System Energy Efficiency Lab seelab.ucsd.edu. Jinseok Yang

Transcription

1 Jinseok Yang

2 Contents SmartGrid and Wireless Sensor Networks Big data = Worth? Data acquisition User query based WSNs Wireless Sensor Actuator Networks Context extraction Context modeling technique

3 SmartGrid ExxonMobile: The outlook for energy: a View to 2040 The IEEE's version of the Smart Grid involves distributed generation, information networks, and system coordination, a drastic change from the existing utility configurations

4 SmartGrid and WSNs Component monitoring is required in SmartGrid Traditional electric-power-system monitoring system Wired : install & maintenance cost Reliable and online information becomes the key factor Share information / Monitoring Wired communication: cost Cost effective wireless monitoring and diagnostic systems Optimizing the management of electric power system Improve system reliability and efficiency Wireless Sensor Networks(WSNs) Low power wireless embedded system Self-functional, distributed control over monitoring area Management layer DATA Self-healing Smart Grid Sensor Sensor Sensor Monitoring Control System Energy Efficiency Lab Power Grid Infrastructure

5 "Big Data" = "Worth?" Thousands of deployed sensors enormous amount of data Extract necessary context from the data Definition of context context is any information that can be used to characterize the situation of an entity Challenges Acquiring information the capability of understanding situations Sensor node has limited battery Context Management Context modeling technique Query based WSNs System Energy Efficiency Lab Query Interface Query / Data Sensor Sensor Sensor Monitoring Power Grid Infrastructure Control Wireless Sensor Actuator Network

6 Query based WSNs Title Researches Descriptions Heterogeneous sensor networks Valuable if they can be effectively used on purpose Model Based BBQ [1], Maintains global statistical model of the data Existing in-network query processing querying Snapshot Intelligence is placed at the edge of network (Approximation) Model based Queries querying [2], Increasing the pull frequency to increase anomaly Sensor-DB system PRESTO [3], detection probability increase energy consumption at Extract only Alexandra valuable information [4] the sensors increase energy efficiency Sensor-DB system (Aggregation) System Energy Efficiency Lab COUGAR [5], Yao [6], TinyDB [7], REED [8] Intelligence is placed at the sensors User queries are disseminated to the sensor nodes based on their selection Each query is executed on the actual sensor data Selecting an appropriate set of sensors where a query would be executed is challenge Hybrid Approach Pervin [9] Probabilistic data model represents sensor data characteristics across the network Selecting sensor nodes to process user queries The query interface is too high-level for users such as system administrator that do not wish to query the sensors but aim at systematically collecting primary datasets for their purposes Architecture for model-based querying in sensor networks [7] Context modeling technique

7 Context modeling technique Context modeling Express infrastructure components, condition, and requirements Extract context based on existing information Approaches Context Modeling Language [10][11] Object Role Language [12] Eclipse: depicts an objects type Box: a role played by an object type Context Management channel[michelle Williams, ] User query based WSNs Query Interface Query / Data Context modeling technique Control Sensor Sensor Sensor Monitoring System Energy Efficiency Lab Power Grid Infrastructure Example of Context Modeling Language [11]

8 Wireless Sensor Actuator Networks Title Sensor and actuator Researches nodes that Descriptions are interconnected over wireless links Sensors gather information about the physical world. From the received Heterogeneous information, the Sensor actuator controllers/actuators Energy perform efficient actions methods/protocols to change that the allow sensor reliability behavior of the coordination environment or physical nodes to select systems and locate an appropriate actuator [13][14][15][16][17] node A lot of coordination methods: single or multiple actuators, actuator location update method Heterogeneous reliability Dynamic Network Topology / Route failure & Transport protocols Actuator actuator coordination [13] [15][16] [18][19] Route failure & Transport protocols [13][16][18][19][20] WSAN Architecture [30] Tasks completes on time is critical Minimize actuator energy consumption is essential Satisfying heterogeneous reliability requirements of both sensor actor and actor actor communication Combined congestion detection mechanism based on both average node delay calculation and local buffer level monitoring of the sensor nodes to accurately detect congestion in the network

9 Summary Component monitoring is required to ensure resilience of power grid The terabyte torrent of data will only be valuable if they can be effectively used on purpose Find grained sensing without context-aware discharge sensor s battery Information gathering: Query based WSNs Model based / Sensor-DB / Hybrid Context aware/context modeling Context modeling language Different application has different interests Control power grid: WSANs minimize delay and maximize lifetime Context Management Query Interface Query / Data Control Sensor Sensor Sensor Monitoring Power Grid Infrastructure

10 Reference [1] A. Deshpande et al, Model-driven data acquisition in sensor networks. IVLDB [2] Y. Kotidis et al,. Snapshot queries: towards data-centric sensor networks. In Data Engineering, ICDE 2005 [3] M. Li et al, Presto: feedback-driven data management in sensor networks, NSDI 2006 [4] A. Meliou et al. Approximat-ing sensor network queries using in-network summaries. IPSN, 2009 [5] P. Bonnet et al, Towards sensor database systems., MDM 2001 [6] Y. Yao et al, The cougar approach to in-network query processing in sensor networks. SIGMOD 2002 [7] S. R. Maddenet al, Tinydb: an acquisitional query processing system for sensor networks. ACM Transaction on. Database System, 2005 [8] D. J. Abadi et al, Reed: robust, efficient filtering and event detection in sensor networks. VLDB 2005 [9] S. Pervin et al, Hybrid in-network query processing framework for wireless sensor networks. ICC 2011 [10] K. Henricksen et al, Model-ing context information in pervasive computing systems. Pervasive 2002 [11] K. Henricksen et al, Generating context management infrastructure from high-level context models. MDM [12] T. Halpin. Information modeling and relational databases: from conceptual analysis to logical design. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, [13] T. Melodia et al., Communication and coordination in wireless sensor and actor networks. Mobile Computing, IEEE Transactions [14] G. A. Shah et al., Cluster-based coordination and routing framework for wireless sensor and actor networks. Journal Wireless Communications & Mobile Computing, 2011 [15] Y. Zeng et al., A real-time architecture for automated wireless sensor and actuator networks. IICWMC 2009 [16] T. Melodia et al., Handling mobility in wireless sensor and actor networks. IEEE Transactions on Mobile Computing, 2010 [17] T. Melodia et al., A distributed coordination framework for wireless sensor and actor networks. MobiHoc, 2005 [18] V. C. Gungor et al. A real-time and reliable transport (rt) 2 protocol for wireless sensor and actor networks. IEEE/ACM Trans [19] E. Ngai et al., A delay-aware reliable event reporting framework for wireless sensor-actuator networks. Ad Hoc Networking., 2010 [20] E.-H. Ngai et al., A real-time communication framework for wireless sensor-actuator networks. In Aerospace Conference, 2006