Components of a sensor node

Immune Network

The immune network theory proposes that the immune system has a dynamic behavior even in the absence of external stimuli.
It is suggested that the immune cells and molecules are capable of recognising each other, what endows the system with an eigen-behaviour that is not dependent on foreign stimulation.

BACKGROUND OF IMMUNE SYSTEM

Concept came from biological background
Very important computational perspective.
Nervous system
inspired the development of ANN
Immune system
Led to the emergence of AIS as a novel computational intelligence paradigm.
Artificial immune systems developed using ideas, theories, and components, extracted from the immune system.

WHAT IMMUNE SYSTEM DOES?
All living organisms are capable of presenting some type of defense against foreign attack.
Main task
Search for malfunctioning cells from their own body (e.g., cancer and tumour cells), and foreign disease causing elements (e.g., viruses and bacteria)
Made up by great variety of molecules, cells, and organs spread all over the body

NEGATIVE SELECTION

After T-cells are generated, they migrate into the thymus where they mature. During this maturation, all T-cells that recognize self-antigens are excluded from population of T-cells; a process termed negative selection.
Negative selection presents an alternative paradigm to perform pattern recognition by storing information about the complement set (non self) of the patterns to be recognized (self)

Forecasting Powerdemand Using Artificial Neural Networks

Electricity load forecasting is very important for the reliable operation of electricitycompanies. Load forecasting helps to make important decisions for generatingelectric power. Load forecasts are extremely important for energy suppliers, financialinstitutions, and other participants in electric energy generation, transmission,distribution, and markets.

Load forecasts can be divided into three categories:

1. Short-term forecasts - usually from one hour to one week
2. Medium forecasts - usually from a week to a year and
3. Long-term forecasts - longer than a year



The forecasts for different time horizons are important for different operations. Short-term load forecasting can help to estimate load flows and to make decisions
that can prevent overloading. Such decisions can be used to the improvement of network reliability and to the reduced occurrences of equipment failures. Long term forecasting helps to know the electric load that want in the future and take necessary actions like building more power stations, for fulfill the needs of people in the country. Artificial Neural networks have been known of for a long time. Today, neural networks are broadly used in science and commercial products. It can be forecasted a daily peak load by using actual load of some similar days of target day. But in that case, there are limitations to improve forecasting accuracy by these methods. Neural networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. So ANNs can be applied for the forecasting problems.

Development of Sensor networks

It can be seen rapid advances in micro-electro-mechanical systems (MEMS) technology,
wireless communications, and digital electronics recently. It has enabled the
development of low-cost, low-power, multifunctional sensor nodes. They are small
in size and communicate in short distances. These tiny sensor nodes are capable of
sensing, data processing, and communicating components. The idea of sensor networks
is based on collaborative effort of a large number of nodes. Sensor networks
represent a significant improvement over traditional sensors.


Type of sensors
Sensor networks may consist of many different types of sensors such as seismic, low
sampling rate magnetic, thermal, visual, infrared, and acoustic and radar. They are
able to monitor a wide variety of conditions that include the following:
ˆ Temperature
ˆ Humidity
ˆ Vehical movement
ˆ Lightning condition
ˆ Pressure
ˆ Soil makeup
ˆ Noise levels
ˆ The presence or absence of certain kinds of objects
ˆ Mechanical stress levels on attached objects
ˆ Current characteristics such as speed, direction, and size of an object


Application areas
Sensor nodes can be used for continuous sensing, event detection, event ID and
location sensing. The concepts of micro-sensing and sensor networks open many new
application areas. It can be categorize the applications into agriculture, military,
environment, health, home and other commercial areas. It is possible to expand this
classification with more categories such as space exploration, chemical processing
and disaster relief.
For example, the physiological data about a patient can be monitored remotely
by a doctor[5, 6]. Sensor networks can also be used to detect foreign chemical agents
in the air and the water. They can help to identify the type, concentration, and
location of pollutants. If it say directly, sensor networks will provide the end user
with intelligence and a better understanding of the environment.
Sensor networks and ad hoc networks
Sensor network applications require wireless ad hoc networking techniques. Although
many protocols and algorithms have been proposed for traditional wireless
ad hoc networks, they are not well suited for the unique features and application
requirements of sensor networks. To illustrate this point, the differences between
sensor networks and ad hoc networks are outlined below :

ˆ The number of sensor nodes in a sensor network can be several orders of
magnitude higher than the nodes in an ad hoc network.
ˆ Sensor nodes are thickly deployed.
ˆ Sensor nodes are prone to failures.
ˆ The topology of a sensor network changes very frequently
ˆ Sensor nodes mainly use broadcast communication paradigm whereas most ad
hoc networks are based on point-to-point communications.
ˆ Sensor nodes are limited in power, computational capacities, and memory.
ˆ Sensor nodes may not have global identification (ID) because of the large
amount of overhead and large number of sensors.

