Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications

Ashish Manchanda, Prem Prakash Jayaraman, Abhik Banerjee, Arkady Zaslavsky, Shakthi Weerasinghe, Guang-Li Huang

TL;DR

This work addresses the challenge of keeping context data fresh in a Context Management Platform (CMP) for real-time IoT applications. It introduces Context Freshness Monitoring System (CFMS), a hybrid framework combining an Analytic Hierarchy Process (AHP)-based Decision Supporting Algorithm (DSA) to weight context attributes and a sliding-window–based Parameters Freshness Processing Algorithm (PFPA) to maintain cache freshness. The experimental evaluation demonstrates improved cache performance, with higher cache-hit ratios and lower cache-expired ratios compared with baseline approaches, and shows scalability under varying workload and memory constraints. The approach is particularly relevant for edge-enabled IoT deployments where memory and latency are critical, with potential extensions to fuzzy AHP and integration into broader CMP ecosystems like CoaaS.

Abstract

Internet of Things (IoT) has seen a prolific rise in recent times and provides the ability to solve several key challenges faced by our societies and environment. Data produced by IoT provides a significant opportunity to infer context that is key for IoT applications to make decisions/actuations. Context Management Platform (CMP) is a middleware to facilitate the exchange and management of such context information among IoT applications. In this paper, we propose a novel approach to monitoring context freshness as a key metric, to improving the CMP's caching performance to support the real-time context needs of IoT applications. Our proposed hybrid algorithm uses Analytic Hierarchy Process (AHP) and Sliding Window technique to ensure the most relevant (as needed by the IoT applications) context information is cached. By continuously monitoring and prioritizing context attributes, the strategy adapts to IoT environment changes, keeping cached context fresh and reliable. Through experimental evaluation and using mock data obtained from a real-world mobile IoT scenario in section~\ref{use case}, we demonstrate that the proposed algorithm can substantially enhance context cache performance, by monitoring the context attributes in real time.

A Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications

TL;DR

This work addresses the challenge of keeping context data fresh in a Context Management Platform (CMP) for real-time IoT applications. It introduces Context Freshness Monitoring System (CFMS), a hybrid framework combining an Analytic Hierarchy Process (AHP)-based Decision Supporting Algorithm (DSA) to weight context attributes and a sliding-window–based Parameters Freshness Processing Algorithm (PFPA) to maintain cache freshness. The experimental evaluation demonstrates improved cache performance, with higher cache-hit ratios and lower cache-expired ratios compared with baseline approaches, and shows scalability under varying workload and memory constraints. The approach is particularly relevant for edge-enabled IoT deployments where memory and latency are critical, with potential extensions to fuzzy AHP and integration into broader CMP ecosystems like CoaaS.

Abstract

Internet of Things (IoT) has seen a prolific rise in recent times and provides the ability to solve several key challenges faced by our societies and environment. Data produced by IoT provides a significant opportunity to infer context that is key for IoT applications to make decisions/actuations. Context Management Platform (CMP) is a middleware to facilitate the exchange and management of such context information among IoT applications. In this paper, we propose a novel approach to monitoring context freshness as a key metric, to improving the CMP's caching performance to support the real-time context needs of IoT applications. Our proposed hybrid algorithm uses Analytic Hierarchy Process (AHP) and Sliding Window technique to ensure the most relevant (as needed by the IoT applications) context information is cached. By continuously monitoring and prioritizing context attributes, the strategy adapts to IoT environment changes, keeping cached context fresh and reliable. Through experimental evaluation and using mock data obtained from a real-world mobile IoT scenario in section~\ref{use case}, we demonstrate that the proposed algorithm can substantially enhance context cache performance, by monitoring the context attributes in real time.
Paper Structure (16 sections, 7 figures, 2 tables, 2 algorithms)

This paper contains 16 sections, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Context Freshness Monitoring System(CFMS)
  4. Decision Supporting Algorithm
  5. Parameters Freshness Processing Algorithm
  6. Inputs and Initializations
  7. Sliding Window Algorithm (SWA) Procedure
  8. Main Loop
  9. Experimental Evaluation
  10. Varying the threshold for the Sliding Window Algorithm
  11. Changing the size of the incoming entries(contextual data)
  12. Modifying the cache capacity
  13. Evaluating the Cache Expired Ratio
  14. Discussion
  15. Conclusion & Future Work
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Context Freshness Monitoring System.
  • Figure 2: Hierarchy of context "Road Work" for decision making.
  • Figure 3: Weights of context attributes of context "Road Work" after DSA.
  • Figure 4: Comparison of Cache hit with a threshold value of Sliding Window.
  • Figure 5: Comparison of Cache hit ratio with Number of Entries and Thresholds.
  • ...and 2 more figures