A Trust Aware Secure Ant Colony Optimization Based Routing Algorithm for Internet of Things

Sharmin, Afsah; Anwar, F.; Motakabber, S. M. A.; Hashim, Aisha-Hassan A.

doi:10.1109/iccce58854.2023.10246057

Cited by 2 publications

(1 citation statement)

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“…Existing ACO based solution in IoT have focus more on energy-efficiency in IoT [29] [32], routing protocols [30] [33], and resource allocation [32] [34]. Adopting self-organization for optimized resource utilization in IoT has however received very little attention and this can be linked to failure of existing works to formulate a realistic approach to assessing the availability of computational resources of IoT nodes [29] , [35].…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

Wilson,

Nunoo-Mensah,

Boateng

et al. 2024

Preprint

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IoT devices relying on cloud-based computing resources for data processing are causing unsustainable growth and latency, thus making their dependence on these computing resources a foremost challenge to real-time and time-critical IoT applications. Researchers have proffered several approaches, but notable amongst them are the bio-inspired optimisation methods due to their ability to model stochastic environments efficiently. This paper proposes a bio-inspired load-balancing optimisation approach for reducing latency in IoT networks. The paper leverages the concept of “stigmergy” in social ants to create a metric for assessing the computational capacity, reliability, and availability of a node’s computing resources in a decentralised Internet of Things (IoT) network. The main contribution of this work is the design of a decentralised serverless computing paradigm for stateful(reliable) task offloading and data processing among end-user nodes in an IoT environment. A novel bio-inspired algorithm is designed for latency reduction and optimal load-balancing in any collaborative data processing environment without a centralised server. This work also introduces the concept of computing pheromones as a metric for assessing computation resource capacity and availability in decentralised data processing environments, which paves the way for more efficient and reliable IoT data processing solutions. Experimental results show the effectiveness of the proposed IoT data processing approach, highlighting improvements in response time and turnaround time compared to existing approaches.

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