Optimization Techniques in RFID Network Planning
Summary
Radio Frequency Identification (RFID) network planning entails determining the optimal number, placement and configuration of readers and antennas to achieve reliable tag coverage while minimising cost, interference and energy use. The underlying problem is combinatorial and NP-hard, demanding sophisticated optimisation techniques. Early methods relied on integer programming or greedy heuristics, but modern research has embraced metaheuristic and bioinspired algorithms to handle large-scale, multi-objective formulations. These include evolutionary algorithms, swarm-intelligence methods and hybrid paradigms that balance exploration and exploitation. Key objectives span coverage maximisation, interference mitigation, load balancing, deployment cost and reader redundancy reduction. Constraints may represent obstacle-induced signal attenuation, reader capacity and regulatory power limits. Advances in mathematical modelling, algorithmic strategy and parallel computing have enabled planners to address dynamic environments, heterogeneous tag populations and real‐time adaptation. Practical deployments in smart factories, logistics hubs and retail inventory systems illustrate the global significance of robust, energy-efficient RFID infrastructures.
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Optimization Techniques in RFID Network Planning publication trend
The graph below shows the total number of articles in optimization techniques in rfid network planning across all publications each year (not limited to Nature Index journals).
Technical terms
Radio Frequency Identification (RFID): Technology using electromagnetic fields to automatically identify and track tags attached to objects.
Swarm Intelligence: A class of optimisation techniques inspired by collective behaviours of social organisms such as birds, fish and insects.
Firefly Algorithm: A metaheuristic inspired by the bioluminescent communication of fireflies, used to explore and exploit search spaces.
Particle Swarm Optimisation (PSO): An iterative method modelling social behaviour to adjust candidate solutions according to individual and group best positions.
Multi-objective Optimisation: The process of simultaneously optimising two or more conflicting objectives to obtain a set of Pareto-optimal solutions.
References
- RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review. IEEE Access (2024).
- Optimization Method of RFID Reader Antenna Deployment in Obstacle Environment Based on Improved FA. International Journal of Antennas and Propagation (2022).
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