Energy-aware resource allocation in machine to machine system-based NOMA using hybridized shark smell with lemur’s optimization

Non-Orthogonal Multiple Access (NOMA) approaches are becoming increasingly popular for future networks because they can efficiently connect multiple users on a Resource Element (RE). NOMA techniques meet stringent requirements like low latency, high spectrum efficiency, and connectivity for numerous devices. Additionally, Machine-Type Communication Devices (MTCDs) in Machine-to-Machine (M2M) communication networks are anticipated to provide diverse services, clustering methods, and various Quality of Service (QoS) levels. Resource allocation and clustering pose significant challenges in M2M communication systems based on NOMA. Moreover, one significant challenge is the issue of interference organization. In NOMA systems, multiple users distribute the similar frequency and time resources, leading to potential interference between devices. Managing this interference effectively is crucial to ensure reliable communication between machines. Another challenge is the complexity of resource allocation in NOMA-based M2M systems. Allocating resources such as power, bandwidth, and time slots optimally to multiple devices while considering the varying quality of service requirements can be a challenging task. This complexity increases as the number of devices in the system grows, requiring sophisticated algorithms for efficient resource allocation. Addressing these challenges in M2M systems requires advanced algorithms, efficient interference management techniques, and robust resource allocation strategies to optimize system performance and ensure reliable communication between devices. Therefore, in this research work, efficient allocation of resources in M2M communication is developed considering the total energy consumption of the system. The power control task is done with the aid of Hybrid Shark Smell with Lemur’s Optimization (HSSLO). The HSSLO algorithm is effective in addressing the challenges faced in the existing models. HSSLO combines the strengths of the Shark Smell Optimization (SSO) and Lemur’s Optimization (LO) algorithm to tackle complex optimization problems efficiently. By leveraging the exploration and exploitation capabilities of the SSO algorithm and the search efficiency of the LO algorithm, HSSLO can effectively allocate resources such as power, bandwidth, and time slots to multiple devices in a balanced and optimized manner. Moreover, HSSLO’s ability to handle interference management is crucial in NOMA systems. The algorithm’s optimization techniques can helps to mitigate interference between devices by intelligently adjusting transmission parameters and resource allocation, thereby enhancing overall network performance and reliability. The foremost aim of optimization is to minimize the total energy consumption of the network using energy harvesting strategy. The proposed model HSSLO analyzes the amount of energy utilized to perform a task in an M2M communication system. Experimental analyses are carried out separately for with energy harvesting and without energy harvesting strategies. The total energy consumption of the proposed HSSLO is 41.39%, 16.5%, 80.95%, and 16.03% more effective than ROA, FF, SSO, LOA, E-NOMA, and FMB-ROA, respectively. These experimental results show that the proposed resource allocation using HSSLO in NOMA produces optimal energy consumption of the network when compared to the existing models.