# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Proactive (Link-State-Based) Routing Protocols  
In proactive routing, each node is exchanging information about the current network topology with other nodes to update its own routing table. Thus, proactive routing can immediately find the shortest path as the route discovery process has no delays. Based on the algorithm used, proactive routing can be categorized into groups: (1) link-state-based routing protocols, such as OLSR; (2) routing schemes, which are based on the distance-vector algorithm, such as DSDV. In this subsection, we focus on the first group and analyze OLSR-based energy-efficient routing approaches, since the OLSR is the leading proactive (hop-by-hop) routing protocol for MANETs. In OLSR, each node distributes topology control (TC) messages all over the network. This information is used by individual nodes to compute routes to all destinations. To reduce TC message overheads (traffic), the OLSR routing algorithm selects a small set of nodes (called Multi-Point Relays (MPRs)) among one-hop and two-hop neighbor sets of host, while the MPR selection algorithm is based on topological information. MPRs are responsible for forwarding link-state information and improving flooding in the network. To further reduce the number of TC messages, [PERSON] et al. [155] suggested two policies to improve the MPR selection algorithm. Both policies are employed to select MPR by using a simple modification in the OLSR protocol without extra signaling overheads. The improved OLSR variants outperform simple OLSR and cooperative OLSR in terms of routing cost and the number of TC messages. Many OLSR-based algorithms have tried to optimize the selection of MPR sets for efficiently reducing the consumption of network topology control, the delivery rate of data packets, and the end-to-end delay of packet transmission between nodes. In their attempt to make OLSR more energy-aware, [PERSON] and [PERSON] [42] introduced an energy-efficient routing scheme that is based on accurate state information about the available energy levels of nodes. This routing scheme exploits the nodes' energy levels as QoS metrics for route selection. To increase the accuracy of the energy levels at all traffic rates, the authors proposed two new techniques: (1) Prediction; (2) Smart Prediction. In the first technique, the energy level of a node is regulated based on its previous consumption rate. The second technique is an improved version of the first technique, where a node's energy level is adjusted based on the average of all known consumption rates for other nodes if no consumption rate can be determined for the node. However, both techniques have similar overheads as those in the simple OLSR since they use the same MPR selection mechanism. Moreover, both techniques do not take into account load balancing or other QoS metrics which should be involved in the routing process. Consequently, these techniques are incomplete as they address only the energy level issue. [PERSON] et al. [43] proposed the OLSR-energy-aware (OLSR_EA) routing scheme, in which the route calculation algorithm selects paths based on a composite energy cost. This cost is considered the energy routing metric and computed by combining the residual energy and consumed transmission power of each node. OLSR_EA uses the auto-regressive integrated moving average time-series method to measure and predict the per-interval energy consumption. Similarly, [PERSON] et al. [156] introduced a proactive forwarding for MANETs that is based on a multi-metric criterion. Another proactive routing scheme for MANETs that incorporates an energy conservation mechanism was proposed in [157]. This scheme exploits the new Energy Conserving Advanced Optimized Link State Routing (ECAO) model used for the prediction of the energy consumption level of the node. Such a prediction is used for the calculation of the energy cost. The performance of the ECAO model was compared with the existing OLSR and other advanced OLSR models.