![DAWN Lab / UMBC 34
Diffusion Basics, contd.
Design Issues:
How does a sink express its interest in one or
more events?
How do sensor nodes keep track of existing
interests from multiple sinks?
When an event occurs, how does data get
propagated from source(s) to sink(s)?
Can in-network data processing (e.g. data fusion),
data aggregation and data caching help improve
performance?
[Intanagonwiwat et. al.; ACM MobiCom 2000]](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-33-320.jpg)
![DAWN Lab / UMBC 35
Diffusion Basics, contd
Example Task
{Type = Animal; Interval = 20ms; Time = 10s;
Region = [-100, 100, 200, 400] }
The above task instructs a sensor node in the
specified region to track for animals; If animal is
tracked/detected, then send observations every 20
ms for 10s
The above task is sent via interest messages and all
sensor nodes register this task.
When a node detects an event, it then constructs a
Data Event message](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-34-320.jpg)
![DAWN Lab / UMBC 36
Diffusion: Basics, contd
Data Event Example:
{Type = Animal; Instance = Tiger;
Location = [101, 201]; Intensity = 0.4;
Confidence = 0.8; Timestamp = 2:51:00}
Interests and Gradients:
For each active task that a sink is interested in:
Sink broadcasts interest to its neighbors
Initially, to explore, it could set large interval (e.g 1s)
Sink refreshes each interest, using timestamps
Each sensor node maintains an interest cache
Interest aggregation is possible](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-35-320.jpg)
![DAWN Lab / UMBC 57
S-MAC [Ye et. Al. 2002]
Sensor-MAC Protocol proposed in 2002
Assumptions
Network consists of several small nodes,
deployed in an ad hoc manner
Nodes dedicated to a single or few collaborative
applications: Per-node fairness is not critical
In-network processing assumed: e.g. data fusion,
data aggregation, collab signal processing
Long idle periods and occasional burst of data:
higher latency may be tolerated](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-55-320.jpg)
![DAWN Lab / UMBC 75
Exposure Problems
Exposure is related to the coverage
Exposure may be defined as the expected ability of
observing a target in the sensor field
Formally defined as the integral of the sensing
function (depends on distance from sensors) on a
path from P_s to P_d
Sensing function depends on nature of sensors
Sensor model:
s
node
sending
from
p
point
of
distance
is
)
,
(
and
constants;
are
,
)]
,
(
[
)
,
(
p
s
d
k
p
s
d
p
s
S k
](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-73-320.jpg)
![DAWN Lab / UMBC 77
Exposure along a path
Suppose object O is traveling from point p(t1) to
p(t2) along path p(t).
Exposure for object O during interval t1 to t2 along
p(t) is defined as:
2
2
)
or
(
2
1
)
(
)
(
)
(
then
y(t))
(x(t),
p(t)
If
length
arc
of
element
the
is
)
(
)
(
))
(
,
(
]
,
),
(
[
2
1
dt
t
dy
dt
t
dx
dt
t
dp
dt
t
dp
dt
dt
t
dp
t
p
F
I
t
t
t
p
E
t
t
C
A](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-75-320.jpg)
![DAWN Lab / UMBC 78
Exposure: Properties
Consider only 1 sensor at location (0,0). Let
Determine the path from a=(1,0) to point b=(X,Y)
with minimum exposure
Determine x(t), y(t) such that x(0) = 1; y(0) = 0;
x(1) = X; y(1) = Y and the exposure function is
minimized.
Lemma 1: If b=(0,1), then the minimum exposure
path is
2
2
)
,
(
1 1
)]
,
(
),
0
,
0
(
[
y
x
y
x
p
s
S p
s
d
2
E
and
2
sin
,
2
cos
t
t](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-76-320.jpg)
![DAWN Lab / UMBC 102
More info…
Reality check: Questions to ask wireless sensor network
vendors
[From http://www.networkworld.com/news/2005/100305-
wireless-sensors.html]
How complex is deployment vs. that of conventional wired
networks?
How stable are standards like Zigbee?
Why go with standard-based approaches vs. possibly
more flexible proprietary mesh networking protocols?
Will radio interference be a factor with multiple sensor nets
with hundreds or even thousands of nodes?](https://image.slidesharecdn.com/newwsn-250228163712-732ae4c0/85/Wireless-Sensor-Network-WSN-Power-Point-ppt-99-320.jpg)
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- 1. Tutorial: Wireless Sensor Networks November 2005 Krishna M. Sivalingam, Associate Professor Dept. of CSEE University of Maryland, Baltimore County (UMBC) Baltimore, MD 21250 [email protected] www.cs.umbc.edu/~krishna; dawn.cs.umbc.edu Please do not distribute Copyright by Prof. Sivalingam. The softcopy may be used for personal research/academic purposes only.
