# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 5 Taxonomy for Energy-Efficient and Energy-Balanced Routing Protocols
### Single-Path Technique  
The single-path technique is another class of the load-balanced tree routing protocol. In this case, the source node finds a single energy-efficient and energy-balanced route to the BS as depicted in Figure 9. The single-path technique is easy to implement and exhibits low complexity in comparison to other multi-hop communication methods. Moreover, the technique avoids delay in conveying the sensed information to the BS, which makes it very suitable in real-time communication. Other advantages of the single-path load-balanced tree technique include congestion and interference control, and minimum resource utilization. Although the design of a single-path load-balanced tree routing protocol can be quite simple in comparison to the other multi-hop communication methods, selecting the design decision variables used in the path creation can be a challenging problem, as in the case of the other multi-hop communication methods. Besides choosing an energy-efficient and energy-balanced path from the source node to the BS, the chosen path must guarantee reliable data transmission. Since the message is routed through a single path, the path must be reliable because there is no room for fault-tolerance, as in the case of the multipath technique. As such, the design decision variables must be carefully selected in order to ensure reliable data transmission and to extend the network lifetime for a rational period. Therefore, most of the studied single-path routing protocols select the path to the BS using different mathematical formulations. These mathematical formulations are derived using different combinations of the mentioned decision variables in Table 5. As represented in Table 5, the design decision variables used by these single-path multi-hop routing protocols to select an energy-efficient and energy-balanced path to the BS are grouped under \"residual energy (RE)\", \"hop-distance\", \"hop count (HC)\", \"total energy (TE)\", \"received signal strength/transmission power (RSS/TP)\", \"throughput\" and \"average network energy (AE)\". These parameters have different strengths and weaknesses; hence, researchers attempt to balance the trade-offs in their selection. For example, the authors in [166; 167] balance the trade-off in using the residual energy and the distance between nodes in selecting the path from the source node to the BS. Their algorithms forward the sensed information to the nearest node with high residual energy. In so doing, they firstly reduce the transmission distance; the algorithms also ensure that the forwarding SN has sufficient energy to convey the sensed message, which equally guarantees a reliable data transmission. Having mentioned the advantages of the approach in [166; 167], using the residual energy and shortest distance between nodes as criteria does not necessarily balance the load in the network; this approach might involve many hops or relay node chains to reach the BS. Thus, more energy will be consumed in receiving (Equation (6)) and conveying (Equation (4)) the sensed information to the BS. This implies that the total energy consumed in the network might increase. So, it is desirable to keep the hop count between the source node and the BS as small as possible. With this in mind, some literature [168; 169; 170] combines the RE, distance, and HC in selecting the path from the source node to the BS. In addition, as shown in Table 5, while some authors combine two or more decision variables in choosing an energy-efficient and energy-balanced route, some authors use a single decision variable to select the path from the source node to the BS. The authors in [171; 172] use the hop count and distance respectively as the only decision variable in selecting the path between the source node and the BS. Likewise, the authors in [173] use the throughput only to select the next hop. Their algorithm [173] alternately selects the nodes with the highest throughput as the next hop towards the BS.