Smart Globally-Connected IoT Devices

Olli Apilo, Jukka Mäkelä, and Pekka Karhula
January 20, 2020

 

It has been estimated that the number of long-range IoT connections will be approx. 5.4 billion by the end of 2025 [1]. To meet the growing demand for low power wide area (LPWA) IoT connectivity, 3GPP released their Narrowband IoT (NB-IoT) and LTE for Machine-Type Communications (LTE-M) specifications in 2016 (Release-13) to support low-power IoT in cellular networks.

After a slow start, these technologies are rapidly entering the IoT market with the compound annual growth rate (CAGR) of 25 % for the number of subscriptions [1]. By September 2019, 114 operators globally have launched NB-IoT or LTE-M networks [2]. NB-IoT and LTE-M are optimized for low power consumption and modem cost as well as wide coverage and a large number of connected devices. Instead of developing new technologies for massive machine-type communications (mMTC), NB-IoT and LTE-M have been adopted as part of 5G to fulfill the LPWA requirements [3]. In addition to mMTC and LPWA, the IoT in 5G is designed to support more demanding uses cases such as automated driving and industrial automation that require high reliability, low latency and higher data rates that could be provided by NB-IoT or LTE-M. Technologies for such critical IoT are being specified in 5G Release-16 and beyond.

Globally-Connected Devices Enabled by eSIM

Typically, cellular IoT devices are manufactured at one or several factories and then shipped all across the world. In addition, some of these devices, such as tracking devices, also move from a country to another when they are used. It is highly desirable that connectivity should work automatically after the device is taken into use throughout the device's lifetime. The traditional way of enabling this is to equip the device with a SIM card from a mobile operator and rely on its roaming agreements. The issue with this approach is that the cellular IoT roaming agreements are just recently being made between the operators [4] and the coverage still limited. In addition, the roaming prices have traditionally been much higher than the local prices, at least for consumer devices. Another alternative is that the device is initially equipped with the profile of a bootstrapping operator that has negotiated with local operators all across the world for the use of their cellular IoT network. The use of local profiles instead of roaming requires that the operator profile including international mobile subscriber identity (IMSI) and the security keys can be changed when needed. This can be enabled by the GSMA embedded SIM (eSIM) solution [5].

The eSIM standard consists of two variants, the consumer solution and the machine-to-machine (M2M) solution [5]. In the consumer solution, the end-user has the direct choice of the operator providing connectivity. The M2M solution is designed especially for IoT devices that have no direct end-user interaction. The main difference between the solutions is that the M2M solution lacks the local profile assistant (LPA) and the possibility to locally select one of the pre-loaded operator profiles. Ideally, eSIM is a non-removable physical circuit integrated to the device but it can also have the form factor of a conventional SIM card. The integrated circuit enables smaller hermetically sealed devices not to be opened during their lifetime. Both consumer and M2M solutions support the remote over-the-air (OTA) secure provisioning of the operator profiles and the secure local storage for the operator profiles.

Demonstrating Cellular IoT Smart Products

In our preliminary use case, the aim is to demonstrate the technical feasibility of the Smart Product concept [6] where a set of sensors together with LPWA cellular IoT connectivity are integrated to products already during manufacturing. Depending on the phase of the product lifecycle, the sensors enable different services such as

  • support for automated testing during manufacturing,
  • location tracking,
  • warnings about harmful delivery or usage conditions,
  • remote control for the end-user,
  • feedback on the actual usage conditions and history to improve the product development of the manufacturer
  • support for product maintenance,
  • warranty claim validation.

Depending on the service and the phase of the product lifecycle, the sensor data is transmitted to the manufacturer and to the different players in the supply chain. The product can be off the electric grid for long periods during the storage, delivery and in some cases also during the end usage. Thus, it is extremely important that communication is based on low-power connectivity options. An example of the services throughout the lifecycle is shown in Figure 1.

Figure 1: Globally-connected local services.

Figure 1: Globally-connected local services.

As a first step for enabling and demonstrating the Smart Product operation during its whole life cycle, we focus on the scenario where the Smart Product moves to a port for shipping. The Smart Product is initially connected to the public cellular network but is allowed to access the private port network as illustrated in Figure 2 because it can bring additional value with its sensing capabilities to the port area. The technical challenges to be studied include the dynamic change of the public network to the private network with help of eSIM, inter-operability between different network configurations, multicasting of service requests for NB-IoT and LTE-M devices, as well as novel service concepts enabled by the Smart Products. The demonstration is planned to be implemented using commercial prototyping boards with cellular IoT connectivity, eSIM support, integrated sensing and positioning. The devices connect to the 5GTN network [7] with multiple virtual operators, radio access network (RAN) sharing and eSIM subscription management emulation. The feasibility to implement the demo in public and Port of Oulu private cellular networks is also evaluated.

Figure 2: Example of the adaptation of the connected network and sensing in the port scenario.

