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1、Sensor Networks,金仲達教授 清華大學資訊系統與應用研究所 九十三學年度第一學期,Pervasive Computing,Sensor Networks-1,Sources,“Comm n Sense: Research Challenges in Embedded Networked Sensing,” D. Estrin, http:/lecs.cs.ucla.edu “A Survey on Sensor Network,” I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Georgia Institute
2、of Technology IEEE Communications Magazine, Aug. 2002,Pervasive Computing,Sensor Networks-2,Introduction,Mark Weiser envisioned a world in which computing is pervasive What we need is to instrument the physical world with pervasive networks of sensor-rich, embedded computation Such systems fulfill t
3、wo of Weisers objectives: Ubiquity: by inject computation into the physical world with high spatial density Invisibility: by having the nodes and collective of nodes operate autonomously,Pervasive Computing,Sensor Networks-3,Introduction,What is required is the ability to easily deploy flexible sens
4、ing, computation, and actuation capabilities into our physical environments such that the devices themselves are general-purpose and can organize and adapt to support several application types,Pervasive Computing,Sensor Networks-4,Embed numerous distributed devices to monitor/interact with physical
5、world Exploit spatially and temporally dense, in situ, sensing and actuation,Network these devices so that they can coordinate to perform higher-level tasks. Requires robust distributed systems of hundreds or thousands of devices.,Vision,Pervasive Computing,Sensor Networks-5,Sensor Nodes and Network
6、s,Sensor nodes = sensing, data processing, and communicating capacity Sensor network: a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it Sensor node position not engineered or predecided protocols or algorithms must be self-organizing Cooperativ
7、e effort of sensor nodes with in network processing,Pervasive Computing,Sensor Networks-6,Applications,Scientific: eco-physiology, biocomplexity mapping,Infrastructure: Contaminant flow monitoring,Engineering: adaptive structures,www.jamesreserve.edu,Pervasive Computing,Sensor Networks-7,Other Appli
8、cations (I),Environmental Forest fire detection, biocomplexity mapping of the environment, flood detection, precision agriculture Healthy Telemonitoring of human physiological data, tracking and monitoring doctors and patients inside a hospital, drug administration in hospitals Military: Monitoring
9、friendly forces, equipment and ammunition; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; nuclear, biological and chemical attack detection and reconnaissance,Pervasive Computing,Sensor Networks-8,Other Applications (II),Home Home automa
10、tion Smart environment Commercial Environmental control in office buildings Interactive museums Detecting and monitoring car thefts Managing inventory control Vehicle tracking and detection Monitoring product quality Monitoring disaster areas .,Pervasive Computing,Sensor Networks-9,Challenges,Tight
11、coupling to the physical world and embedded in unattended “control systems” Different from traditional Internet, PDA, mobility applications that interface primarily and directly with human users Untethered, small form-factor, nodes present stringent energy constraints Living with small, finite, ener
12、gy source is different from fixed but reusable resources such as BW, CPU, storage Communications is primary consumer of energy Sending a bit over 10 or 100 meters consumes as much energy as thousands/millions of operations,Pervasive Computing,Sensor Networks-10,New Design Themes,Long-lived systems t
13、hat can be untethered and unattended Low-duty cycle operation with bounded latency Exploit redundancy Tiered architectures (mix of form/energy factors) Self-configuring systems that can be deployed ad hoc Measure and adapt to unpredictable environment Exploit spatial diversity and density of sensor/
14、actuator nodes,Pervasive Computing,Sensor Networks-11,Approach,Leverage data processing inside the network Exploit computation near data to reduce communication Achieve desired global behavior with adaptive localized algorithms (i.e., do not rely on global interaction or information) Dynamic, messy
15、(hard to model), environments preclude pre-configured behavior Cant afford to extract dynamic state information needed for centralized control or even Internet-style distributed control,Pervasive Computing,Sensor Networks-12,Why cant we simply adapt Internet protocols and “end to end” architecture?,
16、Internet routes data using IP addresses in Packets and Lookup tables in routers Humans get data by “naming data” to a search engine Many levels of indirection between name and IP address Works well for the Internet, and for support of Person-to-Person communication Embedded, energy-constrained (un-t
17、ethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection,Pervasive Computing,Sensor Networks-13,vs. Ad Hoc Networks,Large number of sensor nodes (several orders of magnitude higher) Densely deployed Prone to failures Network topology changes very frequently
