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  • Programming Sensor Networks: A Tale of Two PerspectivesRamesh [email protected] Networks Laboratoryhttp://enl.usc.edu

  • Wireless SensingMotes: 8 or 16 bit sensor devices32-bit embedded single-board computers

  • Platform ChallengesWireless communication is noisy Loss rates far higher than in wired environmentsBattery life is a constraint, and determines network lifetimeOther constraints on the mote platforms Processors (16 bit, low MIPS) Memory (a few KBs to few MBs)

  • State of SoftwareMotes are programmed using TinyOS nesCTinyOS An event-based OS with non-preemptive multi-taskingnesC A dialect of C that has language support for TinyOS abstractions

  • But, lots of applications!VolcanoCitySoilHabitatBridgeBirds Nests

  • Wireless Sensing ResearchLots of research!

  • some of it from our Lab

  • But, there is a problem!Six pages of 158 pagesof code from a wireless structural dataacquisition system called WisdenProgramming these networks is hard!

  • Three ResponsesEvent-based programming on an OS that supports no isolation, preemption, memory management or a network stack is hard.Therefore, we need OSes that support preemption and memory management, we need virtual machines, we need higher-level communication abstractions.

    OS/Middleware

  • Three ResponsesTiny sensor nodes (motes) are resource-constrained, and we cannot possibly be re-programming them for every application.Therefore, we need a network architecture that constrains what you can and cannot do on the motes.Networking

  • Three ResponsesToday, were programming sensor networks in the equivalent of assembly language.What we need is a macroprogramming system, where you program the network as a whole, and hide all the complexity in the compiler and the runtime

    Programming Languages

  • Three ResponsesProgramming LanguagesOS/MiddlewareNetworkingThe Tenet ArchitectureThe Pleaides Macroprogramming System

  • The Tenet ArchitectureOmprakash Gnawali, Ben Greenstein, Ki-Young Jang, August Joki, Jeongyeup Paek, Marcos Vieira, Deborah Estrin, Ramesh Govindan, Eddie Kohler, The TENET Architecture for Tiered Sensor Networks, In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (Sensys), November 2006.

  • The ProblemSensor data fusion within the network can result in energy-efficient implementations

    But implementing collaborative fusion on the motes for each application separately can result in fragile systems that are hard to program, debug, re-configure, and manage

    We learnt this the hard way, through many trial deployments

  • An Aggressive PositionWhy not design systems without sensor data fusion on the motes?

    A more aggressive position: Why not design an architecture that prohibits collaborative data fusion on the motes?

    Questions:How do we design this architecture?Will such an architecture perform well?

  • Tiered Sensor NetworksEnable flexible deployment of dense instrumentationMotesLow-power, short-range radiosContain sensing and actuationAllow multi-hop communicationMasters32-bit CPUs (e.g. PC, Stargate)Higher-bandwidth radiosLarger batteries or poweredProvide greater network capacity, larger spatial reachMany real-world sensor network deployments are tieredReal world deployments at,Great Duck Island (UCB, [Szewczyk,`04]),James Reserve (UCLA, [Guy,`06]),Exscal project (OSU, [Arora,`05]),Future large-scale sensor network deployments will be tiered

  • Tenet PrincipleMulti-node data fusion functionality and multi-node application logic should be implemented only in the master tier. The cost and complexity of implementing this functionality in a fully distributed fashion on motes outweighs the performance benefits of doing so.Aggressively use tiering to simplify system !

  • Tenet Architectureand may return responsesMotes process data, Applications run on masters, and masters task motes

  • What do we gain ?Simplifies application development

    Application writers do not need to write or debug embedded code for the motesApplications run on less-constrained masters

  • What do we gain ?Enables significant code re-use across applications

    Simple, generic, and re-usable mote tierMultiple applications can run concurrently with simplified mote functionality

    Robust and scalable network subsystemNetworking functionality is generic enough to support various types of applications

  • ChallengesMore bits communicated than necessary? Communication over longer hops?Not an issueTypically the diameter of the mote tier will be smallCan compensate by more aggressive processing at the motesIn most deployments, there is a significant temporal correlation Mote-local processing can achieve significant compression but little spatial correlation Little additional gains from mote tier fusionMote-local processing provides most of the aggregation benefits.The costs will be small, as we shall see