Figure 2: Example of the adaptation of the connected network and sensing in the port scenario.

This activity is hosted by 5G-VIIMA and 5G-FORCE projects as a part of the Finnish nationwide 5G test network 5GTNF [8]. 5G VIIMA is a Finnish research project studying the potential of the developing 5G technology for industrial applications [9] while 5G-FORCE provides a platform including cutting edge technologies on 5G radio, networking, machine learning and security to facilitate experiment of verticals [10]. The companies participating in the 5G-VIIMA project provide real industrial environments and plenty of real-time industrial challenges to be solved with 5G connectivity.

Conclusion

IoT devices move globally when shipped from the manufacturer to the end-user. In some applications, such as asset tracking, the IoT devices also cross country borders in their targeted use cases. eSIM remote provisioning provides a secure way to manage cellular IoT subscriptions throughout the device lifetime. The planned Smart Product demonstrations provide valuable practical experiences on cellular IoT remote operator profile provisioning, inter-operability between different public and private networks as well as the potential for new IoT service innovations.

References

  1. “Ericsson Mobility Report,” White Paper, Ericsson, Nov. 2019.
  2. “NB-IoT and LTE-M: Global market status,” GSA Report, Global mobile Suppliers Association, Sep. 2019.
  3. “Mobile IoT in the 5G future - NB-IoT and LTE-M in the context of 5G,” GSMA White Paper, GSM Association, Apr. 2018.
  4. (2019, Oct.) Landmark deal broadens collaboration between leading carriers, laying the groundwork for millions of global Internet of Things connections. [Online]. Available: https://www.vodafone.com/business/news-and-insights/press-release/att-and-vodafone-business-in-commercial-inter-carrier-arrangement-for-nb-iot-roaming-across-us-and-europe
  5. ”eSIM whitepaper: The what and how of remote SIM provisioning,” White Paper, GSMA, Mar. 2018.
  6. M. E. Porter and J. E. Heppelmann, ”How smart, connected products are transforming competition,” Harvard Business Review, vol. 92, no. 11, pp. 64-88, Nov. 2014.
  7. (2019, Dec.) 5GTN - 5G Test Network. [Online]. Available: https://5gtn.fi/
  8. (2019, Dec.) 5GTNF - 5G Test Network Finland. [Online]. Available: https://5gtnf.fi
  9. (2019, Apr.) Giant project to clarify 5G network industrial potential. [Online]. Available: https://www.oulu.fi/university/news/5gviima
  10. (2019, Dec.) 5G Finnish Open Research Collaboration Ecosystem 5G-FORCE. Available: https://5g-force.org/

 

Olli ApiloOlli Apilo has worked on research and development of wireless communications at the VTT Technical Research Centre of Finland since 2006. He has been involved in various projects with topics ranging from the energy efficiency of MIMO cellular communications to the development of LTE radio resource management software for commercial base stations. More recently he has been working on projects where the practical feasibility of cellular IoT technologies is evaluated for different applications. He has also been involved in developing cellular IoT testing capabilities at VTT’s 5G test network.

 

Jukka MakelaJukka Mäkelä is working at the VTT Technical Research Centre of Finland as a principal scientist and a project manager. He has extensive experience in different fields of advanced communications networks including, for example, 5G, network infrastructures, intelligent network management, Multi-access Edge Computing and Internet of Things solutions. He has also a strong experience in leading R&D prototyping and field trial activities. His scientific activities include 40 published scientific articles including conference papers, book chapters and journal papers related to his research and development work. He is also supervising Ph.D. and diploma thesis workers at VTT.

 

Pekka KarhulaPekka Karhula works as a Research Scientist at the VTT Technical Research Centre of Finland, where his current research topics include 5G and beyond communications, edge computing and IoT. He received his M.Sc. degree in Mathematical Information Technology from the University of Jyväskylä in 2016 and is currently pursuing a Ph.D. degree at the University of Oulu. His Ph.D. topic focuses on improving resource efficiency in distributed systems and edge networks. He was a visiting scholar at the Columbia University for nine months in 2018/2019. His other research interests include IoT protocols, wearables, distributed AI, light-weight virtualization and live migration of services.

 

 

When the IoT Meets IRS: Intelligent Reflecting Surfaces for Massive IoT Connectivity

Muhammad Ali Jamshed and Furqan Jameel
January 20, 2020

 

The concept of Intelligent Reflecting Surface (IRS) has emerged as a transformative and revolutionary technology for providing seamless coverage and to achieve energy and spectrum efficiency for cost-effective wireless networks.

The architecture of the IRS comprises of arrays of passive and reconfigurable antenna elements printed on a low-cost substrate [1]. Each element in the IRS structure is equipped with a phase shifter to control the transmission of the incident signal [2]. The IRS reflects the incident plane wave in the form of a beam in the desired direction by adjusting the phase difference between each passive element [3]. Although the IRS itself is in the infancy phase, its applications and utility in other domains are growing exponentially.

