This invention relates to the design of energy efficient technique using wireless sensor network using HANT algorithm and multiple mobile sinks. More specifically the invention relates to the transfer of data from one node to sink through shortest possible path.
BACKGROUND ART
[002] A technique introduced in routing protocol named SERA (Secure Energy Efficient Routing Algorithm) for examining the performance of DHWSNs (Dynamic HWSNs). This protocol mainly emphasis on energy and the normalized distance factor to identify the optimal CHs. The re-rotation of CHs was also obtained by SEAR. The simulation outcomes proved that the protocol SERA was efficient and reliable for DHWSNs. The QoS (Quality of Service) in the network was preserved by maximizing the throughput, network lifetime, also minimizing the energy consumption of SNs.
[003] An EERH approach was developed (Energy Efficient Cooperative Routing Scheme for HWSNs), which was named as an energy-saving routing framework. Routing paths were recognized based on the residual energy of the underlying sensors and transmission directions of event packets. Thus, the outcomes from simulation proved that EERH efficiently prolongs the lifetime of an HWSN. Author discussed a

clustering protocol for HWSNs based on the concept of network division. The nodes that are heterogeneous in nature were inserted first into the network and then divided into regions. Then CH selection was performed. This was done by considering the remaining energy and CH as well as node distance. From the simulation outcomes, it was proved that the proposed protocol had offered better results towards energy consumption and lifetime.
[004] A clustering process is introduced on the basis of WPA (Wolf Pack Algorithm), which was otherwise known as CLWPA. However, this algorithm enhances the network life cycle and stability cycle and obstructs the premature CHs death. Also, CLWPA offers extra-ordinary performance and utilized in harsh environments. Clustering based Energy efficient (EE) routing protocol for homogeneous-heterogeneous WSNs were discussed. For the selection of CH, a threshold with a balanced probability was introduced. Thus, simulation outcomes offered better performance in homogeneous as well as heterogeneous WSN in terms of network lifetime, energy efficiency, and a number of transmitted messages.
[005] A technique is formulated DET (Distance based Enhance Threshold) - SSEP (Sensitive Stable Election Protocol), which has outperformed the protocol ETSSEP (Enhance Threshold SSEP). DETSSEP has attained enhanced throughput as well as network lifetime. A multi-level HWSN model was introduced in order to offer energy efficiency at an improved level. Dual parameters (Primary, Secondary) were proposed for energy and density determination. This HWSN model minimizes energy

consumption and extra time, respectively.
[006] An analysis was performed on the routing protocol named CREEP (Cluster-Head Restricted EE Protocol) to boost the lifetime of HWSNs. This protocol overcomes the problem of high complexity, which was caused by CHs selection and computation. PSO (Particle Swarm Optimization) based coverage control algorithm was developed to minimize energy consumption and to improve the rate of coverage. Initially, the sensors were deployed randomly, and then the network was divided into grids. In order to change the sensing radius of sensor nodes, PSO was adopted to overcome the wastage of energy.
[007] An algorithm was designed with a technique named IDGS (Intelligent Data Gathering Scheme) combined with DF (Data Fusion). This proposed model was aimed at minimizing energy consumption as well as enhancing the lifetime of WSNs. IDGS-DF model has been utilized for the detection of a forest fire. An enhanced algorithm was introduced, which were characterized as PEGASIS (Power Efficient Gathering in Sensor Information Systems). This algorithm was presented to overcome the problem of hot-spot and proved that PEGASIS had performed well in terms of energy consumption, lifetime, and network latency.
[008] A data collection algorithm is designed by merging the Hamilton loop and the PEGASIS algorithm. MA (Mobile Agent) was in charge of fusing as well as receiving a packet from the CHs on the path. The performance outcomes proved that the proposed

