Superframe Scheduling with Beacon Enable Mode in Wireless Industrial Networks
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In this paper, we develop a method to check the schedulability superframe with beacon-enabled mode in ISA100.11a Wireless Industrial Networks environment. Maximum length of time slot is added in the superframe to reduce the overhead, and deadline monotonic scheduling approach is proposed to analyze the system scenario. The simulation results indicate that our proposed method required less number of beacon for message scheduling. Therefore, more data can be sent in the network with our proposed method. Full paper can be find in http://okadanil.com/publications
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Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
Scalable Parallel Computing on Clouds
Iterative computations are at the core of the vast majority of data-intensive scientific computations. Recent advancements in data intensive computational fields are fueling a dramatic growth in number as well as usage of such data intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable environment for the scientists to perform data intensive computations. However, clouds by nature offer unique reliability and sustained performance challenges to large scale distributed computations necessitating computation frameworks specifically tailored for cloud characteristics to harness the power of clouds easily and effectively. My research focuses on identifying and developing user-friendly distributed parallel computation frameworks to facilitate the optimized efficient execution of iterative as well as non-iterative data-intensive computations in cloud environments, alongside the evaluation of heterogeneous cloud resources offering GPGPU resources in addition to CPU resources, for data-intensive iterative computations.
secon15_slides_v1.1
This document proposes a pre-processing based approach to offload computer vision tasks from smartphones to servers to improve energy efficiency and response time. It discusses how downscaling images as a pre-processing technique can reduce data transfer sizes but also decrease processing accuracy. An analytical framework is developed to determine the optimal pre-processing level based on energy, time constraints, and network conditions to maximize accuracy. Evaluation shows the approach achieves near-optimal accuracy while satisfying constraints, outperforming alternative instant upload and check approaches.
Training ImageNet-1k ResNet50 in 15min pfn
This document summarizes research conducted by Preferred Networks to train a ResNet-50 model on the ImageNet dataset using a minibatch size of 32,000 across 1024 Tesla P100 GPUs. They were able to complete 90 training epochs in 15 minutes, achieving a validation accuracy of 74.9%. To enable training with such a large minibatch, they employed techniques like RMSprop warmup, slow start learning rates, and batch normalization without moving averages. The training was conducted on Preferred Networks' in-house cluster MN-1 which has 128 nodes each with 8 GPUs connected via InfiniBand FDR interconnect.
Modeling Uncertainty For Middleware-based Streaming Power Grid Applications
The document describes modeling uncertainty in middleware-based streaming applications for power grids. It presents a discrete-event model built in Ptolemy II to capture uncertainty from sources like middleware latency, network delays, and number of sensor streams. Monte Carlo simulations are run over this model by varying parameters like middleware concurrency and sensor streams. Regression analysis is then used to understand the relationship between these influential parameters and the end-to-end application run time.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
Clone cloud
CloneCloud proposes a flexible architecture that seamlessly uses cloud resources to augment mobile applications in an energy-efficient manner. It clones a mobile app and partitions it at runtime to migrate computation-heavy threads to the clone in the cloud. This allows threads to leverage faster CPUs and hardware accelerations remotely while keeping other functionality local. A static analyzer identifies legal partitions, a dynamic profiler collects execution data to build cost models, and an optimization solver picks optimal partitions to minimize time and energy. The prototype delivers significant speedups and energy reductions without requiring programmer involvement in partitioning. However, it does not give programmers flexibility over partitioning and may not cover all parameter combinations.
Energy Monitoring With Self-taught Deep Network
This document discusses using a self-taught deep learning approach for energy monitoring tasks like anomaly detection and appliance detection using unlabeled time series data. It proposes using a neural network to learn representations of the raw time series data by predicting timeslot labels derived from time of day. These learned features capture important time-based patterns and provide a better representation for downstream tasks. The approach is shown to improve performance on appliance detection compared to using the raw data directly. It is also applied to anomaly detection using an autoencoder model trained on the learned features of windows. Finally, an overall system architecture is proposed to implement this approach at scale across data collection, feature extraction, training and various energy monitoring applications.
Saving Energy for Cloud Applications in Mobile Devices using Nearby Resources
A presentation of a paper titled "Saving Energy for Cloud Applications in Mobile Devices using Nearby Resources", which was presented in the 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2018), Cambridge, UK, March 2018.
DOI: 10.1109/PDP2018.2018.00091
Error Permissive Computing
1) The document explores a new concept called error permissive computing that improves computing capabilities and reduces power consumption by allowing and managing hardware errors through system software instead of eliminating errors through general purpose hardware error correction.
2) It describes several approaches for implementing error permissive computing including a software framework called BITFLEX that enables approximate computing, an FPGA-based memory emulator for evaluating new system software mechanisms, and techniques for sparse and topology-aware communication that can accelerate large-scale deep learning and reduce communication costs.
3) The goal is to take a holistic approach across hardware and software layers to perform lightweight error correction at the software level while eliminating general purpose error correction in hardware for improved efficiency.
