A Wireless Sensor Network Computer Science Essay

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A wireless sensor network has a distributed autonomous sensor to monitor all the conditions such as physical, environment etc. At first the WSN was initially motivated by military application as battlefield surveillance, but today WSN is used in industrial and consumer applications such as processing , controlling , monitoring , machine health monitoring etc .WSN is built of "nodes", in which one node is connected to another node or one node connected to several other nodes. Each sensor node has a Transceiver with an internal or the external antenna, microcontroller, and an energy source as battery. The sensor size may range from the shoebox to the small dust grain particle. The topology used in WSN varies from simple star network to an advanced multihop wireless network. The propagation technique mostly used in WSN is "routing or "flooding".

The wireless sensor networks are typically self organizing and self healing. The self organizing is the approach in which it can allow the node to automatically join the network without any manual intervention. But the self healing is the process in which reconfigured their link association and find the alternative path, around power drained or failed nodes.

CHARACTERISTICS OF WIRELESS SENSOR NETWORK

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The main characteristics of WSN includes,

Ability to cope up with mode failure

Power consumption constraints for nodes using batteries

Mobility of nodes

Communication failure

Heterogeneity of nodes

Scalability to large scale of deployment

Must have ability to withstand to harsh environmental conditions

Ease of use

Power consumptions

ADVANTAGES

The advantage of WSN includes,

It avoids lot of wiring procedure.

It can be able to accommodate lot of new devices at anytime, (i.e...Scalable).

It is flexible to go through the physical partitions

Can be accessed through the centralized monitors.

DISADVANTAGES

The disadvantage of WSN includes,

It is very easy for hackers to hack the communication as we can't able to control the propagation waves.

Connectivity is very low, because WSN can easily get distracted since it is wireless network.

Can easily get distracted by various elements like Bluetooth.

Very costly.

APPLICATION

The wireless sensor network used in variety of application, the request constant monitoring and detection of the specific events. The application includes

MILITARY

The wireless sensor network in military application involves battlefield surveillance and monitoring, detection of attack by weapons of mass destruction as nuclear or chemical.

ENVIRONMENT

The wireless sensor network in environment application involves as forest fire detection and flood detection, habitat exploration of the animals.

PATIENT diagnosis and monitorinG

Patient can wear the small sensor in the body so that, it can able to monitor the heartbeat or pulse rate, blood pressure of the patient and it send to the automated monitoring system through network if any anomaly is detected then the alert message is got and the appropriate treatment is given. Sensor also made to correctly indentify allergies and prevent wrong diagnosis.

The attacks that are possible in wireless sensor network are denial of service, attacks on information intransit, sysil attack wormhole attack. The feasible securities for the wireless sensor network are cryptography, steganography, physical layer secure access, holistic security in Wireless Sensor Network.

The Wireless Sensor Network has two kinds of networks namely, homogeneous wireless sensor network and heterogeneous wireless sensor network. In homogeneous Wireless Sensor Network, all the computers use the same type of processors and the operating system. While the heterogeneous Wireless Sensor Network have different computers and have different processing system.

Since the WSN is used in various applications, such as military, healthcare and other important fields the lifetime is an important thing that has to consider as a important one. The lifetime is an important one, if we minimize consuming more energy during communicating or processing then the lifetime of the WSN can be easily maximized. The lifetime of the homogeneous WSN can be easily maximized but for the heterogeneous WSN the lifetime for the WSN is difficult to maximize the lifetime.

Several techniques have been introduced to maximize the lifetime for the heterogeneous wireless sensor network; one of the ways to maximize the lifetime is to use the Swarm intelligent technique.

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Artificial bee colony algorithm is one of the parts of the swarm intelligent process. Swarm intelligent is the part of the artificial intelligent that is based on the action of individuals in a various decentralized systems. The decentralized system is composed of physical individuals or the virtual systems in which they communicate among themselves cooperate, collaborate exchange information and knowledge and perform some task in the environment.

BEE colony optimization algorithm was introduced in the field of the swarm intelligent based technique. The bee colony technique is the "BOTTOM UP" approach to modeling where special kinds of artificial agents were created by analogy with bees. The artificial bee agents, which collaboratively solve the complex the combinatorial optimization problem.

The artificial bee colony optimization algorithm plays a important role in maximizing the life time of the wireless sensor network .ABC is one of the newest algorithm based on foraging behavior of the of the insects. The ABC technique tries to model the natural behavior of the real honey bee in food foraging. The honey bees use several mechanisms such as waggle dance to locate the food source and to search the new food source. Through the waggle dance the bees not only share the information about the direction and the distance of the food source but also it informs about the amount of nectar available. This kind of information is shared among the bees, so that they can able to find the optimal solution.

In the ABC the collective and the cooperate behavior of the bees forms the optimal algorithm. Since the ABC algorithm is very simple in concept, easy to understand and have limited parameters to use, it is used in many of the applications. It is used in large number of the optimization problems. The colony of the bee algorithm consists of three bees:

1) Employed bee

2) Onlookers

3) Scouts.

