Context aware caching scheme cacs for real time health monitoring systems
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Context-aware caching scheme (CACS) for real-time health monitoring systems. Aarti Munjal, Aravind Kalavagattu Arizona State University CSE 535: Mobile Computing Project presentation. Problem Statement. Formally,

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Context-aware caching scheme (CACS) for real-time health monitoring systems

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Context aware caching scheme cacs for real time health monitoring systems

Context-aware caching scheme (CACS) for real-time health monitoring systems

Aarti Munjal, Aravind Kalavagattu

Arizona State University

CSE 535: Mobile Computing

Project presentation


Problem statement

Problem Statement

Formally,

To design a context aware caching scheme for real-time health monitoring of patients in a community setting. The system has to be scalable, with minimum time-lag for information discovery, robust in handling the network traffic and ensure high amount of accuracy.


Tasks involved

Tasks involved..

  • Topology

  • CACS

  • Employing Context awareness

  • Search & Update

  • Analysis

  • System Design

  • Simulation/Implementation


Topology

Topology

  • To make it scalable, we adopt a multi-tier architecture

    • PDAs at leaves

      • Patient with sensors

    • Server as root

      • Doctor

    • Hubs sit at intermediate levels

Server

H7

H8

H1

H2

H3

H4

H5

H6

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

Figure 6: Multi-tier architecture


Context aware caching scheme cacs

Context Aware Caching Scheme (CACS)

  • Aim:

    • To ensure data availability even in case of disconnections and failures.

  • Cache Resolution: Data request aimed to be solved at the highest level possible.

    • Query should be served by the top-level hubs with out going to the patient PDAs always.

  • Cache Management: Decision regarding purging less important data to make room for the critical information.

    • In case of overflow in traffic, critical data needs to be stored as compared to less critical ones


Data structures used for caching

Data Structures Used for Caching

  • Path

    • Hub sequence is stored to reach the patient PDA


Employing context awareness

Employing Context-awareness

  • Context:

    • Environmental Conditions

    • Patient’s health vulnerabilities

    • Range of each sensor values (if it is within the safe limit or not)

      • Eg: Temperature of 98-99 F is safe, but we need attention if it crosses beyond

  • Context parameters

    • Priority = f(v,m,e) [used for admission control at hub level]

    • TTL = g(v,m,e) [used to make sure the values get

      refreshed timely]

      where,

  • Defined at the PDA level.

    • v = variance of data values

    • m = mobility of PDAs (patients)

    • e = environmental factor


Search and update

Search and Update

  • Search:

    Request (pid,sid,path) from Server level reaches the last hub in path.

    do {

    go to immediate parent hub;

    broadcast to the child hubs;

    }

    while (entry_not_found)

  • Update:

    Register Packet from pda (with ret = 1) is sent to hub. Hub adds the entry in its data table, appends its own id in ‘path’ field and forwards the packet to next-level hub.

  • Packet is forwarded until it reaches the server.

Server

H7

H8

H1

H2

H3

H4

H5

H6

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

Figure: Multi-tiers


Analysis comparison with simple caching scheme

Analysis: Comparison with Simple Caching Scheme

  • Performance Measures:

    • Average cost for search and update

    • Request Satisfaction factor

  • Parameters for Evaluation

    • N = Total No. of Nodes

    • n = No. of levels

    • r = Branching Factor

    • p = Probability that each hub has data

    • λr = Request Generation Rate

    • λ = PDA Mobility Rate

    • c = Average No. of levels for broadcast

    • a = Probability that path to PDA is known


Analysis contd

Analysis Contd…

  • Average Cost Function for CACS:

    C = λc[(1-p)n-1{an+(1-a)(c+ r-1)} + (1-(1-p)n)(n-1)/2] + λm(n)

  • Average Cost Function for Simple Caching Scheme:

    C = λc[(1-p){an + (1-a)(N-1)/2}] + λm(n)


Analysis contd1

Analysis contd…

  • Search cost: λc[(1-p)n-1{an+(1-a)(c+ r-1)} + (1-(1-p)n)(n-1)/2]

    • (1-p)n-1 - probability that none of IM hubs has data

    • n - no of hops travelled if path to pda is known (probability a)

    • o/w we search path by broadcasting (probabilty (1-a))

    • 1- (1-p)n - probability that at least one of the IM hubs has data

    • (n-1)/2 - # of hops to travel on an average

  • Update cost: λm(n)


Results

Results


Simulation

Simulation

  • Simulation tool: Network Simulator (Ns2)

  • Simulation in Ns2 is agent-based, where agents communicate with each other through message-passing.

  • Three Agents:

    • Pda Agent (lowest level)

    • Hub Agent (Intermediate levels)

    • Server Agent (Highest level)

  • Two types of packets:

    • Request packet

    • Data packet


Simulation contd

Simulation contd…

  • Storage

    • Tables

  • Communication

    • Packet Structure

  • Timer Events (TTL-based)

    • PDA: Refreshes data and sends to hub

    • Hub: Request sent to PDA

  • Timer & Values hashed using <pid,sid> pairs


Tasks accomplished

Tasks: accomplished

  • Mathematical analysis of CACS

    • Its comparison with the traditional simple caching scheme

    • Proved: CACS performs much better than the traditional caching scheme.

  • Implemented the design of system for simulation

    • Created three agents

    • Data Structures used to store data at each level

    • Timers to refresh TTL

    • Incorporating Context-awareness using rules

    • Data admission control based on priority


Future work

Future Work

  • Request Satisfaction Factor (rs)

    • Number of requests satisfied/ Number of requests generated

    • Simple Caching Scheme:

      • Data stored using FCFS.

    • CACS:

      • Critical data always given preference.

  • rs for CACS > rs for Simple Caching Scheme

    • Due to the context-aware caching: stores most frequently asked or critical data all the time.

  • Simulating the system for a large community of patients for testing and validating the mathematical analysis.


References

References

  • Krishna Venkatasubramanian, Guofeng Deng, Tridib Mukherjee, John Quintero, Valliappan Annamalai and S. K. S. Gupta, Ayushman: A Wireless Sensor Network Based Health Monitoring Infrastructure and Testbed, IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), 2005

  • Y. Du and S. K. S. Gupta, COOP - A cooperative caching service in MANETs, In Proc. of ICAS-ICNS 2005. Joint International Conference on, Tahiti, French Polynesia, pp 58-63, Oct. 23-28, 2005

  • Anurag Kahol, Sumit Khurana, Sandeep K.S. Gupta, and Pradip K. Srimani, A Strategy to Manage Cache Consistency in a Disconnected Distributed Environment, IEEE Transactions On Parallel And Distributed Systems, VOL. 12, NO. 7, JULY 2001

  • A. Skordylis, N. Trigoni and A. Guitton, A Study of Approximate Data Management Techniques for Sensor Networks, Proc of the 4th Workshop on Intelligent Solutions in Embedded Systems, 2006.

  • Guanling Chen and David Kotz. A Survey of Context-Aware Mobile Computing Research, Department of Computer Science, Dartmouth College, Dartmouth Computer Science Technical Report TR2000-381


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