چكيده به لاتين
The Internet of Things (IoT) is an emerging technology that makes it easy to perform human tasks by connecting objects such as home appliances, ventilation systems, office machines, industrial and agricultural machinery, sensors, and vehicles. Due to its wide range of applications, today, the Internet of Things, both academically and industrially, is one of the leading topics of interest to researchers and craftsmen. One of the applications of the Internet of Things is in the industrial field, which has led to the fourth generation industrial revolution (Industry 4.0.). On the Industrial Internet of Things (IIoT), communications are in the form of machine-to-machine links, so they need to meet requirements such as reliability and real-time. One of the main requirements of the Industrial Internet of Things is to minimize access delay to vital data and optimal energy consumption. But today's centralized industrial networks are unable to meet the basic needs of the Industrial Internet of Things.
Because data management methods on the Industrial Internet of Things, suffer from such deficiencies as, Lack of intermediate node capacity to store data, Lack of simultaneous reduction of the delay constraint and the goal of energy reduction and Lack of calculationg energy consumption on the side of data producers nodes and intermediate nodes. At this thesis, in order to reduce energy consumption and access delay we introduce a method which data generates from data producers, to store in the middle nodes, to access data consumers such data. With the goal of being able to minimize energy consumption, which consumes for routing data from data producers toward intermediate nodes and from intermediate nodes toward data consumers and energy, which consumes for turning intermediate nodes on. In order to reduce energy consumption and access delay from distributed data management networks, we have introduced and modeled our problem. Since the time complexity of this problem is high, we have used the Ant Colony Optimization (ACO) algorithm, which is a metaheuristic algorithm based on collective intelligence.
We have used CPLEX and MATLAB tools as well as the proposed algorithm to evaluate the proposed problem model. The results of the evaluation of this thesis show that the proposed method, for the objective function criterion, has improved by 47% compared to the related method and is only 11% away from the optimal method.