The employed bee goes and searches for food source and it visit the food source. The onlookers wait in the dancing area to make a decision to choose the food source. The scout is the one which randomly search and discover the new source. The position of the food source gives the solution for the optimization problem. The nectar amount of the food source corresponds to the quality or fitness of the associated solution. In ABC algorithm the first half constitutes the artificial employed bee and the second half constitutes the onlookers bee. The number of onlookers and the employed bees should be equal to the number solution in the population. The employed bee without food source becomes the scout bee.

The ABC algorithm perform search in cycles. Each cycle consists of the following three steps: 1) the employed bee flies to the food source, collect the nectar and then return to the hive. In the hive we measure the nectar amount.2) information on collected nectar amounts are on a disposal to all artificial bees. Based on this information the onlookers bees select the food source.3) chosen bees that become the scout bees fly to the possible food source.

In the ABC algorithm the initial population of the solution is generated randomly. On the subsequent cycles the employed bees and the onlookers bees probabilistically create a modification in the initial solution.

CHAPTER 2

LITERATURE REVIEW

2.1 OVERVIEW

A literature review is a body of text that aims to review the critical points of current knowledge including substantive findings as well as theoretical and methodological contributions to a particular topic. Literature reviews are secondary sources, and as such, do not report any new or original experimental work. In this report I have taken a survey about what are all the projects that have been used using ABC and what are all the techniques that is used to maximize the life time of Wireless Sensor Network.

The localization of unknown nodes by mobile anchor using ABC technique [8] with some advantages of Genetic Algorithm.2D space is considered all unknown sensor randomly deployed in 2D and its location is unknown. The mobile anchor could move anywhere and reach any place in this space. The unknown node is localized and the target is reached based on the minimum hop count. In other methods the distance between the mobile anchor and the unknown nodes are calculated, that may result in error but this paper describes about the coincide if nodes with each other rather than focusing the distance.

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When initializing the process at the beginning the mobile anchor moves from the point of vicinity it is considered as the beginning of path. Later by using the ABC, minimum distance is found (least hop count).Then, mobile anchor moves along the optimal path to traverse the whole unknown nodes. Finally, all unknown nodes could get the location information.

The advantage includes that it improves localization accuracy without any additional requirement of nodes, high convergence, and stronger global search. The disadvantage includes that it just reduces the error during localization, but it does not fully eliminates the error. The future enhancement involves that this approach is used for other such implementations as 3D space.

Since the WSN development increases, the problems related to these networks is also increases. One of the major issues is Dynamic deployment, which also affects the performance of the WSN. To solve this problem and to increase the performance of the WSN ABC method [3] is used. WSN is used in much application as tracking, weight, pressure and direct the objects move in the area of interest. However, it is used in many applications the success is that identifying the sensor position, this deployment of the network.

Nodes can be deployed in dynamic or n the statistic manner. When we considered about the static deployment, GPS system is used in order to determine its position and also its neighbor position. But when we considered about the dynamic deployment the problem comes here in order to improve the performance, the coverage rate should be maximized.

When we considered about the dynamic deployment no priori information is obtained .so the initial deployment is chosen randomly. Each sensor knows its position by using information from others. The ABC technique is used to dynamic deployment and also helps to maximize the coverage rate are, detecting the radii of sensors are the same, all sensors have ability to communicate with each other. All sensors are mobile, in this way ABC is used to increase the coverage rate and the performance of WSN.

The advantage includes by using the ABC technique the deployment of WSN is easy to good deployment. The disadvantage includes that it is not suitable for the stationary one. The future work involves using the ABC technique is used for stationary one also.

ABC algorithm is used for solving the capacities vehicle routing problem[9]. The ABC algorithm is swarm based heuristic, which involve the foraging behavior of honey bee. Enhanced ABC is also used in this paper to improve the solution quality. In this paper both the concept that is ABC and the enhanced ABC is used and the results are then compared. The enhanced ABC gives good solution when compared with ABC .ABC algorithm was recently introduce, so before applying to the performance ABC algorithm was evaluated for solving the CVRP .

It evaluates the performance of ABC using classical benchmark; enhanced ABC algorithm was also introduced in order to make the performance better to solve the CVRP. The application to CVRP is solution representation, search space and cost function, initial solution, neighborhood operators and the selection of the food resource.

The advantage here is using the technique of the ABC the solution obtained is very efficient. The disadvantage in this paper is it needs more computation.

The performance of ABC algorithm is tested on fuzzy clustering[4]. It applied ABC algorithm fuzzy clustering to classify different data sets, cancer, diabetes and the heart from UCI database. Clustering is an important tool for variety of application in data mining data analysis, data compression and the other fields. Popular clustering algorithms as K-MEAN algorithm which is a centre based, simple, fast algorithm.

Another important algorithm is fuzzy c-mean is effective algorithm, the random selection in centre points makes iterative process falling into the local optimal solution easily, To tackle this problem several swarm intelligence algorithm is used ,one of them is ABC algorithm in which it includes the foraging behavior of honey bees for the numerical optimization problems.

The classification performance of ABC algorithm is tested on training neural networks and clustering and the result obtained is then compared with widely available technique.

