MMB & DFT 2012 Workshop Proceedings

Recommendations

Info

We aim to use network calculus to model energy replenishing sensor nodes inorder to derive their service curves. By concatenation of the individual servicecurves we are then able to define the end-to-end service for a specific data flowand thus bound the time it takes until a measurement or event is reported to thesink. This allows to check if requirements are met and enables to take appropriateaction in the network design already before deployment.As mentioned above, harvesting defines the energy replenishment of a sensornode and therefore restricts the possible service by upper bounding the energythat can be spent. On the other hand, the service that is required by a flowdefines how much energy is spent by the sensor node and how much could bepreserved to increase performance in the subsequent periods of operation. Ourmain effort lies in modeling the mutual dependency between performance andenergy consumption in order to derive an accurate service curve representation.During the time the sensor is running on its initial energy budget, it can offerforwarding services according to a fixed service curve β. The delay an arrivingdata flow that is characterized by the input process A suffers when crossing asensor node on its path to the sink is then lower bounded by the horizontal deviationof its arrival curve α and the sensor’s service curve, i.e., h(α, β). However,as soon as the battery was depleted first, the service is additionally depending onthe sensor node’s energy replenishment and we cannot simply use the ordinarynetwork calculus descriptions for service and arrival to derive the delay bound.The service demand and thus the energy consumption of the data flow A mustbe characterized in order to assure for any time instance that the energy budgetis not exceeded and the service curve has to be adapted accordingly.Energy consumption can be characterized by the output of the sensor nodeA ′ ≥ A ⊗ β. If the sensor’s service β would cause an output that is too energyexhaustive, we aim to limit the amount of data that enters the system and thuscan consume energy in order to prevent the depletion of the sensor’s battery. Wecall this arrival restriction A restr . For any time instance the effective input servedby a node’s forwarding capabilities is then bounded by A∧A restr and the outputaccordingly by (A ∧ A restr ) ⊗ β which is guaranteed to drain less energy thanavailable. Our model for the mutual dependency therefore resembles a feedbacknetwork [2] where the input depends on the output and vice versa.The problem of a service restriction according to the behavior of a replenishingenergy source resembles a window flow control mechanism [1]. Just like awindow flow controller (WFC) we want to limit the amount of data that entersthe system, but in contrast to a WFC we do not intent to do so according to apredefined and well-known artificial restriction. Thus we aim to model a moregeneral setting of external restrictions to a sensor node’s service.References1. Rajeev Agrawal and Rajendran Rajan. Performance bounds for guaranteed andadaptive services. IBM Research Report, 1996.2. Francois Baccelli, Guy Cohen, Geert Jan Olsder, and Jean-Pierre Quadrat. Synchronizationand Linearity: An Algebra for Discrete Event Systems. Wiley, 1992.8
Network Calculus: Application to an IndustrialAutomation NetworkSven Kerschbaum 1 , Kai-Steffen Hielscher 2 , Ulrich Klehmet 2 and Reinhard German 21 Siemens AG, Industry Sector (Industry Automation Division)Nürnberg, GermanyEmail: sven.kerschbaum@siemens.com2 Department of Computer Science 7 (Computer Networks and Communication Systems)University of Erlangen-NürnbergEmail: {klehmet, ksjh, german}@informatik.uni-erlangen.deIntroductionDue to company-wide information processing, in recent years the office and automationworld have increasingly merged (“office meets factory”). Processes in industrialautomation plants often require hard deadlines. Classical performance modeling approaches,e.g. simulation and queuing theory, are not able to provide these bounds. Onthe contrary, Network Calculus (NC) is a formal modeling method to obtain worst-casebounds [1]. Its results allow to plan and dimension industrial plants which fulfill therequired deadlines even when using non-real time capable network components.Industrial AutomationHistorically, industrial automation networks were mainly based on specific networkscalled fieldbuses, e.g., Profibus and Modbus, that interconnected programmable logiccontrollers, robot controllers, I/O devices, etc. to exchange data for monitoring, controllingand synchronizing industrial processes. The fieldbus protocols ensured that theend-to-end message delays remained within specific limits and met the requirementsof industrial processes. As a consequence, industrial automation networks were deterministicand allowed their end-to-end delays to be determined. Nowadays, automationsystems are connected via Ethernet. The use of Ethernet is becoming more and morecommon for connecting the devices at the field level. Despite all affords to include qualityof service aspects into the Ethernet standard, e.g. priority tagging, Ethernet remainsnon-deterministic, which is the main requirement of industrial networks.The Network Calculus EngineAfter evaluating different NC tools ([2,4]), we decided to implement a specific toolfor industrial automation networks. Our Network Calculus Engine (NCE) provides aframework to model and analyze networks. Until now, the total (TFA) and separatedflow analysis (SFA) [2,3], methods have been employed. The NCE is highly structuredand consists of various modules. The most important modules are the network,curve and analysis module. The network module provides basic elements, e.g. nodes9
Page 1 and 2: MMB & DFT 2012Workshop ProceedingsW
Page 3: Table of ContentsPreface . . . . .
Page 7: WoNeCa
Page 12 and 13: and links, that can be used to buil
Page 14: whereR ⊗ ˜S(t) = inf τ[R(τ) +
Page 18 and 19: A function f is approximated by a c
Page 21: PILATES
Page 24 and 25: 2 Joint PHY Layer Security, PU Auth
Page 26: 4 Joint PHY Layer Security, PU Auth
Page 29: Key Generation from Wireless Channe
Page 32 and 33: Sender Preamble SFD Header 0 0 0 0
Page 35: MACoRN
Page 39: The Monte Carlo EM Method for the P
Page 44: ISBN: 978-3-00-037728-0

MMB & DFT 2012 Workshop Proceedings

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?