Introduction of agriculture: - History and characteristics of the Indian agriculture sector

and its transition from traditional to commercial agriculture and the problems it faces, can only

be understand through its literature review. Modern agricultural practices and the relationship

with environmental depletion have also been assessed. The developmental challenges faced by

the Indian agriculture sector in particular and developing nations in general –

Illiteracy

poor socioeconomic conditions

lack of technical knowledge and awareness

small land holdings and disasters leading to rural poverty

weather-dependent farming systems

low per capita income

Underdeveloped physical infrastructures and inefficient technical procedures associated

with the comparatively high cost of agricultural production.

The Indian Agricultural Research Institute (IARI) was established in 1905 that was responsible

for research leading to the Green Revolution of the 1970s.The Indian Agricultural Statistics

Research Institute develops new techniques for the experimental design of agricultural, analyses

data in agriculture, and specializes in statistical techniques for animal and plant breeding.

Recently the Government has set up a Farmers Commission to evaluate the agriculture program.

However their recommendations have had a mixed reception. Agricultural subsidies and taxes

often change without notice for short-term political ends. Adoption of modern agricultural

practices and use of technology is inadequate, hampered by ignorance, high costs and

impracticality in the case of small land holdings. Natural disasters and human beings induced

environmental degradation are closely associated with improved farming systems. With the

development of agricultural implements and techniques, production will be increase rapidly. In

India, farming practices are too messy and non-scientific. Farming needs some plans before

implementing any new technology. The inspection of technology is important since all

innovations are not relevant or attractive to all areas. It is important to inspect them according to

the geographical area. The local context of agriculture will be affected from new technology and

let the local Kisan Vigyan Kendra (KVKs) promote it. There only appropriate technologies need

to be adopted.

As India has second largest agricultural land after the china in the world, therefore the agriculture

sector is most important part of employment and mean of living of Indian population. Slow

agricultural growth is a matter of concern as current agricultural practices are neither

economically nor environmentally sustainable and India's yields for many agricultural

commodities are low. Farmer’s access to markets is hampered by poor roads, rudimentary

market infrastructure, and excessive regulation. According to the World Bank, India's large

agricultural subsidies are obstructing productivity-enhancing investment as well as investments

in rural infrastructure, and the health and education of the rural people. Farmer’s lack of

knowledge and awareness about new technology are also responsible for low productivity and

also adding to the problem of poverty among farmers. Other causes are the slow progress in

implementing land developments, inefficient finance and marketing services for farm produce

and inconsistent government policy. Double monsoons led to two harvests being acquired in one

year.

Irrigation

Agriculture provides food, without that no one can be alive. The growth of population is about

2.5% per year, thus it is necessarily important that production of food should be increase about

3.5% per year. In India, near about 50% of agricultural area is under nourished irrigation and for

remaining 50%, there is no advanced method of irrigation followed by farmer. In India irrigation

can be mostly classified into two parts that are having different issues. Irrigation facilities are not

enough and there is no effective system management for water that how much water is stored,

how much is used for irrigation or what value can be added to this water. Even most of the

farmers are depends on the monsoon which further depends on nature. But as we know that

system of nature is changed due to the pollution and global warming on the earth. Due to the

extreme change in nature there is a need of developing efficient method of irrigation. A good

monsoon results in robust growth for the economy as a whole, while a poor monsoon leads to

sluggish growth. For better water management there is a need of high energy costs and less water

supplies. We can say that for farming, farmers are mostly depends on rain water in India. Small

farmers are using great effort which results in wastage of human being’s hard work and time

also. Therefore to reduce the requirements of manpower and to get better crops, farmer need to

start the use of various technology. Soil, climate factors and land topography are main issues of

the productivity of crops. In various areas the amount of rainwater are not enough to fulfill the

moisture requirement of crops and irrigation is important to raise crops to fulfill the requirement

of food. Rainfall is always doubtful and unforeseeable. Due to uneven distribution of rainfall, it

is going to be very difficult for a farmer to observe the actual supply of water in the whole farm.

In India monsoon begins in July, after the 1st week of rain, farmers deeply plow the field to yank

out weeds and allow water to seep into the ground that breaks the capillaries, so that ground

water can’t rise to the surface. In September before the last rain of the season, farmers plow the

land again that breaks the capillaries on top but keeping a layer of water close to the root. This

method of irrigation of farmer uses more water. With groundwater, the major problem is of

equity. Those who are better able to extract water take away suspiciously from groundwater,

causing various problems. One is that if groundwater is close to coastal areas, it may get mixed

with salt. In other places, the groundwater level drops drastically and wells go dry, making it

difficult to get drinking water.

Water is a basic need of human being and a natural resource, so it is necessary that farmers don’t

be waste water through farming. Irrigation system is planned to fulfill the water requirement of

crop. When farmers doing water supply through water motor pump, they actually do not aware

from soil moisture, structure and temperature require for individual crop. That’s why they don’t

know that at what level of water they keep motor start. If the farmer is not present on the

agricultural land, then how he will be know about the actual situation of his land. Thus, an

effective and exact management of water for crops is necessary. Earlier there were no such

advance equipments of measuring soil moisture and temperature was available in India. A

traditional method of farming goes unable to booting the crops, so new technologies required. To

increase the productivity of water and to have high water efficiency micro irrigation is one of the

most significant methods. Micro irrigations are most suitable methods for the level of soil

moisture and environmental temperature. It is divided in two parts one is sprinkle/surface

irrigation and other one is drip irrigation. Remote sensing and modeling are two modern tools

that are used for smart management and irrigation demand monitoring. Wireless sensor network

can be act as a monitoring tool for providing measurements of climate data and other required

parameters. 1

In India in every part of agriculture greenhouse based commercial irrigation systems are

required. As generation evolved, various different method of irrigation is also developed. In the

present days it is necessary to save natural resources like water for human beings. Thus, we have

to use advance method that able to conserve water as well as energy in the supply of water for

irrigation. We can reduce the wastage by knowing the status of soil that controls the flow of

water in the field. There is no ideal method available for all weather condition, variety of crop

culture and soil texture. Green house technology is exactly suitable for this problem.

