# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 6 Discussion  
In the localization process, IACS utilizes normal and seed nodes similar to LCC and MSL*. The proposed IACS improves LCC and MSL* by reducing the number of normal nodes used in the localization process. To reduce the number of normal nodes, IACS selects nodes with the total intersection set and complement set that is above the average value and one of the nodes that has a duplicate intersection set and complement set. This decreases the cost of computation, communication and energy consumption while maintaining the estimation accuracy as in LCC and MSL*. Based on the simulation results, the number of valid samples used for localization can affect the costs of localization and the accuracy slightly. For example, utilizing a low number of samples will slightly decrease the accuracy and costs of localization while utilizing a high number of samples will increase the accuracy and cost of localization. However, the overall cost of computation, communication, and energy consumption is inversely proportional to the total number of samples. The default maximum number of samples in MCL, MSL* and LCC was set to 50 since it is proven experimentally as the optimum number of samples needed for localization in MCB [17]. The simulation results show that no obvious improvement is observed for all methods in terms of localization accuracy when the maximum number of samples is equal and larger than 50. For the cost of computation, communication, and energy consumption, IACS shows the least values compared with LCC and MSL* while maintaining the localization accuracy even with high sample numbers produced for the localization process. Localization accuracy can be improved in a mobile WSN environment. Blind nodes can obtain more location information from more nodes for the localization process. However, accuracy can only improve when the sensor node velocity is slow. In the fast speed mobile network, location of the blind nodes neighbor will be out of range as the nodes are moving far away from each other in a shorter time. As the previous blind nodes neighbor is out of range, the previous sample will be inadequate to estimate the current blind node location, thus, the new samples are required for the estimation of the current location. Valid samples are harder to obtain when the mobility speed of the sensor node is high, resulting in a repetitive sampling process until enough sample is obtained. The communication cost is low when the mobility speed is high, this causes a less number of nodes being selected for the localization process. Less communication means less energy consumed. Compared to MSL* and LCC, IACS requires the least costs requirement. DOI value is the variability of radio range's signal strength and direction of the sensor node. The slightest irregularity of radio range and direction can contribute to the increments of the size of overlap area of radio transmission and reduce the number of neighbors of each node, thus reducing the localization accuracy and communication possibility. From the results (Figure 9c), computation cost is at maximum when DOI value is at 0.3, then it drops at 0.4 and above due to difficulty in obtaining valid samples. As the DOI increases, each node faces difficulties to detect neighbors, thus causing the disability to produce enough valid samples in the localization process. In our simulation, IACS has shown to be more suitable than LCC and MSL* in a high DOI value. IACS also requires the least cost of computation, communication and energy consumption, when compared toLCC and MSL*. Scalability is one of the crucial issues in designing a WSN network. We consider the scalability as the ability to maintain the cost of the localization process while increasing the size of the network. Therefore, IACS is simulated on different node densities and the results are shown in Section 5.5.5. The accuracy of localization improves as the nodes density increases. This is possible since the localization process of each node depends on the information from its neighbors.