Алгоритм максимізації часу життя сенсорної мережі з використанням концепції віртуальних вузлів

Abstract

Розглянуто концепцію віртуальних вузлів, які є копіями реальних вузлів, кожен з яких здатний виконувати певне завдання і відправляти отримані дані до наступного віртуального вузла. Представлено функції витрат енергії для зчитування, оброблення та передавання інформації. Введено умову обмеження, яка необхідна, коли географічна зона контролюється певною кількістю вузлів, проте не потрібна інформація від усіх вузлів. Запропоновано алгоритм щодо максимізації часу життя безпровідних сенсорних мереж, який дає змогу звести до мінімуму витрати енергії вузлів. One of the main problems of the requirements of quality of service of wireless sensor networks is to provide fault tolerance. Based on research on energy nodes and routing data, efficiency is paramount to increase the lifetime of the network [1]. In this paper an algorithm of network lifetime maximization is proposed as a promising solution towards a distributed application deployment in wireless sensor networks. There are three cost functions: reading, processing and transmission information; the concept of virtual nodes, which are copies of real nodes. To assess the effectiveness of the algorithm considers three cases the tests are the most common, such as: 1) uniform power consumption and uniform primary energy in each node; 2) irregular power consumption and uniform primary energy in each node; 3) uniform power consumption and uneven primary energy in each node. Nodes are randomly following a uniform distribution. Each unit is equipped with sensors to measure temperature, humidity and light. To minimize energy consumption must have exact information about the network topology, the distance between them and the number of parameters: energy consumption in the processing, reading and transmission of information, residual energy node, the working frequency, data rate. Modeling was performed for two cases: A) when the information is processed and stored on the receiving node and B) when data from this node is analyzed and processed by specialists, while the node itself provides only basic processing. In cases B2 and B3, units selected to perform the proposed algorithm processing, will be those who weigh less on the network, regardless of whether they are heads of clusters or not. In particular, the best results in terms of energy consumption networks with diverse options that are the most common type of network in real conditions. In the case of A, where more detailed processing and the number of instructions for each process higher energy savings lower than B.