Our main motive to find challenges faced by farmers in irrigation and provides controlling of

field using WSN applications. For crop irrigation farmers have to aware from the basic terms of

field such as soil moisture, humidity, temperature, pressure etc. The main aim of irrigation is to

improve the productivity of agriculture. In various place of India there is wastage of irrigation

water and traditional methods uses high quantity of water. To overcome these problems faced by

farmer, WSN sensors technologies are used. WSN sensors include soil moisture sensor, humidity

sensor, light sensor (LDR) and temperature sensor. Soil moisture status related to irrigation

control by soil moisture sensor. Wireless sensor based technique has an important role in

enhancing the productivity of agriculture. To implement the proposed system AVR

microcontroller is used. GSM with wireless sensors used for controlling of irrigation. The

sensors, installed to monitor the real data of a particular crop are controlled by sending SMS via

the GSM module. Through this proposed system irrigation mechanisms are controlled more

effectively.

Modern irrigation method:-

For equally management of water drip irrigation is very good and low cost solution, which is

currently in use. Drip irrigation system can called as modern irrigation system. Drip irrigation is

applied under different range of field conditions which reduce the more requirement of water.

Root zone of a plant which receives moisture from the soil will define the characteristics of soil

which further define the dripping nature of the plants. Water requirement varies with different

level of growth of crops and quality of soil. Drip irrigation save water because supply of water

continuously gives at slow rates to the surface of land nearest to the root of crop as shown in the

below figure. The dripping system overcomes the wastage of water, reducing the cost of labor

and increasing the yield. Drip irrigation having various advantages over sprinkle irrigation such

as welfare use of fertilizers, saves water and labor work, yield better quality of plant and control

windy atmospheric condition.

Drip irrigation with wireless sensor network:-

Drip irrigation system have a controller which control values related to a particular crop, which

further control the water supply in the field and manage the moisture level of the soil that helps

to produce crop in minimum time duration. Water supply to the crop fields mainly based on the

soil conditions. Drip irrigation system with WSN is designed by using AVR Atmega 16 and

GSM module as shown in the block diagram. Through this irrigation system farmers can be

easily know about the environmental conditions which will be maintain uniformly

Why energy conservation is required in WSN

Frequent change in environment changing measuring parameters because of that more energy is

used to repeat the procedure of parameter measurement. Conservation of energy is required

because power source in WSN is not rechargeable. To increase the life time of sensors and WSN

also there is a need of more energy so conservation of energy is necessary. Energy conservation

is one of the most things that required to being discussed. Main challenge in WSN is large

energy consumption. There are many factors that consume energy in WSNs as follows:-

When two packets are delivered at the same time at common sensor then retransmission must

be needed.

When sensor received data while it is not ready to process.

Idle listing:-When a sensor node waiting for the traffic.

Overhearing: - It occurs when distance between the sensor nodes is too short. Thus, two nodes

receive same information.

Collision: - When node receives multiple data packets at the same time.

Traffic fluctuation: - when network is working on its maximum capacity delay rises to its high

level.

Abstract: - Nowadays various challenges faced in WSNs. Some of the main WSNs challenges

are: no requirement of addressing of global ID, IP based protocol does not apply, Nodes are

stationary; Constraints of energy, Constraints of storage and processing capacity, High

redundancy of data in different sensors and batteries are not chargeable and can’t be replaced.

Consumption of energy is one of the main challenges. Many issues are formulated in networking

as basic problem is to optimize multidimensional networks. Wireless sensor network (WSN) is a

network of independent nodes used for environment monitoring but network dimension is

increased in terms of number of nodes. At the same time WSN developers face challenges that

comes from failure of communication link and limited energy. Routing is responsible for energy

consumption require for data communication. Bio inspired principle and algorithms are used to

conserve energy required by sensor nodes. Biological systems give inspirations to designing the

different parts of the network such as clustering, time synchronization, routing and protection,

etc. Bio inspired principles have got their way into designing of nodes of network. These

principles and methods can be applied to address the problems of scalability of individual nodes

in a network sensor system. Bio inspired algorithm provide energy efficient methods for data

transmission. One of the bio inspired algorithm named as Quorum Sensing algorithm minimizes

energy expenditure of each sensor node and also balances available energy for the entire

network.

DIFFERENT METHODS THAT ARE USED FOR ENERGY CONSERVATION AS

FOLLOWS:-

Life of the sensor nodes is directly proportional to the energy resources. Sensor nodes consist of CPU (processing), memory (storage) and battery. There are various techniques to reduce energy consumption: -

1. To increase the lifetime and to save energy of wireless sensor networks (WSNs) clustering

method is used. For the local aggregation of data sensor nodes are together forms group that is

called clustering. Clustering is an effective and practical method to enhance the performance of

the system of WSNs. A cluster head decide for each cluster by the group of clusters (sink).

Aggregation of data at the cluster head while at the each cluster reduced the large number of

data transmission and further increases the energy efficiency of the network. In this whole

process the sensor nodes are clusters and leader node is cluster head. Cluster head received

data from number of clusters and aggregate all the data and send that to the base station.