- 2. DAWN Lab / UMBC 2 General Overview Introduction to Wireless Sensor Networks Data Dissemination and Routing Protocols Data Gathering Medium Access Control Protocols Locationing and Coverage Testbeds/Applications Security in Wireless Sensor Networks Summary & Discussion
- 3. DAWN Lab / UMBC 3 Motivation GOAL: Deeply Networked Systems or Pervasive Networking 98% of all processors are not in traditional desktop computer systems, but in house-hold appliances, vehicles, and machines on factory floors Add reliable wireless communications and sensing functions to the billions of physically embedded computing devices to support ubiquitous networked computing Distributed Wireless Sensor Networks is a collection of embedded sensor devices with networking capabilities
- 4. DAWN Lab / UMBC 4 Introduction to WSN
- 5. DAWN Lab / UMBC 5 Background , contd. Sensors Enabled by recent advances in MEMS technology Integrated Wireless Transceiver Limited in Energy Computation Storage Transmission range Bandwidth Battery Memory CPU Sensing Hardware Wireless Transceiver
- 6. DAWN Lab / UMBC 6 Background, contd.
- 7. DAWN Lab / UMBC 7 Sensor Nodes, contd.
- 8. DAWN Lab / UMBC 8 Sensors (contd.) The overall architecture of a sensor node consists of: The sensor node processing subsystem running on sensor node main CPU The sensor subsystem and The communication subsystem The processor and radio board includes: TI MSP430 microcontroller with 10kB RAM 16-bit RISC with 48K Program Flash IEEE 802.15.4 compliant radio at 250 Mbps 1MB external data flash Runs TinyOS 1.1.10 or higher Two AA batteries or USB 1.8 mA (active); 5.1uA (sleep) Crossbow Mote TPR2400CA-TelosB
- 9. DAWN Lab / UMBC 9 Overall Architecture of a sensor node Appl i cat i on Layer Net work Layer M AC Layer Physi cal Layer Com m uni cat i on SubSyst em W i rel ess Channel Sl ow Seri al Li nk Sensor Sensor Node CPU Radi o Board Forward Packet Pat h
- 10. DAWN Lab / UMBC 10 Wireless Sensor Networks (WSN) Distributed collection of networked sensors
- 11. DAWN Lab / UMBC 11 Networked vs. individual sensors Extended range of sensing: Cover a wider area of operation Redundancy: Multiple nodes close to each other increase fault tolerance Improved accuracy: Sensor nodes collaborate and combine their data to increase the accuracy of sensed data Extended functionality: Sensor nodes can not only perform sensing functionality, but also provide forwarding service.
- 12. DAWN Lab / UMBC 12 Applications of sensor networks Physical security for military operations Indoor/Outdoor Environmental monitoring Seismic and structural monitoring Industrial automation Bio-medical applications Health and Wellness Monitoring Inventory Location Awareness Future consumer applications, including smart homes.
- 13. DAWN Lab / UMBC 13 Applications, contd. ALERT Beam Formation cooperative ALERT COM MAND LEVEL SENSING COM MUNICATIO N THREAT processing M U L T I -H O P cooperative signalling THREAT
- 14. DAWN Lab / UMBC 14 Applications, contd.
- 15. DAWN Lab / UMBC 15 Characteristics and challenges Deeply distributed architecture: localized coordination to reach entire system goals, no infrastructure with no central control support Autonomous operation: self-organization, self-configuration, adaptation, exception-free TCP/IP is open, widely implemented, supports multiple physical network, relatively efficient and light weight, but requires manual intervention to configure and to use. Energy conservation: physical, MAC, link, route, application Scalability: scale with node density, number and kinds of networks Data centric network: address free route, named data, reinforcement-based adaptation, in-network data aggregation
- 16. DAWN Lab / UMBC 16 Challenges, contd. Challenges Limited battery power Limited storage and computation Lower bandwidth and high error rates Scalability to 1000s of nodes Network Protocol Design Goals Operate in self-configured mode (no infrastructure network support) Limit memory footprint of protocols Limit computation needs of protocols -> simple, yet efficient protocols Conserve battery power in all ways possible
- 17. DAWN Lab / UMBC 17 WSN vs. MANET Wireless sensor networks may be considered a subset of Mobile Ad-hoc NETworks (MANET). WSN nodes have less power, computation and communication compared to MANET nodes. MANETs have high degree of mobility, while sensor networks are mostly stationary. Freq. node failures in WSN -> topology changes Routing protocols tend to be complex in MANET, but need to be simple in sensor networks. Low-power operation is even more critical in WSN. MANET is address centric, WSN is data centric.
