MIMO and IRS: Enablers for Energy Efficient Wireless Sensor Networks

Muhammad Ali Jamshed and Masood Ur Rehman
July 23, 2021


The commercialization of 5G new radio (NR) will be able to support three types of connectivities with heterogeneous traffic requirements, i.e., massive machine-type communication (mMTC), ultra-reliable low-latency communication (URLLC), and extended mobile broadband (eMBB).

The mMTC and URLLC are the pillars and play a pivotal role in supporting different internet of things (IoT) connectivity. In contrast, the URLCC also aims to provide extremely high reliability in transmitting the small data packets for the mission-critical IoT traffic, boiling down the packet error rate to 10-9. Energy efficiency is one of the critical issues in IoT-based wireless sensor networks (WSN), as the lifetime of these battery-powered IoT-based sensor nodes is scarce. A plethora of research efforts have been concentrated to enhance the energy efficiency of WSN, i.e., by utilizing effective routing and scheduling techniques. Strategic deployment of sensors is yet another technique that improves the energy efficiency of WSN. Providing optimal network coverage, i.e., line-of-sight (LoS) communication, reduces the transmission of redundant sensing information and maximizes the battery life of IoT-based sensor nodes. However, with the rapid changes in the environment (indoor and outdoor and due to human and nature activities), achieving optimal coverage is nearly impossible, which reduces the lifetime of WSN. These rapid changes create blind spots for already deployed IoT-based sensors and increase the need for retransmitting the redundant sensing information. To overcome such issues, the use of multiple-input-multiple-output (MIMO)/massive-MIMO (mMIMO) and the intelligent reflecting surfaces (IRS) have proven to be effective.

In this newsletter, we have explored the possibilities and effectiveness of using the IRS and MIMO/mMIMO in improving the coverage of WSN, which in return enhances the energy efficiency of WSN.

MIMO/Massive MIMO for IoT Applications

The MIMO/ mMIMO is considered one of the key enablers of 5G technology due to its diverse features, enhancing the energy and spectral efficiency of any wireless system(s). The MIMO/mMIMO achieves higher data rates and spectral efficiency by exploiting many degrees of freedom. The usefulness of MIMO/mMIMO in mobile communication has been heavily used by academia and the industry. However, the effectiveness of MIMO/mMIMO in IoT applications is still an open debate. The constraints and the requirements of IoT applications are significantly different compared to mobile communication and require a different set of approaches to exploit the usefulness of MIMO/mMIMO. The work of [1] identifies the challenges and opportunities of using MIMO/mMIMO in the IoT domain and shows that the MIMO/mMIMO can bring immense opportunities for IoT-based applications but requires significant integration of the physical-layer techniques with the protocol design.

Figure 1: An Illustration of the combined use of MIMO and IRS to overcome blind spots for WSN.

Figure 1. An Illustration of the combined use of MIMO and IRS to overcome blind spots for WSN.

IRS for IoT Applications

With the advent of research on future wireless communication or the so-called 6G, the concept of using IRS has emerged as a promising solution to coverage issues. An IRS is capable of tuning a wireless signal as per user requirements and can provide a user with more control over the wireless signal omitting the need of decoding a signal [2]. The characteristics of an IRS make any wireless system more energy-efficient, cost-effective, and secure. Therefore, the use of IRS for IoT is gaining more interest. It proves to be more beneficial as it can benefit these power constrained IoT devices with more coverage and decreases the need for continuous retransmission of data packets. Furthermore, it has been shown in [3] that an IRS-enabled IoT network can increase the energy efficiency of a wireless network compared to a traditional relay-enabled wireless network. Hence, these features of IRS make them an effective choice to enhance the lifetime of power-constrained IoT-based WSN without compromising the quality of service (QoS).