The future enhancement plan to apply ABC algorithm on more problems and compare the performance of the algorithm not only with FCM algorithm but also with other well-known optimization technique.[[

The wireless sensor network plays a vital role in much application, the lifetime has to be considered as an important factor, and WSN uses batteries for their energy consumption. If sufficient energy is not yet possible, then replacement and recharging of the batteries is not possible, so in order to make more energy, one of technique used is called dynamic power management method [15]. It investigate the power dissipated at each node and analyses the strength and limitation of selective switching, dynamic frequency, voltage scaling.

Power consumption problem can be addressed in two ways. The large number of energy - efficient communication protocol, Local dynamic power management method - to recognize and minimize the impact of wasteful and inefficient activities in an individual node. A wasteful inefficient activity includes software, hardware and also overhearing of neighboring nodes. These results to non optimal solution and this may also results in more power consumption. To avoid this problem dynamic power management technique was used.

There are two kinds of DPM techniques are used. The first one is the selective switching technique which minimizes idle state power dissipation of hardware components and achieved by estimating power of individual components. Advantage of this technique is that it can be implemented in software components, since hardware components provide well defined interface to be dynamically configured. - Disadvantage is that cost of power transition is high and more delay occurs.

The second technique is dynamic power and frequency scaling. In this the active state power requirement of hardware components is adapted to present and workload. Extra hardware is required to undertake this job is the major disadvantage of this technique.

ABC is a optimization algorithm, is a family based search algorithm. In paper [11] golden section search mechanism is incorporated with ABC to improve global convergence and prevent to stick on to a local solution. The proposed variant termed as ILS-ABC. The result shows the variant can be successfully applied to solve real life problem.

The ABC algorithm is good at exploration and exploitation, so this proposed is used to improve the convergence speed and to avoid local search. So the exploration and the exploitation is balanced. The introduction of golden section improves the global and prevents the local search. This technique focuses on the onlooker bee, here the scaling factor is used and it is mainly focused on the high quality of food source.

Advantage of this paper is that it is applied for the real life engineering design problem, better convergence speed is obtained, it is very efficient.

The general formula for the lifetime of wireless sensor network which independently holds underlying network model which includes protocols, network architecture, data collection, initiation, channel fading and lifetime[14]. This formula identifies two parameters. One of the parameter is channel state and the other parameter is residual energy consumption. This not only gives the steps but also tell us the guidelines to increase the lifetime of sensors. Based on this formula, the authors proposed the MAC protocol to exploit both channel state information and residual energy information.

In wireless sensor network, sensors monitor and collect the data and store at access point, where the end user can access the data. In this process, replacement or recharging battery is not possible. So in order to maximize the lifetime, lifetime maximizing protocol is used.

The energy consumption can be done in two forms. One of the forms is continuous and the other form is reporting energy consumption. In the former form, minimum energy is needed to sustain network lifetime without data collection (i.e.) in the form of battery leakage. But in reporting energy consumption the additional energy consumed in transmission, reception and the rate of collection of data.

The usage of maximizing lifetime protocol (i.e.) MAC also increases the lifetime of sensor is the only advantage in this paper. Only considerable lifetime can be improved. So it leads to some disadvantages. In order to maximize the lifetime in future, artificial bee colony algorithm can be used.

To address target coverage problem in wireless sensor network with adjustable sensing range and consume energy and the lifetime is also increased. In [6] a large amount of sensor with adjustable sensing range that is randomly deployed to monitor the number of targets. The adjustable range set covers the entire target. The authors proposed a mathematical model solution to address this problem and design heuristics that efficiently compute the sets. Power saving technology falls into three categories. They are scheduling the sensor nodes, to alternate between active and sleep mode and adjusting the transmission or sensing range of nodes.

The scheduling mechanism is designed in such a way some sensor are active while others are in sleep mode. This paper deals with target coverage by dividing the sensor into a number of set. Using the property the sensors have adjustable sensing ranges. The goal is to set up the minimum sensing range for the active sensor, satisfying coverage requirements. This method also covers the density of active nodes, thus decrease the inference

The AR-SE problem is solved by using relaxation and rounding techniques. These two techniques are centralized and distributed (localized solution) are given for computing the set covers. Once the sensors are deployed, they send their coordination to base station then it computes and broadcast back the sensor schedule. In distribution and localized algorithm each sensor node determines its schedule based on communication with one hop neighbor. Advantage: greedy algorithm is used, so the result obtain is efficient Problem addressed is to determine maximum network lifetime when all target are covered and sensor energy resource are constrained. Integrate the sensor network connectivity requirement, maintaining connectivity among the selected sensors that has an advantage in facilitating the exchange of information between sensor and base station is their future work.

In recent years, wireless sensor networks play a vital role in many applications like environmental monitoring, animal tracking, military application, patient health monitoring etc. In all these applications, sensors are used to collect details about the particular thing and all information is collected by base station. But the only disadvantage in this is that it requires more energy and it is difficult to recharge node batteries.

In some applications, it does not need information to be collected by sensor from time to time. It only needs aggregate data to be reported to the base station. So by reporting this aggregated data, the communication overhead will be reduced and it leads to significant energy saving.