Using IRS for the Internet of Things (IoT)?

Typically, the Internet-of-things (IoT) applications range from monitoring services in remote locations to continual sensing of the environment [4]. However, the limited power of such IoT devices restraint the transmission range. The monitoring services can be implied in different industries, which may vary from health management using wearable devices, tackling emergencies in disaster scenarios, etc. requires information to be uploaded without being lost. To effectively handle such situations, the key property of IRS, i.e., coverage enhancement, may be helpful for enhancing the functionality of IoT devices. Therefore, for the uninterrupted operations of IoT devices with improved coverage, the use of the IRS with the IoT is one of the cost-effective and feasible options.

IRS Replacing the Relays in IoT Networks?

Due to the low-power nature of IoT devices, relays provide an alternative path (non-line of sight) to overcome the channel impairments that occurred due to the presence of obstacles between the source and the destination device. There are several protocols of relay communication, e.g., amplify-and-forward, decode-and-forward, compress-and-forward, etc. The presence of relays provides an additive advantage to the IoT network and brings an exponential increase in the performance. However, there are some disadvantages to using them, which are often overlooked. We have pointed out the following key issues that may arise due to the presence of relays in the IoT network;

  1. The amount of interference in the IoT network may increase by adding an excessively large number of relay devices [5].
  2. Optimal deployment of relays in the IoT networks plays a critical role and, hence, affects the coverage. Generally, the requirement of the relays increases the overall cost of the IoT network.
  3. Some relay protocols require decoding the signal prior to the retransmission. This kind of relaying techniques may impact the privacy of the IoT devices and make them vulnerable to attacks.
  4. Relays require a permanent source of power to perform their operations which may result in increasing the power budget of the IoT network.

IRS can be considered as a complementary technology to overcome cost, security, power, and energy efficiency issues by providing more control over the single propagation using software-controllable reflections, omitting the need to perform decoding of the signal [6]. Looking from the IoT perspective, the devices which are IoT enabled having power constraint can be benefitted from the IRS. For instance, the authors in [7] compare the performance of a network-enabled with a decode-and-forward relaying protocol against the network. It was claimed that an IRS-enabled network can achieve higher energy efficiency in comparison to decode-and-forward relay for very high data rates and it has been shown in [7] using the following figure.

Figure 1: Comparison of energy efficiency vs. data rate for IRS and decode-and-forward relaying.

Figure 1: Comparison of energy efficiency vs. data rate for IRS and decode-and-forward relaying.

Future of IRS

The merger of IRS in IoT networks is still a new and less-evolved area of communications. However, it has the potential to open new avenues and provide massive opportunities for the research community. We hope that the above discussion will open a new chapter in the domain of the IRS and would provide useful guidance for investigating future IoT networks. The power constraint among the IoT devices can be optimized by using the IRS technology, hence it would be critical for improving their functionality.

References

  1. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis and I. Akyildiz, "A New Wireless Communication Paradigm through Software-Controlled Metasurfaces," in IEEE Communications Magazine, vol. 56, no. 9, pp. 162-169, Sept. 2018.
  2. Wu and R. Zhang, "Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming," in IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394-5409, Nov. 2019.
  3. M. Pozar, S. D. Targonski and H. D. Syrigos, "Design of millimeter wave microstrip reflectarrays," in IEEE Transactions on Antennas and Propagation, vol. 45, no. 2, pp. 287-296, Feb. 1997.
  4. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M. Ayyash, "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications," in IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347-2376, Fourthquarter 2015.
  5. Wang, W. Chen, H. Tang and Q. Wu, "Joint Optimization of User Association, Subchannel Allocation, and Power Allocation in Multi-Cell Multi-Association OFDMA Heterogeneous Networks," in IEEE Transactions on Communications, vol. 65, no. 6, pp. 2672-2684, June 2017.
  6. Tan, Z. Sun, D. Koutsonikolas and J. M. Jornet, "Enabling Indoor Mobile Millimeter-wave Networks Based on Smart Reflect-arrays," IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, 2018, pp. 270-278
  7. Bj ̈ornson, ̈O. ̈Ozdogan, E. G. Larsson, Intelligent reflecting surface vs. decode-and-forward: How large surfaces are needed to beat relaying?, arXiv: 1906.03949.
  8. Subrt and P. Pechac, "Controlling propagation environments using Intelligent Walls," 2012 6th European Conference on Antennas and Propagation (EUCAP), Prague, 2012, pp. 1-5.
  9. Basar, "Transmission Through Large Intelligent Surfaces: A New Frontier in Wireless Communications," 2019 European Conference on Networks and Communications (EuCNC), Valencia, Spain, 2019, pp. 112-117.

