International Journal of Computer Networks and Applications

Anshu Vashisth ¹, Balraj Singh ¹, Ranbir Singh Batth ²

Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, Punjab, IN
2 Menzies Institute of Technology, Melbourne, AU

Abstract

The design of efficient routing strategies for Unmanned Aerial Vehicle (UAV) Networks is a multidomain task that involves analysis of node-level & network-level parameters, and mapping them with communication & contextual conditions. Existing path planning optimization models either showcase higher complexity or cannot be scaled for larger network scenarios. Moreover, the efficiency of these models also reduces w.r.t. the number of communication requests, which limits their scalability levels. To get a better result over these challenges, this article provides an idea to design an efficient Q-Learning model to improve the routing efficiency of UAV networks via bioinspired optimizations. The model initially collects temporal routing performance data samples for individual nodes and uses them to form coarse routes via Q-Learning optimizations. These routes are further processed via a Mayfly Optimization (MO) Model, which assists in the selection of optimal routing paths for high Quality of Service (QoS) even under large-scale routing requests. The MO Model can identify alternate paths via the evaluation of a high-density routing fitness function that assists the router in case the selected paths are occupied during current routing requests. This assists in improving temporal routing performance even under dense network conditions. Due to these optimizations, the model is capable of reducing the routing delay by 8.5%, improving energy efficiency by 4.9%, and reducing the routing jitter by 3.5% when compared with existing routing techniques by taking similar routing conditions.

Keywords

UAV, Routing, Delay, Energy, Mayfly, Optimization, Jitter, efficiency, Complexity.

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Gagandeep Kaur ¹, Balraj Singh ¹, Ranbir Singh Batth ²

Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, Punjab, IN
2 Menzies Institute of Technology, Melbourne, AU

Abstract

In the transformative landscape of mobile edge computing (MEC), where the convergence of computation and communication fuels the era of ubiquitous connectivity, formidable challenges loom large. The burgeoning demand for real-time, data-intensive applications places unprecedented pressure on existing infrastructure, demanding innovative solutions to address the intricate web of challenges. This paper embarks on a compelling journey through the realm of MEC, uncovering the multifaceted challenges that have hitherto impeded its seamless integration into our digital lives. As the proliferation of mobile devices and their insatiable appetite for data strain the network's capacity, latency becomes a formidable adversary, threatening the integrity of applications requiring split-second responsiveness. Furthermore, the capricious nature of mobile devices and their mobility introduces an unpredictable dynamism into the network topology, rendering traditional traffic control approaches ineffective. The consequence is a tangled web of congestion, resource underutilization, and compromised Quality of Service (QoS), all of which hinder the realization of MEC's full potential. In response to these challenges, we unveil a pioneering solution—a QoS-aware Adaptive Data Dissemination Engine (QADE) paired with Dynamic Traffic Flow Control (DTFC). This synergistic model augments the capabilities of MEC deployments by harnessing the power of content-based routing and advanced optimization techniques. QADE, with its innovative utilization of Elephant Herding Particle Swarm Optimizer (EHPSO), refines data dissemination processes with an unprecedented focus on QoS metrics. Temporal delay, energy consumption, throughput, and Packet Delivery Ratio (PDR) become our guiding stars in the quest for routing efficiency. By harnessing this wealth of information, QADE emerges as a beacon of efficiency, driving latency to its lowest ebb, magnifying bandwidth, mitigating packet loss, elevating throughput, and rationalizing operational costs. DTFC complements this endeavor by dynamically steering traffic flows by edge processing capacity, thereby circumventing congestion pitfalls and achieving resource utilization efficiency hitherto considered unattainable. In a series of exhaustive evaluations, our proposed QADE with DTFC emerges as a beacon of hope, surpassing traditional methodologies. With an 8.5% reduction in latency compared to RL, a 16.4% reduction compared to MTO SA, and an impressive 18.0% reduction compared to HFL, it ushers in a new era of real-time data dissemination. By championing QoS awareness, adaptability, and efficiency, this study catapults mobile edge computing into a future defined by resource optimization and stellar network performance, ushering in an era where challenges bow before innovation processes.

Keywords

Data, Dissemination, Trust, Routing, Data Flow, Control, Scenarios.

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