Machine-to-Machine Communications and Internet-of-Things for Future Generations of Wireless Networks

下一代无线网络的机器对机器通信和物联网

• 批准号: RGPIN-2018-06735
• 负责人: Hamouda, Walaa
• 金额: $ 2.84万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752113
• 关键词: Machine; Communications; Internet; Things; Future

Recently, the Internet technology has undergone enormous changes and has become an important communication infrastructure targeting anywhere, anytime connectivity. Given that human-to-human communication was mainly based on voice communication, traditional wireless networks are built and optimized for human-oriented traffic characteristics. Recently, an entirely different paradigm of communication has emerged with the inclusion of "machines" in the communications landscape. The exchange of any machine-generated traffic is known as machine-to-machine (M2M) communication. As a result, the speculations about the number of devices expected to access the Internet in the near future is increasing every day. This is indeed supported by the Internet-of-things (IoT) framework which allows a tremendous number of "Things" to generate and communicate information among each other without the need of human interaction. The projected massive introduction of communicating machines has to be planned for and accommodated with applications requiring a wide range of requirements and characteristics such as mobility support, reliability, required data rate, power consumption, hardware complexity, and device cost. Other planning and design issues for M2M communications include the future network architecture, the massive growth in devices, and the various device requirements that enable the concept of IoT in future generations of wireless networks. In this proposal, we investigate important problems related to the development of M2M communications as being a key enabler for the IoT and the future 5G systems. To address these problems, the proposed program is divided into three complementing themes, all targeting the realization of future M2M communications. One of these themes is concerned with the development of the machine learning theory in connected machines to improve the overall performance of M2M networks. The second theme represents one step closer to 5G systems where we consider the concept ultra-dense networks (UDNs). In this, we focus on problems associated with UDNs and their effect on M2M applications. Using the theory of stochastic geometry, we offer solutions to these problems such as interference management and coordination of human-to-human and M2M communications. Given that the major challenges of M2M are due to design aspects such as dynamic spectrum management, heterogeneity, power-efficient designs, and adaptive configuration capabilities, the third theme in this proposal deals with the application of cognitive radio to M2M where challenges and solutions of cognitive M2M network are provided. Through industrial collaborations, the techniques developed in this program will be incorporated to build prototypes for verification and further development to provide industry with efficient and fast implementations over the wide range of applications.

近年来，互联网技术发生了翻天覆地的变化，已成为随时随地实现互联互通的重要通信基础设施。鉴于人与人之间的通信主要基于语音通信，传统的无线网络是针对以人为本的流量特性进行构建和优化的。最近，随着“机器”在通信领域的出现，出现了一种完全不同的通信范式。任何机器生成的流量的交换都被称为机器对机器（M2M）通信。因此，关于在不久的将来有望接入互联网的设备数量的猜测每天都在增加。物联网（IoT）框架确实支持这一点，它允许大量的“事物”在不需要人类交互的情况下相互生成和交流信息。通信机器的大规模引入必须规划并适应需要广泛需求和特性的应用程序，例如移动性支持、可靠性、所需的数据速率、功耗、硬件复杂性和设备成本。M2M通信的其他规划和设计问题包括未来的网络架构、设备的大规模增长，以及在未来几代无线网络中实现物联网概念的各种设备需求。在本提案中，我们研究了与M2M通信发展相关的重要问题，M2M通信是物联网和未来5G系统的关键推动者。为了解决这些问题，拟议的计划分为三个互补的主题，都以实现未来的M2M通信为目标。其中一个主题是关于机器学习理论在互联机器中的发展，以提高M2M网络的整体性能。第二个主题代表了我们向5G系统迈进了一步，我们考虑了超密集网络（udn）的概念。在本文中，我们关注与udn相关的问题及其对M2M应用的影响。利用随机几何理论，提出了解决这些问题的方案，如干扰管理和协调的人对人和M2M通信。鉴于M2M的主要挑战来自动态频谱管理、异构性、节能设计和自适应配置能力等设计方面，本提案的第三个主题涉及认知无线电在M2M中的应用，其中提供了认知M2M网络的挑战和解决方案。通过工业合作，该项目中开发的技术将被整合到构建验证和进一步开发的原型中，为工业提供广泛应用的高效和快速实现。