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Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application

Mangasuli, SushantKaluti, Mahesh
IAES International Journal of Artificial Intelligence (IJ-AI) (Sinta 1)Vol. 0 No. 01 Juni 2024
DOI10.11591/ijai.v13.i2.pp1348-1357

Abstrak

Climate change poses several environmental threats like floods to urban environment; thus, effective and reliable communication of emergency information is needed during massive breakdown of network infrastructure. This paper presents a mobile adhoc network (MANETs) based effective information such as calls, image, and videos communication system that is compatible with current 3GPP and 5G communication network. Here in maintaining connectivity the information is communicated between different MANET nodes in a multi-hop manner. However, designing radio propagation is challenging considering higher local emergency request congestion at different terrain with varying speed of users. The current radio propagation model is designed without considering the effect of line-of-sight between communicating device and are not adaptive to different environment considering urban disaster management environment. This paper develops an adaptive radio propagation (ARP) model namely expressway, city and semiurban. Then, in reducing congestion and improving network performance efficiency the work introduced an adaptive medium access control (AMAC) protocol. The MAC incorporates a dynamic network controller (DNC) to optimize the contention window size in dynamic manner according to current traffic demands. The AMAC protocol achieves much improved throughput with lesser packet loss in comparison with existing MAC (EMAC) model considering different radio propagation model introduced in this work.

Kata Kunci

5G communicationAdaptive radio propagation dynamic network controllerMobile adhoc networkRadio propagation

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Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application | IAES International Journal of Artificial Intelligence (IJ-AI) | Publiora