Neuro-Adaptive Learning Fuzzy-Based System for Actor Selection inWireless Sensor and Actor Networks

Elis Kulla, Donald Elmazi, L. Barolli
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引用次数: 2

Abstract

Wireless Sensor and Actor Networks (WSANs) is becoming an important part of our technological reality as an autonomous systems, due to the advances of new technologies, such as 5G, Internet of Things (IoT) and ArtificialIntelligence (AI). One of the main challenges in autonomous systems is power management. Self-healing is a key feature of WSAN, which improves network connectivity and lifetime, by assigning actors tasks such as to connect separated network components, or recharge the sensors whose battery power is exhausted. In this paper, we propose a framework for actor selection in WSAN, which consists mainly of an adaptive neuro-fuzzy inference system. It considers network conditions when selecting actors for different tasks regarding network's connectivity restoration.
基于神经自适应学习模糊的无线传感器和行动者网络行动者选择系统
由于5G、物联网(IoT)和人工智能(AI)等新技术的进步,无线传感器和行动者网络(wsan)作为一个自主系统正在成为我们技术现实的重要组成部分。自主系统的主要挑战之一是电源管理。自我修复是WSAN的一个关键特性,它通过分配参与者连接分离的网络组件或为电池电量耗尽的传感器充电等任务,提高了网络的连通性和使用寿命。本文提出了一种基于自适应神经模糊推理系统的无线局域网行动者选择框架。针对网络连通性恢复的不同任务,在选择参与者时考虑网络条件。
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