- 18. DAWN Lab / UMBC 18 Why not port Ad Hoc Protocols? Ad Hoc networks require significant amount of routing data storage and computation Sensor nodes are limited in memory and CPU Topology changes due to node mobility are infrequent as in most applications sensor nodes are stationary Topology changes when nodes die in the network due to energy dissipation Scalability with several hundred to a few thousand nodes not well established GOAL: Simple, scalable, energy-efficient protocols
- 19. DAWN Lab / UMBC 19 Focus: Radio Transceiver Usage The wireless radio transceiver is typically in three modes: Transmit – Maximum power consumption Receive Idle Turned off – Least power consumption Sensor node exists in three modes: Active, standby, and battery dead Turnaround time: Time to change from one mode to another (esp. important is time from sleep to wakeup and vice-versa) Protocol design attempts to place node in these different modes depending upon several factors Sample power consumption from 2 sensor nodes shown next
- 20. DAWN Lab / UMBC 20 Rockwell Node (SA-1100 proc) MCU Mode Sensor Mode Radio Mode Power(mW) Active On Tx(36.3mW) 1080.5 Tx(13.8mW) 942.6 Tx(0.30mW) 773.9 Active On Rx 751.6 Active On Idle 727.5 Active On Sleep 416.3 Active On Removed 383.3 Active Removed Removed 360.0 Sleep On Removed 64.0
- 21. DAWN Lab / UMBC 21 UCLA Medusa node (ATMEL CPU) MCU Mode Sensor Radio(mW) Data rate Power(mW) Active On Tx(0.74,OOK) 2.4Kbps 24.58 Tx(0.74,OOK) 19.2Kbps 25.37 Tx(0.10,OOK) 2.4Kbps 19.24 Tx(0.74,OOK) 19.2Kbps 20.05 Tx(0.74,ASK) 19.2Kbps 27.46 Tx(0.10,ASK) 2.4Kbps 21.26 Active On Rx - 22.20 Active On Idle - 22.06 Active On Off - 9.72 Idle On Off - 5.92 Sleep Off Off - 0.02
- 22. DAWN Lab / UMBC 22 Energy conservation Physical layer • Low power circuit(CMOS, ASIC) design • Optimum hardware/software function division • Energy effective waveform/code design • Adaptive RF power control MAC sub-layer • Energy effective MAC protocol • Collision free, reduce retransmission and transceiver on-times • Intermittent, synchronized operation • Rendezvous protocols Link layer Network layer Application layer • FEC versus ARQ schemes; Link packet length adapt. • Multi-hop route determination • Energy aware route algorithm • Route cache, directed diffusion • Video applications: compression and frame-dropping • In-network data aggregation and fusion See Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001; See Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of Mobile Computing, Ivan Stojmenovic, Editor, 2002.
- 23. DAWN Lab / UMBC 23 Network Architectures
- 24. DAWN Lab / UMBC 24 Network Architectures Layer 1 Layer 2 Layer 3 Layered Architecture Base Statio n Clustered Architecture Base Statio n Larger Nodes denote Cluster Heads
- 25. DAWN Lab / UMBC 25 Clustered network architecture Ti er 1 Ti er 0 Ti er 2 Ti er 1 Ti er 0 Sensor nodes autonomously form a group called clusters. The clustering process is applied recursively to form a hierarchy of clusters.
- 26. DAWN Lab / UMBC 26 Cluster architecture (contd.) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) ( ( ) ) Base St ati on Cl ust er- head Cl ust er-head Cl uster-head Sensor Cl uster Cl ust er Cl ust er Example - LEACH protocol It uses two-tier hierarchy clustering architecture. It uses distributed algorithm to organize the sensor nodes into clusters. The cluster-head nodes create TDMA schedules. Nodes transmit data during their assigned slots. The energy efficiency of the LEACH is mainly due to data fusion.
- 27. DAWN Lab / UMBC 27 Layered Network Architecture A few hundred sensor nodes (half/full duplex) A single powerful base-station Network nodes are organized into concentric Layers Layer: Set of nodes that have the same hop-count to the base-station Additional Mobile Nodes traversing the network Wireless Multi-Hop Infrastructure Network Architecture (MINA) A 10 node sensor network depicting cluster of node 3; there are 2 mobile nodes
- 28. DAWN Lab / UMBC 28 MINA, contd. Set of wireless sensor nodes create an infrastructure – provide sensing and data forwarding functionality Mobile soldiers with hand-held units access the sensors for data and also to communicate with a remote BS BS is data gathering, processing entity and communication link to larger network Shorter-range, low-power transmissions preferred for covert operations and to conserve power
- 29. DAWN Lab / UMBC 29 Data Dissemination Architectures and Protocols
- 30. DAWN Lab / UMBC 30 Data Dissemination In ad hoc networks, traffic is peer-to-peer Multi-hop routing is used to communicate data In WSN, other traffic models are possible: Data Collection Model Data Diffusion Model Data Collection Model: Source sends data to a collection entity (e.g. gateway): periodically or on-demand Data Diffusion Model: Source: A sensor node that generates data, based on its sensing mechanisms’ observations Event: Something that needs to be reported, e.g. in target detection; some abnormal activity Sink: A node, randomly located in the field, that is interested in events and seeks such information
- 31. DAWN Lab / UMBC 32 Data Diffusion: Concept Sources Sink 1 Sink 2
- 32. DAWN Lab / UMBC 33 Diffusion: Basics Data-centric vs. address centric architecture Individual network address is not critical; Data is important and is accessed as needed User can pose a specific task, that could be executed by sensor nodes Concept of Named Data: (Attribute, Value) Pair Sink node requests data by sending “interests” for data Interests are propagated through the network, setting up gradients in the network, designed to “draw” data Data matching the interest is then transmitted towards the sink, over multiple paths (obtained by the gradients The sink can then reinforce some of these paths to optimize
- 33. DAWN Lab / UMBC 34 Diffusion Basics, contd. Design Issues: How does a sink express its interest in one or more events? How do sensor nodes keep track of existing interests from multiple sinks? When an event occurs, how does data get propagated from source(s) to sink(s)? Can in-network data processing (e.g. data fusion), data aggregation and data caching help improve performance? [Intanagonwiwat et. al.; ACM MobiCom 2000]
