CSI 5148 Sink Mobility in Wireless Sensor Networks Andres Solis Montero

CSI 5148

Sink Mobility in Wireless Sensor Networks

Andres Solis Montero

Data Gathering

Introduction

IntroductionSink Mobility

Overview

Problem

Solutions

Sink Mobility in Wireless Sensor Networks Andres Solis Montero 1/23

• Is a fundamental task in Wireless Sensor Networks (WSNs), here its function is to send sensor

readings from sources to sinks nodes.

Questions

References

Energy Consumption

Problem


Overview

Problem

Solutions


• Sensors near the sink deplete their battery power faster than those far apart due to the heavy traffic of relaying messages.

- When a sink’s neighbours deplete their battery power, farther away nodes may still have more than 90% of their initial energy -

Ingelrest et al., (2004)Luo and Hubaux, (2005)Olariu and Stojmenovic, (2006)Vincze et al., (2007)

Questions

References

Energy Consumption

Problem


Overview

Problem

Solutions


• Sink isolation, network failure.

• Energy holes, degraded network performance.

• Manually replace/rechargesensor batteries is ofteninfeasible.

It is desired to minimize and balance energy usage among sensors.

Questions

References

Power-aware Routing

Solutions ?


Overview

Problem

Solutions


Power AwareNon UniformSink Mobility

Singh et al., (1998)Stojmenovic and Lin, (2001)Buragohain et al., (2005)

• Longer network life time.

• Balances energy consumption.

Limitation:Critical nodes are not avoidable.

Questions

References

No uniform node distribution

Solutions ?


Overview

Problem

Solutions



Stojmenovic et al., (2005)Lian et al., (2006)Wu et al., (2008).

• Mitigates message relay load.

• Increases network lifetime.

Limitation:Reduces coverage which is the basis of any sensor network.

Questions

References

Sink Mobility

Solutions ?


Overview

Problem

Solutions



Akkaya et al., (2005);Luo and Hubaux, (2005);

Vincze et al., (2007);Banerjee et al., (2008);

Basagni et al., (2008);Hashish and Karmouch, (2008);

Friedmann and Boukhatem, (2009)

• Improves network lifetime, without bringing negative impacts mentioned in the other approaches.

• Network coverage preserved.

• There are no ‘critical’ nodes around a sink due to its mobility.

Questions

References

Sink Mobility

Taxonomy

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Data Gathering in real-time WSN

Data Gathering in delay-tolerant WSN

• Habitat monitoring.• Water quality

Monitoring.

• Battlefield surveillance.• Forest fire detection.

Questions

References

Delay Tolerant WSN approachesTaxonomy

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Stochastic TSP Label Covering Fixed Track Tree-Based Clustering

Direct Contact Data Collection

Rendezvous basedData Collection

Sink Tours RP Selection Methods

Direct Contact

Rendezvous based

Sinks visit (possibly at slow speed) all data sources and obtain data directly fromthem.

Sinks may visit only a few selected rendezvous points (RPs).

Shah et al. (2003); Gu et al. (2005); Nesamony et al. (2007); Sugihara and Gupta. (2008).

Kansal et al. (2004)Xing et al. (2008), (2007)

Questions

References

Delay Tolerant WSN approachesDirect Contact data Collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Stochastic TSP Label Covering

Direct Contact Data Collection

Sink Tours

Direct Contact

Rendezvous based

Sinks visit (possibly at slow speed) all data sources and obtain data directly from them.

Shah et al. (2003); Gu et al. (2005); Nesamony et al. (2007); Sugihara and Gupta. (2008).

• Eliminates the message relay overhead of sensors, and thus optimizes their energy savings.

Limitation:• It has a large data

collection latency for slow moving sinks.

Concern:• Find best sink trajectory

that covers all sensors minimizing data collection delay.

Questions

References

Stochastic

Direct Contact data collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based

Shah et al. (2003);

• Each sensor buffers their measurements and waits for a sink (beacon).

• Sinks move randomly sending beacons and collect data from encountered sensors in communication range.

