Abstract
With the enormous use of internet of things-based devices for enabling smart agriculture, there is a significant need for efficient systems in order to improve agricultural practices. It can help efficiently to develop optimal web-based information system using the data of field monitoring. But, the collection of such data in the presence of connectivity disruptions poses new challenges for users. This paper targets to determine such offloaders with less infrastructural costs to enable smart agriculture based on network heuristics. Although, few works contribute to the trust established, most of them are applicable only for static networks. This paper explores a trust-based solution for mobile data offloading. This paper identifies the need and impact of trust determination using the trust model algorithm. The proposed algorithm outperforms the hybrid trust-based mobility aware clustering algorithm for trust-based offloaders with up to 13% better offloading potential saving a minimum of 8 pJ energy per user with just 25% contributors with 50% lesser time delay.
Highlights
The use of smart agriculture techniques provides a promising solution for crop monitoring and environmental data gathering, while addressing the challenges of less human resources, climate change, and economic barriers etc
The main concern in this paper is to model a realistic behavior of the users in opportunistic network by considering the opinion based trust of the users
We have considered the Crawdad WTD data-trace for our simulation purpose, which considers the opportunistic network of PDA users
Summary
The use of smart agriculture techniques provides a promising solution for crop monitoring and environmental data gathering, while addressing the challenges of less human resources, climate change, and economic barriers etc. We have identified that there is a limited research focus on trust management for efficient data offloading. This aspect needs to be explored more with possible improvements. The results establish the fact that it is not possible to consistently identify all the significant users to optimize delivery. This is because, the users are highly environment-dependent i.e., the networks are dynamic and keep evolving with time especially in vehicular networks. This involves excessive network resource utilization and high computations due to the use of application-specific trust management
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