- 34. DAWN Lab / UMBC 35 Diffusion Basics, contd Example Task {Type = Animal; Interval = 20ms; Time = 10s; Region = [-100, 100, 200, 400] } The above task instructs a sensor node in the specified region to track for animals; If animal is tracked/detected, then send observations every 20 ms for 10s The above task is sent via interest messages and all sensor nodes register this task. When a node detects an event, it then constructs a Data Event message
- 35. DAWN Lab / UMBC 36 Diffusion: Basics, contd Data Event Example: {Type = Animal; Instance = Tiger; Location = [101, 201]; Intensity = 0.4; Confidence = 0.8; Timestamp = 2:51:00} Interests and Gradients: For each active task that a sink is interested in: Sink broadcasts interest to its neighbors Initially, to explore, it could set large interval (e.g 1s) Sink refreshes each interest, using timestamps Each sensor node maintains an interest cache Interest aggregation is possible
- 36. DAWN Lab / UMBC 37 Diffusion: Interests When a node receives an interest, it: Checks cache to see if an entry is present. If no entry, creates an entry with a single gradient to neighbor who sent this interest Gradient specifies the direction and data rate. Resend interest to a subset of its neighbors This is essentially flooding-based approach Other probabilistic, location-based and other intelligent forwarding approaches possible Similar to multicast tree formation, at sink instead of at source
- 37. DAWN Lab / UMBC 38 Diffusion: Interest Propagation Sources Sink 1 Sink 2
- 38. DAWN Lab / UMBC 39 Diffusion: Data Propagation When a sensor node detects a target, it: Searches interest cache for matching entry If found, computes highest requested event rate among its gradients Instructs sensor sub-system to generate data at this rate Sends data to neighbors on its gradient list Intermediate nodes maintain a data cache Caches recently received events Forwards event data to neighbors on its gradient list, at original rate or reduced rate (intelligently)
- 39. DAWN Lab / UMBC 40 Diffusion: Reinforcement When sink gets an event notification, it: Picks a suitable set of neighbor(s) (best link, low delay, etc.) and sends a refresh interest message, with higher notification rate (e.g. every 10 ms instead of every 1s) This will prune some of its neighbors (since interests in a node’s cache will expire) Each selected neighbor forwards this new interest to a subset of its neighbors; selecting a smaller set of paths Negative reinforcement also necessary to de-select weaker paths if a better path found.
- 40. DAWN Lab / UMBC 41 Part III: Data Gathering Algorithms
- 41. DAWN Lab / UMBC 42 Problem Definition Objective: Transmit sensed data from each sensor node to a base station One round = BS collecting data from all nodes Goal is to maximize the number of rounds of communication before nodes die and network is inoperable Minimize energy AND reduce delay Conflicting requirements Sensor Nodes Base station
- 42. DAWN Lab / UMBC 43 Energy*Delay metric Why energy * delay metric? Find optimal balance to gather data quickly but in an energy efficient manner Energy = Energy consumed per round Delay = Delay per round (I.e. for all nodes to send packet to BS) Why is this metric important? Time critical applications
- 43. DAWN Lab / UMBC 44 Direct Transmission Direct Transmission All nodes transmit to the base station (BS) Very expensive since BS may be located very far away and nodes need more energy to transmit over longer distances Farther the distance, greater the propagation losses, and hence higher the transmission power All nodes must take turns transmitting to the BS so delay is high (N units for a N-node network) Better scheme is to have fewer nodes transmit this far distance to lower energy costs and more simultaneous transmissions to lower delay
- 44. DAWN Lab / UMBC 45 LEACH Low Energy Adaptive Clustering Hierarchy Two-level hierarchy Base Station Larger Nodes denote Cluster Heads
- 45. DAWN Lab / UMBC 46 Scheme #1: PEGASIS Goals of PEGASIS (Power-Efficient GAthering for Sensor Information Systems) Minimize distance nodes must transmit Minimize number of leaders that transmit to BS Minimize broadcasting overhead Minimize number or messages leader needs to receive Distribute work more equally among all nodes
- 46. DAWN Lab / UMBC 47 PEGASIS Greedy Chain Algorithm Start with node furthest away from BS Add to chain closest neighbor to this node that has not been visited Repeat until all nodes have been added to chain Constructed before 1st round of communication and then reconstructed when nodes di Data fusion at each node (except end nodes) Only one message is passed at every node Delay calculation: N units for an N-node network Sequential transmission is assumed
- 47. DAWN Lab / UMBC 48 PEGASIS Start End
- 48. DAWN Lab / UMBC 50 Scheme #2: Binary Scheme Chain-based as described in PEGASIS At each level node only transmits to another node All nodes receiving at any level rise to the next level Delay: O(log2 N) Step 4: c3 BS Step 3: c3 c7 Step 2: c1 c3 c5 c7 Step 1: c0c1 c2c3 c4c5 c6c7
- 49. DAWN Lab / UMBC 51 Scheme # 3:Chain-based 3 level For non-CDMA sensor nodes, binary scheme is not logical Construct chain as described in PEGASIS Divide chain into 10 groups (for the 100-node) To space out simultaneous transmissions to minimize interference In each group, nodes will transmit one at a time Finally, one node out of each group at each level will contain all the data and will rise to the next level until finally the leader will transmit to the BS Total delay = 15 units (9+4+1+1) for 100-nodes
- 50. DAWN Lab / UMBC 52 Chain-based 3 level scheme Third Level Two nodes rise to top and non-leader transmits to leader Leader transmits to BS c18 BS c18c68 c8 c18c28c38c48 c58 c68 c78 c88c98 c1c2…c7c8c9 c10c11…c18c19 …c90 c91…c98 c99
- 51. DAWN Lab / UMBC 53 MAC Protocols for WSN
- 52. DAWN Lab / UMBC 54 MAC Protocols What is fundamentally different for MAC Protocol design in WSN? Low-power operation is even more critical Reduced coordination and synchronization is beneficial Resilience to frequent node failures Suitably blend with the network architecture Probably application dependent Scalability to support large number of nodes Thousands of nodes likely Limited bandwidth availability Would the 802.11 family of protocols work?
