# Managing Consensus-Based Cooperative Task Allocation for IIoT Networks

**Authors:** Carlos Pedroso, Yan Uehara de Moraes, Michele Nogueira, Aldri Santos  
**arXiv:** [2006.09320](https://arxiv.org/abs/2006.09320)  
**Date:** 2020-06-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2006.09320v1/

## Key points

- The paper addresses the challenge of task allocation in the Internet of Things (IoT) industry
- Task allocation aims to enhance information quality and maximize task performance
- Existing approaches do not consider information quality and distributed allocation across cluster services
- The paper proposes a mechanism called CONTASKI for task allocation in Industrial IoT (IIoT) networks
- CONTASKI uses collaborative consensus and similarity capabilities to allocate tasks
- CONTASKI achieved 100% task allocation in scenarios with 75 and 100 nodes
- On average, more than 80% of clusters successfully performed tasks within a low response time
- The approach presented in the paper effectively addresses the challenge of task allocation in IIoT networks

## Summary

The paper titled "Managing Consensus-Based Cooperative Task Allocation for IIoT Networks" addresses the challenge of distributing and managing task allocation among objects in the rapidly growing Internet of Things (IoT) industry. While current IoT services often require objects to perform multiple tasks, the exponential increase in the number of objects poses a significant challenge. The main objective of task allocation is to enhance information quality and maximize task performance. Although there are existing approaches that optimize and manage node dynamics, not all consider the quality of information and distributed allocation across cluster services. To address this gap, the paper proposes a mechanism called CONTASKI for task allocation in Industrial IoT (IIoT) networks. CONTASKI relies on collaborative consensus to allocate tasks and assesses similarity capabilities to determine which objects are suitable for accomplishing specific tasks. To evaluate the effectiveness of CONTASKI, it was implemented on NS-3 simulation platform. The results showed that CONTASKI achieved 100% task allocation in scenarios with 75 and 100 nodes. On average, more than 80% of clusters successfully performed tasks within a low response time. Overall, this paper presents an innovative approach to address the challenge of task allocation in IIoT networks by leveraging collaborative consensus and similarity capabilities. The evaluation results demonstrate its effectiveness in achieving high task allocation rates and efficient task execution within clusters.