Good start to the day! Got to learn something new. The hummingbird algorithm!

I was looking to explore optimal clustering methodology for IoT devices and came across this paper. https://lnkd.in/gXBN4Dt5

In the context of the Industrial Internet of Things (IIoT), clustering of sensor/IoT devices (grouping nodes into clusters with designated cluster heads (CHs)) helps with resource use, managing network complexity, and meeting real‐time data processing needs.

One major challenge: energy consumption and network lifetime (battery‐powered nodes, long deployment periods) in large, industrial‐scale IoT environments.

The challenge here is that the selection of CHs and optimal routing among nodes is combinatorially hard (NP‐type), and previous methods often have limitations (random CH selection, limited routing adaptation, neglect of certain QoS or reliability factors) in realistic IIoT settings.

The authors present a novel clustering/routing framework: the Energy Efficient Quantum‐Informed Artificial Hummingbird Optimization Algorithm (EEQIAHBOA). Phew! Such a long acronym.

This algorithm builds on the metaphor of the hummingbird optimization algorithm (a swarm intelligence method) enhanced with quantum‐informed techniques to better explore the search space (improved exploration + exploitation balance).

Key components of the proposed solution are:

A. Multi‐objective fitness evaluation considering: residual energy of nodes, distance between nodes and CHs, energy consumption in CH selection and routing, packet delay, and packet loss rate.

B. Weight‐determination of criteria via the CRITIC method (evaluating how criteria correlate and deviate) to assign proper importance to each metric.

C. Encoding and evolutionary‐search mechanisms: the CH selection is encoded, and the algorithm iteratively updates solutions (clusters + routes) to optimize the fitness function.