Energy Efficiency of Wireless Sensor Network: A Critical Issue

In our ongoing project to combat deforestation (please see acknowledgment section for more detail on the project), we heavily rely on WSN, where energy efficiency is one of the critical issues. A plethora of research efforts has been put up to maximize the lifetime of battery-powered WSN. In [4], a detailed survey has been carried out on nature-inspired algorithms to maximize the efficient use of energy of WSN. In [5], a scheduling technique has been proposed to increase the wireless sensor nodes' lifetime effectively. Overall, obtaining optimal coverage for these sensor nodes is a challenging task but can increase the lifetime of WSN by removing the burden of retransmissions of redundant sensing information. However, the network suffers from blind spots (an illustration of such blind spots can be seen in Figure 1. scenario 1), which burdens the network with retransmission of redundant data. The combined use of MIMO and IRS (an illustration of which can be seen in Figure 1. scenario 2) can remove these blind spots and enhances coverage for IoT-based WSN. Therefore, the combined use of MIMO and IRS can solve the problems related to the energy efficiency of WSN.


This work has been carried out under the development of ICRG PAK-UK Education Gateway (2020) funded Project No. 310366 - Deforestation in Pakistan: Combating through Wireless Sensor Networks (DePWiSeN). We would also like to acknowledge the support of the Communication, Sensing and Imaging group at the University of Glasgow for this work. For more information and updates on the project, follow us on Twitter.


  1. Bana, A. S., De Carvalho, E., Soret, B., Abrao, T., Marinello, J. C., Larsson, E. G., & Popovski, P. (2019). Massive MIMO for Internet of things (IoT) connectivity. Physical Communication, 37, 100859.
  2. Zhao, J., 2019. A survey of intelligent reflecting surfaces (IRSs): Towards 6G wireless communication networks. arXiv preprint arXiv:1907.04789.
  3. Jamshed, M.A. and Jameel, F., 2020. When the IoT Meets IRS: Intelligent Reflecting Surfaces for Massive IoT Connectivity.
  4. Singh, A., Sharma, S. and Singh, J., 2021. Nature-inspired algorithms for wireless sensor networks: A comprehensive survey. Computer Science Review, 39, p.100342.
  5. Jamshed, M.A., Amjad, O. and Zeydan, E., 2017, November. Multicore energy efficient scheduling with energy harvesting for wireless multimedia sensor networks. In 2017 International Multi-topic Conference (INMIC) (pp. 1-5). IEEE.



Muhammad Ali JamshedMuhammad Ali Jamshed received the B.Sc. degree in electrical engineering from COMSATS University, Islamabad, Pakistan, in 2013 and the M.Sc. degree in Wireless communications from the Institute of Space Technology, Islamabad Pakistan, in 2016, and a Ph.D. degree from the University of Surrey, Guildford, U.K, in 2021. He was nominated for Departmental Prize for Excellence in Research in 2019 at the University of Surrey. He served briefly as Wireless Research Engineer at BriteYellow Ltd, UK, and then moved to James Watt School of Engineering, University of Glasgow, as a Research Assistant. His main research interests include EMF exposure reduction, low SAR antennas for mobile handsets, machine learning for wireless communication, Backscatter communication, and wireless sensor networks. He is serving as a Reviewer for IEEE Wireless Communication Letter. Moreover, he served as a Reviewer, TPC, and the Session Chair, at many well-known conferences, i.e., ICC, WCNC, VTC, GlobeCom etc., and other scientific workshops.


Masood Ur RehmanMasood Ur Rehman (Senior Member, IEEE) is an Assistant Professor in the James Watt School of Engineering at the University of Glasgow. He received the B.Sc. degree in electronic engineering from the University of Engineering & Technology, Lahore, Pakistan, in 2004 and the M.Sc. and Ph.D. degrees in electronic engineering from Queen Mary University of London, London, UK, in 2006 and 2010, respectively. His research interests include compact antenna design, radiowave channel characterization, electromagnetic wave interaction with humans, mm-Wave and nano-comm for body-centric networks, and D2D/H2H communications. He has worked on several projects supported by industry/research councils and is the lead PI on ICRG-funded DePWiSeN project. He has contributed to a patent and authored/co-authored 4 books, 11 book chapters, and more than 120 technical articles in leading journals and peer-reviewed conferences. He is a Fellow of the Higher Education Academy (UK), an associate editor of the IEEE Access, IET Electronics Letters and Microwave & Optical Technology Letters.