# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Collection Utilizing Mobile Sink and Unmanned Aerial Vehicle (UAV)
#### 5.4.2 Cluster-Based Data Forwarding  
Rather than the mobile sink node traversing the network between all the sensors, a different approach groups the sensors into clusters and selects cluster-heads (CH), which collect the data from all cluster members, thus decreasing the traverse of the mobile sink. In general, data gathering in a clustered WSN imposes a tradeoff between the energy consumed during data collection within each cluster and the energy consumed by the mobile sink. On the one hand, the higher the number of clusters (smaller clusters), the less energy is consumed during data collection by the cluster-heads (CH); on the other hand, it follows that the UAV will have to access more CHs on its route, and consequently, the energy spent by the UAV will be higher in that case. This trade-off is one of the main challenges in addressing the joint problem of clusterization and CH selection, jointly with finding the path traversed by the UAV, as reflected in several papers discussed below. [PERSON] et al. [184] propose a two-phase algorithm, termed DGOB, for data gathering in WSNs in an environment impaired by obstacles, which utilizes a mobile sink that traverses the network and collects the data. Both phases, node clusterization and MS trajectory construction, exploit artificial intelligence (AI) schemes. [PERSON] et al. [185] utilize a small-scale drone to gather the data from scattered sensors. Like in several other papers that rely on UAV to collect data from cluster-heads, the challenge in [185] is two-fold: (i) clusterization and CH selection (in contrast with other papers, this paper allows multi-hop clusters, such that cluster members are up to a predefined number of hops from their CH); and (ii) plan of the drone trajectory for traversing through all CHs with minimum energy consumption (e.g., minimum path length). Even though the two problems are related, the authors solve the two issues sequentially. They first solve the CH selection by formulating an optimization problem that seeks the minimal set of cluster-heads that guarantee that all nodes are at most \(h\) hops from a CH in the set. After determining the CH set via linear programing, they utilize the 2-opt heuristic, which relies on a simple local search algorithm for solving the traveling salesman problem, in order to find the optimal travel trajectory for the drone between the selected CHs. [PERSON] and [PERSON] [186] also suggest a data-gathering-by-mobile-sink scheme in WSN. The model in [186] assumes that the mobile sink is moving along a pre-specified path with constant speed and can collect data while traveling. The paper denotes all the sensors that are in transmission range from the mobile-sink traversed path, and can therefore relay traffic to it, as sub-sinks. All other nodes need to forward their data to these sub-sinks, possibly through multi-hop communication, in order for them to relay the data to the mobile sink. The paper suggests two different models: the first assumes that the mobile sink can receive data from only one sensor at a time, while the second assumes that the mobile sink can receive data from multiple sensors simultaneously. Both suggested algorithms comprise three phases: (i) identify the relay nodes (sub-sinks) that are within transmission range from the mobile sink trajectory (unit disk graph model), and partition the path into segments that are the union of all the transmission disks of all the sub-sinks; (ii) determine the communication time each sub-sink can have with the mobile sink, and accordingly, the amount of data it can transmit; (iii) utilize a network flow approach to determine which sub-sink transmits to the mobile sink in each of the mobile sink's path segments. [PERSON] et al. [187] aim to optimize total transmission power between WSN cluster-heads in an IoT network. The problem is split into subproblems, which include CH assignment, building of the forwarding tree within each cluster, and optimal UAV trajectory calculation.