System chosen themselves to be cluster head at given time. These cluster head broad cast their

status to the other sensor nodes which are acting as clusters in the network. Each node in

network determines which clusters want to be used minimum communication energy to belong

by choosing the cluster head. All the nodes are organized as clusters; each cluster had created a

schedule for the nodes in its cluster so those radio components of each non-cluster head node

become turn off at all the time excepting its transmitting time. Thus energy consumption in each

sensor being minimizes. [3] So, the cluster based routing is more energy conservative because it

short the distance for transmission and re –transmission. There are various protocol for the

conservation of energy like LEACH, TEEN, APTEEN, PEGASIS etc. these are already being used. [5]

2. Data communication consumes the biggest part of the energy than the other things. Energy can

only be saved by applying communication protocol such as MAC layer protocol, routing protocol

and transport protocol. Energy is wasted when sensor nodes receives more than one packet at

the same time and also when receiver received packet from the other destination node. In

Wireless sensor networks (WSNs) the extreme data are manage by packet sequence numbers.

To maintain long life of the WSNs, changed battery power regularly is not the solution of energy

conservation then there is only solution is to use the available energy to the maximum extent[1]

[2]. We can be developing an energy efficient routing algorithm which is supported by the

mobility of multiple sinks. Further we will be work for the movement of the sinks in the area of

finding optimal path. [1] Generally routing protocol based on the following parameters:

Negotiation Based

Multi-path Based

Query Based

QoS Based

Coherent Based

So by reducing number of hop it is possible to save energy of the nodes which in terms enhance the lifetime of the whole network. As the appropriate level of mobility factor is not addressed so it is obvious that most of the routing protocols consider the nodes to be unchanged. [1][2]

3. Clusters can be formed dynamically and periodically in this way LEACH protocol is used. LEACH

stands for “Low Energy Adaptive Clustering Hierarchy”. LEACH is a self organized routing

protocol that distributes the energy load equally among the sensor nodes. In LEACH, the sensor

node operated as clusters with one of them acting as a cluster head. LEACH is a hierarchical

protocol and wants support of MAC layer. The transmission of bit stream from one node to the

other node throughout the communication link requires synchronization between the nodes.

The cluster head must have to know the rate at which bits are being received. Generally

clustering algorithm it is see that the sensors those have short life are chosen to be cluster

heads would die quickly and ending the useful lifetime of all other cluster nodes. Cluster will be

fixed through the whole life of the system. Thus LEACH define randomized rotation of cluster

head position such that it rotate among the each sensor node and not drain the battery of single

node. LEACH perform local data fusion to minimize the amount of data which being sent to the

base station from the cluster. In terms, further reducing the energy consumption and increasing

the system lifetime. The main energy saving of the LEACH protocol is due to combining data

routing with the lossy compression. There is clearly a trade-off between the quality of the

output and the amount of compression achieved. In this case, some data from the individual

signals is lost, but this results in a substantial reduction of the overall energy dissipation of the

system. [2][3][4]Since in a network, there are only a few cluster heads this only affect a small

number of nodes. We are examining, this is high energy transmission. In order to distribute

energy over multiple nodes cluster head nodes are fixed. A set of nodes decide to being a cluster

head at time but at other time a new set of nodes decided to being a cluster heads. The decision

of being a cluster head depends on the energy remains at sensor node. [2]

In such a way, nodes with more energy will perform energy intensive functions of the network. The optional number of cluster will depends on various parameters. In a network if there are more cluster heads, the nodes have to reach the nearest cluster head dose not reduce substantially, yet there are more cluster head that have to transmit data the long haul distance to the base station. If there are some of cluster head, some nodes in the network have to transmit their data very far to reach the cluster head, causing the global energy in the system to be large. LEACH Algorithm: - The function of LEACH is divided into rounds where each round has two phases, a set-up phase and a steady state phase. The steady state phase is initializing the round when cluster are organized in the network and continue with the steady state phase when data transfer to the base station. In order to minimize overhead, the duration of steady state phase is longer than to the set up phase. When cluster are initially designed, each node take its own decision that whether or not to become a cluster head for the current round. This decision is based on the energy required by the network and also on what number of times the node act as a cluster head. This decision is independently taken by the node by a random number between 0 and 1. If the number is less than threshold the node become a cluster head for the current round. During each round each node has a probability of becoming a cluster head. The nodes which are cluster heads for the current round must not be cluster head for the next possible round.[2][5] By examine the whole algorithm we can be include an energy based threshold for the inconsistent energy nodes. In this instance, we are pretend that all nodes begin with the same amount of energy and being cluster head (CH) eliminate almost same amount of energy for each node. Each node that has designated as cluster head for the exiting round advertises a message to the remaining nodes. For this purpose, the cluster head use a CSMAMAC protocol and each cluster head using same amount of energy to sent message. During this phase of set up, the non cluster head node have to keep their receiver to hear the message from all the cluster head nodes. After this stage is complete all non CH node decides to which cluster it will belong for the exiting round. This decision is based on the intensity of received message signal.[5]

4. MAC protocols used for establish the communication link between sensor nodes. There are two

main problems with communication nodes. First, is an almost node are battery powered and it is

very difficult to change batteries for all the nodes. Second one is that nodes are often not

deployed with pre-planning method. Also to apply many applications large number of nodes

would be required. Because of the large node congestion problem may be occurred. To

overcome all above characteristic problems the tradition TDMA based MAC protocols are not

suitable. Therefore, CSMA based MAC protocol is used for the communication architecture

system. [4][5]

Fig.: - Different approaches to energy conservation in WSNs [10]

5. Power management has been addressed in both hardware and software with new electronic

designs and operation techniques. The selection of a microprocessor becomes important in

power aware design. Modern CMOS and micro-electro-mechanical systems (MEMS)

technologies allowed manufacturers to produce a enhance generation of circuits by integrating

sensors, signal conditioning, signal processing, digital output options, communications, and

power supply units . For example, the parallel combination of a battery and a super capacitor

has been used to extend the runtime of low-power wireless sensor nodes. In recent years, WSN

nodes have been designed using low power micro-controllers such as the MSP430 from Texas

Instrument or Cool RISC.