- 53. DAWN Lab / UMBC 55 TDM-Based MAC Considered for Clustered architecture Nodes are organized into clusters Each cluster has a clusterhead, that communicates directly with gateway or BS node TDMA MAC The cluster head knows its members’ IDs Creates a simple TDM schedule, allocating time slots to members Broadcasts schedule to members Schedule may be periodically updated Rotating cluster heads possible
- 54. DAWN Lab / UMBC 56 TDM-Based MAC, contd. Advantages: Simple to coordinate within cluster No collisions Can be more energy-efficient: members wake up only when they have to send/receive data Disadvantages: Adjoining clusters need to coordinate to operate in different channels (or frequencies) TDM is not very scalable to large number of nodes: high delays possible Nodes need to be synchronized within each cluster
- 55. DAWN Lab / UMBC 57 S-MAC [Ye et. Al. 2002] Sensor-MAC Protocol proposed in 2002 Assumptions Network consists of several small nodes, deployed in an ad hoc manner Nodes dedicated to a single or few collaborative applications: Per-node fairness is not critical In-network processing assumed: e.g. data fusion, data aggregation, collab signal processing Long idle periods and occasional burst of data: higher latency may be tolerated
- 56. DAWN Lab / UMBC 58 S-MAC details, contd. Periodic Listen and Sleep Mode of operation Each node sleeps for a while; wakes up and then communicates with its neighbors, as necessary. Periodic synch among neighbors to reduce drift Pair-wise or group-wise node synch Nodes exchange schedule by broadcast MAC is still needed to avoid collisions
- 57. DAWN Lab / UMBC 59 Localization (Location Discovery) Algorithms
- 58. DAWN Lab / UMBC 60 Location Information It is essential, in some applications, for each node to know its location Sensed data coupled with loc. data and sent We need a cheap, low-power, low-weight, low form- factor, and reasonably accurate mechanism Global Positioning Sys (GPS) is not always feasible GPS cannot work indoors, in dense foliage, etc. GPS power consumption is very high Size of GPS receiver and antenna will increase node form factor
- 59. DAWN Lab / UMBC 61 Indoor Localization Use a fixed infrastructure Beacon nodes are strategically placed Nodes receive beacon signals and measure: Signal Strength Signal Pattern Time of arrival; Time difference of arrival Angle of arrival Nodes use measurements from multiple beacons and use different multi-lateration techniques to estimate locations Accuracy of estimate depends on correlation between measured entity and distance
- 60. DAWN Lab / UMBC 62 Indoor Localization Examples of Indoor Loc. Systems RADAR (MSR), Cricket (MIT), BAT (AT&T), etc. Some approaches require a priori signal measurement and characterization and database creation Node obtains distance estimate by using database Not always practical to have database loaded in the individual node; only some nodes (e.g. gateway) might carry it.
- 61. DAWN Lab / UMBC 63 Sensor Net. Localization No fixed infrastructure available Prior measurements are not always possible Basic idea: Have a few sensor nodes who have known location information These nodes sent periodic beacon signals Other nodes use beacon measurements and triangulation, multi-lateration, etc. to estimate distance Following mechanisms presented in Savvides et. al. in ACM MobiCom 2001
- 62. DAWN Lab / UMBC 64 Sensor Net. Localization, contd. Receiver Signal Strength Indicator (RSSI) was used to determine correlation to distance Suitable for RF signals only Very sensitive to obstacles, multi-path fading, environment factors (rain, etc.) Was not found to have good experimental correlation RF signal had good range, few 10metres RF and Ultrasound signals The beacon node transmits an RF and an ultrasound signal to receiver The time difference of arrival between 2 signals is used to measure distance Range of up to 3 m, with 2cm accuracy
- 63. DAWN Lab / UMBC 65 Localization algorithms Based on the time diff. of arrival Atomic Multi-lateration: If a node receives 3 becaons, it can determine its location (similar to GPS) Iterative ML: Some nodes not in direct range of beacons Once an unknown node estimates its location, will send out a beacon Multi-hop approach; Errors propagated Collaborative ML: When 2+ nodes cannot receive 3 beacons (but can receive say 2), they collaborate
- 64. DAWN Lab / UMBC 66 Multi-lateration examples Beacon Nodes Unknown Nodes Beacon Nodes Unknown Nodes
- 65. DAWN Lab / UMBC 67 Exposure; Coverage and Deployment
- 66. DAWN Lab / UMBC 68 Coverage Problems Coverage: is a measure of the Quality of service of a sensor network How well can the network observe (or cover) a given event? For example, intruder detection; animal or fire detection Coverage depends upon: Range and sensitivity of sensing nodes Location and density of sensing nodes in given region
- 67. DAWN Lab / UMBC 69 Coverage, contd. Worst-Case Coverage: Areas of breach (lowest coverage) Can be used to determine if additional sensors needed Best-Case Coverage: Areas of best coverage Can be used by a friendly user to navigate in those areas
- 68. DAWN Lab / UMBC 70 Coverage, contd. Given: A field A with sensors S, where for each sensor $s_i in S$, its location (x_i, y_i) is known (How? Based on the Localization Techniques described earlier). Areas I and F are initial and final locations of an agent traversing the field. Problem: Identify P_B, the maximal breach path in S, starting in I and ending in F P_B is defined as the locus of points p in the region, where p is in P_B if the distance from p to the closest sensor is maximized. I and F are arbitrarily specified inputs. Solution: Determine the Voronoi diagram corresponding to the sensor graph. The path P_B will be composed of line segments that belong to the Voronoi diagram.
