Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions

Besides enabling an enhanced mobile broadband, the next generation of mobile networks (5G) are envisioned for the support of massive connectivity for heterogeneous Internets of Things. These IoTs are envisioned for a large number of use cases including smart cities, environment monitoring, smart vehicles, and so on. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges, especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge cloud. Moreover, we identify major open problems and research directions that need to be explored toward enabling the support of massive heterogeneous IoTs through 5G systems.

[1]  Chunyan Feng,et al.  Sparsity Order Estimation and its Application in Compressive Spectrum Sensing for Cognitive Radios , 2012, IEEE Transactions on Wireless Communications.

[2]  Yunfei Chen,et al.  A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios , 2016, IEEE Communications Surveys & Tutorials.

[3]  Jose F. Monserrat,et al.  5G Mobile and Wireless Communications Technology , 2016 .

[4]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[5]  Zhijin Qin,et al.  Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes , 2016, IEEE Transactions on Signal Processing.

[6]  Shaojie Tang,et al.  Data gathering in wireless sensor networks through intelligent compressive sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[7]  Mumtaz Yilmaz,et al.  Determination of spectrum utilization profiles for 30 MHz–3 GHz frequency band , 2016, 2016 International Conference on Communications (COMM).

[8]  Mohsen Guizani,et al.  Replisom: Disciplined Tiny Memory Replication for Massive IoT Devices in LTE Edge Cloud , 2016, IEEE Internet of Things Journal.

[9]  Yasser Gadallah,et al.  Classification of LTE Uplink Scheduling Techniques: An M2M Perspective , 2016, IEEE Communications Surveys & Tutorials.

[10]  H. T. Kung,et al.  Scaling network-based spectrum analyzer with constant communication cost , 2013, 2013 Proceedings IEEE INFOCOM.

[11]  M. Zorzi,et al.  The challenges of M 2 M massive access in wireless cellular networks , 2015 .

[12]  Mohsen Guizani,et al.  Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[13]  J. Torsner,et al.  Internet of Things in the 5G Era: Enablers, Architecture, and Business Models , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Maria Rita Palattella,et al.  Internet of Things in the 5G Era: Enablers, Architecture, and Business Models , 2016, IEEE Journal on Selected Areas in Communications.

[15]  Andrea Zanella,et al.  The challenges of M2M massive access in wireless cellular networks , 2015, Digit. Commun. Networks.