## 1 Introduction
### Related Works
The authors have targeted a specific type of data packets that are broadcasted in the network in order to propagate routing information across the network. In traditional multi-path routing networks, routes are calculated by broadcasting the Route Request packets (RREQ) to the neighbor devices, which led to massive energy loss. The proposed protocol minimizes the collision by disjointed multi-path routing and only selects two paths for the broadcasting of this information, i.e., RREQ to those two devices which consequently saves energy. Devices are able to save up the residual battery energy according to the proposed algorithm, and is compared to the Ad-hoc On-demand Distance Vector (AODV) routing protocol. The authors in [32] employed an ant colony-based meta-heuristic and swarm intelligence to optimize the routing protocol for energy efficiency in wireless networks. This proposed protocol could self-configure in order to adapt to alterations in the network, including topological and geographical changes, as it is based on real ant behavior in which ants wander randomly in a small geographical region. Rather than sending its current residual battery status to other devices, it takes into account the required battery power to deliver a single packet along the path and decides the optimization routing protocol based on the computed information. In the article [33], the authors introduced an Energy-Efficient Cloud-Assisted Routing Mechanism (EECRM) for Cloud Assisted-Mobile Ad-hoc Networks (CA-MANETs). The authors exploited the Bellman-Ford algorithm for quick path recovery and detection processes in order to discover the alternate paths when the link failure occurs between the devices. Service schedule, information update, and information notice functions have been applied to maintain connections in the CA-MANETs. The proposed routing protocol achieved efficient energy consumption, extended network coverage, and the ability to prolong the lifetime of CA-MANETs. To address the challenges of traffic load variations and energy availability conditions, the authors in [34] proposed an effective Energy-Harvesting-Aware Routing Algorithm (EHARA) for heterogeneity of the IoT networks. The EHARA protocol selects the optimal path based on harvested energy, consumed energy, and the residual energy cost metric functions of the intermediate device for data transmission toward the destination device. The proposed protocol improved energy efficiency and prolonged the device and network lifetime for mobile distributed IoT networks. Another work has been carried out in [35], where the authors proposed the mechanisms for D2D communication session setup and management involving procedures in the Long-Term evolution Advanced cellular network (LTE-A) System Architecture Evolution (SAE) that control and limit the interference to the primary cellular network. In this paper, the authors have studied the additional changes required for the SAE procedures and functions to facilitate the D2D communication session setup. Both the uplink and downlink period of time division duplex operation of the LTE-A cellular network issues are addressed due to their different natures in multiple access interference and power control. The results show that the overall throughput of the network is increased with limited interference for the primary cellular network by allowing the D2D communication to underlay cellular networks. Meanwhile, the authors in [36] have proposed a game-theoretic framework in order to determine an efficient solution for the problem of uplink transmission power control and interference mitigation in D2D communication enabled cellular network. The proposed framework has utilized the control intelligence and decision-making process for the realization of mobile user self-optimization functionalities. An iterative distributed heuristic algorithm is exploited to determine the Nash equilibrium point for the non-cooperative power control game.