6. Network lifetime and energy saving is major challenges for wireless sensor network. Solar

energy harvesting is most suitable method for save energy. Because solar energy harvesting is

produced by sunlight and then energy converted into the electricity. Solar energy harvesting

wireless sensor network proposed eco friendly solution of the challenges faced by convention

WSN. To improve lifetime of nodes of the network solar energy harvesting (SEH) sensor node

proposed a low loss low energy adaptive clustering hierarchy centralized protocol (LLEACH-C).

This protocol proposed a vice cluster head mechanism for the recovery of the cluster head

during the communication operation. This vice cluster head mechanism (VCHM) and LLEACH-C

further form a new protocol named as Solar low loss low energy adaptive clustering hierarchy

centralized protocol (SLLEACH-C). This SLLEACH-C protocol proposed to increase lifetime of

network up to 79% and decrease the packet loss. Furthermore, Enhanced SLLEACH-C protocol

becomes more suitable for solar environment, which used solar cluster head recovery

mechanism (SCHRM) for recover cluster head during the operations. Through these protocol the

lifetime of node increases if the number of SHE sensor node increases.[8]

7. Performance of wireless sensor network is directly proportional to their lifetime. Neural

networks are use in energy efficient approaches of WSNs. Neural networks gives dimensionality

reduction and data prediction of sensor from the output of neural network algorithm. These

algorithms can lead to lower communication costs and energy conservation. In WSNs, energy

conservation is a very important anxiety which have been considered in all aspects of these

networks. Neural networks give great compatibility with WSNs characteristics. Neural networks

having various type of topologies such as self organizing map, back propagation neural network,

recurrent neural network, Radial basis functions etc. The important applications of neural

networks in WSNs are sensor data forecast, sensor fusion, path finding, sensor data

classification and clustering of nodes. Artificial neural networks basically defined as arithmetic

algorithms, which are capable to verified complicated mapping between input and output or

they can categorized input data in an unverified manner. A number of algorithms build up within

the classical artificial neural network which can be accept to WSN platforms and also fulfill the

requirements of sensor networks such as distributed storage, data robustness, simple parallel

distributed computation and classification of sensor readings. As we know that WSNs have

centralized nature in which all the sensor nodes often have to be sent their data to the base

station. Neural networks have capability in calculation of sensor readings at base station which

can reduce unwanted communication of data from sensor nodes and save significant energy.

These networks also lead to gain deeper understanding and more perceptions by applying

neural network paradigm in the area of sensor networks. So we can see that whole wireless

sensor network run as a neural network with sensor nodes to decide on the output action. [6]

8. To make use of energy efficiency of biological systems to create a new type of wireless sensor

networks there is a evaluation of a combined RF-Biological system. RF receiver is one of the

major devices which consume more energy in WSNs. For the synchronization and respond to the

request of master node techniques fall on periodically waking up the receiver. Basically, a sensor

node ought to go in a full sleep mode that use negligible energy and come around on external

request only. to design and build a combined RF-biological nano-power sensing device, the

energy efficiency of biological system must be compete that bring closer to the ideal wireless

sensor node. This device will go in full dead mode but by using a fairly long rang RF signal, it can

still be get up. this idea convert a poor Electro-Magnetic (EM) signal into a biological signal using

change in concentration of ions in the vicinity of excitable cell. It also uses a biological device

which demodulate the information which present in the actual EM signal. This whole process is

carried out by the Bio-Mechanical Signal Interpreter (BMSI). Recognize the sensor node address

and generate a signal to wake up the main sensor nodes are major functions of BMSI. This

system consumes considerably lower energy than today's wireless sensor node. Further the bio-

enabled wake up mechanism processed slow that may be take tens of seconds. This is a robust

on the capacity and delay of the wake up channel. In the specific background of networks of bio-

enabled devices which have more than one master nodes competing for the same RF channel,

there is the substitution between capacity, delay and energy. To satisfy the network capacity,

application delay and system energy limitation, full sleep mode and periodic wake ups are

combined. Also, multiple transmitter scenarios that include carrier sensing and collision

detection mechanism is an extension for the communication model. [7]

9. One of the solution of energy conservation between the nodes is bio inspired principles and

algorithms. Bio inspired algorithm inspired from biological system. By using different biological

systems give inspirations to designing the different parts of the network such as clustering, time

synchronization, routing and protection, etc. Bio inspired algorithms are the set of rules which

are based on the nature of the bio mimic. Particle e.g. Swarm Optimization (PSO), Ant Colony

Optimization (ACO), Bees Optimization Algorithm. Bio inspired research has three main areas as:

Bio-inspired computing: - It is a class of algorithm focusing on optimization processes. Bio-inspired system: - It is a class of system architectures for distributed sensing and exploration.Bio-inspired network: - It is a class of strategies which form efficient and scalable networking for autonomic organization.

