# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Static Sink-Based Frameworks
#### 2.2.3 Hybrid Energy-Efficient Distributed (HEED) Clustering  
[PERSON] et al., proposed a distributed clustering algorithm called HEED [58]. This algorithm works in a distributed manner inside every sensor node and forms a cluster with  
Figure 2: Working mechanism of the PEGASIS protocol.  
a CH. HEED is a clustering algorithm that has been proposed for homogeneous sensors. The primary assumption of this clustering algorithm is that the sensor nodes have multiple transmission mechanisms. To model the algorithm, the authors used the parameters from Berkeley Motes [59]. The HEED algorithm operates in three phases: initialization, main processing, and finalization. In the initialization phase, the algorithm forms a neighboring set, computes its own cost, and broadcasts it to its neighbors. The CH probability is also assigned in this phase. The following equation is used to compute the probability of the CH:  
\[CH_{p}=\max\biggl{(}C_{p}\times\frac{E_{res}}{E_{M}},p_{min}\biggr{)} \tag{1}\]  
where \(CH_{p}\) is the CH probability, and \(C_{p}\) is a random number. The number of CHs depend on the value of \(C_{p}\). \(p_{min}\) is the minimum value assigned to nodes when the value of the residual energy goes beyond a threshold limit. \(E_{res}\) is the residual energy, and \(E_{M}\) is the maximum amount of energy that a node has after deployment. The primary parameter for CH selection is the energy, the secondary parameter being the node degree in the case of the fixed transmission power inside a cluster. Based on the node degree, HEED has the ability to balance the number of cluster members (CMs) in a cluster. When the power range is a variable for intra-cluster communication, the cost is determined by the minimum average power of all the nodes of a CH. In the iteration phase, HEED attempts to determine the final CH from the list of candidate CHs a node has within its vicinity. If a node does not find any tentative CH in its transmission range, it simply declares itself as the CH. The probability of the CH doubles in every iteration. When the value of \(CH_{p}\) reaches one, a tentative CH declares itself as the final CH. The sensor nodes ultimately select a CH and join the cluster. Finally, if a sensor node does not find any final CH, it declares itself as the final CH and completes the clustering process. Figure 3 shows the working mechanism of the HEED clustering algorithm. The figure shows the basic clustering outcome using the HEED algorithm. It should be noted that some nodes are not left alone and do not join any cluster. This is shown in order to depict the trivial single CH cluster problem of the HEED algorithm.  
Figure 3: Working mechanism of the HEED clustering algorithm.  
Advantages: This protocol does not utilize assistance from any positioning system. It selects neighbors based on the received signal strength indicator (RSSI) value. As a distributed algorithm, the deployment of sensor nodes is possible in remote areas.  
Limitations: The proposed algorithm increases the chance of developing a single-node CH. A single-node CH directly transmits the sensed data to the sink. This requires a long-range transmission. In addition, a sensor node could have changed its transmission power level and may join another CH to remove rapid energy depletion.