4 and
HomeRFLite above) are the most
used
● Sensors: are becoming smaller,
more powerful and consume less
energy.
● Software:
● Arduino / Processing to control
spots
● You can also use the NESC language
(similar to the C language) on
embedded operating systems like
TinyOS.
6. WSN in Agricultura
Architecture de una red WSN
Wireless networks of motes perform
a self-organizing communication
They pass their sensory input
and information they receive
from neighboring spots to other
spots until it reaches the
gateway.
7. WSN in Agriculture
Measurable parameters in Agriculture
Pressure
Temperature
Humidity
Level og solar Radiation
Soil parameters using the right
sensors
8. WSN in Agriculture
Aplications:
Selective harvest opportunity
Measuring Vegetation Indices
Monitoring Temporal Stability
Harvest
Variability of quality
9. WSN en Agricultura
Aplicación WEB
Posibibility of developing a web application
to monitor on a terrain map parameters
DIGATIC 1.0
measured
Connecting to a database for storing
parameters, historical consultation, etc.
Integration with statistical computing
software such as VESPA
Geostatistical analysis by interpolation of
the measured points (Kriging)
10. WSN in Agriculture
Limitations:
The motes are components of very limited
resources: they have a small CPU and
limited storage capacity
Communication bandwidth, limited.
Their lifespan is determined by its ability to
conserve energy (not to waste the battery.)
Normal state: in hibernation except when
making measurements and transmitting the
results
11. WSN in Agriculture
New aplications
Vineyards / Wineries
Farm animals
E-health: measurement of body parameters in elderly
people
Home Automation: Control lighting, temperature in
each room, etc.