Bioinspired network has different bioinspired techniques or algorithms. To optimize design of network WSN already have various technique. These techniques incorporate evolutionary model and having applications in different area of WSN. Actually these evolutionary techniques are come from the environment and can be used in equally behavior of WSN. To improve life time of network and QoS parameters, the evolutionary techniques included in WSN that requires perfect choice of algorithm or optimizer. These evolutionary techniques are basically dependence on bio mimic optimization strategies. We studied various optimization algorithms those are used in WSN. Some of these are: -Particle Swarm Optimization (PSO): - This algorithm simulate by having group (swarm) of various particle. PSO is an algorithm inspired by behavior of foraging of bird swarm. In this search procedure particle flies in the searching area. They fly and adjust seed according to past experience. Past experience includes their past best position and experience of neighbor particle. Movement of swarm derived from the movement of particle which guided by their position in the searching area. Particle Swarm Optimization is a multidimensional algorithm. This algorithm is mostly used in energy aware clustering and data aggregation.Ant Colony Optimization (ACO): - This algorithm generally based on the foraging behavior of some Ant species. One ant finds the place of foraging goes through the path. Ant marks her path from nest to the destination place and live trail of pheromone that is a chemical hormone. Because of that trail of pheromone other ants take the shortest path for food foraging. Thus routing and ant foraging are the similar phenomena. This algorithm is very adaptable and decentralized in type. Ant colony optimization (ACO) is a stochastic approach for solving combinatorial optimization problems like routing in computer networks. The idea of this optimization is based on the food accumulation methodology of the ant community. Position based routing algorithms (POSANT) had some significant loopholes to find route like it never guarantees the route would be the shortest one, in cases while it is able to find it. The routing algorithms which are based on ant colony optimization find routing paths that are close in length to the shortest paths. The drawback of these algorithms is the large number of control messages that needs to be sent or the long delay before the routes are established from a source to a destination. Zone based ant colony routing algorithm using cluster is more efficient than POSANT routing algorithm by comparative Overhead study of POSANT and Zone based Ant using clustering concept with respect to varying Node Number, Zone Size and Mobility. Bees Optimization Algorithm: - This algorithm based on the food foraging nature of population of swarm of honey bees. To accomplish the food foraging, the colony sends investigator bees for searching of food location. After finding the location of large food resources, the investigators return back and the information to colony.[9]Artificial Immune System: - Artificial immune system inspired by the biological immune system. Immune system is the main part of the body functioning. In nature two immune responses are present. One is to lunch non specific innate immune response to invading pathogens. Artificial immune system based algorithms exploit the immune system characteristics of memorization. The immune system is in simplest form, a cascade of detection, culminating in a system that is remarkably effective.

10. The most effective energy-conserving operation is putting the radio transceiver in the (low-

power) sleep mode whenever communication is not required. Ideally, the radio should be

switched off as soon as there is no more data to send/receive, and should be resumed as soon

as a new data packet becomes ready. Every sensing node can be in active (for receiving and

transmission activities), idle and sleep modes. In active mode nodes consume energy when

receiving or transmitting data. In idle mode, the nodes consume almost the same amount of

energy as in active mode, while in sleep mode, the nodes shutdown the radio to save the

energy. [4][5].

11. For energy efficient data collection, an adaptive and cross layer approach can be used in WSN.

This approach includes an energy aware adaption module that configures MAC layer. Adaptive

and cross layer approach implemented by using ADaptive Access Parameters Tuning (ADAPT)

algorithm that is basically based on IEEE 802.15.4 standard. ADAPT is very simple and uses

information from local to the sensor nodes. It is well distributed so very suitable for sensor

nodes those are resource constrained. ADAPT algorithm involves an adaption module that

communicate with the different layer of WSN protocol stack. IN this approach, layered

architecture involves vertical component to sharing information between different layers. This is

avoids duplicate of the interstate information and lead to designing of more efficient systems.

These receiving by the adaptive module so can be used for protocol's functions optimization.

ADAPT algorithm estimate the current traffic condition and according to that change MAC

layer's parameters.

12. For better power management we used low power strategies and hierarchical routing protocol

in wireless air pollution system and caused the motes to sleep during idle time. There are two

states for each sensor mote, ACTIVE and SLEEP. In the ACTIVE state the whole part of the mote

is ON, whereas in SLEEP state, all the parts including the radio transceiver are in OFF state. The

working principle of this wake-up hardware is provided. All the motes will be in SLEEP state until

it gets a wake-up signal. Data transfer will always be initialized by one of the base stations. we

make the system into sleep mode for fixed time, mote will be in sleep mode for 15 minutes if

pollution level is under control or 5 minutes if it is high and then use interrupt to wake-up

system to take current data.

13. Energy can be save by data acquisition approach which define adaptive sampling, hierarchical

sampling and model driven active sampling. By knowing the adaptive sampling rate of the data,

collision can be avoided by lowering the sampling rate which further reduced data in network

processing, power can be conserve. Also, data prediction and compression approach will be save

energy.

14. Wireless Sensor Networks are a new and very tough research field for embedded system design

automation, as their design must enforce severe constraints in terms of power and cost. For

better power management we used low power strategies and hierarchical routing protocol in

wireless air pollution system and caused the motes to sleep during idle time. The traditional air

quality monitoring system is analytical measuring equipment which is costly, time and power

consuming, and can seldom be used for air quality reporting in real time. There are two states

for each sensor mote, ACTIVE and SLEEP. In the ACTIVE state the whole part of the mote is ON,

whereas in SLEEP state, all the parts including the radio transceiver are in OFF state. The working

principle of this wake-up hardware is provided. All the motes will be in SLEEP state until it gets a

wake-up signal. Data transfer will always be initialized by one of the base stations alternatively.

Vehicular air pollution monitoring is not a delicate application; hence we make the system into

sleep mode for fixed time, mote will be in sleep mode for 15 minutes if pollution level is under

control or 5 minutes if it is high and then use interrupt to wake-up system to take current data.

It uses an Air Quality Index (AQI) to categorize the various levels of air pollution. It also

associates meaningful and very perceptive colors to the different categories, thus the state of air

pollution can be communicated to the user very easily. The system also uses the AQI to evaluate

the level of health concern for a specific area. Wireless Air Pollution Monitoring System uses a

novel technique to do data aggregation in order to tackle the challenge of power consumption

minimization in WSN.

