Unmanned Aerial Vehicles (UAVs) are the upshot of the swift advance in leading fields, such as computing, sensing, communication technologies, etc. Years, the military field served as the sole realm holding UAVs applications. However, the advent of small and mini UAVs brought the coda of such an exclusive use case, and the civilian experience has been pushed up. For higher yield, a swarm of UAVs, aka Multi-UAV system, is formed fulfilling a cooperative screenplay. For bulk cases, the Ad-hoc technology is used as the gist of the networking layer connecting the UAVs, forming the new-brand Unmanned Aerial Ad-hoc Network (UAANET). By dint of the limited battery supply, the energy constraint is deemed the UAVs’ primary impediment, which has acquired all-embracing attention since it may significantly restrict the applications’ scope and affect their efficiency, yield, and performance. This points out the optimization of energy saving as a necessity, of great resonance, in such an environment. To make our proposed solution gets an extremely potent form of efficiency, we highlight the communication impact on energy consumption from two distinct outlooks. The first is the explicit impact in relation to the communication protocol. The second is the implicit impact in which we involve the mobility model as an aspect through which we boost energy saving, where basic parameters in relation to mobility and, at the same time, are seen as networking tones, have been considered. Such a trend of highlighting the communication impact is deemed to be the first in the UAVs’ literature, which makes our work more vital and significant. In this paper, we propose a new ElectriBio-inspired Energy-Efficient Self-organization model for UAANET (EBEESU), which is an amalgam of Electrical-inspired and Bio-inspired models blending a mobility model and a cluster-based communication algorithm with two-level data aggregation, wherein energy saving is the intrinsic aim. Via simulation scenarios, our proposed model proved its efficiency in reducing energy consumption, giving increased network lifetime. Besides, the packet loss ratio and the average End-to-End delay are significantly minimized, optimizing thus the network’s quality of service, making our solution particularly fitting for the UAANET.
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