Data aggregation cannot be done by individual node. All the nodes in the network are partitioned into small groups called clusters. Normally every cluster has cluster head and members[10]. Members send their report to cluster head and it in turn aggregates the data and forwards it to observer through other cluster head. The clusters will improve network life time. Nodes in the wireless sensor network are adhoc in nature. So it gains only neighborhood information by distributed clustering protocols. Techniques used: Iteration clustering technique: In this the node waits for a specific event to occur or certain nodes to decide their role before making a decision. Probabilistic clustering technique: This is used to achieve balanced cluster size. LEACH Protocol: This is an application specific clustering protocol which is used to improve network lifetime.

Now-a-days Wireless Sensor Network places a significant role. So its lifetime is critical process. Many WSN need redundant sensor nodes to reach QOS and fault tolerant, but redundancy is not possible in multihop communication. In order to address this problem VBS [7] is used which a novel sleep is scheduling. It forms multiple overlapped backbone sensor nodes.

All sensor nodes have 3 components as sensing, computing and radio. At the radio part only more energy is consumed, because it is involved in communication. So in VBS the backbone is formed by connected dominating set, forwards the traffic and the rest of the sensor turns off their radio to save energy. Rotating of multiple backbones, make sure that energy is consumed less and the lifetime is reduced. The scheduling problem in VBS is given as maximum lifetime backbone scheduling (MLBS), but it is NP hardness problem.

So, it proposes 2 approximation algorithms. 1) Schedule transition graph 2) Virtual scheduling graph and iterative local replacement in distributed implementation. In Schedule Transition Graph model the schedule in Wireless Sensor Network states in each round is equal to number of backbones of each state has back and energy levels and is one to one mapping. The residual energy is obtained by subtracting the energy consumed by energy at starting level

In Virtual Scheduling Graph, a sensor is converted to multiple virtual nodes, which are connected so that their degree represents the energy of nodes. It is obtained by applying any Connected Dominating Set on VSG. Advantage: It is an efficient way to maximize the lifetime. Disadvantage: It is a complex process.

The swarm based technology is used for locating good solution efficiently with the reasonable running time. [5] uses Artificial Bee colony that includes the foraging behavior of the bees it is used to solve the multi-dimensional numerical problems. This paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear plant, thus maximizing the number of correct classification of transients.In order to illustrate the application of the ABC algorithm to complex problems in nuclear engineering, this work presents the utilization of the ABC algorithm as a tool for the development of a new event classification solution to help operators in the task of identifying possible plant transients in a nuclear power plant, thus making easier the decision-making process regarding to the plant safety in risk situations.

This work is a search-based that looks for the best prototype positions that represent the centroids of the accident signatures, maximizing the number of correct classifications. The easy way to implement and the small number of control parameters makes the ABC algorithm an interesting option of optimization technique. It also can be used in high dimension and complex multimodal search spaces, as in this work in which the ABC algorithm was used to implement a search-based method for the problem of transient classification.

The advantage of this paper is this mechanism provides the ABC algorithm global search ability and prevents the search from premature convergence problem hence; there is a good balance between the local search process carried out by artificial onlooker and employed bees and global search process managed by artificial scouts also robust for solving numerical complex problems.

The reduction of the power loss when reconfiguring the network of the distributed systems is described in[12]. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm.

A new population based artificial bee colony algorithm (ABC) has been proposed to solve the network reconfiguration problem in a radial distribution system. The main objectives considered in the present problem are minimization of real power loss, voltage profile improvement and feeder load balancing subject to the radial network structure in which all loads must be energized.

The main advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate etc, as in case of genetic algorithms,. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. The main disadvantage is differential evolution and other evolutionary algorithms and these are hard to determine in prior.This Ideas presented in this paper can be applied to many other power system problems also.

The sheep and goat disease database is created using rule-based techniques and machine-learning algorithms (ABC and PSO) [13]. These techniques are also applied on this database to develop expert systems to diagnose the diseases affected to sheep and goat animals. The system diagnoses the diseases for the different symptoms entered by the user dynamically. If the symptoms entered by the user matches to the rules already available in the Knowledge base designed by the expert, it displays the actual disease with which sheep is suffering with. Else it displays a message saying that the knowledge is insufficient

Database is the connectivity between the user and the expert system. Here the problem is divided into two aspects one is static part and the other is dynamic part. In static part, the user can get all the static information about different Common Diseases, Common Symptoms, Preventions to be taken, and some Frequently Asked Questions (FAQ's) about different diseases of sheep and goat. In Dynamic Part, the user is having an interaction with the expert system online about sheep & goat farming, the user has to answer the questions asked by the Expert System in Self -Help option in the menu.

The main advantage have a well designed interface for giving health related advices and suggestions in the area of any disease field by providing facilities like dynamic interaction between expert system and the user without the need of expert at all times about sheep and goat. The disadvantage here is security.

In future audio/video interface can be provided for the system, so that user can interact directly with the expert both in audio, visual modes.

Travelling salesman is an important thing in many applications, and is very challenging one to find the optimal path with the minimal cost. Paper [2]proposes a new ABC algorithm called combinatorial ABC to for travelling salesman problem. In ABC algorithm each food source represents a possible solution for the problem and the fitness value of the solution corresponds to the nectar amount of this food source. The new combinatorial version of the ABC algorithm, CABC, employs the same basic steps as the bees do. The aim of TSP optimization is minimizing the total closed tour length.

