A Comprehensive Exploration of Resource Allocation Strategies within Vehicle Ad-Hoc Networks

Abstract

In recent years, a surge in the utilisation of vehicle-to-vehicle (V2V) communication has been observed, serving as a pivotal factor in facilitating automatic control of vehicles without human intervention. This advancement has notably curtailed accident rates, mitigated traffic congestions, and augmented vehicular security. Consequently, a meticulous survey has been orchestrated in the domain of Vehicle Ad-Hoc Networks (VANETs), particularly as autonomous vehicles pervade urban landscapes. The necessity for resources to assure secure and consistent operations of an escalating fleet commensurately intensifies with the enlargement of the fleet itself. Intelligent Transportation Systems (ITS) hinge upon VANETs to furnish travellers with secure and pleasant journeys, pertinent information and entertainment, traffic management, route optimisation, and accident prevention. Nevertheless, a plethora of challenges inhibits the delivery of an adequate Quality of Service (QoS) within vehicular networks, such as congested and interrupted wireless channels, a progressively saturated and sprawling spectrum, hardware inconsistencies, and the swift expansion of vehicular communication systems. Contemporary networks and energy grids are subject to strain from daily and recreational activities. As demand perpetually ascends, a necessity arises for more refined tools and methodologies for resource management and a more precise distribution system. This investigation offers an exploration of the most recent practices and trends in VANET resource allocation, with the objective of garnering insights into the existing research landscape and its impelling forces.

A Comparative Review of Internet of Things Model Workload Distribution Techniques in Fog Computing Networks

“…It is therefore investigated whether or not the distributed computer LB problem of the F-RAN may be solved. With regard to the F-RAN design, the simulation demonstrates that the LB strategy is more effective on the battlefield [29,30]. Figure 7 shows the functional structure of IoBT in the FC network.…”

Enhancing 5G LTE Communications: A Novel LDPC Decoder for Next-Generation Systems

“…The BP decoding technique is depicted in Figure 9 as a flow chart. The process goes back to step 1 if the number of repetitions is the maximum and if it is not a valid code word [59][60][61][62][63]. The BP algorithm works well for codes such as LDPC, but the tanh and tanh-1 operations are too difficult to implement in hardware.…”