To managing sensor networks, clustering is a good way because in clustering, individual sensor

readings and operations takes away. This method gives more physical meaning of data with

respect to environment. It uses desired operation of sensors to give accurate data. How bacteria

cells (Quorum) get into clusters while the sensor finally allows to recognize patterns in the

environment that basically control the building process of cluster. There is a requirement of

development of an algorithm.

Biologically inspired clustering algorithm was developed to control the data aggregation in a

sensor network installed for monitoring of surroundings. This algorithm run each and every

resource limited sensor network that in terms excited to the sensor nodes to carry out

complicated and operational sensing task together. This whole concept is similar to many natural

situations in the environment. Thus, this algorithm mainly based on two bioinspired system

processes: -

1. Quorum sensing (QS)

2. Local activation and lateral inhabitation (LALI)

By permitting the measurement of gradients between two neighbor sensors rather than between

sensors and cluster heads that all were placed at long distance, the new form of clustering

algorithm have been formed. This algorithm was consequently changed to create the clusters

formed more convenient and expressive in terms of the spatial characteristics of the signal. This

new form of algorithm with measurement of gradient in combination with quorum sensing

further required taking an initial input from the end user about the range of gradients allocated to

clusters. To decide that how the clusters should be formed, sensor nodes depends on consider

values. This gone be very tuff for a user to be observe about the signal if nothing is known.

In spite of having different visions of environment that what going on, the sensor nodes should

be decide on that parameter. It is define actually that sensor nodes in a particular area may

measure a small or different value of gradients while sensor nodes in the other area may measure

a large range of gradients. Conversely, to stop the overlapping of clusters the sensor nodes in a

group must have the similar values or range of gradients. For example, if a cluster controls the

signal with gradient range from 5 to 15 signal units per unit distance, then next cluster should be

control either 0 and 5 signal units per unit distance or 15 and 25 signal units per unit distance. To

overcome this problem of gradients range a biological phenomena representing the appearance of

ordered pattern that is provided random conditions.

There are various examples of biological or self organizing phenomena they are connected with a

thread that is Local-Activation and Lateral Inhibition (LALI) mechanism. LALI is a mechanism

where deliberation of pattern forming material gives more confidence for more build up in a area

but slow down (inhibits) the buildup in other area that is far away from previous area. Initially

substance distribution is uneven and a small boost up in substance that reached above the

average concentration will cause enhance growth in local area but it doesn’t affect to other area

of activation. These small variations will take place when local activation is in balanced state

with inhibition that in terms make the system stable. The one only one requirement is that the

inhibition has longer range than self activation and they both are self organized.

The LALI mechanism was included in clustering algorithm with two parameters. One is activator

(AC) and the other one is inhibitor (IN). The incorporation of LALI in clustering algorithm can

easily be understood by taking an example.

Let two sensor nodes named with P and Q, gradient range of both sensor nodes are r1 and r2

respectively. IN represent inhibitor and AC represent Activator. If sensor node P has gradient

range r1 and receives a message than sensor node Q is‘d’ unit distance away and has gradient

range r2 . By changing the IN and AC and comparing gradient ranges r1and r2 sensor node P

respond according to the following rules: -

If r1≠ r2 and d > 1hop, then increment AC

If r1> r2 and d=1hop, then increment IN

Once node P has accepted these rules for all its nearest sensor nodes then received message from

neighbours that have nearest range tor1, one which is greater shown as rH and one which is

smaller shown as r L . These lower and higher values further can be used with the collected value

of IN and AC to change r1by using following steps: -

IN = 10 (AC - Difference)

If difference>0, then r1= rH

If difference<0, then r1= r L

If difference=0, then r1 will be same

These values or range of gradients will change continuously until it is reach at identical range for

whole network.

Figure: - behaviour of quorum sensing at low and high cell density

Bacteria foraging optimization algorithm define quorum sensing by assemble function between bacteria. Quorum sensing is basically a cell to cell communication between bacteria. Cell to cell communication process produce, release and detect the chemical signal molecules that are define as autoinducers on a community of wide scale. QS Allows population of bacteria to control the expression of gene that controls the behavior of bacteria. Bacteria foraging optimization algorithm was anticipated by the researchers those are worked on the foraging behavior of bacteria. Foraging behavior of bacteria goes away through four stages: -

1. Chemotaxis

2. Swarming

3. Reproduction

4. Abolition and distribution

In the quorum sensing system fluctuation in cells for detection and response involved three basic

steps: -

First step is intracellular integration of autoinducers. Second, these autoinducers actively hide

outside of the cell and internally released. As population increases the number of cell also raise

so that concentration of molecule increases respectively. Third, detection of autoinducers starts

when concentration level of molecules of autoinducers reaches at minimum threshold level. At

the same time similar receptor joined the autoinducers and trigger signal transduction tumble and

in terms show the current change in gene expression. Thus, quorum sensing makes easy process

for regulation of cells in synchronization and bacteria coordination in a population. Also QS

define the collective behavior of bacteria.

Figure: - The three steps present in diffusion-based molecular communication

Quorum sensing mechanism is first time described in 1977. It is basically is the communication

of information mechanism that define how bacteria swarm density and biological function

through signaling molecular called autoinducers. This process of bacteria gives a new thought

and method for the research of intelligence emergence of complex system and self adaptive

feature. There are different mechanisms that are based on quorum sensing: -

Environment dependent quorum sensing mechanism

Quorum sensing mechanism with quantum behavior

Cell-to-cell communication

Multi colony communication

Density perception mechanism

Systems inspired from quorum sensing are: -

1. Harmonic Quorum systems (HQs)

Geometric quorum system

2. Heterogeneous quorum based wake up scheduling system

Cyclic quorum system pair (cqs-pair)

Grid quorum system pair (gqs-pair)

3. Quorum based dynamic clustering

Harmonic quorum system is implemented with TinyOS. As performance of every system can

easily be explain by using simulating tool. To manage data storage, energy efficiency and

balance load HQS using query data process in an on-demand manner. Harmonic quorum systems

have been applied in wireless sensor networks. Each node of sensor choose a set of other nodes

(Write quorum) to repeat its sensing data and a end user also put a set of node (Read quorum) on

one side for further requirement of any data. To return data query successfully read quorum must

be overlap with write quorum that stores queried data.