The advantage includes that experimental results show that this approach gives good solutions for this NP-hard combinatorial optimization problem.As a future work, the algorithm will be applied on more complex test problems and this algorithm will be hybridized with some local search heuristics to find better results.

The design of garlic expert systems using machine learning algorithms [1] is to advice the farmers in villages through online. An expert system is a computer program that simulates the judgments and behavior of a human or an organization that has expert knowledge and experience in a particular field. Artificial Bee Colony (ABC) Algorithm is one of the mostly used Machine Learning Technique. By using this ABC Algorithm we developed a new 'Garlic Expert Advisory System'. This system is mainly aimed to identify the diseases and disease management in garlic crop production to advise the farmers in the villages on line to obtain standardized yields. This system is designed by using Java Server Pages (JSP) as front end and MYSQL as backend.[

In this system, the user can directly communicate with the system and can get the appropriate solution suggested by the experts through online. In this system the user will submit the symptoms observed by him in the garlic crop to the system through online and system processes the information provided by the user and suggests him with appropriate solution provided by the experts. The system uses the ABC Algorithm as a backend machine learning technique for finding the appropriate solutions for the symptoms provided by the user.

The advantage includes Algorithm of machine learning technique gives a better solutions compared with general Rule Based Algorithm. The algorithm used in the present system can be treated as quite effective; in most of the cases it finds a solution which represents a good approximation to the optimal one.

The future enhancement is by the thorough interaction with the users and beneficiaries the functionality of the System can be extended further to many more areas in and around the world.

2.2 SUMMARY OF LITERATURE SURVEY

TABLE 1.1 Summary of Literature Survey

S.NO

AUTHOR NAME/YEAR

METHODOLOGY/

PROBLEM IDENTIFIED

PROBLEM IDENTIFIED/ SOLUTION

FUTURE WORK

1

Rongbinq,sijinli,tianyima and fengqian/2012.

To localize the unknown nodes by using ABC with some advantages of genetic algorithm.

The mobile anchor with the help of ABC technique moves around and find unknown nodes by calculating minimum hop distance in 2D space.

Can also be implemented to locate the unknown node in 3D space.

2

[

Celal ozt'urk,Dervi Karaboga,Beyza Gorkeml/2011

It definesthe dynamic deployment of the node which also degrades the performance of the WSN.

For deploying the nodes, ABC algorithm is used and is archived by increasing the coverage rate.

This can be also used in the deploying nodes in stationary node .

3

Szeto, WY; Wu, Y; Ho, SC/2011

To solve the capacities vehicle routing problem by using the ABC and enhanced ABC.

The use of enhanced ABC and ABC leads to more computation.

Solving the CVRP problem with less computation

4

Dervis Karaboga and Celal Oztur/2010

ABC algorithm is used with fuzzy clustering algorithm to classify different data, cancer, and diabetes from UCI database.

ABC is used with FCM and is tested on neural networks, so more computation is required.

It not only compares with FCM but also with all other optimization technique and proves their efficiency.

5

Waltenegus Dargie/ 2012

Dynamic power management is used to reduce the energy that releases during idle state power dissipation and hardware power dissipation which leads to minimize the lifetime of WSN.

Cost of power transition and the delay occur is more

To minimize the delay and the cost transition various other techniques related to maximizing the lifetime is used.

6

Tarun Kumar Sharma, Millie Pant, V.P.Singh / 2012

ABC along with golden section search mechanism avoids local search and it leads to global search and can also be used in the real life problem.

Since it mainly focuses on onlookers bee, the high quality expecting is not fully obtained.

In order to obtain the high quality of food source ,it can focuses on full bee hive process.

7

Yunxia Chen and Qing Zhao/2005

The lifetime of WSN can be increased by using MAC protocol by consuming in two forms continuous and reporting energy consumption.

Only considerable amount of energy can be maximized.

To maximize further, other technique such as swarm intelligence is used.

8

Yaxiong Zhao, Jie Wu, Fellow, Feng Li, and Sanglu Lu /2012

To maximize the lifetime of the WSN virtual backbone scheduling algorithm is used.

Since forming backbones lead to NP hardness problem, it uses schedule transition graph and virtual scheduling graph is Used.

Since it is one to one mapping later it can be used to one to many Mapping many.

9

A. Dhawan, C. T. Vu, A. Zelikovsky, Y. Li, S. K. Prasad

The target coverage problem is addressed by using the adjustable sensing range.

Problem addressed is to determine maximum network lifetime when all target are covered and sensor energy resource are constrained.

Integrate the sensor network connectivity requirement maintains connectivity among the selected sensors that has an advantage to exchange the information between sensor and base station is their future work.

10

Ossama Younis,Marwan Kirunz and Srivasan Ramasubramanian /2006

Clustering technique is used to cluster the sensor nodes for sending the aggregated data to base station.

Nodes in wireless sensor network are ad hoc in nature. So it gains only neighborhood information.

Using more than one technique (to maximize network lifetime) must be avoided. Because it is quite difficult to follow more techniques.

…

11

Iona Maghali.S de Oliveira,Roberto Schirru and Jose A.C.C .de Medeiros/2009

The algorithm has the best ability to find the best centroid positions which correctly identifies an accident in the nuclear power plant

Two parameters are used if any one of them exceed then it is very complex .