18、 Mainly use a broadcast paradigm vs. point-to-point in ad hoc networks Limited in power, computational capacities, and memory May not have global identification (ID),Pervasive Computing,Sensor Networks-14,Communication Architecture,Factors of design consideration Transmission media Production costs
19、Power consumption Fault tolerance NW topology HW constraints Environment Scalability,Pervasive Computing,Sensor Networks-15,Fault Tolerance,The ability to sustain sensor network functionalities without any interruption due to sensor node failures The reliability Rk(t) or fault tolerance of a sensor
20、node can be modeled with the Poisson distribution to capture the probability of not having a failure within the time interval (0,t) Rk(t) = exp(-kt) , for node k,Pervasive Computing,Sensor Networks-16,Scalability,The number of sensor nodes 10 - 100 - 1000 - 10000 - . Depending on the application New
21、 schemes must be able to utilize the high density The density (R) = (N . R2)/A A: region area R: radio transmission range N: the number of scattered sensor nodes,Pervasive Computing,Sensor Networks-17,Production Costs,The cost of a single node is very important to justify the overall cost of the net
22、work The cost of a sensor node should be much less than US$1 The state-of-art technology allows a Bluetooth radio system to be less than US$10 10 times more expensive the the targeted price,Pervasive Computing,Sensor Networks-18,Hardware,4 basic units: sensing unit, processing unit, transceiver unit
23、, power unit Sensing: sensors, Analog-to-digital converters (ADCs) Additional application-dependent units Location finding system, power generator, mobilizer.,Pervasive Computing,Sensor Networks-19,Hardware Constraints,Constraints Size Power Operate in very high densities Low cost Dispensable Autono
24、mous Adaptive to environment,Pervasive Computing,Sensor Networks-20,Sensor Network Topology,Topology maintenance and change in 3 phases Predeployment and deployment phase Be thrown in as a mass or placed one by one Post-deployment phase Change in sensor nodes position, reachability, available energy
25、, malfunctioning, and task details Redeployment of additional nodes phase Additional sensor nodes can be redeployed,Pervasive Computing,Sensor Networks-21,Environment,Nodes are densely deployed either very close or directly inside the phenomenon to be observed Usually work unattended in remote geogr
26、aphic areas in the interior of large machinery at the bottom of an ocean in a biologically or chemically contaminated field in a battlefield beyond the enemy lines in a home or large building .,Pervasive Computing,Sensor Networks-22,Transmission Media,Often by wireless medium Radio: Used by most sen
27、sors AMPS sensor uses a Bluetooth-compatible 2.4 GHz transceiver with an integrated frequency synthesizer Infrared: License-free, robust to interference from electrical devices cheaper and easier to build Optical: Smart Dust mote Both infrared and optical require line of sight,Pervasive Computing,Se
28、nsor Networks-23,Power Consumption,In some application scenarios, replenishment of power resources might be impossible Battery lifetime In a multihop ad hoc sensor network, each node plays dual role of data originator and data router cause significant topological changes require rerouting of packets
29、 and reorganization of the network Power consumption sensing, communication, and data processing,Pervasive Computing,Sensor Networks-24,Design Issues According to Protocol Stack,Physical layer: Simple, robust modulation, transmission, receiving MAC protocol power-aware; minimize collision with neigh
30、bors broadcasts Network layer routing data supplied by transport layer Transport layer maintain flow of data,Pervasive Computing,Sensor Networks-25,Three Management Planes,The power management plane, e.g. Turn off its receiver after receiving a message Broadcasts low in power and cannot participate
31、in routing messages The mobility management plane Detects and registers movement of sensor nodes maintain route back to the user, keep track of their neighbor The task management plane balances and schedules sensing tasks for a specific region They are needed for sensor nodes to work power-efficient
32、ly, route data in a mobile network, share resources between sensor nodes,Pervasive Computing,Sensor Networks-26,Physical Layer,Responsibility Frequency selection, carrier frequency generation, signal detection, modulation, and data encryption. 915 MHz industrial, scientific, and medical (ISM) band h
33、as been widely used Long distance wireless communication can be expensive in terms of power A good modulation is critical for reliable comm. Binary and M-ary modulation schemes Ultra wideband (UWB) or impulse radio (IR) are promising,Pervasive Computing,Sensor Networks-27,Physical Layer Open Issues,