• Data is carried by the sink to access point.

Limitations:• It has a large data collection latency for slow moving sinks.• Constant channel monitoring (beacons) is energy expensive.

Chakrabarti et al. (2003);

If a sink moves along a regular path, sensors can predict their arrival after learning their pattern.

Questions

References

TSP Tour for data collection

Direct Contact data collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based

Nesamony et al. (2006, 2007);

• Equivalent NP- complete Travel Salesman Problem.

• Traveling Salesman with Neighbourhood (TSPN)

All locations are known.

First determine visiting order of the disks.TSP order of the disks. Constrains may apply (energy level, buffer overflow...).

For each disk, a representative points is selected.(center, closest point to starting point, random...)

Algorithm computes the optimum path according the order. B = min(|AB|+|BC|) adjacent edges.

Limitations:• It has a large data collection

latency for slow moving sinks.• TSP – NP complete problem.

Questions

References

Label-covering tour data collectionDirect Contact data collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based

Sugihara and Gupta (2007, 2008)

• All locations are known. No need to visit all nodes once.

• Complete graph is made with sensors andinitial positions.

• Edges have a cost (Euclidean distance) and labels of all nodes (transmission radius) they intersect.

• Minimum set of edges that can collect data fromall nodes.

Proved to have better performance than TSP solutions with large transmission radius.

Limitations:• It has a large data collection latency for slow moving sinks.• Minimum label problem is NP hard.• No restrictions are applied to the algorithm (energy level, buffer

overflow...).

Questions

References

Delay Tolerant WSN approachesRendezvous based data Collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Fixed Track Tree-Based Clustering

Rendezvous basedData Collection

RP Selection Methods

Direct Contact

Rendezvous based

Sinks may visit only a few selected rendezvous points (RPs).


• Avoids long travel distances.

• Reduces time and data collection latency.

Limitation:• More energy consumption

because of multi hop data communication.

Concern:• Trade-off of energy

consumption and time delay.

Questions

References

RP selection by fixed Track.

Rendezvous based data Collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based


• Sink moves through straight lines (fixed track) broadcasting beacon messages initially.

• Sensors build a MST using hop counts. Their resend the min count received.

• The roots are the RPs.

• Sink motion can be slow or temporarily stop in critical data delivery places.

• Each sensor belongs to only one tree.

Limitation:• More energy consumption because of multihop data communication.• Find better fixed track and MST configuration to

balance time and message load.

Questions

References

RP selection by Reporting TreeRendezvous based data Collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based

Xing et al. (2008), (2007)

Greedy algorithm with constrained Reporting Tree pathrooted at BS.

• Need to find a sub-path where the maximum distance traveled by the sink is L. (max L that can travel within D Time).

• Each edge has a weight based on their children.

• Edges are sorted according to their weight. The biggest values <= L are selected.

• RPs are at any point of the final path.

Limitation:• More energy consumption because of multi hop data

communication.• Configuration L input might yield different results.

Questions

References

RP selection by Clustering

Rendezvous based data Collection

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSN

Real Time WSN


Direct Contact

Rendezvous based

Rao and Biswas (2008)

• Framework integrating several algorithms.

• K-hop clusters are constructed.

• Each cluster is a minimum hop tree rooted at its Navigation Agent (NA) with a depth of at least K+1 and at most 2k+1.

• A TSP tour of NA is used for the sink. They use min hop links between clusters.

• Info is collected 1-hop of the NA (data replication,...)

Limitation:• More energy consumption because of multi hop data

communication.• Configuration k input might yield different results.• k=1 direct contact data collection.• k=kmax (n : network size) static sink scenario.

Questions

References

Real Time WSN approaches

Taxonomy

Introduction

Sink Mobility

Taxonomy

Delay Tolerant WSNReal Time WSN


Cluster based Brute Force MILP Tree based Learning-based

Request zone

Sink Relocationstrategies

Data DisseminationTo mobile sinks

Sink Relocation

Data Dissemination

Event-Driven Periphery Tree-based

Multi hop message relay with optimal sink relocation and routing algorithms for data dissemination to mobile sinks.