routing model could equalize resource expenditure, prolong the network lifetime, and decrease the propagation delay. Hot spot problem in WSNs was eliminated by ACMDGT (Asynchronous Clustering and Mobile Data Gathering based on Timer) mechanism. This mechanism makes use of a single mobile sink which examines the optimal path based on the moving and data overflow time of CHs. Thus ACMDGT outperforms TCBDGA and LEACH algorithms by maximizing the lifetime of the network and minimizing energy consumption.
[009] An analysis of the routing protocols in an extensive manner based on the clustering method was performed. The routing protocol used for designing heterogeneous WSN was named as Stable Election Protocol (SEP). Using this protocol, different clustering architectures were proposed, namely T-SEP, Z-SEP, and E-SEP. The performance comparison was made on the basis of network lifetime, stability, and energy efficiency. Comparing all those architectures, the design of T-SEP and SEP were efficient than that of E-SEP and Z-SEP. Therefore, in order to enhance network lifetime and to save energy, T-SEP architecture seems to be a good choice for heterogeneous WSN.
[010] Introduction of hybrid heterogeneous energy-efficient routing approach for tracking animals was developed. The arrangement of a network includes a base station, sensor nodes, and relay nodes. The hybrid energy model was designed using sensor nodes representing diverse energy levels. Based on the energy initialized, the sensor nodes were classified into advance and normal nodes. Also, the performance of the

network was compared with the following protocols, such as Stable Election Protocol (SEP), Sub-Netting Based Routing Protocol (SNRP), and Low Energy Adaptive Clustering Hierarchy (LEACH).
[Oil] A new method was developed in HWSN for EE routing via single and multiple data sinks. The routing protocols were named as Multiple Energy Efficient Cluster protocol (MEEC) and Improved Dual Hop Routing (IDHR). The selection of a cluster head was completed by combining distance, energy with the node density parameter. In MEEC, mainly, the use of multiple sinks avoids dual hop communication among sink and cluster head, which reduces the problem of hot-spot and improves the lifetime of the network.
[012] A protocol was presented named Energy-Coverage Ratio Clustering Protocol (ECRCP) for heterogeneous energy wireless sensor networks to overcome the drawbacks of Low-Efficiency Adaptive Clustering Hierarchical (LEACH) protocol. ECRCP was based on minimizing energy consumption for defining an optimum number of clusters. The principle named regional coverage maximization was used for the selection of cluster head. Thus, the simulation outcomes prove that ECRCP balances network load enhances network lifetime, and minimize energy consumption.
[013] A new algorithm was introduced named ESRA (Enhanced Stable Routing Algorithm) for HWSNs. Cluster head was selected using the distance factor. The proposed algorithm has improved the stability period and lifetime compared with other

protocols. From simulation outcomes, it was proved that the stability period was improved by 15.43%. Also, combining energy with distance factors not only increases the cluster head selection but improves the operation of node.
[014] A method named ECH (Enhanced Clustering Hierarchy) was developed to attain energy efficiency in WSNs by the mechanism of sleeping waking for overlapping and neighbouring nodes. Thus, the data redundancy is minimized, and then network lifetime is maximized. A Fuzzy Logic (FL) based Clustering Algorithm (CAFL) has been presented to enhance the lifetime of WSNs. This method uses FL for selection of CHs and clusters formation processes by means of residual energy and closeness to the sink as fuzzy inputs in terms of CH selection, and residual energy of CH. Simulation outcomes have proved their efficiency and enhanced lifetime.
[015] A technique is developed for data gathering in WSNs via mobile sink (MS) using GTAC-DG (Game Theory and Ant Colony Data Gathering). This approach has combined the game theory and ant colony for choosing the best RPs (rendezvous points) and optimal path for MS. In WSNs, Sustainable Data Gathering Technique (SGDT) based on Nature Inspired Optimization (NIO). Here, a chain-based data gathering and transmission process were performed for inter and intra-cluster communication. Data aggregation process is also presented for redundant data removal, which helps in lessening the transmission cost and overhead of networks

OBJECTIVES
[016] Objective of the invention is to design an HWSN, in order to monitor the harsh environment and to detect forest fires, volcanic eruptions, etc.
[017] Yet another objective of the invention is to propose an adaptive SOA based CH selection and to offer an EE routing scheme for HWSNs.
[018] Yet another objective of the invention is to introduce a hybrid algorithm (HANT) for the route establishment and navigation via the best possible path.
[019] Yet another objective of the invention is to enhance the network lifetime and stability period by minimizing the node's communicative distance.
[020] Yet another objective of the invention is to minimize energy consumption, eliminate the hot-spot problem in the multiple mobile sinks based HWSNs

SUMMARY
[021] HANT algorithm is proposed to improve life time of network and stability period when comparing with existing approaches namely SEP, DSEP, PSEP, and ESRA. An optimal path for the MS is obtained by means of HANT algorithm. The MSs collect data via the optimal path also communicate with CHs through short range communications. With multiple sink mobility, the lifetime of battery-operated devices is also extended the energy consumption is reduced. It is observed that the stability period of HANT algorithm improved by 20.16% comparing with ESRA approach, and the network life time of proposed method is also enhanced when compared to existing methods. Thus, the performance improvement is achieved due to the shorter distance of communication from MSs to each node.