- 69. DAWN Lab / UMBC 71 Voronoi diagrams In 2D, the Voronoi diagram of a set of points partitions the plane into a set of convex polygons such that: All points inside a polygon are closest to only one site. The polygons have edges equidistant from nearby points. Related is Delaunay Triangulation Connect points in V- Diag. whose polygons share a common edge.
- 70. DAWN Lab / UMBC 72 Worst-Case Coverage: Alg. 1. Generate the bounded Voronoi diagram a. Let U and L denote vertex set and links of diag. 2. Create a graph with vertices from set U and links from L a. Weight of link in graph = minimum distance from all sensors in S 3. Do a breadth-first search to determine a path from I to F in the graph, such that the path has maximum edge cost 4. Multiple such breach paths are possible.
- 71. DAWN Lab / UMBC 73 Best-Case Coverage Problem: Identify P_S, the path with maximum support in S, starting at I and ending in F. Solution: Use Delaunay triangulation The best path will be one connecting some of the sensor nodes Similar approach to Max. Breach Path Use Delaunay instead of Voronoi The edge cost in the graph G, will be the length of the Delaunay triangle line segment.
- 72. DAWN Lab / UMBC 74 Examples Fig. on left shows the bounded Voronoi diagram and the maximal breach path Fig. on right shows the Delaunay Triangulation and the maximal support path Question: Once these are determined, how to use these?
- 73. DAWN Lab / UMBC 75 Exposure Problems Exposure is related to the coverage Exposure may be defined as the expected ability of observing a target in the sensor field Formally defined as the integral of the sensing function (depends on distance from sensors) on a path from P_s to P_d Sensing function depends on nature of sensors Sensor model: s node sending from p point of distance is ) , ( and constants; are , )] , ( [ ) , ( p s d k p s d p s S k
- 74. DAWN Lab / UMBC 76 Exposure at a point All-Sensor Field Intensity at Point p in field with n sensors denoted by Closest-Sensor Field Intensity at Point p: ) , ( ) , ( 1 p s S p F I n i i A } ,..., , { 2 1 n s s s ) , ( ) , ( ) , ( ) , ( | min min p S S p F I S s p s d p s d S s S C i i m m
- 75. DAWN Lab / UMBC 77 Exposure along a path Suppose object O is traveling from point p(t1) to p(t2) along path p(t). Exposure for object O during interval t1 to t2 along p(t) is defined as: 2 2 ) or ( 2 1 ) ( ) ( ) ( then y(t)) (x(t), p(t) If length arc of element the is ) ( ) ( )) ( , ( ] , ), ( [ 2 1 dt t dy dt t dx dt t dp dt t dp dt dt t dp t p F I t t t p E t t C A
- 76. DAWN Lab / UMBC 78 Exposure: Properties Consider only 1 sensor at location (0,0). Let Determine the path from a=(1,0) to point b=(X,Y) with minimum exposure Determine x(t), y(t) such that x(0) = 1; y(0) = 0; x(1) = X; y(1) = Y and the exposure function is minimized. Lemma 1: If b=(0,1), then the minimum exposure path is 2 2 ) , ( 1 1 )] , ( ), 0 , 0 ( [ y x y x p s S p s d 2 E and 2 sin , 2 cos t t
- 77. DAWN Lab / UMBC 79 Exposure: Properties Lemma 2: Given a sensor s and two points a and b, such d(s,a)=d(s,b), then the minimum exposure path between a and b is that part of the circle centered as s and passing through a and b. Theorem: Let the sensor be located at (0,0) in a unit field. The minimum exposure path from (1,-1) to (-1,1) is as below: S=(0,0)
- 78. DAWN Lab / UMBC 80 Exposure: Properties Let s be a sensor in a polygonal field with vertices v1,…,vn. For the inscribed circle of the polygon, let edge v_i,v_{i+1} be tangent at point u_i The minimum exposure path from vertex v_i to vertex v_j consists of: Line segment from v_i to u_i Part of inscribed circle from u_i to u_j Line segment from u_j to v_j (OR) in the opposite direction (from v_i to u_j etc) Problem of MEP between 2 points in same corner or between 2 points inside the inscribed circle is open
- 79. DAWN Lab / UMBC 81 Generic Exposure Problem Given a network with randomly placed sensor nodes, how to determine minimum exp. Path Solution: Tessellate the network into a set of equidistant grid points (with varying degree of precision) For each edge in the grid network, assign an edge equal to the exposure along the edge (integrated from the sensor function) Using Dijkstra’s algorithm, determine the shortest path from a source (based on edge weights) This is the min. exposure path
- 80. DAWN Lab / UMBC 83 Security in Sensor Networks What is different ? Unfriendly, unattended environments Severe resource constraints render most of the cryptographic mechanisms impossible PKI is infeasible for sensor networks and have to rely on symmetric key cryptography Security has never been more important! Applications in battlefield management, emergency response systems and so on Key management is the most critical issue Focus of majority of the research Following is review of some key research in the area
