Optimization of Wireless Positioning and Sensing Network (WPSN) Architecture using Machine Learning Technique

无线定位和传感技术正在成为利用磁或电磁响应交换来跟踪对象的最新技术策略。无线阅读器是该技术的关键组件，配备有用于发送和接收信号的天线、收发器和用于解码数据的处理器。最近，Peytec Inc.设计并制造了用于无线定位和传感网络（WPSN）应用的智能传感器，其使用可扩展的无线通信系统来感测和跟踪室外/室内对象位置数据，以创建到库存系统的自动交易。在室内工业环境中感测位置信息需要高精度无线通信系统，并且部分地因为各种对象反射和分散信号而成为具有挑战性的任务，这不利地影响感测和位置数据的通信的可靠性。目前，Peytec Inc.只有在没有障碍物存在时才能收集信息（在10厘米的可接受精度范围内），然而，一旦引入障碍物，随着行程时间的变化，精度急剧下降。该项目的目标是开发机器学习算法，以定位具有所需精度的对象，以及针对室内定位目的的现有静态和动态障碍物来构建最佳的无线定位和传感网络（WPSN）。该项目将汇集瑞尔森大学和Peytec Inc.的人才。并利用瑞尔森的资源，包括经验丰富的研究人员，充满活力和目标驱动的研究生，以及Peytec Inc.的卓越记录。在WPSN的设计和开发中，通过利用历史数据，训练数据，特别是机器学习（ML）方法，共同探索跟踪产品所需的最佳读者数量。成功设计优化问题和ML算法来识别最佳WPSN架构将为Peytec Inc.带来新的差异化产品功能、市场扩展和销售。加拿大安大略。此外，该项目还将为HQP的培训提供一个丰富的环境，让他们从事行业级产品的开发和设计。

Wireless Positioning and Sensing technology is emerging as the latest technological strategy in tracking objects using magnetic or electromagnetic response exchange. Wireless readers that are key components of this technology are equipped with antennas for sending and receiving signals, a transceiver and a processor to decode data. Finding optimal number and placement of these readers for indoor and outdoor purposes are an important issue in these days.Recently, Peytec Inc, designed and fabricated smart sensors for Wireless Positioning and Sensing Network (WPSN) application using scalable wireless communication system to sense and track oudoor/indoor object location data to create automated transactions to the inventory system. Sensing location information in indoor industrial environments requires a high precision wireless communication system and is a challenging task in part because various objects reflect and disperse signals which adversely affects reliability of communication of sensing and location data. Currently, Peytec Inc. is only capable of collecting the information when no obstacle is present (within an acceptable accuracy range of 10 cm), however once an obstacle is introduced the accuracy drops dramatically as the travel time changes.The objective of this project is to develop machine learning algorithms to localize object with required precision, and to architect optimum Wireless Positioning and Sensing Network (WPSN) with respect to existing static and dynamic obstacles for indoor localization purposes. This project will pull together talents from Ryerson University and Peytec Inc. and take the advantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduate students with a track record of excellence of Peytec Inc. in design and development of WPSN to jointly explore the optimal number of required readers for tracking products by utilizing historical data, training data and in particular, Machine Learning (ML) approaches. Successful design of optimization problems and ML algorithms to identify optimum WPSN architectures will enable new differentiated product capabilities, market expansion and sales for Peytec Inc. Ontario, Canada. In addition, this project will provide a rich environment for the training of HQP by having them engaged in development and design of industry-caliber products.