Opportunistic Data Gathering in IoT Networks using Discrete Optimization

Author(s)

Afonso, Edvar ; Campista, Miguel Elias M. ; IEEE

• Document type: Journal article
• Source: 2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC); v. N/A, p. 6-pg., 2020-01-01.

Abstract

The Internet of Things (IoT) is based on data collection for future processing and decision making. In multihop Low-Power and Lossy Network (LLN) scenarios, efficient data forwarding in terms of generated traffic and energy consumption is fundamental. This paper revisits the concept of mobile agents to collect data along the agents's itinerary. The idea is to avoid sending requests to the network when non-expired contents that were opportunistically collected are available in the cache of a central element. In the proposed mechanism, the itinerary is composed of devices of interest and intermediate devices in a closed loop at the origin. Knapsack optimization is used to add unsolicited data opportunistically. The reward is calculated according data popularity. Simulations show that it is possible to reduce network traffic and the energy consumed by devices when compared to the traditional mobile agent data gathering model. (AU)

• FAPESP's process: 15/24490-2 - MC2: mobile computing, content distribution, and cloud computing
• Grantee: Luis Henrique Maciel Kosmalski Costa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants