Data Aggregation Scheme Using Multiple Mobile Agents in Wireless Sensor Network

Wireless sensor networks (WSNs) consist of large number of sensor nodes densely deployed in monitoring area with sensing, wireless communications and computing capabilities. In recent times, wireless sensor networks have used the concept of mobile agent for reducing energy consumption and for effective data collection. The fundamental functionality of WSN is to collect and return data from the sensor nodes. Data aggregation’s main goal is to gather and aggregate data in an efficient manner. In data gathering, finding the optimal itinerary planning for the mobile agent is an important step. However, a single mobile agent itinerary planning approach suffers from two drawbacks, task delay and large size of the mobile agent as the scale of the network is expanded. To overcome these drawbacks, this research work proposes: (i) an efficient data aggregation scheme in wireless sensor network that uses multiple mobile agents for aggregating data and transferring it to the sink based on itinerary planning and (ii) an attack detection using TS fuzzy model on multi-mobile agent-based data aggregation scheme is shortly named as MDTSF model.


Introduction
Data aggregation signifies inspiring and well-researched topics in the wireless sensor network (WSN) [1][2][3][4][5] writings. The energy restrictions of nodes in a sensor network call for fuel-saving data aggregation approaches and encompass the nexus lifecycle. In addition, mobile agents (MAs) [3,6] are projected to better the execution of data assemblage in WSNs. In these methodologies, schedules ensuing that traveling agents mainly control the total accomplishment of the collection of the data management. Gathering data effectively has always been of primary importance in a wireless sensor network. The mobile agent paradigm [7,8] has made it possible to collect and aggregate data in a manner that is proper for real-time applications. Along this line, a number of heuristics have been scheduled to achieve effective itinerary planning for MAs [9].

Overview
Inside the complexity of the sensor, sensor nodes perhaps induce dispensable data; similar packets from various junctions can be accumulated to such an extent that the several broadcasts could be condensed. Data aggregation [1] is the mixture of statistics from different origins by using utilities for example repression (eliminating copies), lowest level, highest level, and median. A few of these tasks can be achieved by the aggregator sensor node, by allowing sensory points to supervise data network depletion. Knowing that calculation would be less power absorbing than transmission, considerable reduction in energy can be achieved by data aggregation [10]. The potency of data aggregation can be deduced using many metrics [11].

Materials and methods
In recent times the concept of mobile agent (MA) was applied by researchers in wireless sensor networks (WSN) to reduce the energy consumption and improve data collection. Mobile agent paradigm has been adopted by researchers as an alternative to traditional client-server paradigm. Data aggregation in WSN is an active research area due to its importance in solving the main drawbacks of using WSNs. This research has the following contributions.
i. An efficient data aggregation scheme by means of itinerary planning (DAS-IP) using ACO-GA was proposed using the concept of single mobile agent for data aggregation and transfer to sink.
ii. A multi-mobile agent-based data aggregation scheme was proposed to overcome the desk delay problem encountered by single mobile agent itinerary approach.

Results and discussion
This section of the chapter offers a comprehensive and concise comparison of the implementation results of the proposed data aggregation schemes with existing ones in terms of the metrics say, delay, energy, drop rate throughput and finally overhead. The performance of the proposed data aggregation schemes are evaluated by contrasting with existing techniques [12,13].

Performance evaluation of data aggregation scheme using hybrid based ACO-GA itinerary planning
The performance of the proposed data aggregation scheme using hybrid ACO-GA itinerary planning is contrasted with the prevailing techniques say, dynamic based data aggregation approach (DMA-DA). The comparison is done concerning the metrics say, energy, drop rate, throughput and overhead. The experimented was executed on NS-2. Figure 1 shows the simulated WSN portraying the clusters along with their member nodes together with mobile agents and sink nodes.
The simulation parameters utilized for the experiment are offered in Table 1.

Results and comparative analysis of the data aggregation scheme using hybrid based ACO-GA itinerary planning
The performance analysis of the proposed method and prevailing DMA-DA results for the disparate metrics comparison is offered in Tables 2 and 3 for the number of nodes 100, 200, 300, 400 and 500. In Figure 2 the propounded data aggregation scheme is contrasted to existing DMA-DA concerning the metrics say, delay, delivery ratio and drop rate for different number of nodes. From the above table the proposed method has a delay value      Table 3 above displays the experimental outcome of the suggested DAS-IP along with existing DMA-DA technique for 100, 200, 300, 400 and 500 nodes respectively. From the table it can be seen that for a simulation of 100 nodes the proposed DAS-IP offered energy of 6.463972 but the existing DMA-DA has 13.61866, for 200 nodes the energy is 7.80906 and 13.192729 for proposed DAS-IP and prevailing DMA-DA respectively. Similarly the energy for the proposed is 6.805129, 6.

Delay for the data aggregation scheme using hybrid based ACO-GA itinerary planning
The delay of the proposed data aggregation scheme is contrasted with existing DMA-DA technique for 100, 200, 300, 400 and 500 nodes as illustrated in Figure 2. The vertical axis gives the delay value whereas the horizontal axis signifies the number of nodes. The bars in the graph represent the comparisons among the various techniques. It can be inferred from the figure that the routing delay increases as the number of nodes increases. On considering 500 numbers of nodes, the delay is too high for the existing technique. But, the delay of the proposed technique varies by 5.162867 values lower than the existing one. Also, for any number of nodes when contrasted to the existing one, the proposed DAS-IP shows less delay for the routing data to the sink.

Delivery ratio for the data aggregation scheme using hybrid based ACO-GA itinerary planning (DAS-IP)
The data delivery ratio is given as the total number of data received at destinations (Sink) divided by the total number of data sent from the source node. Figure 2 offers a comparison among the proposed and existing methods by varying the number of nodes from 100 to 500. In Figure 3 the vertical axis shows the delivery ratio whereas the horizontal axis denotes the number of nodes used for running the experiments. Figure 3 compares the delivery ratio against the number of nodes for the existing DMA-DA and the proposed DAS-IP technique. It can be inferred that the delivery ratio decreases as the number of nodes increases. For lower number of nodes, say 100, the delivery ratio is too high for the proposed DAS-IP technique and its value is 0.709117, but the delivery ratio is too low for the existing one. For 200 nodes proposed DAS-IP offers a delivery ratio of 0.615861 as against 0.272461 for existing DMA-DA technique, similarly when the node increases 300 the delivery ratio is 0.382883 and 0.172134 for proposed DAS-IP and existing DMA-IP respectively. For higher number of nodes, say 500, the delivery ratio decreases when contrasted to the lower number of nodes. But, in terms of delivery ratio, the proposed technique shows improved results. It is obvious from the graph that the proposed technique exhibits superior performance in terms of delivery ratio.

Drop rate value for the data aggregation scheme using hybrid based ACO-GA itinerary planning (DAS-IP)
The drop value comparison is done on varying number of nodes from 100 to 500 as shown on the graph in

Overhead for the data aggregation scheme using hybrid based ACO-GA itinerary planning (DAS-IP)
The Overhead value of the proposed DAS-IP technique is contrasted with existing DMA-DA. The overhead comparison appears in Figure 6. The vertical axis displays the overhead values while the horizontal axis shows the number of nodes in executing the experiment.   for same number of nodes. It is evident from the graph that, as the number of nodes increases, the overhead for the proposed and the existing also increases. The proposed technique has the least overhead in all the cases. Therefore, the proposed demonstrates superior performance on the basis of overhead as compared to existing DMA-DA.

Throughput for the data aggregation scheme using hybrid based ACO-GA itinerary planning (DAS-IP)
The comparison of throughput for the proposed DAS-IP and prevailing DMA-DA appears in Figure 7. The horizontal axis signifies the throughput in kbps while vertical axis signify the number of nodes in running the experiment.  300, 400 and 500 nodes respectively. This proves the superiority of the proposed technique on contrasted to prevailing DMA-DA.

Performance evaluation of multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
The performance of the proposed multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF) is contrasted with the prevailing techniques say, LEACH and T-LEACH techniques. The comparison is done concerning the metrics say, energy consumption, end to end delay, packet drop rate, and throughput and network life time.

Results and comparative analysis of multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
The performance analysis of the proposed MDTSF model and existing LEACH and T-LEACH results for the disparate metrics comparison is offered in Table 4 for the number of attacks 1, 2, 3, 4 and 5.