Genetic algorithm is used along with ABC to solve such complexity.

12

R. Srinivasa Rao, S.V.L. Narasimham, M. Ramalingaraju/ 2008

ABC algorithm is used to determine the sectionalize in order to reduce the power loss in the distributed systems.

The main aim is to reduce the power during the reconfiguration of the network.

This idea can be applied to other power plant system also.

13

Prof.M.,S.Prasad Babu, Prof.M.Ramjee, Sri.S.V.N.L.Narayana Sri.N.V.Ramana Murty/2011

ABC and PSO technique is used to find the disease in sheep and goat.

Can be in two ways static and dynamic, so its complex.

In future work user can interact with the system with audio, video and visual mode.

14

Beyza Gorkemli, Dervis Karaboga/2011

ABC algorithm is used for solving the Travelling Salesman Problem.

It gives good solution to solve NP hardness problem.

In future it works with some local search algorithm to have better results.

15

Dr. A. Arul Lawrence Selvakumar, G. Mohammed Nazer,/2011

ABC is used to find the problems that is occurred in garlic production and it gives solution .

The user can obtain the solution by submitting the symptom through online and get back the solution. Security is the problem.

Can also be extended to other areas also.

CHAPTER 3

SYSTEM ANALYSIS

3.1 EXISTING SYSTEM

Existing system uses ant colony optimization to maximize the lifetime of WSNs, it uses pheromone and heuristic information for finding the connected covers. Heuristic information is associated to each assignment for measuring its utility in reducing constraint violations. Pheromone is deposited between every two devices to record the historical desirability of assigning them to the same subset. In each iteration the number of subsets is adaptively determined as one plus the number of connected covers in the best-so-far solution. The ants concentrate on finding one more connected cover and avoid constructing subsets excessively.

3.1.1 DISADVANTAGE OF EXISTING SYSTEM

Ant Colony Optimization is slower process compared to artificial bee colony algorithm and it consumes more energy than artificial bee colony algorithm.

3.2 PROPOSED SYSTEM

3.2.1 OVERVIEW

ARTIFICIAL BEE COLONY ALGORITHM:

[

In the ABC model, the colony consists of three groups of bees: employed bees, onlookers and scouts. While this may be a correct math model, it neglects the male drone population. It is assumed that there is only one artificial employed bee for each food source. In other words, the number of employed bees in the colony is equal to the number of food sources around the hive. Employed bees go to their food source and come back to hive and dance on this area. The employed bee whose food source has been abandoned becomes a scout and starts to search for finding a new food source. Onlookers watch the dances of employed bees and choose food sources depending on dances. The main steps of the algorithm are given below:

Initial food sources are produced for all employed bees

REPEAT

Each employed bee goes to a food source in her memory and determines a neighbour source, then evaluates its nectar amount and dances in the hive

Each onlooker watches the dance of employed bees and chooses one of their sources depending on the dances, and then goes to that source. After choosing a neighbour around that, she evaluates its nectar amount.

Abandoned food sources are determined and are replaced with the new food sources discovered by scouts.

The best food source found so far is registered.

UNTIL (requirements are met)

In ABC, a population based algorithm, the position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution. The number of the employed bees is equal to the number of solutions in the population. At the first step, a randomly distributed initial population (food source positions) is generated. After initialization, the population is subjected to repeat the cycles of the search processes of the employed, onlooker, and scout bees, respectively.

Fig 3.1 Architectural diagram

An employed bee produces a modification on the source position in her memory and discovers a new food source position. Provided that the nectar amount of the new one is higher than that of the previous source, the bee memorizes the new source position and forgets the old one. Otherwise she keeps the position of the one in her memory. After all employed bees complete the search process; they share the position information of the sources with the onlookers on the dance area. Each onlooker evaluates the nectar information taken from all employed bees and then chooses a food source depending on the nectar amounts of sources. As in the case of the employed bee, she produces a modification on the source position in her memory and checks its nectar amount. Providing that its nectar is higher than that of the previous one, the bee memorizes the new position and forgets the old one. The sources abandoned are determined and new sources are randomly produced to be replaced with the abandoned ones by artificial scouts.

It consists of sensors and the sinks. The sensor collects the information by monitoring all the nodes and report to the sink and the sink finally finds the optimal path to reach the destination so the it takes less energy to reach the node and the lifetime is also maximized.

By using this artificial bee colony algorithm we can find the connect covers with best-so-far solution. Thus it can finds new connected covers in the network. This process consumes less energy compared to ant colony optimization and it is the fastest methodology compared with ant colony optimization.The BeeRP (Bee Routing Protocol) is a bio inspired protocol that is used by bees in order to communicate with other bees to reach the goal and destination. The principle of BeeRP is given as

Fig 3.3 BEE routing protocol for wireless sensor network

Each sensor node is equipped with a compass.

The transmitter is considered as a hive of bees.

The receiver (sink in WSN) is considered as a sun

The transmitter's neighbours that can communicate directly with this later are considered as sources of food.

