Towards a real-time IoT: Approaches for incoming packet processing in cyber–physical systems

Behnke, Ilja; Blumschein, Christoph; Danicki, Robert; Wiesner, Philipp; Thamsen, Lauritz; Kao, Odej

doi:10.1016/j.sysarc.2023.102891

Cited by 5 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have only included studies (including technical articles and excluding review-type articles) focused on both areas. Table 7 summarizes the methodologies and results of the applications [115][116][117][118][119][120][121][122][123][124][125][126][127].…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…To ensure an up-to-date discussion, only works published in 2023 were included in Table 7. In general, existing works have tackled CPS and IoT applications using classification [115,[118][119][120][121][122][123][124]127], regression [125], and deep learning [118][119][120][121][122]126] approaches. More investigations can be conducted using clustering, transfer learning, and data generation algorithms.…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…Methodologies Results [115] Open network connections for real-time packet reception Soft early demultiplexing with packet classification and lazy cache invalidation; priority inheritance scheme to facilitate the communication process; rate limitation scheme to protect the system from unexpected high traffic…”

Section: Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Chui¹,

Gupta

Liu³

et al. 2023

Information

View full text Add to dashboard Cite

The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.

show abstract

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Chui¹,

Gupta

Liu³

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…It is an active protocol that uses IEEE 802.15.4 and is designed for multi-hop and many-to-one communication, while it can also support one-to-one messaging (Arshad et al, 2022;Tyagi et al, 2022a). RPL may handle a variety of connection layers, including limited link layers, possibly lossy link layers, and link layers used in resourceconstrained devices (Behnke et al, 2023). The protocol can establish network routes quickly, communicate routing knowledge, and coordinate topologies efficiently (Wang et al 2023a).…”

Section: Introductionmentioning

confidence: 99%

Priority RPL for IOT Based Smart Manufacturing Industries

2023

JAETS

View full text Add to dashboard Cite

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.

show abstract

Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS

Jiang,

Wang,

Que

et al. 2024

Journal of Systems Architecture

View full text Add to dashboard Cite

Towards a real-time IoT: Approaches for incoming packet processing in cyber–physical systems

Cited by 5 publications

References 26 publications

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Priority RPL for IOT Based Smart Manufacturing Industries

Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS

Contact Info

Product

Resources

About