- 81. DAWN Lab / UMBC 84 SPINS-Perrig et al, Berkeley Complete suite of security protocols for sensor networks SNEP (Secure Network Encryption Protocol) Data Confidentiality Authentication Integrity Freshness μTESLA Lightweight version of TESLA for authenticated broadcast
- 82. DAWN Lab / UMBC 85 SPINS: Applications Authenticated Routing Base station can be authenticated using μTESLA For each time interval, the first packet heard is chosen as parent, which is authenticated later Prevents spurious routing Node-to-Node Key Agreement A sends B a request with a nonce B asks BS for a session key using SNEP BS distributes shared session keys securely to A and B using SNEP with strong freshness
- 83. DAWN Lab / UMBC 86 Key Management Scheme for DSN Eschenauer et al, UMD (CCS 2002) Based on probabilistic key sharing Each node is equipped before deployment with a key-ring chosen randomly from a common key pool Each key has an identifier associated with it Shared secret key is established between two nodes by one of the two ways: Broadcasting the key identifiers and comparing them to find a common key if one exists Sending a challenge encrypted in a key; a valid response is a successful decryption of the challenge establishing a shared key
- 84. DAWN Lab / UMBC 87 Key Mgmt Contd There may not be a shared key between a pair of nodes In such a case a path to one node from the other is established through the secure links already in place A direct secure link is then established If a node is compromised, its entire key-ring is revoked from the network In general for a required probability of 0.5, 75 keys need to be in the key ring chosen from a pool of 10,000 keys.
- 85. DAWN Lab / UMBC 88 Random Key Predistribution Schemes Chan, Perrig et al, CMU, 2003 Proposes three random key predistribution schemes q-Composite random key predistribution Multi-path key reinforcement Random pair-wise scheme q-Composite random key predistribution Builds on the work of Eschenauer and Gligor (referred to as basic scheme) Basic idea is to share q keys between nodes rather than just one key Final key is the hash of all q keys An attacker now needs to capture more nodes in order to eavesdrop on any link with given probability
- 86. DAWN Lab / UMBC 89 q-Composite Predistribution Contd. However choosing size |S| of common key pool is tricky Too large May not find q common keys between every pair of node Too small Attacker can get a large sample of S by capturing just a few nodes Choose largest |S| such that Pconnect ≥ P Pconnect is the probability of two nodes sharing sufficient keys to form a secure link (derived mathematically) P is the desired probability that two nodes form a secure link
- 87. DAWN Lab / UMBC 90 q-Composite Predistribution Contd. q-Composite scheme thus makes small scale attacks less appealing for an attacker Attacker can only gain a little additional information by capturing a few nodes e.g. amount of additional communication compromised when 50 nodes are captured is only 4.74% as compared to 9.52% for basic scheme However makes network more vulnerable if large number of nodes are captured
- 88. DAWN Lab / UMBC 91 Multi-path Key Reinforcement Need to update the key once a secure link has been formed between two nodes To prevent attacker from obtaining and using the old key by capturing other nodes Node A sends j random values over multiple disjoint secure paths to node B The new key is computed from all the j values Attacker has to eavesdrop on j paths in order to construct the key The neighbors on those paths are called reinforcing neighbors
- 89. DAWN Lab / UMBC 92 Multi-path Key Reinforcement Contd Significant network overheads (~10X) The method is not as effective when used with q- Composite Both the methods essentially do the same thing But their weakness compound each other Small key pool and high network overheads Works well in conjunction with the basic scheme Reduces the eavesdropping probability 146 times!
- 90. DAWN Lab / UMBC 93 Random pair-wise Key Scheme Targeted at Node-to-Node authentication without any help from the base station Each node need only save a random set of n*p keys instead of all n-1 keys p is the smallest probability that any two nodes have a shared key such that all nodes have shared keys with some high probability Nodes are predeployed with m random pair-wise keys for m other nodes Node broadcasts its identifier once deployed Mutual key agreement with the neighbors takes place by cryptographic handshake
- 91. DAWN Lab / UMBC 94 Random pair-wise Key Scheme Contd Multi-hop range extension is simple with having neighbors rebroadcast the identifiers further Must be used to a limited number of hops to prevent DoS attack by an adversary Distributed node revocation is possible by having nodes broadcast public votes against a misbehaving node Mechanism for detecting misbehavior assumed at each node If A receives more than a threshold number of votes are against B, it cuts off all communication with B Many practical issues arise!