  • System OverviewTasking SubsystemNetworking SubsystemTaskingLanguageTask ParserTasklets and RuntimeReliableTransportRoutingTask DisseminationTenet System

  • Tasking SubsystemGoalsFlexibility: Allow many applications to be implementedEfficiency: Respect mote constraintsGenerality: Avoid embedding application-specific functionalitySimplicity: Non-expert users should be able to develop applicationsTasking SubsystemNetworking SubsystemTaskingLanguageTaskParserTasklets and RuntimeReliableTransportRoutingTask DisseminationTenet System

  • Tasking LanguageLinear data-flow language allowing flexible composition of taskletsA tasklet specifies an elementary sensing, actuation, or data processing actionTasklets can have several parameters, hence flexibleTasklets can be composed to form a taskSample(500ms, REPEAT, ADC0, LIGHT) Send()No loops, branches: eases construction and analysisNot Turing-complete: aggressively simple, but supports wide range of applications

    Data-flow style language natural for sensor data processing

  • Classes of TaskletsSystemRebootGetSend

    Task ManipulationIssue (Wait, Alarm)DeleteTaskIfDeleteActiveTaskIf

    Sensor/ActuatorSampleActuateData ManipulationArithmeticComparisonLogicalBitwiseStatisticsDeleteAttributeIf

    MiscellaneousStorageCount / Constant

  • Task Examples

    Sample and Send

    With time-stamp and seq. number

    Get memory status for node 10

    If sample value is above 50, send sample data, node-id and time-stamp

  • How many Tenet tasks can a mote run at the same time?+Memory is the constraint, and task complexity matters!Network Monitoring(on Tmote Sky motes)

  • Overview of Networking SubsystemTasking SystemNetworking SubsystemTaskingLanguageTaskParserTasklets and RuntimeTenet SystemGoalsScalability and robustnessGenerality: Should support diverse application needsReliableTransportRoutingTask Dissemination

  • Task DisseminationReliably flood task from any master to all motesUses the master tier for disseminationPer-active-master cache with LRU replacement and aging

  • Reliable TransportDelivering task responses from motes to mastersWorks transparently across the two tiers

    Three different types of transport mechanism for different application requirementsBest-effort delivery (least overhead)End-to-end reliable packet transportEnd-to-end reliable stream transport

  • Putting it all together

  • Application Case Study: PEGGoalCompare performance with an implementation that performs in-mote multi-node fusionPursuit-Evasion GamePursuers (robots) collectively determine the location of evaders, and try to corral them

  • Mote-PEG vs. Tenet-PEGPursuerEvader DetectedRe-task the motesTask the motesEvaderPursuerEvader DetectedEvaderLeader ElectionLeaderMote-PEGTenet-PEG

  • PEG ResultsError in Position EstimateReporting Message OverheadComparable positional estimate errorComparable reporting message overhead

  • PEG ResultsLatency is nearly identicalA Tenet implementation of an application can perform as well as an implementation with in-mote collaborative fusion

  • Why is this surprising?More bits communicated than necessary? Communication over longer hops?Not an issueTypically the diameter of the mote tier will be smallCan compensate by more aggressive processing at the motesIn most deployments, there is a significant temporal correlation Mote-local processing can achieve significant compression but little spatial correlation Little additional gains from mote tier fusionMote-local processing provides most of the aggregation benefits.Can achieve performance comparableto mote-native implementation

  • Real-world Tenet deployment on Vincent Thomas BridgeRan successfully for 24 hours100% reliable data deliveryDeployment time: 2.5 hoursTotal sensor data received: 860 MB

  • Interesting Observations

  • Related WorkArchitectureSNA [Culler,`05], [Polastre,`05], Internet [Saltzer, `84], Virtual MachinesMate [Levis,`02], TaskingVanGo [Greenstein,`06], SNACK [Greenstein,`04]DisseminationDeluge [Hui,`04], Trickle [Levis,`04], Reliable TransportRMST [Stann,`03], Wisden [Xu,`04], RoutingESS [Guy,`06], Centroute [Stathopoulos,`05],

