# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 7 Conclusions  
In this paper, we focused on the conservation of sensor nodes overall localization cost and redundancy management in data sharing and transmission. Our study shows that MSL* uses both seed and normal node to estimate blind node location that is outside the range of seed nodes but within the range of normal nodes. Though the MSL* has the advantage of better localization accuracy, the method will increase the overall communication cost in the network. The MSL* drawback is then mitigated by LCC which reduces the communication cost. However, the filtering process in LCC is exposed to redundancy, where the blind node may have neighbors that share the same information even after the filtering process. Furthermore, LCC neglect the data shared by second hop neighbors, even though the localization process includes the samples from both first and second hop neighbors. Therefore, a method to reduce data redundancy that would affect computation, communication and energy cost of localization process is proposed.  
The proposed method, which is named as IACS, is based on the intersection and complement sets that select the most significant neighboring nodes for the localization process. IACS consists of two main steps namely, duplication cleaning and average filtering. In the duplication cleaning step, the neighbor nodes that contain unique intersection and complement sets are selected for the localization process. If the intersection and complement set of normal nodes are identical, the node with the shortest distance to the blind node will be selected. Then, the average filtering step will further filter the nodes selected in the duplication cleaning step based on their total intersection and complement elements. This process excludes the nodes with the total intersection or complement elements that are lower than the average of intersection or complement elements in the neighborhood, respectively. Through these steps, the proposed IACS allows the selection of the most significant nodes to reduce the costs required for localization of the blind node. From the simulation, IACS reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC. Furthermore, IACS also required the least cost of computation, communication and energy in various scenarios tested compared to MSL* and LCC. Based on the research, there are also research gaps found on the MCL-based algorithm that can be done for future works. Most of WSN localization methods are implemented using simulation due to the high cost for the full-scale hardware environment. Furthermore, the resampling process used in the recent PF algorithm does not prevent the samples deterioration [30]. On the other hand, the presence of obstacles and walls in the area can cause the sensor node to be in a Non-line-of-sight (NLOS) state, where the signal transmission between nodes can derange [31]. Both samples deterioration and deranged signal transmission may lead to localization inaccuracy and failures. On that note, future works to efficiently avoid PF samples deterioration, to prevent deranged signal transmission among nodes caused by obstacles or walls, and full-scale hardware implementation could contribute significantly to the field.  
The concept, design and evaluation of IACS is done by M.Z.M.Z.H.; N.M.N.and M.Y.LI structure the paper and device experiments with selected benchmarking; E.M.T.M.N. contribute in the evaluation.  
The authors acknowledge University of Malaya for the financial support (University Malaya Research Grant (RP036 (A, B, C)-15 AET) and facilitating in carrying out the work.