scispace - formally typeset
Search or ask a question

What is routing protocol in iot network ? 


Best insight from top research papers

In IoT networks, routing protocols play a crucial role in determining the path for data transmission . These protocols are essential for efficient communication between devices by selecting the best route while considering factors like limited resources, network load, and connectivity challenges. Various routing protocols have been proposed and compared in the literature, such as Cluster-Based Routing (CBR), Routing Protocol for Low Power and Lossy Networks (RPL), and others . These protocols aim to enhance network performance by reducing control overhead messages, improving throughput, decreasing end-to-end latency, and increasing efficiency in data sharing . Overall, routing protocols in IoT networks are designed to optimize data transmission, enhance network reliability, and address the unique challenges posed by IoT environments.

Answers from top 5 papers

More filters
Papers (5)Insight
Routing protocols in IoT networks determine how data is forwarded from one device to another. The study compares AODV and RPL, showing RPL's superior performance in throughput and packet delivery ratio.
Routing protocols in IoT networks determine the best path for data exchange. Protocols like RPL, ETSP, CTP, and FAMOUS are efficient for energy consumption, network lifetime, and packet delivery ratio.
FOG-RPL is a fog computing based routing protocol for IoT networks. It optimizes node selection and reduces network load, enhancing efficiency compared to traditional RPL in IoT environments.
Routing protocols in IoT networks determine how data is forwarded from one device to another. The study compares AODV and RPL, showing RPL's superior performance in throughput and packet delivery ratio.
Clustered IoT (CIoT) routing protocol efficiently selects routes in IoT networks, minimizing control overhead by utilizing a Predictive Generated Hello Messages Algorithm (PGHMA) to enhance network performance.

Related Questions

What is the Internet of Things?4 answersThe Internet of Things (IoT) refers to the concept of connecting physical devices or appliances through the internet. It involves a network of connected devices such as home appliances, office equipment, industrial machines, and motor vehicles that can be controlled through embedded software applications. IoT enables the transmission of data among devices, tracking and monitoring devices, and automation of tasks without physical interference. It supports the interconnection of objects and is driven by technologies such as cloud computing, wireless sensor networks, and 5G. IoT has various applications in fields like medical, infrastructure, landscaping, architecture, and education. It allows for the connection of everyday objects via the internet, enabling tracing, positioning, monitoring, administration, and smart recognition. Overall, IoT is a network that connects devices and objects, enabling automation and data transmission for various applications.
What is routing?4 answersRouting is the process of directing network packets from their source to their destination through intermediate network nodes using specific packet forwarding mechanisms. It involves making higher-level decisions to choose the most appropriate communication route for traffic in a network or between networks. Routers play a crucial role in routing by setting up optimized paths among different nodes in the network. Communication devices use route selectors to search for the most suitable communication route and convert data into a compatible format for transmission. In a network system, nodes exchange traffic information through a global bus and switch routing paths based on this information. Routing data packets in a network involves arranging transceiver units in each node, defining static and dynamic criteria, and searching for optimal transmission routes. Overall, routing ensures efficient and effective data transmission within and between networks.
What are the different protocols used for IoT?4 answersThere are several protocols used for IoT. One approach is attribute-based encryption (ABE), which can provide privacy protection for IoT devices by encrypting sensitive information such as device owner's identity and device type. Another important protocol is the Routing Protocol for Low Power and Lossy Networks (RPL), which is known for its performance in terms of energy consumption, network lifetime, reliability, efficiency, and packet delivery ratio. Ad-hoc On-demand Distance Vector (AODV) is another routing protocol that has been compared to RPL in terms of throughput, packet delivery ratio, and average end-to-end delay. These protocols play a crucial role in ensuring secure communication and efficient data routing in IoT networks.
How can machine learning be used to improve the energy efficiency of routing in IoT-enabled wireless sensor networks?5 answersMachine learning algorithms can be used to improve the energy efficiency of routing in IoT-enabled wireless sensor networks. These algorithms optimize data transmission by finding the most efficient paths for data to be routed among sensor nodes. By using machine learning, researchers have developed energy-efficient routing protocols that balance the load among sensor nodes, reduce energy consumption, and extend the lifespan of the network. These protocols consider factors such as energy levels, distance between sensors, and the number of sensor nodes in each cell or cluster. Additionally, machine learning algorithms can be used to optimize data transmission in terms of energy consumption, taking into account the battery levels of the sensor nodes. Overall, machine learning plays a crucial role in improving the energy efficiency of routing in IoT-enabled wireless sensor networks, leading to longer network lifespans and reduced energy consumption.
Why internet of things?5 answersThe Internet of Things (IoT) is gaining popularity due to its potential to transform various industries and improve people's lives. IoT enables the interconnection of objects and devices, allowing them to collect and share data over the internet. It has applications in areas such as smart homes, smart cities, and industrial automation. IoT technology offers benefits such as improved productivity, cost-effectiveness, and optimization of operations. It allows for the tracking and monitoring of objects, providing alerts and ensuring safety. IoT is driven by technologies like cloud computing, wireless sensor networks, and 5G, which enable seamless communication and networking of smart objects. However, there are challenges that need to be addressed, including identity management, standardization, privacy, energy management, and information security. Despite these challenges, IoT has the potential to revolutionize industries, improve economic growth, and enhance the quality of life.
What is IoT connectivity?4 answersIoT connectivity refers to the ability of devices to connect and exchange data within the Internet of Things ecosystem. It involves the use of wired and wireless technologies to establish connections between devices and enable communication. IoT connectivity can be achieved through various network protocols, including wired, wireless, and hybrid technologies. These technologies allow for the connection of numerous smart devices and enable data exchange and information sharing. The connectivity options range from wired technologies such as Ethernet to wireless technologies like Wi-Fi and cellular networks. In the context of IoT, connectivity is essential for enabling new use cases and delivering services across different markets and applications. The evolution of IoT connectivity is also being explored in the context of 5G and future 6G wireless communication systems.

