A Survey on Wireless Sensor Networks

Transcription

1 Reti di Calcolatori LS A Survey on Wireless Sensor Networks Eugenio Magistretti emagistretti@deis.unibo.it Bologna, February 12th Table of Contents WSN Concepts: WSN characteristics Why WSN? WSN architecture and design guidelines Solutions for networking support: Adaptive Topology: GAF Data Dissemination: LEACH, Pegasis, Teen, SPIN In-network processing: Directed-diffusion User Queries and Database Oriented Approach: COUGAR Conclusions and On-going Work 2

2 PART 1 WSN Concepts 3 Goals Link Physical Word and Digital Data Networks, providing distributed network and Internet access to sensors, controls, processors deeply embedded in equipment, facilities and environment Technical goals: scalability, lifetime, adaptability and features Huge number of participants (billions) Generally composed by randomly placed and stationary devices Limited resources (e.g. battery-powered devices) Low bit rate delivery to the user Short message packets suffice 4

3 Devices UCB PC104 based AMD ElanSC400 CPU SDRAM 16MB Flash Disk 16MB Radio Packet Controller 418 MHz Use motes as radio Linux OS Motes: Smart-It Atmel ATmega 128L (8 MHz 8 MIPS) 64Kbyte RAM, 128Kbyte FLASH ROM, 4Kbyte EEPROM Bluetooth radio (57.6 kbps) Analog in: 8 x 10-bit AD converter Digital IO: 16-bit Interrupt lines: 3, edge or level triggered Serial IO at 57.6Kbps Tiny OS 5 Applications Ubiquitous and pervasive computing Environmental monitoring (air, water, soil chemistry; surveillance) 6

4 Applications Home automation (smart houses, virtual neighbor) Inventory tracking (in warehouses, laboratories) Futuristic: Health monitoring (ingested sensors, smart medications) Circulatory Net 7 Comparison to Conventional Networks Network Features WSN Features Cellular MANET Bluetooth Thousands of nodes Nodes are appliances outfitted with sophisticated radio transceivers Mobile nodes greatly outnumber stationary (BS) BS have unlimited power supply, mobiles are battery-operated The primary goal is to provide high QoS, along with high bandwidth efficiency Tens to hundreds of nodes Nodes are appliances outfitted with sophisticated radio transceivers Nodes are fully mobile Energy consumption is of secondary importance Aims to form and maintain a connected multihop network The goal is to provide QoS (throughput vs. delay) Topology is a star network where a master has up to seven slaves (piconet); there are mechanisms to form a multihop topology Nodes are appliances and electronic consumer devices Nodes are short-range mobile Energy isn t generally an issue Goal is to replace cable between devices and provide RF connection between them Hundreds of thousands of nodes Nodes integrate sensors, processors, transceivers with limited resources Nodes are generally stationary after deployment Each node depends on small lowcapacity battery as energy source, and cannot expect replacement The main goal is to prolong the lifetime of the network 8

5 Why WSN? WhyDistributed Sensing? 1. Dispersive media 2. Obstructions 3. Detection theory WhyWireless? 1. Environmental lacks 9 Lifetime: Devices Power Consumption Sensing circuitry Digital processing Radio transceiver Communication dominates energy budget Example: With 3J a node could transmit 1 Kb a distance of 100 meters or efficiently execute 3 million instructions 10

6 Collaborative Processing (1) Nodes organize themselves for purposes of sensing their field of view, and pass information on to some users Goals: high detection probability, false alarm rates Options: 1. Send raw data to a central site 2. Each node perform computation procedures to come to some decisions 11 Collaborative Processing (2) Architecture should limit the information that must flow over the network to conserve battery life and to avoid overwhelming the users 3. Signal Processing Hierarchy: Lower false alarm probability The load on nodes up the communication chain is reduced 12

7 Collaborative Processing (3) Processing Elements Active Processors Noncoherent Coherent Homogeneous Heterogeneous All nodes Special Nodes Raw data will be preprocessed at each node to extract a set of parameters (e.g. data fusion) Low data traffic Raw data will be tagged with timestamp and uploaded to a CN (e.g. beamforming) Long streams More reliability, Lower cost Increased functionality: GPS or longer range radios need not be implemented in every element Savings in overall network power consumption, because routing can be made more flexible and dynamic Increased reliability Uncertain savings on hardware costs 13 Collaborative Processing (4) Beside the design chances presented in the previous slides, two design principles emerge from the effort to achieve reliable decisions with low energy consumption: 1. Play the probability game to the extent you have to 2. The processing hierarchy is closely intertwined with networking and data storage issues 14

8 Self-organization (1) Self-organization refers to the ability of the system to achieve the necessary organizational structures without requiring human intervention Organizational structure is established to enable: 1. Basic sensing 2. Collaborative signal processing 3. Communication network operations to support internode and sensor system/user interaction 4. Resource management 15 Self-organization (2) Self-organization means imbue the commander s intent into the system Many interacting devices give rise to a complex adaptive system in which Emergent Behavior is expected Self-organization tasks: 1. Bring the initial system online 2. Establish needed end-to-end circuits 2. Allow new nodes to be added and reconfigure when existing nodes fail 4. Quickly evolve so as to achieve these functions via low power operations 16

9 Novel Design Features 1. Data-centric Identity Data 2. Application-specific Intermediate nodes perform application-specific tasks 17 Summary of Design Features Computation intensive, less communication Signal processing hierarchy Perform only to the extent we have to Processing hierarchy is intertwined with networking Self-organization Data-centric and application-specific design 18

10 PART 2 Solutions for networking support in Wireless Sensor Networks 19 Protocol Stack Application Network Data-link Physical USER QUERIES In-network aggregation Data dissemination Adaptive topology MAC PHY MGMT 20

11 Adaptative Topology: GAF(2) Geographic Adaptive Fidelity Routing fidelity is maintained as long as any intermediate node is awake Robustness is diminished Node equivalence is determined by dividing the area in virtual grids r=f(r) Needs a location information system (GPS) 21 Information Gathering Models Two are the most important gathering models assumed for autonomous WSNs: All peer nodes with a Base Station (with constant power supply) Only peer nodes with sinks By considering instead WSNs as a network in the Internet cloud, it is possible to explain them as a set of database servers 22

12 Why not an Internet end to end architecture? Internet routes using IP address and Lookup Tables Humans get data by naming data to a search engine Many levels of indirection between data name and IP address Works well in Internet Embedded, energy-constrained, unattended, untethered systems cannot tolerate communication overhead of indirection 23 Data Dissemination Protocols WSNs protocols should be: Application specific Data centric Capable of aggregating data Capable of optimizing energy consumption The suitability of network protocols depends on network topology and radio parameters of the system 24

13 Classification of Dissemination Protocols (1) Based on the type of target applications and mode of operation (Agrawal, Manjeshwar) : Proactive Reactive Hybrid Based on network organizational structure: Clustered Flat Hierarchical 25 Data Dissemination Multicast Unicast Gen. Routing Protocols Proactive Reactive Hybrid Clustered Flat Clustered Flat Clustered Flat SPIN Flooding Gossiping SAR LEACH Direct Pegasis TEEN APTEEN Directed- Diffusion* This protocol provides support for User Queries This protocol provides support for In-network Aggregation and User Queries 26

14 Proactive Clustered Protocols (1) Base station (far) Localized coordination and control for cluster set-up and operation Local aggregation LEACH: Adaptive dynamic clustering Cluster-heads create schedule for the nodes in their clusters TDMA Cluster-heads perform data aggregation Cluster-heads send data to the BS 27 Proactive Flat Protocols Grant a great dynamicity to the network and save the power required for organizational proposes PEGASIS: LEACH evolution Only one leader at a turn Communication chain Advantages: Shorter distances Only one leader c0 c1 c2 6 c3 5 c4 1 7 BS 28