Banerjee et al. (2008), Bi et al. (2007), Vincze et al. (2007), Bogdanov et al., 2004.

Wu and Chen (2007), Kim et al. (2003)Baruah et al. (2004) , Ammari and Das (2005)

Questions

References

Real Time WSN approaches

Taxonomy

Introduction

Sink Mobility

Taxonomy



Cluster based Brute Force MILP

Sink Relocationstrategies

Sink Relocation

Data Dissemination

Event-Driven Periphery Tree-based

Concern:• Reduce multi hop message relay with optimal sink relocation.

• Sinks move through energy-intense areas rather than energy-sparse areas.

Limitation:• More energy consumption because

of multi hop data communication.

• Optimal multi sink placement is NP-Complete problem.

Banerjee et al. (2008), Bi et al. (2007), Vincze et al. (2007), Bogdanov et al., 2004.

Questions

References

Brute Force approach

Sink Relocation

Introduction

Sink Mobility

Taxonomy



Sink Relocation

Data Dissemination

• Algorithm runs periodically to check if sinks should be relocated.

Limitation:• More energy consumption because of multi-hop data communication.• Optimal positions NP-Complete.Friedmann and Boukhatem (2009)

• Sink relocation takes place if and only if the new sink position reduces total cost.

• Each edge is assigned a weight based on the remaining energy and cost of the message transmitting.

• Sinks have a global view of the network and run a centralized algorithm.

Questions

References

Data Dissemination to mobile sinks

Taxonomy

Introduction

Sink Mobility

Taxonomy



Tree based Learning-based

Request zone

Data DisseminationTo mobile sinks

Sink Relocation

Data Dissemination

• It is the problem of data routing to sinks in the presence of sink mobility.

• Data dissemination with mobile sinks is a combined problem of LOCATION and ROUTING.

Wu and Chen (2007), Kim et al. (2003)Baruah et al. (2004) , Ammari and Das (2005)

Concern:• Fast and correct delivery with trade-off energy consumption.

Limitation:• More energy consumption because of multi-hop data communication and sink reposition.

Questions

References

Request Zone

Data Dissemination

Introduction

Sink Mobility

Taxonomy



Sink Relocation

Data Dissemination

Ammari and Das (2005)

Limitation:• More energy consumption because of multi hop data communication and sink reposition.• Needs complete coverage of nodes, routing to c is not

the expected sink position and directional routing could be a problem.

• Sink advertises by flooding with positions a1 and a2 before it starts moving.

• Sink will move from a1 to a2.

• Each sensor computes the circle with diameter d1 and d2. Then it computes the sensors in its transmission area towards c; center of the circle with D=|a1,a2|.

• Directional routing is used from the sensor to the center of the circle.

Questions

References

References

Thanks for Listening...

Introduction

Sink Mobility


Questions

References

1. Ivan Stojmenovic, Amiya Nayak. “Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scalable Coordination and Data Communication”Wiley-Intercience, Chapter 6, pp. 153- 181. 2009. 2. Shah RC, Roy S, Jain S, Brunette W. “Data MULEs: modeling

and analysis of a three-tier architecture for sparse sensor networks”. Ad Hoc Netw 2003;1(2–3):215–233.

3. Chakrabarti A, Sabharwal A, Aazhang B. “Using predictable observer mobility for power efficient design of sensor networks”. Proceedings of the 2nd InternationalWorkshop on Information Processing in Sensor Networks (IPSN), Volume 2634 of LNCS; 2003. pp. 129–145.

4. Nesamony S, Vairamuthu MK, Orlowska ME, Sadiq SW. “On optimal route computation of mobile sink in a wireless sensor network”. Technical Report 465. ITEE, University of Queensland; 2006.

5. Sugihara R, Gupta RK. “Improving the data delivery latency in sensor networks with controlled mobility”. Proceedings of the 4th IEEE International Conference on Distributed Computing inSensor Systems (DCOSS), Volume 5067 of LNCS; 2008. pp. 386–399.

