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Öğe Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment(Elsevier, 2023) Kumari, Rani; Sah, Dinesh Kumar; Cengiz, Korhan; Ivkovic, Nikola; Gehlot, Anita; Salah, BashirThe rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.Öğe Energy efficient medium access control protocol for data collection in wireless sensor network: a q-learning approach(Elsevier Ltd, 2022) Sah, Dinesh Kumar; Amgoth, Tarachand; Cengiz, KorhanIn emerging business markets, the data is becoming new gold for the industries. There are several places such as marketing, manufacturing, and analysis of future trends becoming data-intensive to achieve growth. The tiny sensor placements in the field, cities, industry, buildings and sea are helping to collect data and process it either for information retrieval or decision making. The periodic scheduling of radio transceivers assists in accomplishing efficient energy utilization in sensors often uses Time-division multiple access (TDMA) protocols for node scheduling. Our objective is to reduce the amount of time; the receiver node is in the wake-up state. Besides, the slot which potentially not being used for an extended period can utilize by other nodes. The efficient utilization of slots can help to achieve low power duty cycles with low latency. To accomplish that, we propose the emulation of the classroom learning environment with the Q-learning grading for node scheduling. We proposed an analytical mapping of WSNs to classroom learning. The initial benchmark of the performance has compared to the IEEE.802.11 (TDMA-scheduling). Further, the TDMA driven protocols such as Z-MAC, learning-driven protocols (E-MAC or aloha-Q), i-Queue are compared to evaluate the parameters such as energy consumption, throughput, and latency.Öğe Reinforcement learning infused MAC for adaptive connectivity(Institute of electrical and electronics engineers inc., 2024) Sah, Dinesh Kumar; Nauman, Ali; Jamshed, Muhammad Ali; Cengiz, Korhan; Ivković, Nikola; Uroš, VedranThe beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.Öğe SOHCL-RDT: A self-organized hybrid cross-layer design for reliable data transmission in wireless network(Elsevier, 2023) Cengiz, Korhan; Kumari, Rani; Sah, Dinesh Kumar; Ivkovic, Nikola; Salah, BashirIn this paper, we propose SOHCL-RDT'' which stands for a self-organized hybrid cross-layer design for reliable data transmission in wireless network. The communication paradigm is changing and new approach related to machine learning or other optimization algorithms are being introduce rapidly. The TCP/IP or OSI model is not at all equipped to accommodate such a vast changes in its established protocol stacks. Considering this, we have proposed the hybrid cross layer design where the communication or transmission will be handle using two set of protocol stack. One set for the established classical network, and another using cross layer approach. Our design leverages the strengths of both the physical and MAC layers to optimize packet transmission and minimize energy consumption. An optimization algorithm based on gradient descent is also developed to adjust transmission parameters in real-time. The objective is to invoke the classical model only when it needed; it means until unless gradient descent is able to make classification regarding the node scheduling and achieve the acknowledgment, the TCP/IP protocol stack will be in deactivation. Using this method, we have performed our experiments mainly on two parameters named as packet delivery ratio (PDR), end-to-end delay (E2ED); because these are important aspect of reliability. In addition to that, the energy consumption of network is also compared with the existing algorithms. The results show that the proposed hybrid cross-layer design outperforms the existing algorithms. The performance gain can be attributed to the cross-layer approach and the use of the optimization algorithm. Overall, the proposed hybrid cross-layer design is a promising solution for reliable data transmission in wireless sensor networks, with the potential to improve network performance and prolong network lifetime by reducing energy consumption.& COPY; 2023 Published by Elsevier B.V.Öğe TDMA policy to optimize resource utilization in Wireless Sensor Networks using reinforcement learning for ambient environment(Elsevier B.V., 2022) Sah, Dinesh Kumar; Amgoth, Tarachand; Cengiz, Korhan; Alshehri, Yasser; Alnazzawi, NohaData packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor.
