Data congestion control framework in Wireless Sensor Network in IoT enabled intelligent transportation system

Kavitha, T.; Pandeeswari, N.; Shobana, R.; Vinothini, V.R.; Sakthisudhan, K.; Jeyam, A.; Malar, A. Jasmine Gnana

doi:10.1016/j.measen.2022.100563

Cited by 11 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They act as a bridge between the WSNs and other networks, such as local area networks (LANs) or wide area networks (WANs). The gateway devices can be connected to the internet or other communication networks to enable remote access and control of the IoT system [50]. To enable efficient communication within WSNs, various communication protocols are used.…”

Section: Iot and Wsnsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu,

Kim,

Lin

2024

Sensors

View full text Add to dashboard Cite

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

show abstract

Section: Iot and Wsnsmentioning

confidence: 99%

“…In addition, if FL clients are equipped with different types of communication modules or protocols, allowing all FL clients to participate in the same communication round can be challenging. In this section, we will discuss different approaches for combining WSNs and FL, including edge computing, data fusion, and resource allocation [11,50].…”

Section: Fl In Wsnsmentioning

confidence: 99%

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu,

Kim,

Lin

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The iterative nature of the FL does not eliminate the network congestion and privacy threats problem completely (Kavitha et al, 2022) that occurs during the transfer of weights. Indeed, for complex models, largescale applications, and high-frequency updates, the communication overhead is not negligible and needs to be addressed as stated in (Chhetri et al, 2023).…”

Section: Motivation and Contributionmentioning

confidence: 99%

Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain

Balija,

Nanda,

Sahoo

2024

AAAI-SS

View full text Add to dashboard Cite

Large language models (LLM) have gathered attention with the advent of ChatGPT. However, developing personalized LLM models faces challenges in real-world applications due to data scarcity and privacy concerns. Federated learning addresses these issues, providing collaborative training while preserving the client’s data. Although it has made significant progress, federated learning still faces ongoing challenges, such as communication efficiency, heterogeneous data, and privacy-preserving methods. This paper presents a novel, fully decentralized federated learning framework for LLMs to address these challenges. We utilize different blockchain-federated LLM (BC-FL) algorithms, effectively balancing the trade-off between latency and accuracy in a decentralized-federated learning environment. Additionally, we address the challenge of communication overhead in peer-to-peer networks by optimizing the path for weight transfer and mitigating node anomalies. We conducted experiments to evaluate memory usage and latency in server and serverless environments. Our results demonstrate a decrease in latency by 5X and a 13% increase in accuracy for serverless cases. Comparisons between synchronous and asynchronous scenarios revealed a 76% reduction in information passing time for the latter. The PageRank method is most efficient in eliminating anomalous nodes for better performance of the global federated LLM model. The code is available on GitHub (https://github.com/Sreebhargavibalijaa/Federated_finetuning_LLM-s_p2p_environment)

show abstract

“…Over the years, encroachments in the micro-electro mechanical-system (MEMS) have made it imaginable to construct sensor devices that are limited in power, tiny in size, very limited sensing power, and have computational capabilities. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Different types of sensor devices with these characteristics have been developed which can be used in various applications. Research teams throughout the world have been intrigued by this unusual trait which has prompted a significant amount of attention to be paid to conserving the energy of nodes 3 since it was discovered.…”

Section: Related Workmentioning

confidence: 99%

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

Malik,

Singh,

Manju

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryInternet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

show abstract

Data congestion control framework in Wireless Sensor Network in IoT enabled intelligent transportation system

Cited by 11 publications

References 13 publications

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

Contact Info

Product

Resources

About