Sensor network deploying ways
Sensor networks are deployed in the following two ways .
ˆ Sensors can be positioned far from the actual phenomenon, i.e., something
known by sense awareness. In this approach, large sensors that use some
complex techniques to distinguish the targets from environmental noise are
required.

ˆ Several sensors that perform only sensing can be deployed. The positions
of the sensors and communications topology are carefully engineered. They
transmit time series of the sensed phenomenon to the central nodes. These
central nodes are the place that computations are performed and data are
combined. A sensor network is composed of a large number of sensor nodes,
which are tightly deployed either inside the phenomenon or very close to it.
The position of sensor nodes need not be engineered or pre-determined. This allows
random deployment in inaccessible places or disaster relief operations. Another
unique feature of sensor networks is the cooperative effort of sensor nodes. Sensor
nodes are fitted with an on-board processor. Instead of sending the raw data to the
nodes responsible for locally carry out simple computations and transmit only the
required and partially processed data. The above described features ensure a wide
range of applications for sensor networks.

Reliability consideration in WSN
The reliability of the wireless sensor network is of great importance as it wants to
prevent the loss of data and statistics. Several mechanisms has been implemented
to make sure that all data and statistics are eventually delivered to the gateway.
They are,
ˆ Each node is logging both data and statistics in EEPROM and overwrites it
only once acknowledged.
ˆ The gateway is checking once a day that all the expected data and statistics
packets were received correctly. It uses a network wide acknowledgment to
signal the nodes which portion of their log can be safely reused.

Technological Options
Deployment of wireless sensor networks in agriculture is at its beginning. Currently
three main wireless standards are used. They are: WiFi Bluetooth and
ZigBee. Of these, ZigBee is the most promising standard owing to its low power
consumption and simple networking configuration. However ZigBee standardisation
is not yet complete.

limitations
Some of the main Barriers to implement WSNs are:
ˆ The limitations to power supply in a wireless sensor network
ˆ The significant overhaul of existing IT infrastructure required if wireless sensor
networks are to achieve their full potential
ˆ The potential for the bulk of data generated by thousands of sensor nodes to
overcome the system while providing limited value

ˆ The reliability of wireless sensors in agriculture is unproven and is considered
risky.
Actually, no unique solution that solves all of these problems. However the application
of wireless sensors in land management can raise awareness of the effectiveness
of new technologies in the agricultural field.

Wireless Ad Hoc Sensor Networks

A wireless ad hoc sensor network consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data. Some examples of wireless ad hoc sensor networks are the following:

1. Military sensor networks to detect and gain as much information as possible about enemy movements, explosions, and other phenomena of interest.

2. Sensor networks to detect and characterize Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) attacks and material.

3. Sensor networks to detect and monitor environmental changes in plains, forests, oceans, etc.

4. Wireless traffic sensor networks to monitor vehicle traffic on highways or in congested parts of a city.

5. Wireless surveillance sensor networks for providing security in shopping malls, parking garages, and other facilities.

6. Wireless parking lot sensor networks to determine which spots are occupied and which are free.

The above list suggests that wireless ad hoc sensor networks offer certain capabilities and enhancements in operational efficiency in civilian applications as well as assist in the national effort to increase alertness to potential terrorist threats.

Two ways to classify wireless ad hoc sensor networks are whether or not the nodes are individually addressable, and whether the data in the network is aggregated. The sensor nodes in a parking lot network should be individually addressable, so that one can determine the locations of all the free spaces. This application shows that it may be necessary to broadcast a message to all the nodes in the network. If one wants to determine the temperature in a corner of a room, then addressability may not be so important. Any node in the given region can respond. The ability of the sensor network to aggregate the data collected can greatly reduce the number of messages that need to be transmitted across the network. This function of data fusion is discussed more below.

The basic goals of a wireless ad hoc sensor network generally depend upon the application, but the following tasks are common to many networks:

1. Determine the value of some parameter at a given location: In an environmental network, one might one to know the temperature, atmospheric pressure, amount of sunlight, and the relative humidity at a number of locations. This example shows that a given sensor node may be connected to different types of sensors, each with a different sampling rate and range of allowed values.