15 Reactive Clustered Protocols Only when sensed data exceeds a threshold value, an alerting message is sent to the interested nodes Pay particular attention to time critical attributes Generally these protocols outperform proactive ones under an energy dissipation viewpoint TEEN: LEACH based initialization Hard and soft thresholds 29 Multicast Data Dissemination Flooding 1. Implosion 2. Overlap 3. Resource blindness (1) (2) Gossiping 2. Avoids implosion problem 30

16 Multicast Data Dissemination SPIN: 1. Negotiation 2. Meta-data 3. Resource Adaptation 31 In-network aggregation The goal is to facilitate the communication among sources and sinks Directed Diffusion: Not host based but data-centric Application-specific attribute based naming In-network processing through application specific filters Localized interactions Trade-off: Energy efficiency vs. Robustness and Scalability Data rate proactive Attribute-value pairs event-driven 32

17 Localized Algorithms (1) Collaborative and distributed computation in which sensor nodes communicate with sensors within some neighborhood, yet the overall computation achieves a desired global objective Properties: Scalability Robustness 33 Localized Algorithms (2) Design is hard: Global behavior Parametrical Sensitivity Approaches to overcome these difficulties: Develop intuition by prototyping Develop techniques for characterizing the performance 34

18 Application Example: Remote Surveillance Interrogation: e.g., Give me periodic reports about animal location in region A every t seconds Interrogation is propagated to sensor nodes in region A Sensor nodes in region A are tasked to collect data Data are sent back to the users every t seconds 35 Basic Directed Diffusion Source CLASS_KEY IS INTEREST_CLASS LONGITUDE_KEY GE 10 LONGITUDE_KEY LE 50 LATITUDE_KEY GE 100 LATITUDE_KEY LE 120 SENSOR EQ MOVEMENT INTENSITY GE 0.6 CONFIDENCE GE 0.7 INTERVAL IS 10 EXPIRE_TIME IS 100 Sink Interest = Interrogation Gradient = Who is interested

19 Basic Directed Diffusion Source FilterAttrVec CLASS_KEY EQ DATA_CLASS SENSOR EQ MOVEMENT INTENSITY GE addfilter (FilAttrVec, FilterCallback) 1. subscribe (InterestAttrVec, Callback) InterestAttrVec CLASS_KEY EQ INTEREST_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT 2. subscribe (AttrVec, ApplCallback) Sink Interest = Interrogation Gradient = Who is interested Basic Directed Diffusion Interests Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested

20 Basic Directed Diffusion Sending data Source 4. h = publish (SensedAttrVec) 5. send (h, SensedAttrVec) SensedAttrVec CLASS_KEY IS DATA_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT INTENSITY IS 0.8 CONFIDENCE IS 0.7 Low rate event Sink 39 Basic Directed Diffusion Source m1a 6. FilterCallback.recv (Message m1) m1b m2 m2 m2 CLASS_KEY IS DATA_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT INTENSITY IS 0.8 CONFIDENCE IS sendmessage (Message new) Low rate event 40

21 Basic Directed Diffusion Source 8. ApplCallback.recv (NRAttrVec) Sink Low rate event 41 Basic Directed Diffusion Source and Reinforcing the best path CLASS_KEY IS INTEREST_CLASS LONGITUDE_KEY GE 10 LONGITUDE_KEY LE 50 LATITUDE_KEY GE 100 LATITUDE_KEY LE 120 SENSOR EQ MOVEMENT INTENSITY GE 0.6 CONFIDENCE GE 0.7 INTERVAL IS 1 EXPIRE_TIME IS 90 Sink Low rate event Reinforcement = Increased interest

22 Directed Diffusion and Dynamics Source Recovering from node failure Sink Low rate event High rate event Reinforcement Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event