At a time, if we continuously use a irregular level that is set as a write quorum the data require to

repeat to all the displace components of the set. On the other hand as here we do not assume any

dependency on routing protocol so there is not any path to have any other component of a set

with that source of data be in the right place and read quorum may not be overlapped this

component.

As lack of routing mechanism available for data collection purpose, so that geographic routing

protocol implemented for quorum system design. The major challenge is Hole balancing in WSN

when constructing quorum system. A cross layer design approach have to be take that in terms

construct both quorum system and data routing mechanism. Here routing mechanism is data

centric rather than address that serves on-demand data query from WSN users. Data management

in this system is fully location free as a field guided routing mechanism.

The organization of quorum sensing algorithm is basically a process of clustering algorithm. It is

applied in cluster node, where each data treat as a single cell and use knowledge of local

connectivity to cluster cell to form multiple colonies. As in quorum sensing autoinducers tune

from individual cells like that quorum sensing algorithm in WSN, simulate signals to tune power

radius for individual cluster cell. At the same time core cell distribute their power for cluster

formation and communication between clusters determine identity of each sensor node. This

cluster based quorum sensing algorithm is very flexible to identify static and dynamic data. As

we know that quorum sensing is a biological process that is used in the co-ordination of the

communities of bacterial cells, which have not aware globally. To detect minimum population of

cell, the bacteria themselves organize by transmitting and receiving signaling molecules. This

process has been integrated into the design of clustering algorithm, when there are approx

sensors to form cluster. Quorum sensing (QS) algorithm identifies clustering method in sensor

networks. This algorithm developed less clusters than distributed clustering algorithms. Quorum

sensing algorithm conserves the energy with the increase in network size. Quorums in the sink

node are measurable if user demanding because it is a demanding set functioning with control. A

time frame set for the sensor nodes at which a set of quorum must be awake. The quorum set

arrangement can be done as tree based quorum, majority based quorum, and grid based quorum

and others. Linkage units remain in ON mode when non quorum times frame to save energy.

Sensor nodes wake up only at their required quorum. To reduce energy wastage and set snooze

frequency of the nodal device Quorum MAC protocol is used.

Quorum Sensing: - Quorum sensing is mainly a biological term which describes harmonization

in bacteria. The term ‘Quorum’ defines for minimum population of bacteria cell. Bacteria

coordinate their behavior by emanation and reception of molecules using quorum sensing. This

behavior of bacteria called auto inducers. By quorum sensing a group of bacterial cell has not

any knowledge about global, can coordinate themselves for various applications.

Bioluminescence is a bacterial process in which visible light emitted from the living organism.

Signaling molecules (auto inducers) send out by bacterial cell which diffuse through primary cell

membrane into the nearby area of the organ and increase in concentration. Quorum sensing is a

mechanism which defines a way to produce universal synchronization by mean of molecular

communication in totally circulated manner.

Fig.: - Quorum Sensing

Quorum sensing system consists of three autoinducers and three cognate receptors functioning in

parallel to channel information into a shared regulatory pathway. Beyond controlling gene

expression on a global scale, quorum sensing allows bacteria to communicate within and

between species.

Quorum sensing controls virulence in many bacterial pathogens and typically, activation of

virulence factor expression occurs at high cell density.* Quorum sensing bacteria initiated when

concentration of auto inducers exceeded. Autoinducers provide a circumstance in which cell

introduce themselves to each other and identify the presence of other cell in the environment.

Autoinducers trigger same kind of particles when received. In the quorum sensing, emitter cell

release molecules as a response to a command. These molecules spread through the medium by

mean of impulsive diffusion. A specific signal transducing mechanism required at the receiver

that respond to other particle. Control of bacterial functions facilitate by the quorum sensing.

These control functioning are idle when undertaken by particular bacterium but turn out to be

effective when undertaken by the group.

DIFFERENCE BETWEEN MOLECULAR COMMUNICATION AND TRADITIONAL

COMMUNICATION: -

There is a hypothetical difference between molecular communication and traditional

communication. The global message of both is different in terms of concept. In the quorum

sensing the global message set in the concentration of particles, which is further interpreted.

From the communication point of view quorum sensing defines as a collective communication.

While in the case of traditional communication, concentration of data does not require.

Autoinducers have vast variety that initiates cell to cell communication in species so that only

similar nodes or same kind of species of transmitter will be able to receive message. Quorum

sensing is most suitable option to achieve universal synchronization. As bacteria functions

controlled by quorum sensing so this is method conserve energy in case of sensor nodes. To

make use of this quorum sensing process in any network, quorum sensing algorithm has to be

developed. We have to take some assumption to make quorum sensing algorithm: -

All nodes in the network expend most of the time as sleep and remaining time they are wake up

cyclically to transmit data to their neighbors and after that forwarded that data to the control station (or

Base station)

At least one neighbor must be available for each sensor node and every node must be occupying

different position.

Transmission radius has to be fixed for each node and nodes able to communicate within that

radius.

All nodes have same capability and need not to aware about global message.

Quorum sensing is a valid tool for nanonetwork to achieve synchronization in cluster node as molecular communication. The quorum sensing phenomena can be applied in nanomachines. The molecular communication provides global synchronization in the environment.

