TY - GEN
T1 - Deployment of Bio-Inspired Intelligent Model for Self-organizing Smart Sensory Systems
AU - Guha, Debanksh
AU - Mukherjee, Ishaan
AU - Ghoshrave, Eeshan
AU - Chilluri, Venkata Suresh Babu
AU - Rathore, Rajkumar Singh
AU - Wu, Hang
AU - Chu, Xinlei
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/10/1
Y1 - 2025/10/1
N2 - As Internet of Things (IoT) grows, efficient self-organizing mechanisms are very much needed for the vast and ever-growing dynamic networks without being dependent on centralized algorithms to maintain a control over the systems. This paper introduces a bio-inspired artificial Intelligence (AI) model that leverages natural systems such as ant colonies, swarm behavior, and neural intelligence to upskill the IoT self-organization. It replicates the behavior typically observed in a biome. The proposed IoT model autonomously manages tasks, distributes workloads, and maintains a resilient network environment. This decentralized pathway opens up avenues for scalability, fault-tolerant systems, and adaptability which caters to the need of a critical IoT architecture. Simulation results demonstrate that the bio-driven model drastically outperforms outdated and traditional AI algorithms in terms of resource utilization, fault recovery, and network resilience. These findings suggest that bio-inspired AI models can offer a stalwart foundation for autonomous and resilient IoT systems.
AB - As Internet of Things (IoT) grows, efficient self-organizing mechanisms are very much needed for the vast and ever-growing dynamic networks without being dependent on centralized algorithms to maintain a control over the systems. This paper introduces a bio-inspired artificial Intelligence (AI) model that leverages natural systems such as ant colonies, swarm behavior, and neural intelligence to upskill the IoT self-organization. It replicates the behavior typically observed in a biome. The proposed IoT model autonomously manages tasks, distributes workloads, and maintains a resilient network environment. This decentralized pathway opens up avenues for scalability, fault-tolerant systems, and adaptability which caters to the need of a critical IoT architecture. Simulation results demonstrate that the bio-driven model drastically outperforms outdated and traditional AI algorithms in terms of resource utilization, fault recovery, and network resilience. These findings suggest that bio-inspired AI models can offer a stalwart foundation for autonomous and resilient IoT systems.
KW - Bio-inspired AI
KW - Decentralized networks
KW - Fault tolerance
KW - Internet of Things (IoT)
KW - Self-organizing systems
KW - Swarm intelligence
UR - https://www.scopus.com/pages/publications/105020017314
U2 - 10.1007/978-981-96-6715-4_45
DO - 10.1007/978-981-96-6715-4_45
M3 - Conference contribution
AN - SCOPUS:105020017314
SN - 9789819667147
T3 - Lecture Notes in Networks and Systems
SP - 641
EP - 651
BT - Innovative Computing and Communications - Proceedings of ICICC 2025
A2 - Hassanien, Aboul Ella
A2 - Anand, Sameer
A2 - Jaiswal, Ajay
A2 - Kumar, Prabhat
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Innovative Computing and Communication, ICICC 2025
Y2 - 14 February 2025 through 15 February 2025
ER -