2. Detect the occurrence of events of interest and estimate parameters of the detected event or events: In the traffic sensor network, one would like to detect a vehicle moving through an intersection and estimate the speed and direction of the vehicle.

3. Classify a detected object: Is a vehicle in a traffic sensor network a car, a mini-van, a light truck, a bus, etc.

4. Track an object: In a military sensor network, track an enemy tank as it moves through the geographic area covered by the network.

In these four tasks, an important requirement of the sensor network is that the required data be disseminated to the proper end users. In some cases, there are fairly strict time requirements on this communication. For example, the detection of an intruder in a surveillance network should be immediately communicated to the police so that action can be taken.

Wireless ad hoc sensor network requirements include the following:

1. Large number of (mostly stationary) sensors: Aside from the deployment of sensors on the ocean surface or the use of mobile, unmanned, robotic sensors in military operations, most nodes in a smart sensor network are stationary. Networks of 10,000 or even 100,000 nodes are envisioned, so scalability is a major issue.

2. Low energy use: Since in many applications the sensor nodes will be placed in a remote area, service of a node may not be possible. In this case, the lifetime of a node may be determined by the battery life, thereby requiring the minimization of energy expenditure.

3. Network self-organization: Given the large number of nodes and their potential placement in hostile locations, it is essential that the network be able to self-organize; manual configuration is not feasible. Moreover, nodes may fail (either from lack of energy or from physical destruction), and new nodes may join the network. Therefore, the network must be able to periodically reconfigure itself so that it can continue to function. Individual nodes may become disconnected from the rest of the network, but a high degree of connectivity must be maintained.

4. Collaborative signal processing: Yet another factor that distinguishes these networks from MANETs is that the end goal is detection/estimation of some events of interest, and not just communications. To improve the detection/estimation performance, it is often quite useful to fuse data from multiple sensors. This data fusion requires the transmission of data and control messages, and so it may put constraints on the network architecture.

5. Querying ability: A user may want to query an individual node or a group of nodes for information collected in the region. Depending on the amount of data fusion performed, it may not be feasible to transmit a large amount of the data across the network. Instead, various local sink nodes will collect the data from a given area and create summary messages. A query may be directed to the sink node nearest to the desired location.

Practical implementations of Wireless Sensor Networks

Experimental Projects

This is a project done by the Lofar Agro project[14]. This is a good experimental
project to use sensor networks for agriculture. It has installed several sensor boards
(TNOdes) in a parcel for monitoring the crop(figure:6.2). The nodes are manually
localized so that a map of the parcel can be created. The TNOdes are equipped with
sensors for registering the temperature and relative humidity. To further improve
communication, the nodes are installed at a height of 75cm while the sensors are
installed at a height of 20, 40 or 60cm. In addition to the TNOdes, the field is
equipped with a weather station registering the luminosity, air pressure, precipitation,
wind strength and direction. The humidity of the soil is a major factor in the
development of the micro climate. A number of sensors that measure soil humidity
are also deployed in the field. Finally, an extra sensor measures the height of the
groundwater table.
A TNOde records the temperature and relative humidity every minute. For
energy-efficiency considerations, the nodes are reporting data only once per ten
minutes. To further save energy, the data sent over the wireless links is minimized
by using delta encoding. The TNOdes use TinyOS as operating system. Data is
thus sent using the multihop routing protocol MintRoute available within TinyOS
. The data collected by the TNOdes is gathered at the edge of the field by a field
gateway and further transferred via WiFi to a simple PC for data logging, gateway.
The gateway is connected via wire to the Internet and data is uploaded to a server
and further distributed to a couple of other servers under XML (figure:6.1).


