8 results on '"IoT routing"'
Search Results
2. Intelligence-based optimized cognitive radio routing for medical data transmission using IoT
- Author
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B Naresh Kumar and Jai Sukh Paul Singh
- Subjects
internet of things ,cognitive radio sensor networks ,medical data transmission ,spreading rate-based coronavirus herding-grey wolf optimization ,cluster head selection ,iot routing ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.
- Published
- 2022
- Full Text
- View/download PDF
3. Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things.
- Author
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Ramkumar, Muthuperumal Periyaperumal, Mano Paul, Pauliah David, Maram, Balajee, and Ananth, John Patrick
- Subjects
LUNG cancer ,BEES algorithm ,INTERNET of things ,FRACTIONAL programming ,ANT algorithms ,MATHEMATICAL optimization ,COMPUTER-aided diagnosis - Abstract
Summary: The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Implementation of a LoRa Mesh Library
- Author
-
Joan Miquel Sole, Roger Pueyo Centelles, Felix Freitag, and Roc Meseguer
- Subjects
LoRa ,mesh network ,IoT routing ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
LoRa is a popular communication technology in the Internet of Things (IoT) domain, providing low-power and long-range communications. Most LoRa IoT applications use the LoRaWAN architecture, which builds a star topology between LoRa end nodes and the gateway they connect to. However, LoRa can also be used for the communication between end nodes themselves, forming a mesh network topology. In this paper, we present a library that allows to integrate LoRa end nodes into a LoRa mesh network, in which a routing protocol is used. Thus, an IoT application running on these nodes can use the library to send and receive data packets to and from other nodes in the LoRa mesh network. The designed routing protocol is proactive, and maintains the routing table at each node updated by sending routing messages between neighboring nodes. The implemented library has been tested on embedded boards featuring an ESP32 microcontroller and a LoRa single-channel radio. By using our LoRa mesh library, nodes do not need to connect to a LoRaWAN gateway, but among themselves. This opens the possibility for new, distributed applications solely built upon tiny IoT nodes.
- Published
- 2022
- Full Text
- View/download PDF
5. Intelligence-based optimized cognitive radio routing for medical data transmission using IoT.
- Author
-
Kumar, B. Naresh and Singh, Jai Sukh Paul
- Subjects
INTERNET of things ,WIRELESS communications ,ENERGY consumption ,COGNITIVE radio ,SPECTRUM allocation - Abstract
The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive-routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Optimal Routing and Deep Regression Neural Network for Rice Leaf Disease Prediction in IoT.
- Author
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Vimala, S., Gladiss Merlin, N. R., Ramanathan, L., and Cristin, R.
- Subjects
RICE straw ,INTERNET of things ,RICE ,ARTIFICIAL neural networks ,MATHEMATICAL optimization ,FORECASTING - Abstract
To meet the increasing food demand, production of rice is increased. Unfortunately, rice leaf disease has caused a major problem in the agricultural yield. Various disease prediction strategies are developed in the Internet of Things (IoT) agricultural applications, but accurately predicting the disease causes substantial environmental issues. Therefore, an effective method named Sunflower EarthWorm (S-EWA) optimization algorithm is proposed in this research to predict the disease in the rice crop. The sensor nodes are dispersed randomly in the IoT network of agricultural field, and these sensor nodes collect the agricultural data from the rice crop and are sent to the base station (BS) through the optimal path, which is computed using the proposed Sunflower EarthWorm optimization algorithm. The regeneration, the reproduction, and the dynamic behavior of the optimization algorithm effectively transfer the data through routing using the optimal path. The optimization algorithm uses the fitness function to determine the optimal path based on the position of earthworms. Deep Regression Neural Network uses the artificial neurons and performs the disease prediction of rice leaf at BS. The proposed S-EWA- based DBN attained better performance in terms of accuracy as 95.2, sensitivity as 95.51, and specificity as 94.89 by varying the training percentage, and accuracy as 95.7, sensitivity as 95.86, and specificity as 95.54 by varying the hidden layers, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. Implementation of a LoRa Mesh library
- Author
-
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts, Miquel Solé, Joan, Pueyo Centelles, Roger, Freitag, Fèlix, Meseguer Pallarès, Roc, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts, Miquel Solé, Joan, Pueyo Centelles, Roger, Freitag, Fèlix, and Meseguer Pallarès, Roc
- Abstract
LoRa is a popular communication technology in the Internet of Things (IoT) domain, providing low-power and long-range communications. Most LoRa IoT applications use the LoRaWAN architecture, which builds a star topology between LoRa end nodes and the gateway they connect to. However, LoRa can also be used for the communication between end nodes themselves, forming a mesh network topology. In this paper, we present a library that allows to integrate LoRa end nodes into a LoRa mesh network, in which a routing protocol is used. Thus, an IoT application running on these nodes can use the library to send and receive data packets to and from other nodes in the LoRa mesh network. The designed routing protocol is proactive, and maintains the routing table at each node updated by sending routing messages between neighboring nodes. The implemented library has been tested on embedded boards featuring an ESP32 microcontroller and a LoRa single-channel radio. By using our LoRa mesh library, nodes do not need to connect to a LoRaWAN gateway, but among themselves. This opens the possibility for new, distributed applications solely built upon tiny IoT nodes., This work was supported by the Ministry of Science and Innovation of the Spanish Government through the State Research Agency (AEI) under Project PID2019-106774RB-C21, Project PCI2019-111851-2 (LeadingEdge CHIST-ERA), and Project PCI2019-111850-2 (DiPET CHIST-ERA)., Peer Reviewed, Postprint (published version)
- Published
- 2022
8. A multi-objective Gray Wolf algorithm for routing in IoT Collection Networks with real experiments
- Author
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Thierry Val, Sami Mnasri, Sihem Tlili, Val, Thierry, Réseaux, Mobiles, Embarqués, Sans fil, Satellites (IRIT-RMESS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Faculté des Sciences de Gafsa, Université de Gafsa, University of Tabuk, Université Toulouse - Jean Jaurès (UT2J), Taif University, IEEE Western Saudi Arabia Section, and IEEE Western Saudi Arabia Section - Computer Society Chapter
- Subjects
Multiobjective Grey Wolf ,Optimization problem ,[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,business.industry ,Computer science ,Distributed computing ,media_common.quotation_subject ,Process (computing) ,020206 networking & telecommunications ,Context (language use) ,02 engineering and technology ,Prototyping ,Optimization I ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,IoT Collection Networks ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Network performance ,Quality (business) ,Routing (electronic design automation) ,Internet of Things ,business ,IoT routing ,media_common - Abstract
International audience; The routing is a real-world engineering problem in IoT Collection Networks. It has a direct impact on the network performance. The routing problem can be treated as a multi-objective optimization problem. Our objective is then to study the IoT routing problem using the Multi-Objective Grey Wolf Optimizer (MOGWO) [1], which consists in translating the routing process into a multi-objective optimization problem and implementing its execution with the MOGWO. In this context, the experimental study showed that the MOGWO provides better quality of routing (higher lifetime of the network, more efficient delivery delay and higher number of neighbors).
- Published
- 2021
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