The transmitter that will communicate indirectly with the receiver will use its own compass to select the smallest angle that will be limited between the straight edge (from transmitter to receiver) and edge from transmitter to one of its neighbours. The selected neighbour will do the same task by using its own compass to select the closest of its neighbours that creates a smallest angle to reach the receiver and so on till reaching the final target (receiver). BeeRP which is a bio-inspired algorithm is the same as DIR or Compass routing in its advantages and insufficiencies.

CHAPTER 4

SYSTEM SPECIFICATION

4.1 HARDWARE REQUIREMENTS

TABLE 4.1 HARDWARE REQUIREMENTS

S.No

CATEGORY

SPECFICATION

1

Processor

Intel Processor IV

2

RAM

512MB

3

Hard Disc

80GB

4

Monitor

LCD Monitor

5

Key Board

108 Mercury Keyboard

6

Mouse

Logitech Mouse

4.2 SOFTWARE REQUIREMENTS

TABLE 4.2 SOFTWARE REQUIREMENTS

S.NO

CATEGORY

SPECIFICATION

1

Operating System

WINDOWS 2007

2

Language used

JAVA

3

Front End

Java Swing

4

IDE

Netbeans

5

Tool used

TRM simulator tool

4.2.1 Software Description

4.2.1.1 JAVA

JAVA is an Object Oriented Programming. Java was developed by Sun Microsystems. The most striking feature of the language is that it is a Platform -Neutral language. Java is the first Programming language that it is not tied to any particular hardware operating system. Programs developed in Java can be executed anywhere on the system.

Java is an ideal framework for service-side Web programming:

Portability (well-defined semantics of language and standard libraries)

Platform independence (byte code interpretation)

Secure runtime model (array bound checks, automatic garbage collection, byte code verification...)

Sandboxing security (Security manager)

Dynamic loading (class loader)

Data migration (serialization)

Unicode (as HTML and XML)

Threads, concurrency control

Network access (java.net.*)

Cryptographic security (RSA ...)

Applet

Java's performance has improved substantially since the early versions, performance of JIT compilers relative to native compilers has in some tests been shown to be quite similar the performance of the compilers does not necessarily indicate the performance of the compiled code; only careful testing can reveal the performance issues in any system.

One of the unique advantages of the concept of a run time engine is that errors (exceptions) should not crash the system. Moreover, in run time engine environments such as java there exist tools that attach to the runtime engine and every time that an exception of interest occurs they record debugging information that exited memory at the time the exceptions was thrown.

We call the java as a revolutionary technology because it has brought in a fundamental shift in how we develop and use programs.

4.2.1.2 JAVA SWINGS:

Swing is the widget toolkit for java. It is the part of Sun Microsystems java foundation classes (JFC). It is an API for providing a graphical user interface (GUI) for java programs. Swing was developed to provide a more sophisticated set of GUI components than the earlier abstract window toolkit. Swing provides a native look and feel that emulates the look and feel of several platforms, and also supports a pluggable look and feel unrelated to underlying platform. Swing is platform independent, model view controller GUI framework for java. It follows a single threaded programming model.

4.2.1.3 NETBEANS:

NetBeans refer to both platform framework for java desktop application and also for Integrated Development Environment (IDE) for developing with java, PHP, Java script, and others. The NetBeans IDE written in Java and can run on Linux, windows, Solaris and other platforms that support the compatible JVM. The NetBeans platform allows application to develop from the set of modular software components called modules. The application based on NetBeans platform can be extended by the third party developers.

The NetBeans platform is a reusable framework for simplifying the development of java swing desktop applications. The NetBean bundle for javaSE contains what is needed to that no additional SDK is required it need only NetBean plug in and based applications. Application can install modules dynamically. The platform offers reusable services common to desktop application, allowing developers to focus on logic specific to their application. The features of the platform are:

User interface management

User settings management

Storage management

Window management

Wizard framework

NetBeans visual Library

Integrated development tools

NetBeans IDE is a free, open-source, cross-platform IDE with built-in-support for java programming language. The profiler is based on Sun Laboratories research project that was named JFluid. That research uncovered specific technique that can be used to lower the overhead of profiling a java. One of those techniques is dynamic byte code instrumentation, which is particularly useful for profiling large Java applications. Using dynamic byte code instrumentation and additional algorithms, the NetBeans Profiler is able to obtain runtime information on applications that are too large or complex for other profilers. NetBeans also support Profiling Points that let you profile precise points of execution and measure execution time.

NetBeans 7.1 adds three ANT-based project types for JavaFX:

JavaFX Application,

JavaFX Preloader, and

JavaFX FXML Application

The GUI design-tool enables developers to prototype and design Swing GUIs by dragging and positioning GUI components

TRM simulator:

Trust and Reputation Model simulator TRMSim-WSN (Trust and Reputation Models Simulator for Wireless Sensor Networks) is a Java-based simulator aimed to test Trust and Reputation models for WSNs.It provides several Trust and Reputation models and new ones can be easily added.

It allows researchers to test and compare their trust and reputation models against a wide range of WSNs. They can decide whether they want static or dynamic networks, the percentage of fraudulent nodes, the percentage of nodes acting as clients or servers, etc.

It has been designed to easily adapt and integrate a new model within the simulator. Only a few classes have to be implemented in order to carry out this task.