- 92. DAWN Lab / UMBC 95 Random pair-wise Key Scheme Contd Node replication can be resisted by limiting the max degree of each node Degree counting is modeled in a similar way as vote counting for node revocation Complete resilience against node capture A compromised node does not provide any further information Large network size supported n = m/p where m is the key-ring size of a node and p is the smallest probability that any two nodes have a shared key such that all nodes have shared keys with some high probability
- 93. DAWN Lab / UMBC 96 Testbeds and Applications
- 94. DAWN Lab / UMBC 97 Habitat Monitoring Traditional human monitoring methods for habitats are invasive and cause negative impact Often, repeated visits necessary to collect data Ideal will be to establish a group of wireless sensor networks that sense and wirelessly transmit data Better for environment; Cheaper, Safer, etc. Great Duck Island (GDI) Project by College of Atlantic; Intel and UC Berkeley Monitor usage patterns of nesting burrows Changes in burrow conditions during breeding season
- 95. DAWN Lab / UMBC 98 GDI Project Establishes multiple clusters of sensor networks Each cluster or “patch” has a gateway node Data from clusters forwarded over a wireless LAN (802.11b) connection to a basestation (part of the island’s field station) The base station provides necessary connectivity to Internet Sensors sense light, temp, pressure, infra-red, relative humidity in the burrows Sensor data is archived & queried in real-time Users with mobile devices and remote clients access data
- 96. DAWN Lab / UMBC 99 GDI Project, contd. The sensor nodes are Berkeley Motes (40 Kbps radio, 4 MHz ATMEL chips, 512KB storage) Motes encased in transparent acryclic enclosure As of July 2002, 32 motes (nine in underground burrows) Data collection and evaluation in progress
- 97. DAWN Lab / UMBC 100 Smart Kindergarten project Project at Univ. of California, Los Angeles and an elementary school Plans to develop toys with embedded sensors, that can sense child’s response and wirelessly transmit data to an infrastructure The toy can provide visual, aural, motion feedback and sense child’s speech, physical manipulation, etc. Could enhance education process by providing a better learning environment – individualized, unobtrusive evaluation by teacher, adaptive, etc.
- 98. DAWN Lab / UMBC 101 Other projects Airbee Wireless’s Ronald Reagan Airport project: Every door in the airport could be outfitted with 900-MHz wireless sensors and automated locks, networked to a central point where rules could be set for when a door could be opened, by whom, by time of day, without the need for guards Q: Can a 900-MHz jammer disrupt the system? http://wwwcsif.cs.ucdavis.edu/~yick/research/ applications.html
- 99. DAWN Lab / UMBC 102 More info… Reality check: Questions to ask wireless sensor network vendors [From http://www.networkworld.com/news/2005/100305- wireless-sensors.html] How complex is deployment vs. that of conventional wired networks? How stable are standards like Zigbee? Why go with standard-based approaches vs. possibly more flexible proprietary mesh networking protocols? Will radio interference be a factor with multiple sensor nets with hundreds or even thousands of nodes?
- 100. DAWN Lab / UMBC 103 More info… Can a deliberate jamming attempt shut down the entire net? What tools are available to manage these nets, and to treat them as part of an enterprise IP net? How can data from sensor nets be integrated with existing enterprise applications? How realistic are battery life projections of months or years? What are the total life-cycle costs of sensors nets, including battery replacement?
- 101. DAWN Lab / UMBC 104 More Information Wireless Sensor Networks, An Edited Book Co-Editors: Znati, Sivalingam and Raghavendra Springer Publishers, 2004 18 Chapters contributed by leading researchers in the field Other Books also available Ivan Stojmenovic Feng Zhao S.S. Iyengar
- 102. DAWN Lab / UMBC 105 Coming Soon Near You IEEE Communication Society’s Third Annual Intl Conf. on Sensor and Ad Hoc Comm. & Networks (SECON) www.ieee-secon.org/2006 Reston, VA (Hyatt Reston) near Dulles Sep. 25-29, 2006 Interested in submitting papers, participating in panels, presenting a demo, SPONSORING or anything else related, pl contact Krishna Sivalingam at [email protected] (General Chair)
- 103. DAWN Lab / UMBC 106 Coming Soon Near You IEEE Communication Society and Create-Net (Italy)’s Second Annual Intl Conf. on Security and Privacy for Emerging Areas in Communication Networks www.securecomm.org Baltimore/DC area Sep./Oct., 2006 Interested in submitting papers, participating in panels, presenting a demo, SPONSORING or anything else related, pl contact Krishna Sivalingam at [email protected] (Steering Cmte Co-Chair)
- 104. DAWN Lab / UMBC 107 Unsolicited Plug Crossbow Technology’s Wireless Sensor Network Training Course November 9-10, 2005, Towson, MD Burkshire Marriott Conference Hotel Contact [email protected]
- 105. DAWN Lab / UMBC 108 Other topics … Transport protocols Data compression and data fusion Low-power design issues Simulation toolkits/environments specific to Sensor Networks
- 106. DAWN Lab / UMBC 109 Summary Motivation for Wireless Sensor Networks Data Dissemination and related routing protocols Data Gathering algorithms MAC and Organization protocols Localization algorithms Coverage and Exposure Applications and Testbeds Security Summary
- 107. DAWN Lab / UMBC 110 THANK YOU!