首页> 外文会议>International Conference on Smart City and Emerging Technology >Smart community maintenance ecosystem with self-reporting and self-diagnostic IoT sensors
【24h】

Smart community maintenance ecosystem with self-reporting and self-diagnostic IoT sensors

机译:具有自我报告和自我诊断的IoT传感器的智能社区维护生态系统

获取原文

摘要

Community is a basic social unit taking care of groups of families. Since the day community becomes operational, it has a routine cycle of maintenance. And in turn take services as input from multiple vendors and hence generate improved ambience and fully functional services. And for this it require that maintenance issues are registered timely. But the major concern is recording and registration of the complaints for every community issues. These targeted issues are street lights control, speedy vehicle movements, tracking hijacked or vacant parking space, over grown green belt growth, electricity issues, water management control and growth through IOT. Also it is not easy for residents to register complaints via different portals or apps. This paper intends to provide a smart solution using IOT sensors. These sensors would automatically detect the issues and inform the concerned authorities by using an automated complained recording backend. This backend also would manage the complaint register status for further necessary reviews. Problem statement and its criticality are discussed at the beginning of the paper. The paper also intends to detail how to make the sensor system, low maintenance and self-healing. Then the proposed solutions are discussed case by case. The paper also provide a detailed insight of the iot sensors.
机译:社区是照顾家庭群体的基本社会单位。自从日间社区开始运作以来,它就具有例行的维护周期。进而将服务作为来自多个供应商的输入,从而产生改善的氛围和功能全面的服务。为此,它要求及时记录维护问题。但是,主要关注的是针对每个社区问题记录和记录投诉。这些目标问题包括路灯控制,快速的车辆行驶,跟踪被劫持或空置的停车位,绿化带增长过度,电力问题,水管理控制以及通过物联网的增长。此外,居民通过不同的门户网站或应用程序注册投诉也不容易。本文旨在提供一种使用物联网传感器的智能解决方案。这些传感器将自动检测问题并通过使用自动投诉的记录后端来通知有关当局。该后端还将管理投诉记录状态,以进行进一步的必要审查。在本文开始时讨论了问题陈述及其重要性。本文还打算详细介绍如何制造传感器系统,低维护成本和自我修复。然后逐案讨论提出的解决方案。本文还提供了有关物联网传感器的详细信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号