Figure 6.1: Lofar Agro Setup



Figure 6.2: TNode

Water management
Water management [15] is important factor in agricultural field. Sensor networks
also can be use in water management in the field. Irrigation is a major issue in many
agricultural fields.
Maximizing the net benefits of irrigated crop production through an appropriately
designed agricultural water management program is needed. Growers are challenged
to practice protection practices, reduce runoff and other losses from irrigation. Also
they have to increase crop water use efficiency while meeting the crop water requirements
for maximum net return. It can be help farmers conserve water and energy
resources associated with irrigated crop production by using WSN.
An ideal proactive system would optimize water needs in different areas of the
field with available water particularly because water is a limited, shared resource.
Proactive computing means that design systems that interpret actionable data and
then automatically act on it. E.g.:
ˆ An irrigation system that optimally rations limited ground water
ˆ An automated call to the workers to come in and pick the crop when they’re
ripe
Being able to water plants more selectively and precisely on the basis of individual
plant needs and available water, would save water. This type of precision would be
time consuming for a farmer, so a proactive system that does it automatically.
Watermark sensors(figure:6.3) can be used for monitoring soil water status and
crop water use. The information that collected by watermark sensors can be utilized
for irrigation management.
Similarly, dealing with pests is another opportunity for proactive computing. For
example, It can be used to detect the presence of birds and alert the farmer about
the problem. It only takes a minute or two for a flock of birds to do serious damage


Figure 6.3: Moisture sensors

to a crop. A proactive approach would detect and respond to the bird presence,
perhaps by making big sound.
6.3 Full-Wireless FieldMonitoring Server for Sensor
network
Field Server (FS) is a strong server which can be installed in fields [17]. Field
Monitoring Server (FMS) is a kind of FS for monitoring combined with aWeb server,
sensors, a wireless-LAN and a special housing. FS can be a node of a sensor network
installed at fields (figure:6.4).Solar cell embedded on the top of FMSs have been used
as a light intensity sensors. The power source for FMS is supplied by a cable, since
the solar cell’s power alone is not sufficient to run conventional FMSs. Power supply
cable can be problematic to construct a massively distributed monitoring system by
using wireless sensor network in fields especially in paddy fields. It has developed
power saving technologies so that the solar cell can drive the FMS. Conventional
wired FMSs function as a backbone by using an ad-hoc network, where power is
usually supplied by cable or larger solar cell. Energy consumption of full-wireless
FMSs is half that of conventional FMSs. Sleep mode also is introduced for power
saving. That means FMS is switched off immediately after the Field server-agent
collects data.
For wireless sensor networks, data acquisition devices such as Mote or TINI ,have
been employed. However, casing and combination with sensors are important and,
in fact, difficult in the field. Generally conventional sensor network devices support
only a few sensors and slow speed communication. They are insufficient for
agricultural applications such as model base systems and do not meet the needs of
farmers. Some applications require much faster communication speed, and other
applications require data such as air temperature, humidity, PPFD (Photosynthesis
Photon Flux Density), UV, CO2 concentration, soil-moisture, EC, leaf-wetness, wind

speed/direction and GPS. FS must be water-proof, dust-proof and heat-resistant.
The full-wireless FMS can be installed easily, not only in upland fields also even
in paddy fields. However it has few problems. The first one was related to dew
condensation in the FMS when the inside temperature of the FMS was lower than
outside air temperature.


Figure 6.4: Wireless field monitor server


Figure 6.5: Parts of a field monitor server

This is an another implementation of sensor networks in a vineyard to react
against powdery mildew problem. Powdery mildew risk that can be calculated from
temperature data readings gathered throughout an agricultural field over a period of
time. A map generated in this way could easily demonstrate what areas of the field
were at the highest risk for powdery mildew. It would let the field farmers to spray
pesticides on the specific at risk area to avoid problems. Unanalyzed temperature
data would have been insufficient for this purpose. Because it calculates powderymildew
risk using one of a number of complex models that take temperature data
gathered over time as input. Temperature data could also be used to make heat
unit calculations, which is a factor in deciding when to harvest.
Another example of using wireless sensor networks in agriculture is reacting against
a fungal disease which can enter a agricultural field through a variety of sources.
The development and associated attack of the crop depends strongly on the climate
conditions within the field. Humidity is an important factor in the development
of the disease. Both temperature and whether or not the leaves are wet are also
important indicators. To monitor these three critical factors, it can be instrumented
the field with wireless sensors. The main goal of monitoring is to reveal when the
crop is at risk of developing the disease. Then let the farmer treat the field or parts
of it with fungicide only when absolutely needed.