 

Muhammad Ali JamshedMuhammad Ali Jamshed received his 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. He is currently with the Institute for Communication Systems (ICS), 5G Innovation Centre (5GIC), University of Surrey, Guildford, UK. He was nominated for Departmental Prize for Excellence in Research in 2019 at the University of Surrey.

 

Furqan JameelFurqan Jameel received his B.Sc. in electrical engineering (under the ICT R&D funded program) in 2013 from the Lahore Campus of COMSATS Institute of Information Technology (CIIT), Pakistan. In 2017, he received his M.Sc. degree in electrical engineering (funded by the prestigious Higher Education Commission Scholarship) at the Islamabad Campus of CIIT. In 2018, he visited Simula Research Laboratory, Oslo, Norway. Currently, he is with the Department of Communications and Networking, Aalto University, Finland. He was a recipient of the Outstanding Reviewer Award in 2017 from Elsevier.

 

 

Augmenting Software Engineering Processes Towards Designing Privacy-Aware Internet of Things Applications

Charith Perera and Mahmoud Barhamgi
January 20, 2020

 

The design and development process for the Internet of Things (IoT) applications is more complicated than for desktop, mobile, or web applications. IoT applications require both software and hardware to work together across multiple different types of nodes (e.g. microcontrollers, system-on-chips, mobile phones, miniaturized single board computers, cloud platforms) with different capabilities under different conditions.

IoT applications typically collect and analyze personal data that can be used to derive sensitive information about individuals. Without proper privacy protections in place, IoT applications could lead to serious privacy violations. Thus far, privacy concerns have not been explicitly considered in software engineering processes when designing and developing IoT applications, partly due to a lack of tools, technologies, and guidance. In this newsletter, argues the importance of developing a privacy-aware IoT application design tool to address the challenges mentioned above. This tool should not only transform IoT application designs into privacy-aware application designs but also validate and verify them. We outline how this proposed tool should work in practice and its core functionalities. We also identify research challenges and potential directions towards developing the proposed tool. We anticipate that this proposed tool will save many engineering hours which engineers would otherwise need to spend on developing privacy expertise and applying it. We also highlight the usefulness of this tool towards privacy education and privacy compliance. Let us introduce an example to make our argument more concrete.

A Walking Through Example

Let us consider a simplified use case scenario to highlight the challenges in designing privacy-aware IoT applications. A doctor needs an IoT application which can be used to monitor patients’ rehabilitation process. This use case is inspired by a real-world application called ‘MyPhysioapp’ (myphysioapp.com) [1]. A doctor has compiled his functional requirements as follows. The doctor has difficulties in seeing his patients frequently due to different reasons (e.g., traveling distance, work schedules, etc.). Further, frequent in-person consultations are not necessary for most circumstances. Each in-person visit costs for both the doctor (government) and the patient. Once the initial consultation is performed, the doctor only needs to track the patient's progress and does not need to meet the patient unless there is something exceptional happened. The doctor is only interested in tracking the patient's progress. After evaluating the progress every two weeks, the doctor may ask his specialty nurse to change the exercise plan as necessary. Two software engineers have come up with two different designs as follows to fulfill the above functional requirements. The designs are visually illustrated in Figure 1.

 

  • Design 1: In this design, wearable sensors are used to capture raw data (e.g., accelerometers, gyroscopes) that can be used to identify users’ (patients) activities. Data is then sent to the cloud for activity recognition using a mobile phone as an intermediary device. Next, the cloud services are used to process the raw data and in order to identify the user’s activity patterns. User activity patterns are then compared with doctors recommended a rehabilitation plan to produce a progress report. The doctor can review the progress and make recommendations to the nurse regarding any alterations.
  • Design 2: In this design, wearable sensors are not only used to capture raw data but also to identify activities (using the micro-controllers attached to the wearable). Timestamped activities are then sent to the mobile phone. The nurse, based on the doctors’ recommendations, creates the exercise plan and sends it to the patient’s mobile phone. The mobile phone then compares the timestamped activity data and the exercise plan in order to determine how well the patient is performing the exercises. The mobile phone sends a weekly progress report to the doctor. Based on the report, the doctor gives advice to the nurse and she alters the exercise plan accordingly.

It is important to note that both designs satisfy the doctor’s functional requirements. However, design 2 is certainly ‘better’ than design 1 in terms of privacy awareness. Based on this use case scenario, we extract two research questions as follows:

We consider privacy as a trade-off function. Applying a certain privacy-preserving measure into a certain IoT application may impact the implementations in terms of costs, complexity, usability, fault tolerance, responsiveness, etc. Therefore, our aim is not to prescribe a certain design over others. Instead, we want the developer to be informed about privacy-by-design choices before they make their final design decisions. In this regard, we propose a usable privacy-aware IoT application design tool that will inform the privacy-aware design choices to the developers. Previous investigations have shown that applying privacy principles into IoT applications is time-consuming and difficult [2].

