Optimizing energy in wsn using evolutionary algorithm
Then, the routing table is updated once more by GA while applying the reduction of energy for all the nodes. On the other hand, Figure 6 b pictures the time and the first node is removed. View at Google Scholar A. The density of the clusters is controlled by adopting this measurement, while density is the count of nodes in each cluster.
On the other hand, Figure 6 b pictures the time and the first node is removed. The stochastic nature of GA dictates that different solutions with variant performance are obtained in different runs of the algorithm. The placement of nodes influences the capacity of a network to correctly sense an event as well as the number of possible disjoint paths towards the sink s. The individuals with higher fitness score have higher chance for being selected, the process which results in preferential adoption of the best solution. Also known as a global heuristic algorithm, a generic algorithm estimates an optimal solution through generating different individuals [ 8 ].
Comparing with the other studies, the simulation showed that the proposed fitness function met the objectives. The proposed fitness function was improved by defining at least 3 states for all the node for obtaining the almost exact fitness as. All the sensor nodes located in the environment should have a connection with high energy level nodes. The data collected by cluster members optimizing energy in wsn using evolutionary algorithm aggregated in CH and sent to the sink. Figure 4 a shows the general scheme of coverage in WSN.
In what follows, some important fitness parameters in WSN are discussed. The single-step oriented process is too costly; the more the distance between the node and base station, the more energy is needed and consequently the shorter the lifetime of the network. This value is a kind of penalty for remote nodes. NC is the surcharge of uncovering points; and show the activation status of node and covering status of pointrespectively. Home Journals About Us.
Where, is the total energy consumption and is the total distance between nodes and each cluster is multiplied by total distance between cluster heads. Consistent with other studies, the parameters are defined as close as possible to practical situation [ 12 ]. A technical survey was conducted on these operational stages.
There is an adopted cluster head for managing each cluster. Ineffective layout means waste of energy and financial resources. Afterward, the bit sequence of the other parent is replicated as the second part of children. Ration of energy level and amount of energy required to achieve the proper network are obtained by the formula. The next step is to evaluate connectivity of the network.
Sensor nodes are not efficient choice for long-term transmission as their energy consumption is a super linear function of the distance the data that is transmitted. Figure 4 a shows the general scheme of coverage in WSN. Level of energy consumption and number of active nodes along with live packets over time are listed in Table 3.
All the sensor nodes located in the environment should have a connection with high energy level nodes. Role of number of nodes on genetic algorithm iteration on lifetime of the network is pictured in Figure 4 c. The technique also has other advantages such as improved security, less extra data, and improved scalability of the network.
The final individuals provide route with almost uniform energy consumption. In effect, GA minimum spanning tree and aggregation tree are alike as the former is based on the environment-monitor node developed to examine the best edges toward the BS and to achieve balanced load of data packets to the nodes. To introduce some feasible optimum network topologies with as few as possible constraints e.
It is implied by iterations 50 and 57 that more number of individuals does not necessary result in better solution. Clearly, consumption of energy, reasonably, is subject to the number of nodes and for large WSN the energy is extreme. Necessity of data integrity in WNSs due to support continuous and permanent communication among the sensors has made the lifetime another important parameter in WSNs. Wide range of applications optimizing energy in wsn using evolutionary algorithm resulted in development of variety of protocols which include plenty of flexible parameters.