  • SummaryApplicationsSimplifies application developmentNetworking SubsystemRobust and scalable networkTasking SubsystemRe-usable generic mote tierSimple, generic and re-usable system

  • Software AvailableMaster tierCygwin Linux Fedora Core 3StargateMacOS X Tiger

    Mote tierTmote SkyMicaZMaxforhttp://tenet.usc.edu

  • The Pleaides Macroprogramming SystemNupur Kothari, Ramakrishna Gummadi, Todd Millstein, Ramesh Govindan, Reliable and Efficient Programming Abstractions for Wireless Sensor Networks, Proceedings of the SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2007.

  • What is Macroprogramming?Conventional sensornet programmingNode-local program written in nesCCompiled to mote binary

  • What is Macroprogramming?Central program that specifies application behaviorNode-local program written in nesCCompiled to mote binaryCompilerRuntime+?

  • Change of Perspectiveint val LOCAL;

    void main() { node_list all = get_available_nodes(); int max = 0;

    for (int i = 0, node n = get_node(all, i); n != -1; n = get_node(all, ++i)) { if (val@n > max) max = val@n; } } Easily recognizable maximum computation loop

  • Pleaides Languageint val LOCAL;

    void main() { node_list all = get_available_nodes(); int max = 0;

    for (int i = 0, node n = get_node(all, i); n != -1; n = get_node(all, ++i)) { if (val@n > max) max = val@n; } } Node-local variableCentral variableList of nodes in networkNetwork NodeAccess node-local variable at node

  • Other Language FeaturesSensors

    Timers

    Waiting for asynchronous events unsigned int temp SENSOR(TEMP) LOCAL;unsigned int timer TIMER(100) LOCAL; event_wait(n, &temp, &timer);

  • AdvantagesReadability

    nesC programs harder to readProgrammer has to manage communicationTinyOS event model complicates control flow

  • AdvantagesReliability

    Getting predictable semantics from nesC programs, while still ensuring efficiency, can result in complex programs

  • Car ParkingFind nearest empty parking spot such that no two cars are assigned the same spot

  • AdvantagesExtensibility

    Slightly modifying application semantics is easier in Pleaides than it would be in nesCFind a parking spot such that no two cars are assigned the same spotFind nearest parking spot such that at most two cars are assigned the same spot

  • But

    Not a new idea!Shared-memory models in parallel computing and we all know how that turned out!Are shared memory models workable for sensor networks?

  • Implementing PleaidesThe Pleaides CompilerConcurrencyPartitioning

  • The Need for PartitioningNave approachCode executes hereNode-local variables read from or written to over the networkCan incur high communication cost

  • The Need for PartitioningTake the computation to the dataControl-flow migrationAccess node-local variables from nearby nodesHow does the compiler do this automatically?

  • NodecutsControl-flow graph for max exampleNodecuts generated by the Pleaides compilerProperty: The location of variables accessed within a nodecut are known before its execution

  • Control-Flow MigrationRuntime attempts to find lowest communication cost node to execute nodecutEven sophisticated sensor network programs have a small number (5-7) of nodecuts

  • Implementing PleaidesThe Pleaides CompilerConcurrencyPartitioning

  • ConcurrencySensor network programs can leverage concurrent execution

    ChallengesHow to specify concurrent execution?What consistency semantics to offer?

  • Programmer-Directed Concurrencycfor (int i = 0, node n = get_node(all, i); n != -1; n = get_node(all, ++i)) { if (val@n > max) max = val@n; } } int val LOCAL;

    void main() { node_list all = get_available_nodes(); int max = 0;

    Concurrent-for loop

  • Serializabilitycfor (int i = 0; i < 5; i++) { j = i; j++;} cfor execution cannot be purely concurrent, since program output would be unpredictable0123440132Serializability: cfor execution corresponds to some sequential execution of the loops iterations

  • Ensuring SerializabilityChallenge:To ensure serializability while allowing performance gains through concurrent execution

    Approach:Distributed locking, with multiple-reader/single-writer locks

  • Distributed Lockingcfor (node n = 1; n < 5; n++) { if (val@n > max) max = val@n; } 2010056val@nmax20Technique generalizes to nested cfor loops using hierarchical lockingLocking code generated entirely by the compiler