Figure 1: Motivational Scenario: Different IoT application designs can be developed to fulfill the same functional requirements with different privacy risks associated with them.

Figure 1: Motivational Scenario: Different IoT application designs can be developed to fulfill the same functional requirements with different privacy risks associated with them.

Target Roles and Audience

We believe such a tool can be beneficial to different types of stakeholders as follows:

  • Design Tool for Software Engineers (designers/architects): Primary stakeholders of this tool would be software engineers. We expect them to use this tool to sketch their potential IoT application designs and get validated before moving to the implementation phase. This tool will provide different types of suggestions which engineers can use to improve their IoT application designs in terms of privacy.
  • Compliance Tool: This tool will also be useful to demonstrate certain compliance needs (e.g., General Data Protection Regulation (GDPR)). It will have the capability to automatically generate a compliance report for each IoT application design briefly explaining the design decisions and risks associated with, so the compliance officers can determine whether to approve or not.
  • Education and Awareness Tool: We also expect this tool to be used for enhancing privacy awareness among students from school level to university level. Over the last few years, there have been many program environments and languages been developed to help young children to learn how to code (tynker.com, scratch.mit.edu).

Tool-Assisted Privacy-Aware IoT Applications Design

The tool we propose is something that the engineering community has not seen before. However, it is inspired by many existing tools used by the engineering community (e.g., UML design tools). First, let us illustrate how the proposed tool (and underline technology) is expected to work in practice using Figure 2. (Step 1) Software engineers will draw their application designs using a pre-defined set of notations. Key components will be nodes (device profiles) and data flows. To ease the process, common device profiles will be provided. This process will look like a UML diagram design process. (Step 2) Engineers will then specify the service which they plan to run. (Step 3) They can either assign each service to a node or just leave them unassigned for the algorithms to do that in a later step. (Step 4/5) Optionally, engineers can provide additional information related to data management (e.g. 90 days of data retention) and context (e.g. healthcare domain). Additional information will help the algorithms to better design IoT applications. The rest of the steps are invisible to engineers and triggered by a single click. (Step 6) Algorithms automatically assign each service into nodes appropriately by considering device capabilities, runtime requirements of the services, and other relevant context information. (Step 7) Algorithms incorporate privacy protection features into the design. This step may also reassign the services into different nodes, if necessary. This is one of the key features of this tool. (Step 8) Algorithms examine the privacy awareness at both node and composition levels.

Figure 2: The Workflow of the Proposed Tool.

Figure 2: The Workflow of the Proposed Tool.

Then, all the results will be combined to produce the overall privacy index and presented to the engineers. Engineers may consider changing their initial designs to improve the privacy index. (Step 9) The terms and conditions unique for each IoT application design are automatically generated.

Research Directions

We identify three themes of research challenges: (1) Design Notations and User Interactions, (2) Context-Aware Planning and Adaptation, and (3) Operationalisation, Measuring and Rating:

  • Design Notations and User Interactions: We envision this tool to follow the visual programming paradigm [3]. The proposed tool is expected to be used by engineers to design IoT applications by manipulating program elements graphically. We expect such a design process would be natural for engineers as they are typically familiar with the design approach such as Unified Modelling Language (UML) and Data Flow Diagrams (DFD). Such familiarity will help engineers to quickly familiarize themselves with the tool. Ideally, the visual programming language will be inspired by the data flow diagrams notations. However, it is important to note that DFDs are flexible enough to be represented in different levels of complexities. Therefore, it would be a fine balance between maintaining simplicity while allowing engineers to design their systems in detail. In addition to the DFD based information, the tool should have ways to gather other related contextual information.
  • Context-Aware Planning and Adaptation: The design and development of IoT applications require both software and hardware to work together across multiple different types of nodes (e.g., micro-controllers, system-on-chips, mobile phones, miniaturized single-board computers, cloud platforms) with different capabilities under different conditions (e.g., CPU, memory, energy, data communication, knowledge availability, energy limitations, latency tolerance limitations, domain requirements). Therefore, the privacy-preserving techniques that can be applied on a given node vary depending on the context. The question that needs to be answered is ‘How do we optimally allocate responsibilities to each node based on the context when designing a privacy-aware IoT application?’.
  • Operationalization, Measuring, and Rating: Finally, the challenge is how engineers know, given an IoT application design, whether it is a good design or a bad design (from a privacy perspective). We tend to understand different types of measuring and rating/indexing techniques well (e.g. Body Mass Index, energy ratings, food reference intake and so on). However, no such mechanism is available to measure the privacy awareness of IoT applications. We believe such mechanisms (e.g., privacy index) would be increasingly important for both engineers and end-users.

