# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 4 Data Aggregation and Communication  
We collect data from flying UAVs and match the data to discard similar data sent by multiple sensors in the data aggregation process. Data aggregation is divided into two levels. In level 1, the data are collected using a TDMA schedule when multiple sensors simultaneously communicate data. A near-linear time algorithm is proposed in this paper for the data aggregation level 2 problem in FSNet. The data coming from sensors is converted into a long string. According to our knowledge, this is the leading work to assume nontrivial alignment among the strings and the patterns. Specifically, we demonstrate the data aggregation problem in two ways: First, to sanction approximating \(D[i]\)'s, and second, an additional procedure sanction named partial data move, for the movement of partial data from one position to another in a data. This similarity among the \(X\) and \(Y\) data is known as data match with moves (DMM) and designated as \(d(X,Y)\)[38]. DMM is a powerful data-matching tool that can greatly benefit FSNet data aggregation. By using DMM, network operators can easily match data from different sources, allowing for a more accurate and comprehensive analysis of the data. The DMM algorithm can take advantage of UAV mobility by assigning them to different regions and optimizing their movement patterns to efficiently collect data. Moreover, DMM can provide a wide range of benefits, including reducing the amount of data, reducing bandwidth usage, increasing energy efficiency, and supporting proximity-based data aggregation. There are various applications in computational biology where partial data matches are considered a primeval in multiple situations. Moving a larger subsequence is similar to the insert or delete operation; during text processing, moving a large array together might be assumed, like reordering to deleting or inserting typescripts. Keep in mind that the nontrivial placements are still a challenge for DMM. Hence, \(d(X,Y)\) is the size of the small structure of edit procedures that convert \(Y\) to \(X\); the allowable procedure affects the data stated. The deletion of a character at location, \(loc\), transforms \(X\) to \(X[1]\ldots X[loc-1],X[loc+1]\ldots X[n]\). * The insertion of a character, \"\(c\)\" at a location, \"\(loc\)\", gives \(X[1]\ldots X[loc-1],c,\)\(X[loc]\ldots X[n]\). * The substitution of a character at the location, \"\(loc\)\", with character, \"\(c\)\", results in \(X[1]\ldots X[loc-1],c,X[loc+1]\ldots X[n]\). * The partial data movement with factors \(1\leq loc\leq loc_{2}\leq k\leq n\) converts \(X[1]\ldots X[n]\) into \(X[1]\ldots X[loc-1],X[loc_{2}]\ldots X[loc_{3}-1],X[loc]\ldots X[loc_{2}-1],X[ loc_{3}]\ldots X[n]\). The data are identical when the edit distance between two data is \"0\". The metric represents its measure. The transformation is performed in several operations, and each operation's cost is equal even in the inverse case. Hence, \(d(X,Y)=d(Y,X)\); then, every distance resulting from transforming one datum to another must follow the triangular inequity. The restrictions of the interaction of edit operations are none. These restrictions may be as follows: it is relatively conceivable for a fractional data move to take a fractionaldatum to a different position and then for a successive data move to function on a fractional datum that overlays the relocated fractional datum and its neighboring typescripts. The deterministic algorithm results in the DMM problem, and the running time complexity is \(O(\mathit{nlogn})\). An algorithm returns the equivalent array, \"\(Ar\)\", where every \(Ar[loc]\) is estimated to be close to the \(O(\mathit{lognlog}^{*}n)\) factor. The proposed methodology depends on inserting data to a vector of arrays below \(L1\) metric. The \(L1\) size among these arrays is \(O(\mathit{lognlog}^{*}n)\), the estimate of the DMM between the two original data. The proposed method can further solve several problems beyond the primary data aggregation Level 2 problem.