BMUNPLC: Design of a Bioinspired Model for Improving Usability of New PLC Deployments for IoT Applications

Internet of Things (IoT) have paved the way into households and industries due to their ease-of-use and fine-tuned control characteristics. Every IoT network requires multiple wireless devices that can be connected via a common routing and control protocol. These devices utilize wireless transmitters and receivers to communicate with a central control router, which is connected to the cloud. This router is capable of aggregating data from multiple IoT devices and send it to the cloud for logging and control purposes. Control signals from the cloud are fetched by router and then wirelessly transmitted to IoT devices for seamless connectivity and control. But a major drawback of such scenarios is presence of wireless components at each IoT device, which increases cost and energy consumption of the deployed network under practical use cases. Due to incorporation of wireless devices, delay needed to communicate between router and the device is higher when compared with its wired counterparts. To overcome these limitations, a novel bioinspired device placement model for new electrical sites, is proposed in this text. The proposed model uses power line communications (PLC) in broadband mode to efficiently transfer data and control signals between different electrical appliances. This communication is controlled via use of a hybrid genetic algorithm with teacher learner based optimization (GA TLbO) that assists in efficient placement of IoT router and control of communication data rates on phase lines. The proposed model also discusses design of new age electrical appliances that can directly connect with the proposed GA TLbO based PLC interfaces. These devices are capable of sensing current device status, and communicating that information on the power line via a power line control module (PLCM) that is built using low-power and low-cost components. Integration the GA TLbO model with PLCM is capable of reducing power consumption, reduce deployment costs, and improve data and control signal communication speed, across multiple real-time scenarios. The proposed model was tested on small-scale, medium-scale, and large-scale household & industrial environments, and it was observed that due to integration of PLC with IoT cost of deployment was reduced by 39.5%, energy consumption was reduced by 43.8%, and communication speed was improved by 8.3% when compared with IoT deployments, and averaged over different simulations. This performance was also compared with existing PLC based IoT models, and an energy reduction of 8.5%, cost reduction of 1.9%, and speed improvement of 3.4% was observed via the proposed GA TLbO model on similar electrical loads. Due to this performance enhancement, the proposed model is capable of being deployed at large-scale IoT sites with high-speed control, energy efficiency, and low-cost operations.