References

  1. S. Cowan, “My Physio App: Better communication, understanding and results,” Br. J. Sports Med., vol. 50, no. 21, pp. 1348–1349, 2016.
  2. C. Perera, M. Barhamgi, A. K. Bandara, M. Ajmal, B. Price, and B. Nuseibeh, “Designing Privacy-aware Internet of Things Applications,” Inf. Sci. (Ny)., vol. 512, pp. 238–257, Mar. 2020.
  3. K. Zhang, D.-Q. Zhang, and J. Cao, “Design, construction, and application of a generic visual language generation environment,” IEEE Trans. Softw. Eng., vol. 27, no. 4, pp. 289–307, 2001.

 


 

Charith PereraCharith Perera is a Lecturer at Cardiff University, UK. He received his BSc (Hons) in Computer Science from Staffordshire University, UK and MBA in Business Administration from the University of Wales, Cardiff, UK and Ph.D. in Computer Science at The Australian National University, Canberra, Australia. Previously, he worked at the Information Engineering Laboratory, ICT Centre, CSIRO. His research interests are the Internet of Things, Sensing as a Service, Privacy, Middleware Platforms, and Sensing Infrastructure. He is a member of IEEE and ACM. Contact him at www.charithperera.net charith.perera@ieee.org

 

Mahmoud BarhamgiMahmoud Barhamgi is an Associate Professor of computer science at Claude Bernard University Lyon 1. His research focuses on security and privacy preservation in service-oriented architecture, web, and cloud environments. Barhamgi received a Ph.D. in information and communication technology from Claude Bernard University Lyon 1. Contact him at mahmoud.barhamgi@univ-lyon1.fr

 

 

In-band Full-duplex for Enhanced IoT Connectivity

Leo Laughlin, Himal A. Suraweera, and Ioannis Krikidis
July 23, 2020

 

The Internet of Things (IoT) promises to affect great change across multiple domains, with wireless connectivity being the key feature that unlocks the power of monitoring and computing in a plethora of applications. Whilst wireless is central to IoT, the predicted growth in traffic must be accommodated within the already crowded wireless spectrum, and IoT presents a wide range of differing wireless connectivity requirements.

Today’s IoT devices achieve two-way communication either by transmitting and receiving at different times (time division duplexing - TDD) or on different frequency channels (frequency division duplexing - FDD). These techniques are used to avoid the problem of self-interference, where the devices own transmission leaks into the receiver and obscure the much weaker incoming signal, preventing it from being decoded. However, in recent years there has been growing interest in in-band full-duplex (IBFD) technologies [1] which enable simultaneous transmit and receive on the same frequency at the same time. This could bring a range of potential benefits to IoT networks in particular.

SIC for Enhanced Communication

IBFD enables bi-directional data communication using only half of the spectral resources compared to TDD or FDD. Theoretically, this doubles the link capacity but requires >100 dB of SI suppression. This is far from trivial, and involves combining multiple stages of SIC, using multiport antennas/feeding networks, feedforward radio-frequency (RF) cancellation circuits, and digital baseband cancellation (typically deploying non-linear digital signal processing) [1] (see Fig. 1). Compared to TDD/FDD, IBFD increases the cost, complexity, and power consumption of transceivers, due to the additional hardware and signal processing required. Moreover, feedforward RF cancellation involves tapping and injecting signals in the transmit and receive paths respectively, thereby adding the loss and eating into the link budget, which ultimately affects the range/cell size. For these reasons, SIC is not suitable for all types of IoT devices. In particular, low-cost sensors with extended range and 10-year battery life (e.g. smart-meters and remote monitoring applications) may not be suitable applications for SIC. This is typified by 3GPP NB-IoT, which specifies devices with half-duplex operation only [2]. An exception to this is short-range communication, where transmit power levels are lower, meaning less cancellation is required to achieve IBFD operation. For example, SIC is already used in RFIC readers, and the relatively short-range links within a home make domestic smart-devices a candidate application for IBFD.

Figure 1: A Block diagram of a transceiver using multiple stages self-interference cancellation. In this design, separate Tx and Rx antennas are used to provide some passive isolation, and this is combined with Feedforward RF cancellation and digital baseband cancellation.

Figure 1: A Block diagram of a transceiver using multiple stages self-interference cancellation. In this design, separate Tx and Rx antennas are used to provide some passive isolation, and this is combined with Feedforward RF cancellation and digital baseband cancellation.

But whilst not every device is suited to using SIC, IoT sensor networks may still benefit from SIC by using it in the network infrastructure. SIC enables on-frequency repeaters (IBFD relays), allowing a relay node to simultaneously receive and relay data in the same frequency band. This provides coverage extension with reduced latency and improved spectral efficiency, for example improving coverage for smart meters suffering severe shadowing loss due to their obscured location. Cell-site infrastructure/access points (APs) can also operate using IBFD, but serve a network of half-duplex only devices. In this case, the AP simultaneously receives from one node and transmits on the same frequency to another. This could, for example, increase the number of nodes in a smart city network, but without increasing the circuit complexity of the nodes themselves, although interference between devices needs to be avoided/managed through scheduling.