23 Directed Diffusion and Dynamics Source Sink Recovering from link failure Low rate event High rate event Reinforcement Directed Diffusion and Dynamics Source Stable path Sink Low rate event Reinforcement High rate event Use: Interests set up gradients drawing down data

24 Query Models Historical Queries: they are mainly used for analysis of historical data E.g. What was the watermark two hours ago in the southeast? One-time Queries: they give a snapshot view of the network E.g. What is the watermark in the southeast? Persistent Queries: they are used for monitoring tasks over a time interval E.g. Report the watermark in the southeast for the next four hours 47 User Queries Database Oriented Approaches Assumptions: WSNs have the capability to forward packets in an autonomous manner Each node runs a mini-server Traditional Centralized Approach Sensor Database System Warehouse Sensor DB Sen DB Sen DB Front- End Front- End Sen DB Sen DB Sen DB Sensor Nodes Sensor Nodes

25 COUGAR: User Queries Database Oriented Approaches Devices ADTs e.g. RFSensor(Sensor, X, Y) SQL semantic extended to include new query types New possible plans (location) New metrics (resource usage and reaction time) Otherissues: Stream processing Quality of Service (latency vs. completeness) In-network processing and aggregation 49 Conclusions and On-going Work Lack of capabilities of devices New design guidelines New models (protocol stack) On-going work Energy harvesting techniques Enlarge testbeds Develop new applications Mobile code-based management Interactions with Internet 50

26 References WSN Concepts I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, Aug. 2002, pp J. Pottie, Wireless Sensor Networks, ITW 1998, pp J. Pottie, W. Kaiser, Wireless Integrated Network Sensors, Communications of the ACM, May 2000, pp J. Pottie, Hierarchical Information Processing in Distributed Sensor Networks, ISIT 1998, p. 163 Self-organization L. Clare, J. Pottie, J. Agre, Self-Organizing Distributed Sensor Networks, SPIE Conf. Unattended Ground Sensor Technologies and Applications 1999, pp References Solutions for networking support SMACS K. Sohrabi, J. Gao, V. Ailawadhi, J. Pottie, Protocols for Self- Organization of a Wireless Sensor Network, IEEE Personal Communications, Oct. 2000, pp GAF Y. Xu, J. Heidemann, D. Estrin, Geography-Informed Enery Conservation for Ad Hoc Routing, MOBICOM 2001, pp LEACH W. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energyefficient Communication Protocols for Wireless Microsensor Networks, Hawaiian Int. Conf. Systems Science

27 Pegasis References S. Lindsey, C. Taghavendra, K. Sivalingam, Data Gathering Algorithms in Sensor Networks Using Energy Metrics, IEEE Transactions on Parallel and Distributed Systems, Sep. 2002, pp TEEN A. Manjeshwar, D. Agrawal, TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks, Int. Workshop Parallel and Distributed Computing Issues in Wireless Networks and Mobile Cokmputing 2001 APTEEN A. Manheshwar, D. Agrawal, An Analytical Model for Information Retrieval in Wireless Sensor Networks Using Enhanced APTEEN Protocol, IEEE Transaction on Parallel and Distributed Systems, Dec. 2002, pp References SPIN W. Rabiner Heinzelman, J. Kulik, H. Balakrishnan, Adaptive Protocols for Information Dissemination in Wireless Sensor Networks, MOBICOM 1999, pp Localized Algorithms D. Estrin, R. Govindan, J. Heidemann, S. Kumar, Next Century Challenges: Scalable Coordination in Sensor Networks, MOBICOM 1999, pp Directed Diffusion J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, D. Ganesan, Building Efficient Wireless Sensor Networks with Low-Level Naming, ACM Symp. Operating Systems Principles 2001, pp C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, F. Silva, Directed Diffusion for Wireless Sensor Networking, IEEE/ACM Transactions on Networking, Feb. 2003, pp COUGAR P. Bonnet, J. Gehrke, P. Seshadri, Towards Sensor Database Systems, Int. Conf. Mobile Data Management

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