  • Distributed LockingTechnique generalizes to nested cfor loops using hierarchical locking1020cfor (node n = 1; n < 5; n++) { if (temp@n > max) max = temp@n; } 201056temp@nmaxCompiler generates locking code Deadlocks possible, but dealt with using a simple deadlock detection and recovery scheme

  • DeadlocksDeadlock detectionLock coordinator at the top of the hierarchy can detect a deadlock easily

    RecoveryRestarts the blocked instances sequentially

  • Looser Forms of ConsistencyApplications can use looser forms of consistency for performance

    Accesses to variables annotated LOOSE within a cfor are not serializedunsigned int beacon LOOSE;

  • ImplementationBuilt as an extension to the CIL infrastructure for C analysis and transformation

    Pleaides compiler generates nesC code; each nodecut corresponds to a nesC task

    Experience with some applications: pursuit-evasion, decentralized street parking

  • Pursuit-Evasion in PleaidesPleaides-PEG has comparable error to mote-PEG and about a factor of two higher latency

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    Position error

    Fraction of reports

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    0.2


Energy-Efficient Positioning for Smartphone Applications using Cell-ID Sequence Matching Jeongyeup Paek *, Kyu-Han Kim +, Jatinder P. Singh +, Ramesh Govindan.

Energy-Efficient Positioning for Smartphone Applications using Cell-ID Sequence Matching Jeongyeup Paek *, Kyu-Han Kim +, Jatinder P. Singh +, Ramesh Govindan.

DREAM: Dynamic Resource Allocation for Software-defined Measurement Masoud Moshref, Minlan Yu, Ramesh Govindan, Amin Vahdat 1 (SIGCOMM’14)

DREAM: Dynamic Resource Allocation for Software-defined Measurement Masoud Moshref, Minlan Yu, Ramesh Govindan, Amin Vahdat 1 (SIGCOMM’14)

SIF: A Selective Instrumentation Framework for Mobile Apps Shuai Hao, Ding Li, William G.J. Halfond Ramesh Govindan.

SIF: A Selective Instrumentation Framework for Mobile Apps Shuai Hao, Ding Li, William G.J. Halfond Ramesh Govindan.

Scalable Rule Management for Data Centers Masoud Moshref, Minlan Yu, Abhishek Sharma, Ramesh Govindan 4/3/2013.

Scalable Rule Management for Data Centers Masoud Moshref, Minlan Yu, Abhishek Sharma, Ramesh Govindan 4/3/2013.

Snooze: Energy Management in 802.11n WLANs Ki-Young Jang, Shuai Hao, Anmol Sheth, Ramesh Govindan.

Snooze: Energy Management in 802.11n WLANs Ki-Young Jang, Shuai Hao, Anmol Sheth, Ramesh Govindan.

1 © Ramesh Jain Social Life Networks: Ontology-based Recognition Ramesh Jain Contact: jain@ics.uci.edu.

1 © Ramesh Jain Social Life Networks: Ontology-based Recognition Ramesh Jain Contact: [email protected].

Interference-Aware Fair Rate Control in Wireless Sensor Networks by Sumit Rangwala, Ramakrishna Gummadi, Ramesh Govindan and Konstantinos Psounis

Interference-Aware Fair Rate Control in Wireless Sensor Networks by Sumit Rangwala, Ramakrishna Gummadi, Ramesh Govindan and Konstantinos Psounis

Multi-dimensional Range Query in Sensor Networks Xin Li,Young Jim Kim, Ramesh Govindan (University of Southern California ) Wei Hong (Intel Research Lab.

Multi-dimensional Range Query in Sensor Networks Xin Li,Young Jim Kim, Ramesh Govindan (University of Southern California ) Wei Hong (Intel Research Lab.