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Wireless sensor network (WSN): -

A wireless sensor network is advancement in wireless technology. It is greatly beneficial for the

environment sensing parameters. WSNs are constructed with number of sensor nodes, which are

used to sense different parameters for different kinds of environment situation.

Definition: - Wireless sensor network is a network that defines as collection of sensor node that

can sense different environmental parameter from the particular field and communicate that

sensing information with controller or sink node. Area or volume of the wireless link is limited

with some range. Wireless sensor nodes in the network may be moving or stable or they can be

homogeneous or can be location aware. Sensing information is transmitted from sensor nodes to

base station then multiple hops relaying to sink. This can be done by wireless internet connection

through gateway or router. As the number of sensor node increases, the variety of information

gathered by base station will also be increases with up to capacity of the present network.

Sensor network scenarios totally based on two nodes are: -

Source Node: - Any device or tool that provide measurements/information.

Sink Node: - where source’s information has to be sent or require. Sink node somehow

connected with the internet for wireless communication.

Wireless communication based on single hop as well as multi hop networks such as Ad-hoc or

MANET network. Range of wireless network is limited due to limited transmission energy, path

loss, and/or obstacle arrived in between transmission.

As in single-hop network packets are directly transmitted to destination whereas, in multi-hop

network data packets are transmitted to intermediate node that further transmit packet to

destination. Where packets are stored and further forwarded to multi-h op network. Multi-

hopping is energy efficient approach. It saves more energy than direct communication. But it

needs careful choice of the network.

In the sensor network or Ad-hoc networks applicants were devices that close to a human user or

interacting with human. In Wireless sensor network (WSN) instead of focusing interaction with

human user, it mainly focuses on environment interaction. Wireless sensor network suitably

deployed in environment. Generally, WSN consist number of sensor nodes these nodes are

established with sensing and actuation features that are useful to measure environment. Sensor

node access information and transmit to sink node wirelessly. Wireless sensor network have

following major components: -

Sensors: - Sensors are used to measure physical phenomena as source node and process.

Base station: - It receives data from the source node and that data for future use.

Actuators: - They give response to the data received from the sensor node.

Processing elements: -they examine the data that will be transmitting.

Advantages of wireless sensor networks: -

1. Like other networks WSN is not moving bits

2. WSN provides solution of different problem that is not only in the form of numbers but

also in the form of alphabets or sentence.

3. It has advantages over geographical issues.

4. It is very feasible or practical to maintain sensor nodes.

5. Energy conservation is limited from point of deployment.

6. Solar energy can be used at the place of battery in WSN.

7. Wireless sensor networks are completed their task possibly soon.

8. It also has self monitoring adapt operations.

9. It is robust network in terms of node failure.

10. Wireless sensor networks are suitable for long field as well as short field.

Applications: -

1. WSNs used for emergency operations such as wildfire, temperature map etc.

2. These networks suitable for Habitat monitoring such as wildlife like Great duck Island,

Zebra Net.

3. In precision agriculture, to bring out pesticides, fertilizers and advancement in irrigation.

4. As logistic to get goods and management of asset.

5. In the field of medical for health monitoring, disease monitoring etc.

Wireless sensor network represent insidious computing capabilities using smart, short, cheap

computing and sensing devices. WSN have distributed sensing behavior of sensors, computation

of data and communication of that data wirelessly. Due to technological development in

semiconductor based devices, cost of WSNs has fallen down. Reasons of cost fallen are number

of transistor on chip is increased and minimization of energy power. This is the big advantages

of WSNs that this is a low cost and energy conservative device.

On board processing sensors, wireless sensors and micro sensors are now integrated at very

small scale integrated circuit using comparatively low energy power. For spatially and

provisionally intense environment monitoring, sensor devices can be intensively embedded and

deployed with wide range. The attenuation in the amplitude of the signals will sharp with

distance.

Battery may also be used as backup source when harvested energy system being used in WSN.

There is new order of cellular phone using for internet accessing and networking in WSN.

Wireless sensor network are now affect the life style of general people by implementing as home

appliances.

Internet accessing, home networking and wireless consumer products will created by broad band

and Ad-hoc networks. Wireless communication networks are now covering 4-G digital cellular

system that include physical layer and signal processing issues with time. Wireless

communication network including GSM, IS-95, W-CDMA, Wireless LAN and Bluetooth.

Wireless data enabled networks popularity with exponential growth of internet. These networks

are associated with 3G and 2G (Broad band and Ad-hoc) also. Wireless sensor network require a

basic establishment of physical layer, medium access control (MAC), network planning and

operations, routing algorithm, topology scheme and deployment of sensor nodes.

An information network infrastructure provides the means for exchanging information through

connection of various telecommunication devices. Terminals of a telecommunication device

enable users to run a variety of applications, which can communicate with other terminals

through the information network infrastructure. The future of Information exchange industry will

depend on broad band wireless internet access in multi-media and other evolving technologies

and applications

In Wireless sensor networks (WSNs) routing of different nodes is major process that consumes

more energy. The wireless sensor node has source of energy with some kind of limits which is

difficult to charge or replace. There are many factors that consume energy in WSNs as follows:-

When two packets are delivered at the same time at common sensor then retransmission

must be needed.

When a sensor node waiting for the traffic.

Overhearing problem.

When sensor received data while it is not ready to process.

Various methods of energy conservation

Select a low energy consumption microcontroller unit (MCU)

For radio frequency (RF) module, selection of chip should have steady transreceiving

current.

Energy source itself should be with low power consumption.

System operating frequency and voltage should be low

Make system with sleep and power down mode which still save energy

Reduce initiation time of the transreceiver module.