34、Modulation schemes Simple and low-power modulation schemes Strategies to overcome signal propagation effects Hardware design Tiny, low-power, low-cost transceiver, sensing, and processing units Power-efficient hardware management strategies,Pervasive Computing,Sensor Networks-28,Data Link Layer,Resp
35、onsibility Multiplexing of data streams, data frame detection, medium access and error control Reliable point-to-point and point-to-multipoint Medium Access Control protocol creation of the network infrastructure fairly and efficiently share communication resources Existing MAC protocols cannot be u
36、sed Cellular system: infrastructure-based Bluetooth and mobile ad hoc network (MANET) much larger number, power and radio range, frequent topology change, power conservation needed,Pervasive Computing,Sensor Networks-29,Some Proposed MAC Protocols,Pervasive Computing,Sensor Networks-30,Example MAC P
37、rotocols,Self-Organizing Medium Access Control for Sensor Networks (SMACS) and the Eavesdrop-And-Register (EAR) Algorithm Nodes to discover their neighbors and establish communication without the need for any local or global master nodes No necessity for networkwide synchronization using a random wa
38、ke-up schedule during connection phase and turning the radio off during idle time slots EAR attempts to offer continuous service to the mobile nodes,Pervasive Computing,Sensor Networks-31,Data Link Open Issues,MAC for mobile sensor networks more extensive mobility in the sensor nodes and targets Det
39、ermination of lower bounds on the energy required for sensor network self-organization Error control coding schemes Power-saving modes of operation,Pervasive Computing,Sensor Networks-32,Network Layer,Design principles Power efficiency Sensor networks are mostly data-centric Data aggregation is usef
40、ul only when it does not hinder the collaborative effort of the sensor nodes. An ideal sensor network has attribute-based addressing and location awareness Also providing internetworking with external networks,Pervasive Computing,Sensor Networks-33,Energy-Efficient Route,Available power:PA Energy re
41、quired () Maximum minimum PA node route Min PA is larger than the min PAs Maximum PA route Minimum energy route Minimum hop route,Pervasive Computing,Sensor Networks-34,Data Centric Route,Use interest dissemination Sinks broadcast the interest, or Sensor nodes broadcast an advertisement and wait for
42、 a request Often require attribute-based naming Query by using attributes of phenomenon Data aggregation Solve the implosion and overlap problems,Pervasive Computing,Sensor Networks-35,Proposed Schemes,Flooding Implosion (duplicated message), overlap (both sensors detect the same event), resource bl
43、indness (not considering resource constraints) Gossiping Relay packets to randomly selected neighbor Negotiation (SPIN),Pervasive Computing,Sensor Networks-36,More Schemes,Small minimum energy communication network Sequential assignment routing Low-energy adaptive clustering hierarchy Directed diffu
44、sion,Pervasive Computing,Sensor Networks-37,Protocol Summary,Pervasive Computing,Sensor Networks-38,Application Layer Protocols,Sensor management nodes do not have global identifications and are infrastructureless Providing administrative tasks Introducing the rules related to data aggregation, attr
45、ibute-based naming, and clustering to the sensor nodes Exchanging data related to the location finding algorithms Time synchronization of the sensor nodes Moving sensor nodes Turning sensor nodes on and off Querying the sensor network configuration and the status of nodes, and reconfiguring the sens
46、or network Authentication, key distribution, and security in data communications,Pervasive Computing,Sensor Networks-39,Application Layer Protocols,Task assignment and data advertisement interest dissemination Advertisement of available data Sensor query and data dissemination issue queries, respond
47、 to queries and collect incoming replies Sensor query and tasking language (SQTL) supports 3 types of events Receive defines events generated by a sensor node when the sensor node receives a message every defines events occurring periodically due to timer timeout expire defines events occurring when
48、 a timer is expired Different types of SQDDP can be developed for various applications. The use of SQDDPs may be unique to each application,Pervasive Computing,Sensor Networks-40,Pervasive Computing,Sensor Networks-41,Research Areas,Constructs for “in network” distributed processing system organized
49、 around naming data, not nodes “programming” large collections of distributed elements Localized algorithms that achieve system-wide properties Time and location synchronization energy-efficient techniques for associating time and space with data to support collaborative processing Experimental infrastructure,Pervasive Computing,Sensor Networks-42,Constructs for in NW Processing,Nodes pull, push, store named data (using tuple space) to create effic. processing points in NW e.g. duplicate suppression, aggregation, correlation Nested queries reduce overhead relative to “edge