Recent Development of Max-SNR Opportunistic Scheduling in Cross Layer Design | Chapter 6 | Research Highlights in Mathematics and Computer Science Vol. 7

 Wireless range efficiency is crucial to meet the increasing demand for wireless duties, particularly low-cost natural Internet services. In contrast to connected networks, wireless channels feature location and occasion-varying. At some one time, various channel environments affect miscellaneous wireless users. A cross-tier design technique named opportunistic scheduling is used to impose upon the wireless surroundings's time-varying traits and boost system performance overall while still intersection user-particularized Quality of Service (QoS) and fairness guidelines. Opportunistic algorithms' fundamental premise is to schedule a consumer with the optimum channel condition at a particular moment by imposing upon channel changes. The demand for QoS provisioning cannot be met by a plan that only permits consumers with best choice channel conditions to transmit at extreme transmission capacities.Using the Max-Min fair algorithm, the project's aim is to establish opportunistic scheduling while supporting fairness and QoS requirements. The Media Access Control (MAC) tier has been outfitted with an opportunistic scheduler that engages Max-Min fairness arranging. Here, the base station collects the Signal to Noise Ratio (SNR) of all the nodes and before schedules the users established these SNR values using at top speed-Min algorithm. Strict Priority, a non-materialistic scheduling technique, is therefore used to accomplish the equal scenario. The throughput, fairness, delay, and shake of the two happening algorithms are then contrasted.

Author(s) Details:

T. Praveena,
Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India.

Please see the link here: https://stm.bookpi.org/RHMCS-V7/article/view/10000

Reinforcement Learning and Heuristic Algorithms for Efficient Routing Protocols in Mobile Ad-Hoc Networks: An Advanced Approach | Chapter 5 | Recent Advances in Mathematical Research and Computer Science Vol. 3

 Nodes in mobile ad-hoc networks can freely roam and communicate with each other across a wireless network in their frequency range. The routes are not stable due to the dynamic topology. As a result, one of the primary challenges is sending data packets between nodes. The methods that are consistent with the network changes caused by node migrations are quite important. Route brevity and route stability should both be considered while minimising data packet transmission time between nodes. Our approach to Artificial Intelligence was widely regarded as a novel development in the world of routing protocols more than a decade ago [1,2]. Reinforcement learning was utilised to pick the resilient routing method by determining the optimal choice among neighbour nodes at any given time to transport data packets from source to destination. It uses reinforcement learning to predict the behaviour pattern of the nodes in respect to the target node. The suggested method uses a Q-learning algorithm to assess the value of actions, which is more homogeneous [3].

Author(S) Details

D. Srinivas Reddy
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.

V. Bapuji
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.

A. Govardhan
Department of Computer Science & Engineering, JNTU-Hyderabad, Telangana, India.

View Book:- https://stm.bookpi.org/RAMRCS-V3/article/view/4466

Recent Study on Dynamic Multi Constraint Routing Based Efficient Data Transmission in Manet for Improved Sink Point Streaming | Chapter 12 | Recent Developments in Engineering Research Vol. 11

In today's internet world, the Mobile Adhoc network (Manet) has been recognized as a keen networking scenario. Access to most networking solutions has been made possible through mobile devices. The physical characteristics of mobile nodes continue to alter the network topology every second. The achievement of greater streaming performance, however, is most significant in terms of service quality. There are various methods to route data packets between the nodes of the source and sink/destination, but they suffer from poor performance. A dynamic multi constraint routing algorithm has been proposed in this paper to address the shortcoming. In route selection, the method considers various parameters such as capacity, lifetime, traffic, speed of mobility, direction and hop count. The multi constraint algorithm estimates the streaming support score (SSS) for any route for routing packets according to the above parameters. The algorithm has enhanced routing efficiency and increased Manet's streaming rate. The method calculates the streaming support score value for each route based on the measures estimated. A single route for forwarding the data packet has been selected based on the value of SSs. Higher throughput performance has been provided by the proposed DMR algorithm and reduces latency.

Author (s) Details

Dr. M. Prakash
Department of Computer Science, Vinayaka Mission’s Kirupananda Variyar Arts and Science College, Vinayaka Mission’s Research Foundation Deemed to be University, Salem, India.

View Book :- https://stm.bookpi.org/RDER-V11/issue/view/26