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Wireless Sensor Networks (WSNs) are used in many application fields, such as military,
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TASK & RESOURCE SELF-ADAPTIVE EMBEDDED REAL-TIME OPERATING SYSTEM MICROKERNEL...
Wireless Sensor Networks (WSNs) are used in many application fields, such as military,
healthcare, environment surveillance, etc. The WSN OS based on event-driven model doesn’t
support real-time and multi-task application types and the OSs based on thread-driven model
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software. Furthermore, the sensor nodes are vulnerable to security attacks because of the
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RAIN (Reliable Array of Independent Nodes) technology was developed at Caltech to provide fault tolerance in distributed systems. It includes three main components: 1) data storage across distributed processors that can retrieve data even if some fail, 2) a redundant communication network between processors, and 3) automatic recovery of applications if a processor fails. RAIN aims to minimize the number of nodes between clients and servers, make nodes more robust and independent, and replace faulty nodes. It provides scalability, high availability, and fault tolerance through techniques like redundant storage and fault monitoring. Potential applications include high availability video and web servers.
krishna@GRAPH
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An energy efficient mac protocol for wireless
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Jawad Ali Khan
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50120130406009 2
This document summarizes an article from the International Journal of Computer Engineering and Technology that analyzes the performance of WLAN standards for multimedia applications with multiple subnet scenarios. It describes creating baseline scenarios in OPNET to simulate wired and wireless network frameworks with three subnets. Traffic including video conferencing, web, and other applications was introduced. The performance of overall delay across the three scenario networks was measured. Multimedia load scenarios added video conferencing traffic served by a video server to analyze support for multimedia applications.
Performance evaluation of qos in
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obtaining the performance measures of an end-to-end delay as well as throughput can be used as an
effective tool for this purpose.
Experimental Analysis of Small Internetwork using OPNET 9.1
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The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.IRJET- Development of Redundant Communication Methods for Robots in Nucle...
This document describes the development of redundant communication methods for robots used in nuclear applications. It aims to minimize wiring by serializing communication lines between a master control system and sub-modules like analog front ends and actuators. It implements three redundant communication methods - RS-485, optical fiber communication, and power line communication - to ensure uninterrupted data transfer even if one method fails. The methods are tested by transmitting joystick data between two microcontrollers to control a robotic arm model over each communication channel individually and combined. Results found the system works efficiently over any single channel and provides redundancy by using multiple channels.
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This document discusses increasing the energy efficiency of mobile phones through task offloading to cloud computing. It presents a model where mobile devices can offload computationally intensive tasks to remote cloud servers to save battery power. The key factors in determining whether to offload a task include the processing speeds of the mobile device and cloud server, data transmission time over the network, and power consumption for local processing versus transmission. An analytical model is developed using 802.11 WiFi specifications to estimate transmission times and energy savings from offloading tasks to the cloud. The goal is to provide a way to decide whether offloading a specific task will increase energy efficiency for the mobile phone.
Enhancing Data Transmission and Protection in Wireless Sensor Node- A Review
The document discusses enhancing data transmission and protection in wireless sensor networks. It first provides background on wireless sensor networks and discusses some of the key challenges, including limited energy resources of sensor nodes. It then reviews existing techniques that have been proposed to improve energy efficiency of nodes and provide secure data transmission. These include reducing communication costs, cooperative behavior between nodes, and using cryptographic techniques like DES encryption. Finally, the document proposes using the LEACH protocol to increase node efficiency by implementing clustered routing, and the RC-6 encryption algorithm to securely transmit data in the wireless sensor network.
IRJET- Dynamic Adaption of DCF and PCF Mode of IEEE 802.11 WLAN
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This document discusses assessing whether tasks on mobile phones should be offloaded to the cloud to improve energy efficiency. It presents a model where mobile devices can offload computationally intensive tasks to the cloud via wireless networks. An experiment is described that compares the energy consumption and time taken of a video conversion task performed locally on a mobile phone versus offloading the task or different parts of the task to a cloud. The results show that offloading the entire task to the cloud reduces energy consumption and processing time compared to performing the task locally on the mobile phone. The document concludes offloading tasks to the cloud can increase a mobile phone's energy efficiency and discusses areas for future work.
High performance energy efficient multicore embedded computing
The document discusses high-performance energy efficient multicore embedded computing. It describes the challenges of achieving both high performance and low power consumption. Several approaches are examined at the architectural level, including heterogeneous multicore processors, cache partitioning, and wireless interconnects. Hardware-assisted middleware approaches like power gating and software approaches like task scheduling and migration are also covered. Finally, examples of commercial high-performance energy efficient multicore processors are provided. The document concludes that high-performance energy efficient computing is an important research area with applications from consumer electronics to supercomputing.
