# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Energy Efficiency Protocols
#### 3.1.1 Energy Efficiency Protocols Utilizing Resource Allocation Techniques  
This section is devoted to present energy efficiency achieving protocols which use re-source allocation techniques. The following works employ techniques for the optimal utilization of the system's resources with the aim of energy efficiency maximization. In [43], the authors presented a scenario that the LoRaWAN GW are power supplied by both renewable sources as well as from the grid. They introduced several resource management schemes that aim to improve the GW energy efficiency. Specifically, the number of channels and SF of LoRaWAN are subject to the management schemes. In or-der to achieve this goal, the authors of [43] formulated a grid consumption minimization problem while the system's quality of service (QoS) demands are fulfilled. Due to the high complexity of the problem an online resource management heuristic algorithm is proposed aiming that minimizes the energy consumption from the grid. The constraint that should be met at the same time is that the received SNR needs to be at least greater than a threshold. Because of the channel and energy correlation reinforcement learning (RL)-based adaptable resource management schemes are developed and using weights represent the energy requirement in each device. In [44], the authors reported energy efficient resource allocation policies with the simultaneous use of multiple energy harvesting sources on end-devices. Two problems were formulated: (i) the problem of maximizing the number of scheduled end-devices utilizing the available harvested energy and (ii) the dynamically allocation of SF for each end-device based on the channel coefficients and the residual energy of batteries. To solve these problems, the authors introduced an optimal energy management, device scheduling, and SF assignment algorithm. This algorithm aims to maximize the scheduled devices solving mathematical optimizing problem which utilizes the available recoverable energy, the available SF in each time frame and simultaneously maintaining the minimum SNR required by each device. The presented optimization framework was assessed through Monte Carlo simulations. The results showed that the proposed scheme efficiently consumes the energy harvested and stored. In [45], the authors presented an energy efficiency resource allocation for LoRaWAN in order to optimize the system energy efficiency (SEE) and the minimal energy efficiency (MEE) of end-devices. Their approach is based upon the exploitation of user scheduling, SF assignment and transmission power allocation. First, the authors documented a suboptimal algorithm that implements a low-complexity user scheduling scheme based on matching theory, as well as a heuristic SF assignment approach for users scheduled on the same channel. Next, they presented an optimal algorithm to select transmission power allocation in order to maximize the SEE. Additionally, they introduced an iterative transmission power allocation algorithm using generalized fractional programming and sequential convex programming in order to maximize the MEE. The numerical results revealed that the presented algorithms outperform the corresponding existing schemes in terms of both SEE and MEE. In [46], the authors presented a number of strategies that adopt LoRaWAN transmission parameters, such as the SF, BW, and TP to different deployed LoRaWANs. The introduced problem aims to be applied to star and mesh topologies as deployed networks. Additionally,, in order to compare the two topologies, they investigate the energy consumption of fixed size packets. Specifically, in a star topology, the optimal parameters selected according to the distance between the transmitter and the receiver and in a mesh topology the best parameters selected based on the network density and coverage. The parameters are optimized in order to achieve maximum network's energy efficiency.