## 3 Data Routing Approaches in WSNs
### Energy-Efficient Approaches
#### 3.1.1 Energy Balancing  
In [51] the authors propose a mechanism for energy distribution in WSNs. They criticize the use of a stationary sink and multi-hop relaying as unbalancing energy expenditure. Hence, they have used multiple mobile sinks for solving the problem and proposed a scheme for correct positioning of the sinks and efficient data collection in the network. In general, the proposed approach address two problems: the ability to place a data collector in any region within the sensing environment, moving the data collector and placing it on tracks spanning the sensing area and discovering a search space for data collector locations. In their proposed approach, the sinks can be replaced with any other position in the sensing area and they can be moved anywhere to track the events. Moreover, by utilization of linear programming, the proposed algorithm finds the discovery space of the sinks based on the remaining energy and future residual energy of the nodes. Furthermore, it finds the optimal locations of the data collectors and theflow paths and selects the one in which the total energy is minimum. In order to achieve this, the following function is proposed:  
\[\alpha\ E_{Min}-\ \beta\ E_{total}\]  
Where, \(E_{Min}\) is the minimum residual energy over all nodes at the end of each phase of the approach and \(E_{total}\) is the whole energy consumption of each phase. Also, \(\alpha\) is defined greater than \(\beta\) as below:  
\[\alpha=1\ \text{and}\ \beta=\ \frac{1}{\sum_{0\leq i\leq N\ E_{i}}}\]  
where \(E_{i}\) is a constant; thus, \(0<\beta\ E_{total}<1\) and any increase to \(E_{Min}\) controls the reductions of \(E_{total}\). Therefore, in this approach, the energy level of the nodes is taken into consideration, the energy of the network is balanced and thus, the network lifetime is prolonged. Geodesic Sensor Clustering (GESC) [52] is an energy-efficient distributed clustering approach which is able to balance energy among the nodes according to the network structure and residual energy of the nodes. Clustering and cluster head selection mechanisms in this approach are done hierarchically. A cluster member transmits its data to its cluster head and the cluster heads after performing data aggregation send their data to another cluster head. Moreover, cluster head selection in GESC is carried out according to the significance of the nodes. It finds the significance of the nodes by performing some calculations. The node significance index \(NI\ (v)\) in this approach is calculated as follows:  
\[NI\ (v)\ =\ \sum_{u\
eq v\
eq w\in V}\frac{\delta_{uw}\ (v)}{\delta_{uw}}\]  
In the equation, \(\delta_{uw}\ (v)\) is the number of minimum hop paths from \(u\) to \(w\) wherein some vertices \(v\in V\) are participated. Also, \(\delta_{uw}\) denotes the number of minimum hop routes from \(u\) to \(w\). The significance of a node depends on its ability to reach other nodes through the shortest paths. In GESC, each node discovers its one hop and two hop neighbors along with their residual energy by broadcasting an initialization packet. Then, for selecting its cluster head candidates, it seeks the list of its neighbors and selects the one hop neighbors which can cover at least one two hop neighbor. After detecting the cluster heads candidates, among the set of selected cluster heads, it selects the node with highest residual energy as the final cluster head. This cluster head selection is updated according to the network topology. With this strategy, GESC is able to distribute the energy consistently. M-SPIN (Modified-SPIN) [44] is an improved version of SPIN (Sensor Protocols for Information via Negotiation) protocol [8] in which like in SPIN, data negotiation among the nodes is taken into consideration. It can distribute data between sensors by considering the limited energy resources of the nodes effectively. The nodes which run this protocol name their data by using some meta-data which are high level describers.