How Else Can SIC Improve IoT?

Where power consumption and device cost are less constrained, for example in industrial applications, connected autonomous vehicles, and smart devices in the home, SIC could potentially be deployed at the device level. Here, IBFD could improve the efficiency of spectrum access in IoT networks, as devices can transmit and simultaneously monitor the spectrum for other nearby users. In contrast to traditional cognitive radio systems where sensing and dynamic access are synchronously performed in orthogonal channels, IBFD enables more flexible and agile use of the spectrum, radically changing the future IoT spectrum map [3]. IBFD can also support feedback transmission during data reception, which is beneficial in ultra-low-latency IoT applications and improves the quality of channel state information. In applications with critical secrecy requirements, IBFD enables transceivers to receive a signal, whilst simultaneously transmitting a jamming signal to prevent eavesdroppers from receiving the same [4].  By properly designed the jamming signal and/or its spatial direction, jamming signals degrade eavesdropper’s reception without affecting the desired communication channel. For IoT scenarios with wireless power transfer (WPT) capabilities, IBFD radio can be exploited in several ways [5]. Specifically, since IBFD increases spectral efficiency by allowing more nodes to be active, consequently this can lead to potential energy savings since the harvested energy from WPT devices can also be increased (ambient WPT IoT devices e.g. LPWA).  In other WPT scenarios, IBFD allows APs to generate efficient energy beams (energy beamforming) while receiving data from multiple IoT devices.

Figure 2: (a) A virtual full-duplex relay scenario where pseudo-full-duplex relaying is achieved using two half-duplex relays. (b) In-band full-duplex communication for increased bi-directional link capacity (this could be a link between IoT devices, as shown, or a link between device and infrastructure). (c) An in-band full-duplex access point serving a network of half-duplex devices, providing network capacity gains without requiring SIC to be implemented in devices. (d) In-band full-duplex relaying supporting extended range communication between two IoT devices (could alternatively be used for communication between infrastructure and devices). Image credited to K. W. S. Palitharathna.

Figure 2: (a) A virtual full-duplex relay scenario where pseudo-full-duplex relaying is achieved using two half-duplex relays. (b) In-band full-duplex communication for increased bi-directional link capacity (this could be a link between IoT devices, as shown, or a link between device and infrastructure). (c) An in-band full-duplex access point serving a network of half-duplex devices, providing network capacity gains without requiring SIC to be implemented in devices. (d) In-band full-duplex relaying supporting extended range communication between two IoT devices (could alternatively be used for communication between infrastructure and devices). Image credited to K. W. S. Palitharathna.

Full-duplex Communication Using Visible Light for IoT Devices

In addition to radio waves, visible light communication (VLC) can also be used to wirelessly connect IoT devices [6]. Several features of VLC such as higher bandwidth, better security, and privacy make it an ideal technology for low-cost IoT use cases such as connected toys, smart lighting, and indoor positioning. In VLC, data transmission is accomplished by switching light-emitting diodes (LED) on and off at very high speeds to modulate the light intensity. At the receiver, a photodiode is used for data detection. An important aspect of VLC connected IoT is the support for bidirectional transmission through FD operation. SIC in VLC systems is easier to design than in RF systems since LED and PD devices are directional and thus can be well isolated [7]. Therefore, most works on FD VLC systems have completely neglected the impact of SI. There are several ways of implementing FD VLC links in an IoT device. In large form factor IoT devices, LED and PD pairs can be separated to reduce the effect of SI. In other devices, vertical and horizontal polarizers can be used to construct two isolated links. Some works also propose bidirectional transmission using Red, Green, and Blue LEDs. Since different colors occupy different portions of the spectrum, the crosstalk due to simultaneous transmission and reception becomes negligible. Also in cases where the same light carrier is not preferred for the uplink, infrared of 850 nm wavelength can be used to create sufficient isolation between the uplink and downlink.  In backscatter type IoT tags, PDs, retro-reflector fabric, and LCD shutters can be used to implement low power bidirectional communication. According to this solution, a reader sends interrogation signals to form the LED-to-Tag downlink while at the same time, fallen light is retrospectively reflected by a reflector fabric and subsequently gets modulated by the closing and opening action of the LCD shutter to form uplink communication [8].

Figure 3: Techniques to achieve Tx-Rx isolation for full-duplex operation in VLC IoT devices: (a) placement of LED & PD pairs (b) vertical & horizontal polarizers. Image credited to K. W. S. Palitharathna.

Figure 3: Techniques to achieve Tx-Rx isolation for full-duplex operation in VLC IoT devices: (a) placement of LED & PD pairs (b) vertical & horizontal polarizers. Image credited to K. W. S. Palitharathna.

