# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Aerial Mobile Sink-Based Frameworks
#### 2.4.3 UAV-Based Compressive Data Gathering (UAVCDG)  
[PERSON] et al. [66] proposed an energy-efficient UAV-based WSN data-gathering mechanism named UAVCDG. First, a joint optimization problem of sensor node clustering, data forwarding tree construction, CH selection, and UAV trajectory planning is formulated for data gathering in dense WSNs. The joint optimization problem can be written as follows:  
\[Minimize\ \omega\ \sum_{\begin{array}{c}m\in W\\ h\in V\\ (i,j)e\ E\end{array}}\ \ P_{ij}^{m,h}+(1-\omega)\ \sum_{\begin{array}{c}i\ \&\ j\ \epsilon\ \{\forall\cup\cup D\}\end{array}}d_{ij}U_{ij} \tag{6}\]  
The first part of the problem refers to minimizing the total transmission power of the active links in the forwarding trees, where \(P_{ij}^{m,h}\) is the transmission power required to transmit data from node \(i\) to \(j\) in cluster \(m\) when \(h\) is the CH. \(V\) is the set of sensor nodes, \(E\) is the link between nodes, and \(W\) is the set of clusters. The second part refers to the trajectory distance of the UAV where \(U_{ij}\ \epsilon\ \{0,1\}\\) indicates whether the UAV will fly from node \(i\) to \(j\), and \(d_{ij}\) is the distance between nodes \(i\) and \(j\). \(S\) is the starting point, and \(D\) is the destination sink node. \(\omega\) is a weight parameter for each part that can be adapted. The constraints regarding to the optimization problem can be found in [66]. The joint problem is decomposed into four complementary subproblems to achieve near-optimal results with low complexity. The network environment is considered to be a rural area with a line-of-sight link, which means that no obstacles exist in the network.  
A clustering technique is applied among the sensor nodes to shorten the trajectory of the UAV and balance the energy consumption. The aim of clustering and CH selection is to jointly minimize the overall transmission power for data gathering and UAV trajectory. A compressive data-gathering (CDG) technique is utilized to minimize the amount of data transmission to optimize energy consumption. Through CDG, an encoded sum of the gathered data is received by the sink node, which can be decoded to the original data. To solve the first subproblem of clustering, a K-means clustering algorithm is implemented, and the clusters are updated to almost equal-sized cells. Subsequently, an algorithm is established for CH selection that uses the starting position \(S\) and destination position \(D\) of the UAV's trajectory to select the CH with the shortest distance from the path. To solve the UAV trajectory subproblem, a heuristic algorithm--called the nearest-neighbor algorithm--is proposed. Finally, to solve the subproblem of forwarding tree construction in each cluster, a minimum spanning tree method is considered.  
Advantages: The CDG technique distributes the transmission load across several sensor nodes in the network. This distribution balances energy consumption with an extended network lifetime. The performance evaluation for small, medium, and large network scenarios demonstrates the efficiency of the proposed method.  
Limitations: The residual energy of the sensor nodes is not considered, which is especially important for choosing the CH. Only UAV trajectory minimization is considered for clustering and choosing the CH. Selecting a CH with low residual energy may cause the death of the node, leading to failure in data gathering.