Optimizing the sensor deployment strategy for large-scale internet of things (IoT) using artificial bee colony

Abstract

In the world of Internet of Things (IoT), specifically in the environmental and nature monitoring strategy, recent developments in the low-power and Long range (LoRa) communication systems have ignited up with new possibilities. However, maintenance has been greatly challenged by the flexible climate and long distance travel. Previous studies have shown that higher electronic failure rates are exponentially accelerated by temperature. The cost of maintenance can be as high as 80% of the overall cost of maintenance. Expenses for deployment is extremely high, if careful handling is not there. In this proposed paper, a problem with the deployment of sensors to preventively mitigate maintenance costs thus maintaining tolerable efficiency of the sensing quality metrics as well as the complete connectivity were formulated. A Cost model is proposed for Maintenance considering the degradation for IoT networks and battery depletion in addition to thermal degradation. The spatial phenomenon methodology is analyzed in order to adopt sensing quality metric based on shared results. Although problem proposed is the conceptual one, it is brought out with the sparse nonlinear optimizer form to solve the problem. To make the solution optimal, two algorithms namely CPSO (Canonical Particle Swarm Optimization) and ABC (Artificial Bee Colony) are applied which are population based metaheuristics algorithms. Compared to the current greedy heuristics, our meta-heuristics demonstrate good results for maintenance costs under the same appropriate sensing efficiency.