# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 2 Related Work  
During the last decades, numerous protocols for wireless sensor networks have been proposed. These protocols can be classified into hierarchical, location-based, data-centric, network flow, and Quality of Service-aware routing [13]. Many energy efficient clustering and routing topologies have been proposed to prolong the network lifetime [8]. Low-Energy Adaptive Clustering Hierarchy (LEACH) [14] is one of the most popular hierarchical routing approaches for WSNs. LEACH uses a probability equation to select the CH, and the nodes join a specific cluster according to the Received Signal Strength Indication (RSSI) of CH neighbors. In the stable phase, i.e., when the clustering has been done, each CH forwards an aggregated data packet from all member nodes to the BS. At the periodical interval, the network rotates CHs to balance the network load and to preserve the energy of nodes. If LEACH is well deployed due to its effectiveness and simplicity, it suffers from the hot zone problem, and needs to support long-range communications for all nodes. Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [15] is a hierarchical protocol whose main goal is to cope with sudden changes in sensed values. In this protocol, sensed attributes hard thresholds (HT) and soft thresholds (ST) are defined. Then, TEEN constructs two-level CHs, which use HT and ST values, to determine whether to transmit data to CH and BS. When a node senses a value bigger than HT, it switches on its transmitter and send the value to its CH. When the CH receives data bigger than ST, it switches on its transmitter and transmits the value to the BS. By construction, TEEN reduces the CH load, but this approach is only suitable for sudden changes of measurements and is not suitable for periodical monitoring. Hybrid Energy-Efficient Distributed clustering (HEED) [16] is a clustering algorithm with an explicit consideration of energy-efficiency. In HEED, cluster heads are elected depending on the node's residual energy and intra-cluster communications cost. After a certain operational time, new CHs are uniformly elected for the network. Distributed Energy-Efficient Clustering Protocol (DCE) [17] elects cluster heads depending on a double-phase scheme. In the first phase, tentative CHs are elected depending on the node's residual energy. In the second phase, the final cluster heads are chosen as nodes with the highest residual energy. HEED and DCE are both efficient algorithms to solve the problem of CH energy balancing. However, these protocols rely on the existence of nodes with bigger batteries, defining heterogeneous networks. The Energy-Efficient Multi-level Clustering algorithm (EEMC) [18] is a hierarchical routing algorithm inspired by LEACH. EEMC is based on a multi-level clusters scheme. The SNs send their data to the lowest layer (level-n) CH, and afterwards, all the level-n cluster heads send data to level-(n-1) cluster heads. The level-1 CHs gather all the data and send them to the base station. It is an effective way to balance the cluster head node energy in a huge WSN structure, but the size of clusters is limited to reduce the intra-cluster communications. The level-1 CHs are also weaknesses for the network lifetime. In order to solve the hot zone problem, [PERSON] and [PERSON] [19] investigated an unequal clustering model for balancing the energy consumption of cluster heads in multi-hop sensor networks. The idea is to make the smallest clusters possible, thus reducing intra-communications costs, but this leads to a growing number of CHs. Then, clusters with a smaller size are built near the BS and bigger ones are situated further away from the BS. If the approach demonstrated is to be useful for managing the hot zone, this algorithm is based on static CHs with a large battery deployed in each cluster. The Unequal Cluster-based Routing (UCR) protocol [12] addresses this problem and proposes to compute the competition radius of nodes and builds clusters of different sizes depending on the node distance to the BS.