## 2 Cluster Head Selection
### Self-Organized Schemes
#### 2.1.2.4 Regional Selectivity  
In some other algorithms [29, 30, 31, 32, 33, 34], CH selection is not the initial stage of the clustering algorithm. On the contrary, first each sensor node finds its neighbouring nodes in a predefined radius or number of hops, or performs a rudimentary regional cluster formation stage based on the position of the nodes within the network, and then the most qualified node in each section is selected as CH based on a distributed algorithm. This method improves the evenness of CH dispersion within the network and eliminates re-clustering procedures in network dimensions by rotating the CH role among the nodes within a certain region. However, in distributed algorithms, determining the neighbouring nodes and gathering their resource information delays completion of the setup phase and imposes extra network implementation costs by utilizing localization algorithms [35, 36, 37, 38, 39, 40] or equipping the nodes with localization equipment such as Global Positioning Systems (GPS). Moreover, the decision based on the local information does not necessarily provide the most optimized selection from a network scale perspective. Density and Distance based Cluster Head Selection (DDCHS) that divides cluster area into two perpendicular diameters to get four quadrants is proposed in [29]. By grouping the nodes, the node density in each quadrant is compared and the candidate quadrants are selected. Then based on the distance of the candidate quadrants from the cluster centre, the nearest one is selected as the CH of the quadrant. In [30] the authors also divide nodes into several circular tracks around the BS and in each track, nodes having more energy level and less distance from higher-level CHs are selected to play the CH role. By using the Monte-Carlo localization Box-Redrawing (MCBR) built upon MCB [40], the paper [31] presents the Weighted Clustering algorithm based on Monte-Carlo localization clustering scheme. The algorithm classifies nodes into hexagons based on their distances from their neighbouring nodes. In the next step, the CH is selected in each hexagon according to the weights of the nodes. A four weighting-factors formula is presented, including distance of the node to the centre of the corresponding hexagon, total time in which node has acted as CH, average distance between any two nodes belonging to the same hexagon and the percentage of energy consumed by the node. Nodes with the least weight value are selected as CHs. The localization algorithm proposed by the paper uses some mobile anchors to help in localizing nodes, which is not feasible in all scenarios such as battlefields or harsh environment monitoring. The work presented in [32] proposes an algorithm to select suitable CHs which cover more regions with the smallest average total communication distances. The paper uses the maximum number of minimum hops (Max-min hops) to find nodes located at the centre of the network. In this respect, every node generates a list of its neighbouring nodes, including their minimum hop number distances. When each sensor node has the min-hop number to all other nodes, it finds the maximum hop (Max-hop) number among them and exchanges the value with other nodes within the network. It is seen in Figure 3 that nodes with the minimum Max-hop numbers are located in central area of the network, and thus, are more eligible to be selected as CH. However, the downside of the algorithm is that it may lead to having CHs not adequately apart from each other. To address this issue, the paper includes the distances of CHs from each other as another determinant factor in selection of CHs. Moreover, the cost of broadcasting the heuristic messages and the convergence time of the algorithm in large scenarios with numerous numbers of nodes is not well suited with the constraint resources and real-time applicability of WSNs.