See what other people are reading

1.How can digital twin technology be leveraged to enhance patient privacy while still providing comprehensive care?
4 answers
Digital twin technology in healthcare offers a promising avenue to enhance patient privacy while delivering comprehensive care. By utilizing techniques like intelligent Intrusion Detection Systems (IDS), efficient authentication methods, and privacy by design, digital twins can establish a cyber secure framework that protects patient data. Additionally, the integration of blockchain technology can enhance trust and ensure privacy for both patients and healthcare providers. Furthermore, the use of federated learning and digital twin models can enable the creation of patient digital twin models in a secure and privacy-preserving manner, aiding in the improvement of medical solutions and enhancing medical care while safeguarding sensitive data. These approaches collectively address data security concerns, ensuring patient privacy is maintained while still allowing for personalized and effective healthcare delivery.
Describe The Traffic-Aware Scatter Net Scheduling (TAAS) for Multicast Wireless Protocols methodology?
5 answers
The Traffic-Aware Scatter Net Scheduling (TASS) algorithm is a sophisticated scheduling approach designed to minimize data transmission latency in wireless networks. It combines random-access and deterministic scheduling, randomly assigning nodes to time slots while considering current network traffic to reduce latency. Quality of Service Aware Scheduler (QAS) is proposed for diverse traffic mixes, aiming for balanced QoS delivery with moderate fairness among users. In the context of Wireless Sensor Networks, TSCH mode under IEEE 802.15.4e is explored for Industry 4.0 requirements, with various decentralized TSCH schedulers classified into autonomous, distributed, and Reinforcement Learning-based protocols for handling heterogeneous traffic scenarios. These methodologies collectively address latency reduction, QoS optimization, and efficient scheduling for diverse wireless network applications.
What is the role of working memory in the development and progression of Alzheimer's disease?
4 answers
Working memory plays a crucial role in the development and progression of Alzheimer's disease (AD). Patients with AD exhibit working memory impairments, such as misbinding errors and increased random responses. These impairments can be detected early through clinical tests involving working memory tasks, supported by neuroimaging techniques like EEG and fNIRS. In AD, working memory failures are linked to language-processing difficulties, including challenges in resolving pronouns, which are influenced by working memory capacity. Furthermore, the complexity analysis of multimodal EEG-fNIRS can effectively detect working memory decline in early AD stages, aiding in clinical evaluation and classification of AD patients. Overall, working memory deficits in AD are multifaceted, involving misbinding errors, random responses, language processing difficulties, and can be assessed through neuroimaging techniques for early detection and monitoring.
Um main-in0the-middle é um atauqe a redes móveis?
4 answers
Yes, Main-in-the-Middle (MITM) attacks pose a significant threat to mobile networks. These attacks involve intercepting data shared between legitimate nodes, compromising communication security. In the context of mobile environments, a variation of MITM attacks called MobileApp-in-the-Middle (MAitM) has been identified, extending the threat to mobile platforms without the need for malware installation. These attacks can go undetected by security systems, making them particularly dangerous. To mitigate MITM attacks in mobile ad hoc networks (MANETs), strategies such as message-delayed and message-dropped assaults have been studied, highlighting the severe impact on network integrity and the importance of implementing cryptographic measures to secure inter-node communication. Overall, MITM attacks are a pressing concern for mobile networks, necessitating robust security measures to safeguard against unauthorized interception and manipulation of data.
Whats articles have used montgomery dataset
5 answers
The Montgomery dataset has been utilized in articles related to medical misinformation and news classification. The dataset includes a feature-rich collection of medical news articles and fact-checked claims, enabling studies on misinformation diffusion and characterization. Additionally, the dataset of news articles with hierarchical categories can aid in training machine learning models for classifying news articles by topic, benefiting researchers in news structuring and event prediction. Furthermore, the impact of the Montgomery case on medicine and law has been extensively analyzed, revealing disagreements on various core issues such as legal duties, medical practice, patient experience, and litigation, highlighting the need for further empirical research and doctrinal analysis.
Dos Santos C, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts DOI
5 answers
