Fitness Monitoring System Based on Internet of Things and Big Data Analysis

Physical fitness monitoring is an important tool for disease prevention and early diagnosis and treatment. Efficient physical fitness monitoring can effectively reduce the risks of disease and relieve the medical burden. This paper analyzes the shortcomings of traditional clustering routing protocols, and proposes a new Internet of Things (IoT) clustering routing algorithm using Particle Swarm Optimization (PSO). The calculation method of the optimal number of clusters is granted, and the fitness function is redesigned. This function fully considers the remaining energy of sensor nodes, distance between clusters and node spacing, etc., and specifically introduces the process of applying PSO to select the optimal cluster head combination and the process of PSO cluster routing algorithm. Based on the traditional particle swarm algorithm, this paper proposes a monitoring algorithm based on two-way chaotic search by introducing chaotic search strategy and reverse learning strategy. This algorithm largely avoids the situation of particles falling into local optima. The experimental results show that the method gets closer to the optimal solution than the traditional particle swarm algorithm. A scheme for constructing Hbase secondary index using hidden column family is proposed, and Hbase table join optimization algorithm is proposed based on bloom filter and Hash join algorithm. On the overall load of Hbase, a pre-partitioned Hash algorithm and a load prediction algorithm based on exponential smoothing are proposed to improve the performance of Hbase to meet business needs. Experimental results show that the Hbase particle swarm optimization algorithm proposed in the article can effectively improve the functioning of Hbase from the perspective of data writing and query.