E NERGY -A WARE G ENETIC A LGORITHM FOR M ANAGING W IRELESS S ENSOR N ETWORKS Abhishek Karpate

O VERVIEW  Wireless Sensor Networks are quickly emerging as a technology for tracking and monitoring in many domains.  Military applications  Environmental applications  Health applications  Agricultural applications  Smoke detection  They consist of spatially distributed sensors which cooperate among themselves for any task.  The sensors are small integrated circuits which are embedded in wireless devices.

C HALLENGES Reliability and Robustness Sensor networks are not meant for frequent maintenance. They should be operated by sensors that are reliable and should be deployed in large numbers. Energy conservation Sensor networks have limited computational and communication capabilities. Algorithms need to be collaborative with energy aware communication. Real time data acquisition and processing There is a critical need for efficient data communication and data processing. Some techniques that are used are event ordering and synchronization. Data Management An embedded real time database is needed which stores the data of interest and provides results to different queries. Data privacy and Security The data collected is sensitive. It should be made sure that the data is properly transmitted and collected with no loss.

P REVIOUS W ORK Many Researchers approached the management of wireless sensors problem from one specific angle such as network life span and coverage. Very little has been done to optimize multiple objectives concurrently which is critical in this case, in particular the trade off between performance and consumed energy. Almost all current approaches provide static solutions that lack the flexibility to change priority according to the application domain. Few research studies looks at the impact of the density of the sensor nodes.

A PPROACH Area monitored by single sensor Area monitored by multiple sensors Set of Sensor Nodes Set of Sub-regions

T HE P ROPOSED M ODEL WSN and the set of areas it needs to cover can be represented by a bipartite graph G as follows: G = ( N ∪ A, E) ; where, N  Set of sensor nodes A  Set of area monitored E  Edge which exists between a node in N to a node in A if and only if that particular node monitors that area.

K EY F EATURES OF THE M ODEL  The proposed model provides a flexible mechanism to incorporate various parameters of the problem, such as:  Heterogeneity of the sensor network  Optimization criteria  An any given instance, a solution is given by identifying a subset of nodes representing sensors (active sensors) that dominates the entire set of nodes representing the areas need to be covered.  Such a subset needs to be identified in a way that attempts to optimize various parameters related to performance and energy.

E VOLUTIONARY A LGORITHM  The evolution starts with n randomly generated strings.  The length of the string represents the number of sensors.  The state of each individual sensor is represented by a 1bit binary number called gene.  The gene defines the status of the nodes as follows: s i =

 Randomly generated strings are subjected to mutation.  Mutation is the random alteration of a gene of a chromosome.  Helps in reintroducing the random cells that have been lost. M UTATION Original String: Mutated String:

Crossover is an operator that combines two strings to produce a new string. The purpose behind crossover is that the resulting string takes better characteristics from both the parents The length of each substring to be swapped is taken as input from the user. The number of strings generated depends on the length of the substring Crossovers generated when the length of the substring is one-third the string length C ROSSOVER

As discussed there are the following sets and constants: S = {s 1, s 2, …, s n } the set of sensors A = {A 1, A 2, …, A m } the set of sub-regions We define fitness function, f ∀ S, ∀ A as follows: f = αP + βQ + γR where, P  Percentage of coverage Q  Quality of coverage R  Number of inactive nodes and α, β, γ are tuning parameters to customize the function according to the priority of each application F ITNESS F UNCTION

 The process of selecting better individuals to use as parents for the further generations.  The process of selection is stopped when for a few cycles no strings with better fitness are found. S ELECTION The healthiest strings are considered for future generations  Elitism can occur in one of the following forms: Pure Elitism: The strings that are considered are all obtained from the previous generation Partial Elitism: Some of the strings that are considered are obtained from the previous generation, others are randomly generated

A SSESSMENT A benchmark is needed to compare the performance of the algorithm. The performance of the algorithm is compared with the greedy Round-Robin algorithm. The algorithm is executed as follows: The desired number of sensors that need to be active are taken as input. Accordingly sensors are divided into groups. For any particular time slice a particular group of sensors are turned on.

E XPERIMENTS The simulations are carried out on three types of networks – densely, medium and sparsely populated The simulations are carried out in order to achieve the following: Have a set of options available for any desired situation. As needed, the required numbers of sensors are turned on and the requirement is fulfilled. Control the activation and deactivation of the sensors. A network that swings its main priority from coverage to energy saved.

R ESULTS Densely populated network Sensor networks having more than 50 sensors are classified as dense networks. Input ParametersOutput Parameters % Coverage Quality Coverage Energy Saved % Coverage Quality Coverage Energy Saved Case Case Case Case Case Table 1: Simulation test results using genetic algorithmTable 2: Simulation test results using round-robin algorithm Number of Active Sensors % Coverage Quality Coverage Energy Saved Case Case Case Case Case

Input ParametersOutput Parameters % Coverage Quality Coverage Energy Saved % Coverage Quality Coverage Energy Saved Case Case Case Case Case Table 3: Simulation test results using genetic algorithm Number of Active Sensors % Coverage Quality Coverage Energy Saved Case Case Case Case Case Table 4: Simulation test results using round-robin algorithm Intermediately populated network Sensor networks having less than 50 sensors and more than 25 sensors are classified as intermediately populated networks.

Input ParametersOutput Parameters % Coverage Quality Coverage Energy Saved % Coverage Quality Coverage Energy Saved Case Case Case Case Case Table 5: Simulation test results using genetic algorithm Number of Active Sensors % Coverage Quality Coverage Energy Saved Case Case Case Case Case Table 6: Simulation test results using round-robin algorithm Sparsely populated network Sensor networks having less than 25 sensors are classified as sparsely populated networks.

A NALYSIS OF R ESULTS  Genetic algorithms overall provide better coverage for the same amount of energy used as compared to the round-robin algorithm.  A better quality of coverage is attained by spending equal amount of energy in genetic algorithms which provide a much needed redundancy for critical applications.  The more densely populated a network is the better capable it is in producing a wider range of viable results.  As the network goes more sparse, lower amounts of energy would be saved in order to attain the some level of coverage or quality.  The more number of strings considered in the partial elitism the better are the chances of getting more viable results.  More efficient results are attained if the number of elements in the crossover operation are kept low because then the crossover is capable of producing more offspring.

C ONCLUSIONS  The project proposes a scheme by which the activation/deactivation process in a wireless sensor network can be adjusted to optimize multiple objectives.  According to the specific need at a given instance, the behavior of the wireless sensor network can be changed depicting networks of different priorities.  The proposed evolutionary algorithm presents different solutions to the optimization problem and outperforms standard greedy techniques in finding the most fit solution.  The graph theoretic model along with the algorithm can be further expanded by creating strings that represent various states of the sensors such as transmission, listening, active, sleep.

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