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Sakthivel, T.
- The Impact of Mobility Models on Geographic Routing in Multi-Hop Wireless Networks and Extensions – A Survey
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Authors
Affiliations
1 Firstsoft Technologies Private Limited, Chennai, Tamil Nadu, IN
2 Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, IN
1 Firstsoft Technologies Private Limited, Chennai, Tamil Nadu, IN
2 Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 5 (2021), Pagination: 364-670Abstract
Multi-hop Wireless Networks (MWNs) emerge as an enabling communication technology, evolving rapidly due to the accelerating advancements and creating potential network applications that significantly improve the quality of life. Pure general-purpose MANET laid the theoretical foundation for MWNs, and many extensions are successfully deployed in commercial networks. This article surveys geographical routing protocols and mobility models applicable to MWNs and their recently proposed extensions. Mobility is a significant factor that profoundly impacts the performance of multi-hop geographical routing. This study analyzes various mobility models that significantly influence the performance of geographical routing protocols based on the characteristics and behavior of various network extensions. This survey investigates the primary challenges in designing geographical routing for various mobility models that notably impact the routing performance for a particular network extension. It also explores the enormous potential of geographical routing protocols under each extension and adequately addressing the routing and mobility-related issues. The essential factors that impact geographical routing, the freshness of location information, and the adaptive location update are examined extensively for various network extensions. Finally, the survey concludes with future research challenges and directions.Keywords
Multihop Wireless Networks, Geographical Routing, Mobility Models, MANET, FANET, WSN, VANET, DTN.Full Text
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- Deep Q-Learning Network-Based Energy and Network-Aware Optimization Model for Resources in Mobile Cloud Computing
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Authors
Affiliations
1 Department of Computer Science and Engineering, Amity University, Uttar Pradesh, IN
2 Firstsoft Technologies Private Ltd., Chennai, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Amity University, Uttar Pradesh, IN
2 Firstsoft Technologies Private Ltd., Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 3 (2022), Pagination: 361-373Abstract
Mobile Cloud Computing (MCC) enables computation offloading procedures and has become popular in resolving the resource limitations of mobile devices. To accomplish effective offloading in the mobile cloud, modeling the application execution environment with Quality of Service (QoS) is crucial. Hence, optimization of resource allocation and management plays a major role in ensuring the seamless execution of mobile applications. Recently cloud computing research has adopted the reinforcement learning models to optimize resource allocation and offloading. In addition, several optimization mechanisms have considered the network transmission rate while selecting the network resources. However, mitigating the response time becomes critical among the dynamically varying mobile cloud resources. Thus, this paper proposes a joint resource optimization methodology for the processing and network resources in the integrated mobile-network-cloud environment. The proposed approach presents the Energy and Network-Aware Optimization solution with the assistance of the Deep Q-learning Network (ENAO-DQN). Designing an energy and network-aware resource optimization strategy recognizes the quality factors that preserve the device energy while allocating the resources and executing the compute-intensive mobile applications. With the potential advantage of the Deep Q-learning model in decision-making, the ENAO-DQN approach optimally selects the network resources with the enrichment of the maximized rewards. Initially, the optimization algorithm prefetches the quality factors based on the mobile and application characteristics, wireless network parameters, and cloud resource characteristics. Secondly, it generates the allocation plan for the application-network resource pair based on the prefetched quality factors with the assistance of the enhanced deep reinforcement learning model. Thus, the experimental results demonstrate that the ENAO-DQN model outperforms the baseline mobile execution and cloud offloading models.Keywords
Mobile Cloud Computing, Resource Allocation, Optimization, Energy Consumption, QoS, Deep Reinforcement Learning, Q-learning, Wireless Network Resource.Full Text
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- Towards an Adaptive Routing Protocol for Low Power and Lossy Networks (RPL) for Reliable and Energy Efficient Communication in the Internet of Underwater Things (IoUT)
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Authors
Affiliations
1 Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IN
2 Firstsoft Technologies Private Limited, Chennai, Tamil Nadu, IN
3 Department of Computer Science, Ponnusamy Nadar College of Arts and Science, Thozhuvur, Thiruvallur, Tamil Nadu, IN
4 Department of Computer Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, IN
1 Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IN
2 Firstsoft Technologies Private Limited, Chennai, Tamil Nadu, IN
3 Department of Computer Science, Ponnusamy Nadar College of Arts and Science, Thozhuvur, Thiruvallur, Tamil Nadu, IN
4 Department of Computer Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 5 (2022), Pagination: 578-590Abstract
Internet of Underwater Things (IoUT) is emerging as a powerful technology to explore underwater things. Reliable communication between underwater things is a significant challenge compared to communication at the surface, notably the unique characteristics imposed by the underwater environment, such as water currents, noisy scenarios, and limited resources. Several routing protocols have been suggested to overcome the challenges in IoUT. The previous works mainly focus either on improving the reliability or energy efficiency of the routing process. Concentrating on both parameters makes the routing process too complex with substantial overhead. Routing techniques face challenges in solving the noise and water current issues in the IoUT environment. The proposed work utilizes the potential of the Routing Protocol for Low Power and Lossy Networks (RPL) on IoUT communication by enhancing its Objective Function (OF) to resolve the routing in the underwater environment. The proposed Underwater Adaptive RPL (UA- RPL) turns the inefficient DODAG construction into an efficient under noisy environment by extending DIO message features. Numerous neighboring nodes receive the extended DIO message, and the nodes that fit into the safety zone are decided according to the multiple routing metrics, such as hop count, ETX, and Energy factor. Entire network traffic is partitioned through multiple parent nodes with the best rank values and attains an energy-balancing routing over underwater things. It helps to improve the network lifetime without compromising communication reliability. The proposed work is evaluated to show its advantages over the underwater environment. The simulation results show that the UA-RPL delivers high performance when varying the underwater things from 15 to 60. Moreover, it outperforms the existing schemes under the IoUT environment.Keywords
Routing Protocol, Internet of Underwater Things, Noisy Environment, Water Current, Reliability, Energy Efficiency, RPLFull Text
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