# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Taxonomy of Energy Consumption Solutions
#### 3.7.1 Routing Protocols
An Adaptive Scheduling MAC (AS-MAC) method was carried out by [107]. The proposed protocol is aimed to make nodes to decide that stays active or sleep depending on traffic load. Therefore, if the traffic load is high, AS-MAC can achieve rapid data dissemination and reduce transmission latency by scheduling more transmission. However, if there is a smaller amount of the traffic load, sensor nodes switch to a sleeping mode in a timely manner, such that idle listening is mitigated, and energy conservation is achieved. 2. Optimal Path Selection Several studies have considered optimal route selection for energy-saving in WSNs. The shortest route approach is a commonly used methods for constructing routing trees in the many-to-one WSN [108]. The potential advantages of shortest path are lowest energy consumption and minimum time delay. [PERSON], et al. [109] investigated a heuristic algorithm based on multi-hops that perform geographical routing. This protocol selects a route with the fewer hops and distance from the source node to the target. The proposed scheme reduces the end-to-end node delay. In [110], authors introduced a distributed shortest path routing network from a source node to the ultimate receiver. The resulting algorithm provides best link cost and maximum network lifetime. [PERSON], et al. [111] demonstrated a new routing technique that can calculate the distance between two non-neighbor nodes in multihop WSNs. This method finds all possible routes between a source node and the ultimate receiver with the fewer hops. This leads to minimizing the energy depletion and delay of the network overall. Another study [112] proposed a new centralized energy-efficient clustering algorithm for WSNs. This is the distance energy evaluated (DEE) protocol which selects the CH nodes according to the ratio between remaining energy of a node and distance. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **LEACH** & **HEED** & **PEGASIS** & **References** \\ \hline Type of protocol & Hierarchical & Hierarchical & Hierarchical & [89, 101] \\ Data delivery model & Cluster-based & Cluster-based & Chain-based & [102] \\ Nodes distributed & Random & Random & Random & [103] \\ Node mobility & Fixed & Fixed & Fixed & [102] \\ Multihop & No & Yes & No & [102] \\ Clustering Method & Distributed & Distributed & Centralized & [103] \\ CH selection & Threshold & Residual Energy & Threshold & [83, 84, 89] \\ Relay node & CH & CH and nodes & nodes & [83, 84, 89] \\ Data aggregation & Yes & Yes & No & [102] \\ Scalability & Low & Moderate & Low & [104] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison and classification of some of routing methods in WSN. probability of being CH is determined according to the node's initial and residual energy. The DEE protocol extends the network lifetime by reducing unnecessary traffic. Most studies have not considered the shortest path combined with balancing the load traffic in each node along the path to deliver data. A node that is surrounded with many neighbor nodes (within transmission range) has less energy due to overhearing, neighbor nodes discovery, or a node may be used for many paths to deliver neighbor nodes' data [77]. 3. Manipulating the Location of Base Station Several studies have proposed manipulation of BS/sink location as a means of reducing energy depletion. They found that the network lifetime of the network can be extended by reducing the transmission distances between sensor nodes. In the work of [PERSON], et al. [113], the idea of a mobile sink (MS) was proposed, where the sink moves in a prescribed path to gather the information in the sensing field. In such a protocol, all nodes regardless of distance will establish a direct connection with the sink. Therefore, the total link length of the network will be very high, especially when a node is located on the border of the network, consuming more energy than other nodes which are close to the sink. The optimal location for a mobile sink (OLMS) for WSNs is suggested by [114].