Directed Diffusion for Wireless Sensor Networkingjohnh/PAPERS/Intanagonwiwat03a.pdf · Directed Diffusion for Wireless Sensor Networking Chalermek Intanagonwiwat, Ramesh Govindan,

Directed Diffusion for Wireless Sensor Networkingjohnh/PAPERS/Intanagonwiwat03a.pdf · Directed Diffusion for Wireless Sensor Networking Chalermek Intanagonwiwat, Ramesh Govindan,

Deborah Estrin, Ramesh Govindan, John Heidemann USC/ISI and UCLA SCADDS Staff and Students: Jeremy Elson, Deepak Ganesan, Chalermek Intanagonwiwat, Fabio.

Deborah Estrin, Ramesh Govindan, John Heidemann USC/ISI and UCLA SCADDS Staff and Students: Jeremy Elson, Deepak Ganesan, Chalermek Intanagonwiwat, Fabio.

Scalable Rule Management for Data Centers · Scalable Rule Management for Data Centers Masoud Moshref† Minlan Yu† Abhishek Sharma†∗ Ramesh Govindan† † University of Southern

Scalable Rule Management for Data Centers · Scalable Rule Management for Data Centers Masoud Moshref† Minlan Yu† Abhishek Sharma†∗ Ramesh Govindan† † University of Southern

Next Century Challenges: Scalable Coordination in Sensor Networks Deborah Estrin, Ramesh Govindan, John Heidemann, Satish Kumar (Some images and slides.

Next Century Challenges: Scalable Coordination in Sensor Networks Deborah Estrin, Ramesh Govindan, John Heidemann, Satish Kumar (Some images and slides.

SCREAM: Sketch Resource Allocation for Software-defined ... · SCREAM: Sketch Resource Allocation for Software-defined Measurement Masoud Moshref, Minlan Yu, Ramesh Govindan, Amin

SCREAM: Sketch Resource Allocation for Software-defined ... · SCREAM: Sketch Resource Allocation for Software-defined Measurement Masoud Moshref, Minlan Yu, Ramesh Govindan, Amin

Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Chalermek Intanagonwiwat, Ramesh Govindan and Deborah Estrin (MobiCOM.

Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Chalermek Intanagonwiwat, Ramesh Govindan and Deborah Estrin (MobiCOM.

ASTUTE: Detecting a Different Class of Traffic Anomalies Fernando Silveira 1,2, Christophe Diot 1, Nina Taft 3, Ramesh Govindan 4 1 Technicolor 2 UPMC.

ASTUTE: Detecting a Different Class of Traffic Anomalies Fernando Silveira 1,2, Christophe Diot 1, Nina Taft 3, Ramesh Govindan 4 1 Technicolor 2 UPMC.

1 Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin, Li.

1 Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table Sylvia Ratnasamy, Scott Shenker, Brad Karp, Ramesh Govindan, Deborah Estrin, Li.

Efficient Privilege De-Escalation for Ad Libraries in Mobile Apps Bin Liu (SRA), Bin Liu (CMU), Hongxia Jin (SRA), Ramesh Govindan (USC)

Efficient Privilege De-Escalation for Ad Libraries in Mobile Apps Bin Liu (SRA), Bin Liu (CMU), Hongxia Jin (SRA), Ramesh Govindan (USC)

A Comparison of Application-Level and Router-Assisted Hierarchical Schemes for Reliable Multicast Pavlin Radoslavov Christos Papadopoulos Ramesh Govindan.

A Comparison of Application-Level and Router-Assisted Hierarchical Schemes for Reliable Multicast Pavlin Radoslavov Christos Papadopoulos Ramesh Govindan.

DREAM: Dynamic Resource Allocation for Software · PDF fileDREAM: Dynamic Resource Allocation for Software-defined Measurement Masoud Moshref† Minlan Yu† Ramesh Govindan† Amin

DREAM: Dynamic Resource Allocation for Software · PDF fileDREAM: Dynamic Resource Allocation for Software-defined Measurement Masoud Moshref† Minlan Yu† Ramesh Govindan† Amin

Understanding Congestion Control in Multi-hop Wireless ...€¦ · Sumit Rangwala Apoorva Jindal Ki-Young Jang Konstantinos Psounis Ramesh Govindan University of Southern California

Understanding Congestion Control in Multi-hop Wireless ...€¦ · Sumit Rangwala Apoorva Jindal Ki-Young Jang Konstantinos Psounis Ramesh Govindan University of Southern California