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Routing in wireless networks requires consideration of a wide variety of inter-domain models that include, clustering, geographical node placements, distance evaluation strategies, traffic management, energy considerations, etc. A typical wireless routing model is depicted in Malisetti and Pamula (2020), wherein entire flow of invitation-based routing is visualized. Here, cluster heads (CH) broadcast invitation requests, that contain CH location, energy levels, and other cluster-specific parameters. Nodes respond to these requests with Yes or No acknowledgements, which assist CHs to either accept the nodes or discard them from their cluster lists.
In order to facilitate the transmission of data from one node to another, CHs provide Time Division Multiple Access (TDMA) slots to approved nodes. To complete the node-to-node communication loops, the CHs transmit this information to other nodes or other CHs. In order to execute application-specific routing and communication, models substitute context-specific characteristics like as throughput, PDR, routing overheads, etc. for distance and energy level measurements. In the next part, we'll take a look at some of these models, their intricacies, benefits, and drawbacks, as well as potential directions for future study. To better match current routing models to their deployment requirements, researchers may use this topic to suggest the best models for future deployments. To wrap things off, this article makes some intriguing insights about the analyzed models and proposes a variety of ways to enhance their real-time performance.
Researchers have suggested a broad range of low-power routing protocols, each with its own unique benefits, drawbacks, and study areas. Low-energy adaptive clustering hierarchy (LEACH), Hybrid Energy Efficient Distributed Clustering (HEED), Dynamic Clustering and Distance-Aware Routing (DDAR), etc., are only few of the protocols included in this category. In addition, these protocols use numerous machine learning models, such as swarm intelligence, bioinspired computing, neural networks, etc. Other statistical factors, such as energy consumption, communication delays and throughput as well as packet delivery ratio (PDR) and scalability are also different amongst these protocols. It is difficult for researchers and network designers to choose the best models for their context-aware network deployments because of the large range of performance measures. Thus, the motivation & contributions of this text are,
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An in-depth description of these models, together with the application-specific deployment strengths, is provided in this article in an effort to remove this uncertainty. Researchers will be able to narrow down a list of context-specific routing models based on this debate. Besides that, it compares the examined models with other power-aware routing models and assesses their performance on a variety of performance criteria.
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Researchers and network designers will be able to find routing models that are most suited for installations requiring features such as low latency, high throughput, high PDR, etc., after consulting this comparison.
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This paper also proposes a new algorithm rank score (ARS), which incorporates a variety of assessment indicators to provide a more complete picture of performance. Selecting routing models with high ARS performance allows network designers to implement routing models that maintain performance equilibrium throughout a variety of simulations and tests.
Based on these contributions, readers will be able to identify optimal models for their application-specific & performance-specific deployments.