Dos Santos C, Gatti M. utilized deep convolutional neural networks for sentiment analysis of short texts. This approach is crucial in the field of natural language processing (NLP) due to the increasing importance of sentiment analysis in understanding subjective information from text data. The use of deep learning neural networks, such as convolutional neural networks (CNN) and long short-term memory (LSTM), has shown promising results in sentiment categorization. Additionally, the study by Zhan Shi, Chongjun Fan, highlighted the advantages of Bayesian and deep neural networks in short text sentiment classification, emphasizing the effectiveness of these algorithms in text representation for sentiment analysis tasks. Furthermore, the work by Raed Khalid, Pardeep Singh demonstrated the potential of using S-BERT pre-trained embeddings in combination with a CNN model for sentiment analysis, outperforming traditional machine learning approaches and word embedding models.
Dos Santos C, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts
5 answers
Dos Santos C, Gatti M. proposed the use of deep convolutional neural networks (CNNs) for sentiment analysis of short texts. This approach leverages the power of deep learning in natural language processing (NLP). The study by Raed Khalid and Pardeep Singh also highlighted the effectiveness of CNNs in sentiment analysis, achieving high accuracy by combining S-BERT pre-trained embeddings with a CNN model. Additionally, research by Zhan Shi and Chongjun Fan emphasized the advantages of Bayesian and deep neural networks in short text sentiment classification, showcasing high classification accuracy. These findings collectively support the notion that deep CNNs can be a valuable tool for analyzing sentiments in short texts, offering promising results for various applications in NLP.
What actions can be taken to improve the overall impact of the career development opportunity?
5 answers
To enhance the overall impact of career development opportunities, several actions can be implemented. Firstly, there is a need for better evaluation systems to assess the efficacy of career development services and inform public policy decisions. Secondly, educators should prioritize career development activities across all educational levels to prepare students effectively for their future transitions. Additionally, improving the quality of career services can positively influence national education, employment, and social inclusion priorities, thereby enhancing human capital development. Moreover, reframing professional development trainings as opportunities for professional growth rather than burdens can help employees perceive them more positively, potentially increasing their engagement and completion rates. Lastly, integrating career development processes that focus on individual employee development while enhancing organizational efficiency can lead to sustained improvements in overall organizational performance.
How to improve the ability to pay taxpayers?
5 answers
To enhance taxpayers' ability to pay, several strategies can be implemented. Firstly, tax education and training, service modernization, and maintaining engagement with taxpayers can improve compliance levels. Secondly, wise government spending is crucial as it influences SMEs owners' willingness to pay taxes, ultimately boosting tax revenue. Additionally, understanding the historical development of the ability-to-pay principle is essential to create a fair tax system. Moreover, proposing innovative tax policies, such as dividing transferred assets into classes and providing options for income inclusion, can make the tax system more equitable and prevent the need to sell assets for tax payments. Lastly, factors like tax knowledge, quality fiscal services, and perception of the tax system's effectiveness influence taxpayers' awareness and willingness to fulfill tax obligations.
What role do government policies and funding play in ensuring equitable access to maternal healthcare services in Turkana County?
5 answers
Government policies and funding are pivotal in ensuring equitable access to maternal healthcare services in Turkana County. The Kenyan government's initiatives like the 'Linda Mama' program prioritize improving reproductive, maternal, child, and adolescent health outcomes. Additionally, the adoption of free maternal healthcare policies, such as exempting maternal services from user fees, aims to promote skilled delivery and reduce pregnancy-related mortality. However, challenges persist, including limited access to public services for pastoralists due to their mobile lifestyle and marginalization, leading to poor health outcomes. To address these challenges, a proposed One Health framework in Turkana County integrates human and animal health services, aiming to improve health outcomes through increased vaccine coverage and improved service access. By addressing these issues through comprehensive policies and funding, equitable access to maternal healthcare services in Turkana County can be enhanced.
How is the current avance in DL with forcasting demand curves?
5 answers
The current advancement in Deep Learning (DL) for forecasting demand curves is significant, as evidenced by recent research. Various studies have highlighted the benefits of utilizing DL techniques for demand forecasting, showcasing improvements in accuracy and robustness. These advancements involve the integration of real-life events from news articles, historical sales data, holiday information, and even Google Trends data into multi-modal forecasting networks. Additionally, the use of Recurrent Neural Networks (RNN) with LSTM layers has shown superior forecasting performance compared to traditional regression models like SARIMA-MLR. Furthermore, the application of deep learning techniques in network slicing has led to the development of multi-model-based forecasting frameworks that enhance resource allocation efficiency and guarantee quality of experience in wireless networks.