CHAPTER 5

PROJECT DESCRIPTION

5.1 MODULES

Deploying sensor

Energy consumption

Simulation result

5.2 MODULE DESCRIPTION

1) Deploying Sensor:

This module tells us about that how the sensors are deployed so that it can be used to monitor the neighbor's information and in order to provide the sufficient information to reach the target.

2) Energy Consumption:

This describes how much of the energy is consumed during the node that process from the source to the destination and also the bee hive concept is implemented and the food searching to reach the target is based on artificial bee colony technique.

3) Simulation Result:

Finally the simulation results show that the energy consumed, the path rate and accuracy. It also describes about the commands and the buttons that is used to design the simulation i.e. Trust and Reputation simulator .Thus this module help us to design the page for showing the simulation result.

CHAPTER 6

CONCLUSION

Since the wireless sensor network plays an important role in many fields the lifetime of each node is considered to be important. In this paper I have used the Artificial Bee Colony algorithm (ABC) to improve the lifetime of the wireless sensor network. The ABC technique used is very efficient way to improve the lifetime of the heterogeneous wireless sensor network. In Phase1, I discussed about the literature survey, architecture and design specification and further implementations and the simulations are carried out in the next phase.

CHAPTER 7

APPENDIX

7.1 CODING

OUTCOME PANELS

ENERGY CONSUMPTION PANEL

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ACCURACY PANEL

[[[

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LFTM-SATISFACTION MODEL

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<AuxValue name="FormSettings_generateMnemonicsCode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_i18nAutoMode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_layoutCodeTarget" type="java.lang.Integer" value="1"/>

<AuxValue name="FormSettings_listenerGenerationStyle" type="java.lang.Integer" value="0"/>

<AuxValue name="FormSettings_variablesLocal" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_variablesModifier" type="java.lang.Integer" value="2"/>

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<EmptySpace min="0" pref="400" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

<DimensionLayout dim="1">

<Group type="103" groupAlignment="0" attributes="0">

<EmptySpace min="0" pref="300" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

</Layout>

</Form>

PATH LENGTH PANEL

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<AuxValue name="FormSettings_autoSetComponentName" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_generateFQN" type="java.lang.Boolean" value="true"/>

<AuxValue name="FormSettings_generateMnemonicsCode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_i18nAutoMode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_layoutCodeTarget" type="java.lang.Integer" value="1"/>

<AuxValue name="FormSettings_listenerGenerationStyle" type="java.lang.Integer" value="0"/>

<AuxValue name="FormSettings_variablesLocal" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_variablesModifier" type="java.lang.Integer" value="2"/>

</AuxValues>

<Layout>

<DimensionLayout dim="0">

<Group type="103" groupAlignment="0" attributes="0">

<EmptySpace min="0" pref="400" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

<DimensionLayout dim="1">

<Group type="103" groupAlignment="0" attributes="0">

<EmptySpace min="0" pref="300" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

</Layout>

</Form>

[

LEGEND PANEL

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<AuxValues>

<AuxValue name="FormSettings_autoResourcing" type="java.lang.Integer" value="0"/>

<AuxValue name="FormSettings_autoSetComponentName" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_generateFQN" type="java.lang.Boolean" value="true"/>

<AuxValue name="FormSettings_generateMnemonicsCode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_i18nAutoMode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_layoutCodeTarget" type="java.lang.Integer" value="1"/>

<AuxValue name="FormSettings_listenerGenerationStyle" type="java.lang.Integer" value="0"/>

<AuxValue name="FormSettings_variablesLocal" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_variablesModifier" type="java.lang.Integer" value="2"/>

</AuxValues>

<Layout>

<DimensionLayout dim="0">

<Group type="103" groupAlignment="0" attributes="0">

<EmptySpace min="0" pref="400" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

<DimensionLayout dim="1">

<Group type="103" groupAlignment="0" attributes="0">

<EmptySpace min="0" pref="300" max="32767" attributes="0"/>

</Group>

</DimensionLayout>

</Layout>

</Form>

[

NETWORK PANEL

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<Properties>

<Property name="border" type="javax.swing.border.Border" editor="org.netbeans.modules.form.editors2.BorderEditor">

<Border info="org.netbeans.modules.form.compat2.border.LineBorderInfo">

<LineBorder roundedCorners="true"/>

</Border>

</Property>

</Properties>

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<AuxValue name="FormSettings_autoSetComponentName" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_generateFQN" type="java.lang.Boolean" value="true"/>

<AuxValue name="FormSettings_generateMnemonicsCode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_i18nAutoMode" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_layoutCodeTarget" type="java.lang.Integer" value="1"/>

<AuxValue name="FormSettings_listenerGenerationStyle" type="java.lang.Integer" value="0"/>

<AuxValue name="FormSettings_variablesLocal" type="java.lang.Boolean" value="false"/>

<AuxValue name="FormSettings_variablesModifier" type="java.lang.Integer" value="2"/>

</AuxValues>

<Layout>

<DimensionLayout dim="0">

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<EmptySpace min="0" pref="398" max="32767" attributes="0"/>

</Group>

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<DimensionLayout dim="1">

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7.2 SCREENSHOT