## 3 Data Routing Approaches in WSNs
### Energy-Efficient Approaches
#### 3.1.1 Energy Balancing
Also, the nodes can decide about their communications with both the information of the application and their available resources. This can enable the sensors to distribute their data effectively. This protocol begins when a node achieves new data and decides to transmit it. Thus, it chooses a name for the new data and sends an advertisement message (ADV) to its neighbors. While receiving the ADV message, the neighbor node checks to see whether it has received or requested such data before or not. If not, the neighbor node transmits a request message (REQ) to the sender node and asks it to send its new data. Finally, the DATA message will be sent from the sender node and the process will be finished (Figure 1). On the other hand, M-SPIN could reduce the number of packet transmissions in comparison with SPIN. SPIN performs negotiation before data transmission but in M-SPIN, the number of nodes which transmit REQ messages in response to ADV message is limited only to the nodes which are nearer to the sink. Also, the neighbors of the sink are identified by a distance discovery algorithm which operates based on the hop counts of the nodes to sink. Therefore, by finding the sink's neighbors, instead of broadcasting the data packet, it is transmitted toward the sink or its neighbors. The main problem of this approach is the considerable loads on sink's neighbors which may lead to their failure. In [53] the authors proposed a hybrid routing algorithm which benefits from both hierarchical and flat routing mechanisms. Their proposed approach aims to solve the _hotspot_ problem in WSNs. Hotspot in this approach is defined as the area in the interior of the maximum transmission distance of the sink node. In other word, the area of hotspot only contains the nodes which are located in the radio range of the sink. They have illustrated that failure of the nodes in the hotspot ceases the connection between sink and other nodes; hence, there should be an energy-efficient scheme to be aware about the energy level of the nodes in the hotspot area. The authors propose a flat routing approach in this area for selecting the most energy-efficient path and balancing energy among the nodes. Also, they have used a hierarchical structure for the nodes beyond the hotspot area. Figure 2 depicts this scheme. In the figure, the dotted circle indicates the hotspot area. For selecting the next hop neighbor in their flat routing mechanism, they consider the links cost based on the remaining energy of the nodes. The calculation of link cost is performed as below:  
\[ \begin{gathered} linkcost\left(\mathrm{i,j}\right)=e_{S}\left(i \right)+\ e_{r}\left(j\right)\\ e_{s}\left(i\right)=\varepsilon_{1}d_{i,j}\ ^{2}+\ \varepsilon_{2}\\ e_{r}\left(j\right)=\varepsilon_{3}\end{gathered} \]  
In this equation, _linkcost_ (i, j) is the energy cost of transmitting a unit of data from node i to node j. \(e_{s}\left(i\right)\) is the consumed energy of node i for sending its data to node j which is proportional to the  
Figure 1: Process of data transmission in SPIN. square of the distance between the sending node i and the receiving node j. \(e_{r}\left(j\right)\) denotes the energy consumed by node j for receiving the data of node i. Finally, \(\varepsilon_{1},\varepsilon_{2}\) and \(\varepsilon_{3}\) are constant values which are characteristic of the sensor node's transmitting and receiving circuitry. Beyond the hotspot area, the proposed scheme uses a clustering mechanism similar to LEACH [54] to reduce the transmission power by compressing the volume of data. The compression is done based on the correlation of data, since more correlation leads to more compression. In addition to the mentioned approaches which focus on residual battery status, a number of works have prototyped the use of solar energy to power wireless sensor networks and balancing energy among the nodes [55, 56, 57, 58].