Application of Sensor Networks in aggriculture

Agriculture is the main income of the most people in developing countries. Therefore
improvement of this field is directly affecting the development of the economy of
the country. It is worth to find out new approach to improve agricultural efficiencies.
Sensors, such as yield monitors, and remote sensed images are helping farmers’
better manage their land. Linking sensors to not only record, but act on information,
further improves efficiencies. For example, sensors that detect weeds and spray them
reduce herbicide use, and save time. Thus, water quality can be improved without
the expense of fencing. Sensors linking temperature, balance, pressure, motion,
tension, flow, moisture, or pests (bacteria, fungi, insects, rodents) to time and location
will aid the agricultural manager. Groups of sensors, varying in type, will
provide added value. In agriculture, sensor systems must be reliable, easy to use,
cost effective, and deliver the information in a timely and consumable way. Wireless
communication systems tied to the Internet will likely be agriculture’s sensor
communication backbone. Keeping the nation’s agriculture healthy is important for
our nation both economically and environmentally. Sensors are playing a key role
for do that.

(Figure 1)

In agriculture it should be have knowledge of the soil and crop characteristics.
Traditional soil and plant sampling and analysis methods are very expensive, tedious,
and time consuming for obtaining soil and crop parameters on an agricultural field
and at a short time scale. Sensors capable of gathering information quickly are
needed. They will be particularly useful to measure parameters that vary faster
in time, such as nitrogen and soil water content (Figure: 1) . Sensors are being
developed and used for the following applications:

• ˆ Soil Properties Sensing: Soil Texture, Structure, and Physical Condition; Soil Moisture; Soil Nutrients.
  

• ˆ Crop Sensing: Plant Population; Crop Stress and Nutrient Status.

• ˆ Yield Monitoring Systems: Crop Yield; Harvest Swath Width; Crop Moisture

• ˆ Variable Rate Technology Systems: Fertilizer flow; Weed detection, pressure

sensors.
Different technologies are in development. The most prominent are those based
on electromagnetic induction, electric conductivity, ion selective field effect transistors,
optoelectronic sensors, ultrasonic displacement sensors, vision systems, and the
combination of these technologies. Contact sensors, ultrasonic sensors, force, pressure,
linear displacement, and optoelectronic sensors are usually applied to obtain
indirectly the flow of produces in the combine and to evaluate the yield on the go.

With ground level soil moisture sensor networks, growers can tell in real time exactly
how much irrigation is required and when. The sensor network can even turn on an
irrigation system automatically when soil moisture reaches a certain threshold. It
not only saves water it can be get higher yields because they’re able to keep moisture
at a consistent level.

Sensor networks communication architecture

The sensor nodes are usually scattered in a sensor field as shown in Fig. 2.



Each of these scattered sensor nodes has the capabilities to collect data and route data back to the sink and the end users. Data are routed back to the end user by a
multihop infrastructureless architecture through the sink as shown in Fig. 2. The sink may communicate with the task manager node via Internet or Satellite.
The protocol stack used by the sink and all sensor nodes is given in Fig. 3.

K.P.M. Madhugeeth

Index No. : 04000579

E-mail : madhugeeth kpm@yahoo.com

Helpout Sri Lankan Farmers via sensor Networks

1 Abstract.

The objective of this survey is to ¯nd out, how can help out Sri Lankan
farmers, by using sensor networks. It can be used environment-related infor-
mation for the improvement of farming strategies. Choice of crop varieties,
sowing and harvest periods, prevention of pests and diseases, efficient use of
irrigation water etc. is depends on that environment-related information.

2 Introduction.
The majority of people in Sri Lanka carry their lives by doing farming. Farm-
ing in Sri Lanka is highly depends on the amount of rain fall. Therefore the
crop yields are highly unreliable due to the variability in both rainfall amount
and its distribution. Information on the temporal and spatial variability of
environmental parameters, the impact on soil, crop, pests, diseases and other
components of farming play a major role in formulating the farmers' strategy.

Today, large mechanized farms in developed countries use advanced tech-
nologies, including in-field sensors, geographic information system (GIS), re-
mote sensing, crop simulation models, prediction of climate and advanced
information processing and telecommunications. Similar information can be
highly useful to farmers in developing countries like Sri Lanka. However, the
techniques that used by developed countries like above are difficult to apply
to Sri Lanka that have small land holdings.This survey is going to look at
how can improve productivity of agriculture in Sri Lanka by using wireless
sensor network technology for environment monitoring.
Real-time data collection in rural area and farms is need for management
of agricultural production. Air-temperature, humidity, solar-radiation inten-
sity, soil temperature, soil moisture, CO2 concentration, UV, IR, leaf-wetness
and soil moisture are some directly caused factors for agriculture.Can it uses
Sensor networks for sense that factors and can it gives a better solution to
the farmers using that sense data? The purpose of this report is to find out
an answer for this question....