6. Kansal A, Somasundara AA, Jea DD, Srivastava MB, Estrin D. “Intelligent fluid infrastructure for embedded networks”. Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services (MobiSys); 2004. pp. 111–124.

7. Xing G, Wang T, Jia W, Li M. “Rendezvous design algorithms for

wireless sensor networks with a mobile base station”. Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc); 2008. pp. 231–239.

8. Xing G, Wang T, Xie Z, Jia W. “Rendezvous planning in mobility-assisted wireless sensor networks”. Proceedings of the 28th IEEE International Real-Time Systems Symposium (RTSS); 2007.pp. 311–320.

9. Rao J, Biswas S. “Joint routing and navigation protocols for data harvesting in sensor networks”. Proceedings of the 5th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS); 2008. pp. 143–152.

10. Friedmann L, Boukhatem L. “Efficient multi-sink relocation in wireless sensor network”. Ad Hoc & Sens Wirel Netw 2009. To appear.

11. Ammari HM, Das SK. “Data dissemination to mobile sinks in wireless sensor networks: an information theoretic approach”. Proceedings of the 2nd IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS); 2005. pp. 314–321.

Question 1

Delay Tolerant WSN >> Direct Data Collection >> TSP tour .

Introduction

Sink Mobility

Question 1

Question 2

Question 3

Sink Mobility in Wireless Sensor Networks Andres Solis Montero

Questions

References

1. In Delay Tolerant WSNs, direct data collection approaches try to minimize the trip made by the sink visiting all sensors in the network. Knowing a min TSP tour of ‘1,2,3,4’ ; construct a minimal path using the TSPN (Travel Salesman Problem with Neighbourhood) algorithm.

Question 1


Introduction

Sink Mobility

Question 1

Question 2

Question 3


Questions

References

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Introduction

Sink Mobility

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Introduction

Sink Mobility

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Sink Mobility in Wireless Sensor Networks Andres Solis Montero 1/Y

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Introduction

Sink Mobility

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Question 2

Question 3

Sink Mobility in Wireless Sensor Networks Andres Solis Montero 1/Y

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Question 1


Introduction

Sink Mobility

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Introduction

Sink Mobility

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Introduction

Sink Mobility

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Introduction

Sink Mobility

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Question 1


Introduction

Sink Mobility

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Question 2

Question 3


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Question 2

Delay Tolerant WSN >>Direct Data Collection >> Label-covering tour.

Introduction

Sink Mobility

Question 1

Question 2

Question 3


Questions

References

2. Having the same sensors and sink configuration but this time without a minimal TSP tour; would it be possible to give the shortest path using a Label-covering tour approach? If yes, give the shortest path using such an algorithm.

Question 2


Introduction

Sink Mobility

Question 1

Question 2

Question 3


Questions

References

Question 2


Introduction

Sink Mobility

Question 1

Question 2

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Questions

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Question 2


Introduction

Sink Mobility

Question 1

Question 2

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References

Question 3

Real Time WSN >> Data Dissemination >> Request Zone

Introduction

Sink Mobility

Question 1

Question 2

Question 3


Questions

References

3. Data dissemination to mobile sinks deals with the problem of correctly routing data to sinks. In the Request Zone algorithm, the sink, before it starts moving, will flood its starting and ending position. Eventually, all nodes will have that information and they will route messages to point c. (center of the circle formed by the diameter determined by |s,e|). The routing solution given by this algorithm will fail in the following scenario starting from the gray node. Why? Is it possible to correct the data delivery starting from the gray sensor using the routing algorithm studied in class?

Question 3


Introduction

Sink Mobility

Question 1

Question 2

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Questions

References

Question 3


Introduction

Sink Mobility

Question 1

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Andres Solis Montero

THANKS !! GRACIAS!!

Sink Mobility in Wireless Sensor Networks

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CSI 5148 Sink Mobility in Wireless Sensor Networks Andres Solis Montero