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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

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)
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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