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Lead-acid battery is widely utilized to provide electricity
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### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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Superframe Scheduling with Beacon Enable Mode in Wireless Industrial Networks
- 1. Superframe Scheduling with Beacon Enable Mode in Wireless Industrial Networks Oka Danil Saputra, Soo Young Shin Wireless & Emerging Networking System (WENS) Laboratory, School of Electronic Engineering, Kumoh National Institute of Technology, Gumi, South Korea. 1 International Workshop on Wireless, Mobile and Sensor Network-2014 Istanbul, Turkey - August 22-23, 2014
- 2. Keywords • Superframe • Scheduling • Beacon • ISA100.11a 2
- 3. Wireless Industrial Networks (WINs) 3 Figure source: • The ISA100 Standards Overview & Status • http://www.yokogawa.com/pr/news/2012/img/20120207-1en.jpg Control Room
- 4. WHY WINs 4 • Offer low cost • Routing is never an issue. • Flexibility • Easy to Installation Figure source: https://www.nics.uma.es/images/research/WSN-sensors-industrial-c1.jpg
- 5. Focus Area 5 Wireless Industrial Networks Technology Wireless HART MAC Layer: Complies with the IEEE 802.15.4-2006 MAC MAC Layer: Modified IEEE 802.15.4-2006 MAC ISA100.11a CSMA/CA Sent Aperiodic Message TDMA Sent Periodic Message Focus Area • CSMA/CA: Carrier Sense Multiple Access with Collision Avoidance • TDMA: Time Division Multiple Access
- 6. Related Work 6 Paper Limitation [4] The knowledge about industrial data have characteristic “periodically” didn’t address by paper. [6] That proposed method requires more beacon (overhead) . • [4] F.P. Rezha and Soo Young Shin. Performance evaluation of isa100.11a industrial wireless network. In Information and Communications Technologies (IETICT 2013), IET International Conference on, April 2013. • [6] F. Dewanta, F.P. Rezha, and Dong-Sung Kim. Message scheduling approach on dedicated time slot of isa100.11a. In ICT Convergence (ICTC), 2012 International Conference on, pages 466–471, Oct 2012.
- 7. Contribution • Paper discuss clearness about superframe scheduling inspired by deadline monotonic scheduling. • Our proposed method reduce the overhead without degrading the network performance. 7
- 8. Network Model 8 𝑀2 𝑀1 𝑀3 𝑀4 𝑀 𝑛 𝑛 : the maximum number of node in covered by gateway.
- 9. Message Characteristic 9 Where: 𝑟 𝑀: start release time. 𝑐 𝑀: computation time. 𝑑 𝑀: deadline time. 𝑡 𝑀: period of message. 𝐼𝑗: Iteration with index-𝑗.
- 10. Proposed Scheme 10 Beacon: • Synchronization. • More scalability. • Self-organization. [3] Wireless systems for industrial automation: Process control and related applications. ISA100.11a Working Group, pages 1–817, 2009. 10 ms [3] Length of superframe 25 [3] Superframe: 𝑆 = 𝐵 + 𝑁=1 𝑁=𝜂 𝑀 𝑁
- 11. Deadline Monotonic Scheduling 11 Figure source: http://groups.inf.ed.ac.uk/teaching/slipc10-11/maciej.php?page=docs/design
- 12. Scheduling Analysis Theorem IV.1: Superframe is schedulable if and only if sufficient Proof: Suppose that the condition is insufficient thereby 𝑐 𝑀 𝑛 > 𝑑 𝑀 𝑛 , then message 𝑀 𝑁 cannot finish computation time before deadline 𝑑 𝑀 𝑛 . Therefore node misses to deliver data and unschedulable. The condition schedulable is passed while message have enough time to finish the execution. 12 ∀𝑴 𝑵: 𝒄 𝑴 𝒏 ≤ 𝒅 𝑴 𝒏 Note: Beacon, Message 1, Message 2, Message 3. 𝑀1 𝑀2 𝑀3 Miss Deadline
- 14. Simulation Parameter 14 • Simulation Tool: Matlab Where: 𝑟 𝑀: start release time. 𝑐 𝑀: computation time. 𝑑 𝑀: deadline time. 𝑡 𝑀: period of message.
- 15. Simulation Result 15 Figure Message scheduling [6] is applied by deadline monotonic scheduling [6] F. Dewanta, F.P. Rezha, and Dong-Sung Kim. Message scheduling approach on dedicated time slot of isa100.11a. In ICT Convergence (ICTC), 2012 International Conference on, pages 466–471, Oct 2012. More beacon needed.
- 16. Simulation Result 16 Figure The proposed scheme, superframe scheduling, is applied by deadline monotonic scheduling Less beacon needed.
- 17. Simulation Result 17 Figure Comparison beacon required between message Scheduling scheme with proposed method 4 8 11 15 18 22 25 29 33 36
- 18. Conclusion • Check the schedulability of superframe in network using deadline monotonic. • Our method guarantee that exchange of data in network successfully without overlap each other with condition superframe passed the schedulability. • By reducing overhead, more data (message) can be sent in network. 18
- 20. THANK YOU 20