DETAILED DESCRIPTION
[025] Transfer of data from nodes to sinks for the purpose of collecting data is done using HANT (Hybrid Harris Hawk and Ant Colony Optimization) algorithm-based sink moving strategy is used to find the shortest travel path. In this process for the transfer of data from node to sink is done with the help of multiple mobile sinks which reduces energy consumption and number of hops required in the process. To ensure higher connectivity sinks are placed closed to nodes that is receiving end and transmitting end are close to each other.
[026] To start the process of transfer of data from node to sink firstly the nodes are arranged in heterogenous form mainly advanced nodes, normal nodes and super nodes [001]. While sinks are further arranged into multiple mobile sinks [002] which reduces energy consumption and number of hops. Once the nodes and sinks are arranged the threshold value is calculated [003] which is compared to a random number which is generated [005]. If the value of random number is less than threshold value [004] then that cluster head is selected [007] for the transmission of data to that cluster head [008] but if the random number is more than threshold value in this case the node becomes cluster member [006]. As now the data is available at a cluster head it determines the nearest mobile sink with minimum distance [009] which is calculated with the help of Hybrid Harris hawk and ant colony optimization algorithm [010] which then sends the data to sink [011] for data collection to keep the process active the network is check if it is alive [012] the whole process repeats else the data is saved and no further action is

done.
[027] For the transfer of data cluster head selection is one of the major steps required in the process, the cluster head selection is done using K-medoid with ASOA. Value of K is selected for n data points as medoids [013] which further calculate distance between every two data points of all objects [014] which is done with a formula:
Distance= Yi=i(Ti — Si)2 [015] using this formula every object is allocated to the nearest medoid value which helps in formation of initial cluster [016] once the initial cluster is formed the overall distance is reduced estimating new medoids in every cluster [017] further allotting every object to nearest medoid thus clustering is attained [018].

DESCRIPTION OF DRAWINGS
Figure 1 denotes complete working flowchart
1 denotes Arrangement of heterogenous nodes
2 denotes Arrangement of multiple mobile sinks
3 denotes Calculation of threshold value
4 denotes If condition
5 denotes Random number generation
6 denotes Nodes becomes cluster member
7 denotes Cluster head selection
8 denotes transmission of data to cluster head
9 denotes determine the nearest mobile sink at minimum distance
10 denotes Hybrid harris hawk and ant colony optimization algorithm based route formation
11 denotes transmission of data to mobile sink
12 denotes Is network Alive?
Figure 2 denotes Working of K-medoid algorithm
13 denotes Select value of K for n data points as medoids
14 denotes Calculate distance between every two data points of all objects
15 denotes Formula
16 denotes Allocating every object to the nearest medoid value- formation of initial cluster

17 denotes Estimating new medoids in every cluster (reduce overall distance)
18 denotes Allotting every object to nearest medoid (Clustering is attained)
Figure 3 denotes Working of HANT algorithm
019 denotes HHO initialization for each HHs to perform tracing, Encircling,
Approaching and Attacking activities from source to sink node
20 denotes Route selection to the sink node (attained using FTHO)
21 denotes Initialize ACO algorithm pheromone information using paths or else paths
22 denotes FANT moves from source to sink node each FANT selects next node according to PSTR
23 denotes BANT is created as soon as FANT reaches sink node which moves beside the route of navigation of FANT
24 denotes Update amount of pheromone to start next iteration

CLAIMS
I/We Claim that
1. Design of Heterogeneous Wireless Sensor Network to enhance network
lifetime and stability period;
method of which is by minimizing the node's communicative distance from
mobile sinks;
wherein present network monitors unfavourable environment and detects
forest fires;
Multiple mobile sinks technique to transfer data from nodes to sink with less
energy consumption and reduced number of hops, optimal for battery
operated devices;
2. A K-medoid with an adaptive SOA based Cluster Head selection design to
achieve an energy efficient routing scheme;
Hybrid Harris Hawk and Ant colony optimisation [HANT] to establish route
and navigate via best possible path, increasing the stability period by
20.16%;
Design of mentioned algorithm to further enhance lifetime and stability
period by minimization of the node's communicative distance;