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

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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.