Location-Centric Storage for Wireless Sensor Networks

Kai Xingn1, Xiuzhen Cheng1, and Jiang Li2

1Department of Computer Science, The George Washington University2Department of Systems & Computer Science, Howard University

The 2nd IEEE International Conference on Mobile Ad-hoc and Sensor SystemsMASS 2005 Reporter: Shin-Wei Ho

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Outline

Introduction Example Applications Location-Centric Storage Performance Analysis Simulation Conclusion

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Introduction

Nevertheless, sensor networks pose many new challenges.

One of the challenges is how to Store data efficiently to facilitate user query. On-demand warning across the entire sensor net

work.

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Introduction(cont’d) There exists three canonical data storage

methods Local Storage (LS) External Storage (ES) Data-Centric Storage (DCS)

These studies indicate that no one outperforms the other two in all situations.

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Introduction(cont’d) In fact, none of these methods targets the

application scenarios considered in our LCS design.

For example, on-demand warning requires Zero delay High reliability

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Introduction(cont’d) Location-centric storage (LCS),

Efficiently disseminate aggregated data based on the intensity of the data.

On-demand Warring Applications

Example Applications

Context-Dependent Information Dissemination for Pervasive Computing On-Demand Warning in Surveillance Sensor Networks Roadway Safety Warning

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Example Applications-- Context-Dependent Information Dissemination for Pervasive Computing “The Computer for the 21st Century”, 1991

Where is the most closest gas station?

I would like to pay $X.

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Example Applications-- On-Demand Warning in Surveillance Sensor Networks

Enemy

Enemy

EnemyAllied Force

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Example Application-- Roadway Safety Warning

“Zero Fatality, Zero Delay”, the World Congress on ITS (Intelligent Transportation Systems and Services)

Car crashes

Where should I

go ?

Location-Centric Storage

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Location-Centric Storage

Assumption Sensors can obtain their own geometric coordinat

es (Sx, Sy) using GPS or other techniques.

A robust broadcasting protocol is in place such that event records can be properly disseminated.

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Location-Centric Storage

When detecting an event, the home sensor creates a record with the following five fields: The time indicating when the event occurs. The location (i.e. the coordinates (Sx, Sy)) of the event.

For simplicity, we assume an event collocates with its home sensor.

An integral intensity value (σ) that characterizes the event. Intensity values are application-specific. Ex: the time needed to clear the road in highway safety warnin

g. A Time-To-Live (TTL) as the expiration time (relative to the

current moment) of the record. The event type bearing other information of the event.

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Location-Centric Storage

Event

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Location-Centric Storage

Event

1 3

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Location-Centric Storage

Event

1 3

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Location-Centric Storage

Event

1 3

Query

User

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Location-Centric Storage(cont’d)

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Performance Analysis

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Performance Analysis(cont’d)Store both

data

odd even

1 1

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Performance Analysis(cont’d)1 1

Store both data

odd even

Contradicts

2

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Performance Analysis(cont’d) There are at most 4 different X coordinates.

The same argument holds true for the Y coordinate.

There fore there are at most 16 pairs of coordinates at which the nodes store both records.

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Performance Analysis(cont’d)

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Performance Analysis(cont’d) Remark: Theorem 5.1

No matter how big the intensity value is, there will be a fixed number of sensors that store the same records. (as long as the two event locations are not colinear in X and Y directions)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d) Remark: Theorem 5.2

The average number of records stored in each node at any time is independent of the network size.

Therefore, the protocol is efficient Storage requirement Power consumption Highly Scalable

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Performance Analysis(cont’d)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d) Remark: Theorem 5.2 & 5.3

LCS is fair to all nodes in storage space. Records are uniformly and independently generated

This is an intrinsic difference compared with DCS.

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Performance Analysis(cont’d)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d)

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Performance Analysis(cont’d) Remark: Theorem 5.4

When the user resides in the broadcast region of an event, the query distance is no more than distance between the user and the home location of this event.

A user can only be notified of the events that occur within certain distance from the user.

Simulation

LCS Performance Evaluation Comparative Study

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Simulation-- LCS Performance Evaluation Simulation setup

200 seconds λ=2i x 10-3, where i is one of 0,…,8 The intensity σ is randomly chosen from [0, 6] The TTL value is randomly chosen from [1, 100] in

seconds. The TTL value decreases by 1 every second. A record is removed when it’s TTL value reaches

zero.

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Simulation-- LCS Performance Evaluation Max-vs-averge storage ratio:

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Simulation-- LCS Performance Evaluation

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Simulation-- LCS Performance Evaluation

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Simulation-- LCS Performance Evaluation

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Simulation-- Comparative Study Comparison:

External Storage Local Storage

The authors did not compare LCS with DCS Target different application scenarios Employ a totally different set of input parameters.

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Simulation-- Comparative Study For example, the message overhead in DCS depends on

The number of event types The hash function exploited

But in LCS, events are stored and disseminated based on its home location and its characteristics Seriousness Price Intention

Therefore, the authors found that it is almost impossible to design a simulation study for fairly comparing LCS and DCS.

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Simulation-- Comparative Study

The total number of messages generated vs. The network size

The network size (N) = 40000

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Simulation-- Comparative Study

The total number of messages generated vs. The number of quires

The number of queries (Q) = 50

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Conclusion LCS: A novel distributed location-centric data storag

e protocol for sensor networks.

The protocol has many nice features, as indicated by theoretical performance analysis and simulation study.

Several simple application scenarios of LCS Safety warning in highway sensor networks On-demand warning in surveillance networks context-dependent information mining in pervasive computi

ng.

Thank you !Question ?