End to end delay for multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
The end to end delay of the proposed MDTSF model is contrasted with existing LEACH and T-LEACH models for 1, 2, 3, 4 and 5 attacks as illustrated in Figure 8. The vertical axis gives the delay value (in seconds) whereas the horizontal axis signifies the number of attacks. The bars in the graph represent the comparisons among the various techniques.

Energy consumption for multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
EC in the proposed MDTSF technique is contrasted with existing LEACH and T-LEACH technique. The EC is given in the graph as illustrated in Figure 9. The vertical axis signifies the EC values in Kilowatts-hour (KWH) whereas the horizontal axis shows the number of attacks in running the experiments.   while existing LEACH uses 9.2384, 10.4587, 9.5647, 10.9874 and 11.2689 respectively. Similarly the energy consumption for existing T-LEACH is 11.2856, 12.8516, 10.2587, 9.2587 and 10.2658 for 1, 2, 3, 4 and 5 attacks respectively. The above graph signifies that the proposed model attained the lowest energy consumption. For the number of attacks, EC has increases and decreases gradually, but it is very low while compared to the existing LEACH and T-LEACH. For better communication, the value of EC should be low to prevent the node from network failure. The proposed method achieves this lower energy consumption for every attack.

Packet drop rate for multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
The packet drop rate (PDR) comparison of the proposed MDTSF and existing LEACH and T-LEACH is done by the varying number of attacks from 1 to 5 as shown on the graph in Figure 10. The vertical axis specifies the packet drop rate values and the horizontal axis shows the number of attacks in running the experiment. Figure 10 portrays the PDR by varying the number of attacks from 1 to 5. It is evident from the graph that the PDR has decreased in the proposed MDTSF model when contrasted to the existing methods. For optimal transmission of network, the PDR should be low. For first attack, the PDR of existing LEACH and T-LEACH are 0.8 and 1.12%, but the proposed method has 0.25% of packet drop rate. The graph confirms that the proposed method has the least PDR value than existing techniques for remaining four attacks. PDR of proposed method slowly increase son contrasting to other existing techniques. The least PDR of the proposed technique confirms its predominance over existing ones.

Throughput for multi-mobile agent-based data aggregation scheme using TS fuzzy model (MDTSF)
The comparison of throughput for the proposed MDTSF and prevailing LEACH and T-LEACH appears in Figure 11. The horizontal axis signifies the throughput in kbps while vertical axis signifies the number of attacks in running the experiment.   Figure 12 shows the comparison of the proposed MDTSF and existing LEACH and T-LEACH in terms of network lifetime. Network lifetime should be high for achieving an optimum network performance. In this case, performance of the network is evaluated based on the number attack occurring in the course of data aggregation in the network and lifetime of the network diminishes linearly when number of attacks are increase as shown in the graph. But the lifetime of the proposed technique is higher than existing methods. Hence it is proved that the MDTSF has highest lifetime.

Packet delivery ratio of the multi-mobile agent-based data aggregation
scheme using TS fuzzy model (MDTSF) Figure 13 offers a comparison among the proposed and existing methods by varying the number of attacks from 1 through 5. In Figure 13 the vertical axis shows the packet delivery ratio whereas the horizontal axis denotes the number of attacks used for running the experiments.  Figure 13 compares the packet delivery ratio performance of the proposed and existing techniques. For efficient data transmission in the network, packet delivery ratio should be high. If the packet delivery ratio has a highest value then, all the information are obtained at the receiver side without any data loss. From Figure 13, the graph clearly shows that the proposed MDTSF achieves the best value of 0.9485% but the existing techniques achieve less delivery ratio. Thus, it can be proves that the proposed MDTSF offers superior performance on compared to other existent methods.

Summary
In WSN, the communication cost is mostly greater than computational cost. Data aggregation is an ideal way of optimizing the communication cost. This can be achieved by accumulating the sensor readings. In this given thesis, an efficient data aggregation scheme based on itinerary approach is presented using multiple mobile agents aimed at accumulating and transferring data to the sink. Inside the proposed strategy a hybrid ACO-GA aimed at itinerary planning is engaged, cluster formation was done by aid of FCM algorithm. A progression of experiments is led and the outcome for the proposed data aggregation scheme is compared with existing ones. The experimental outcomes were compared with existing techniques to demonstrate the