# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 3 Qualitative Comparison
### Optimization Criteria and Evaluation Parameters  
The investigated schemes are intended to optimize the data-gathering process in WSNs from different perspectives. Table 3 shows a comparison of the optimization techniques utilized, and the performance evaluation parameters considered in the reviewed schemes. An implementation of CTP with performance evaluation has been provided in [67]. However, to be efficient for WSNs, the most crucial feature of a data-gathering protocol is minimizing energy consumption and thereby extending the network lifetime [68]. The sensor nodes are mostly powered by a non-rechargeable battery. Energy is consumed via sensing, internal calculations such as aggregation, and signal transmission. Consequently, different schemes have been attempted to minimize energy consumption in different ways, as shown in Table 3. TEEN and PEGASIS techniques consider the rotation of the CH and leader node, respectively, which means that all sensor nodes participating in becoming the CH or leader node, distributing energy consumption evenly in the network. Along with the communication cost from the CH to the sink node, the HEED protocol also considers the intra-cluster communication cost, which is responsible for the energy consumption in the network. The CPDC protocol limits re-clustering in the network under the condition of maximal weight. With the IoJHR protocol, data gathering from sensor nodes to the mobile sink is performed by only two hops in each virtual grid. One hop is for sensor nodes to the CH and another for the CH to the mobile sink, reducing transmission energy consumption. For energy balancing in the EPMS protocol, the selection of a CH is carried on to the next round of data transmission. NDCMC uses a VH along with a CH--which is used when a CH is out of power--rather than selecting another CH. UEWDC minimizes energy consumption by minimizing the node wake-up schedule, in which only one node wakes up at each transmission time. The goal of UAVCDG is to shorten the UAV trajectory to minimize the communication energy of the UAV. The data-gathering techniques in the reviewed schemes are performed mostly by different clustering techniques, except for the PEGASIS, UEWDC, and CTP protocols. Data gathering through clustering helps to reduce the number of transmissions in the network. The data are aggregated in the CH and then transmitted to the sink node--instead of data transmission to the sink node via all of the sensor nodes--which reduces the overall network energy consumption. PEGASIS performs data gathering by chain formation among the sensor nodes via data fusion from neighboring nodes. UEWDC conducts energy-efficient data gathering by proposing a suboptimal solution for UAV trajectory minimization and node wake-up scheduling. None of these investigated protocols considered sensor node mobility, although the mobility of sensor nodes can be an issue in WSNs. Location awareness means that the location information of CHs or sensor nodes is required for data gathering to the sink node. However, the locations of sensor nodes are required only in protocols where sink nodes have mobility. The mobile sink node must traverse the sensor nodes to collect data. The energy consumption in the sink node is also an issue, especially in the case of mobile sink nodes, which are battery-powered, similar to the sensor nodes. Therefore, energy constraints in the sink node must be considered, but only UAVCDG considers sink node energy constraints. The residual energy is the remaining energy of a sensor node. While selecting a node for data transmission, the residual energy consideration is necessary, as it has a significant impact on the network lifetime. The HEED, CPDC, IoJHR, EPMS, and NDCMC protocols consider the residual energy of a sensor node for CH selection. Selecting the node with the highest residual energy as the CH of a cluster helps in transmitting data through that CH for a longer time. The number of hops while optimizing data gathering in WSNs also increases the network lifetime significantly [69].