Open Research Issues

  • Features such as small form factor and low power introduce considerable challenges for SIC implementation in IoT devices. Not only must the power consumption of the SIC hardware and DSP be kept to a minimum, but the losses in the transmit and receive paths must be minimized to avoid degrading the transmitter efficiency, and receiver sensitivity, respectively. This is especially difficult when size is constrained, which limits the use of antenna/propagation-based techniques for SI avoidance. Advances in high dynamic range and low-loss RF circuit techniques, low power digital signal processing, and machine learning are expected to contribute towards implementing efficient SIC solutions.
  • Dynamic environments and device mobility pose a significant challenge for SIC, as cancellation circuits and algorithm coefficients must dynamically adapt to compensate for rapidly changing self-interference coupling. Further work is required to develop reliable and power-efficient SI tracking algorithms, drawing on existing knowledge in the similar problem of wireless channel estimation, prediction, and equalization.
  • In addition to the SI, the full-duplex operation can create significant inter-user interference especially in massive IoT networks [9]. Interference will lower the performance gains unless efficient power control schemes, optimization techniques, and massive multiple access protocols are implemented network-wide. Moreover, interference identification/classification and prediction using deep learning is a promising area for future research.
  • The implementation of FD radio as a technique to boost physical layer secrecy in IoT applications is another promising research direction. The combination of FD radio with sophisticated signal processing techniques as well as channel coding and high-layer cryptographic algorithms seems to be an efficient solution for secure IoT communications. 
  • Factors such as non-ideal hardware, placement errors, and misalignment can reduce the performance of full-duplex VLC. Further, in VLC systems, manufacturing defects cause LEDs to emit photons outside of its field-of-view (FoV) and thus makes SI removal a challenging task. Therefore, new fabrication and LED packaging processes and focus control techniques are required to limit these performance losses in VLC transceivers.

References

  1. K. E. Kolodziej, B. T. Perry and J. S. Herd, "In-Band Full-Duplex Technology: Techniques and Systems Survey," IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 7, pp. 3025-3041, July 2019.
  2. Y.-P. E. Wang et al., "A Primer on 3GPP Narrowband Internet of Things," IEEE Communications Magazine, vol. 55, no. 3, pp. 117-123, March 2017.
  3. G. Zheng, I. Krikidis, and B. Ottersten, ``Full-Duplex cooperative cognitive radio with transmit imperfections,'' IEEE Transactions on Wireless Communications, vol. 12, pp. 2498-2511, May 2013.
  4. G. Zheng, I. Krikidis, J. Li, A. P. Petropulu, and B. Ottersten, ``Improving physical layer secrecy using full duplex jamming receiver,'' IEEE Transactions on Signal Processing, vol. 61, pp. 4962-4974, Oct. 2013.
  5. M. Mohammadi, B. K. Chalise, H. A. Suraweera, C. Zhong, G. Zheng, and I. Krikidis, ``Throughput analysis and optimization of wireless-powered multiple antenna full-duplex relay systems,'' IEEE Transactions on Communications, vol. 64, pp. 1769--1785, April 2016.
  6. M. Haus, A. Yi Ding, J. Ott, "LocalVLC: Augment smart IoT services with practical visible light communication,” in Proc. IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), Washington DC, June 2019, pp. 1-9.
  7. J. Zhang, X. Zhang and G. Wu, "Dancing with light: Predictive in-frame rate selection for visible light networks," in Proc. IEEE Conference on Computer Communications (INFOCOM 2015), Kowloon, Hong Kong, April 2015, pp. 2434-2442.
  8. Liu, J. Li, G. Shen, C. Sun, L. Li, and F. Chao, “Retro-VLC: Enabling Low-power Duplex Visible Light Communication,” in Proc. 16th International Workshop on Mobile Computing Systems and Applications (HotMobile '15), Santa Fe, MN, Feb. 2015, pp. 21-26.
  9. S. Goyal, P. Liu, S. S. Panwar, R. A. Difazio, R. Yang and E. Bala, "Full duplex cellular systems: Will doubling interference prevent doubling capacity?," IEEE Communications Magazine, vol. 53, no. 5, pp. 121-127, May 2015.

 

Leo LaughlinLeo Laughlin (GS’13–M’15) is Co-founder and CEO of Forefront RF Ltd, a company developing self-interference cancellation technologies for wireless applications. He was previously a Research Fellow at the University of Bristol, U.K., where he developed novel transceiver architectures and control algorithms for in-band full-duplex and tunable frequency division duplex. He has a Ph.D degree from the University of Bristol and an M.Eng degree from the University of York, U.K.

 

Himal A SuraweeraHimal A. Suraweera (S’04, M’07, SM’15) received the B.Sc. Eng. Degree (Hons.) from the University of Peradeniya, Sri Lanka, in 2001, and the Ph.D. degree from Monash University, Australia, in 2007. Currently, he is a Senior Lecturer with the Department of Electrical and Electronic Engineering, University of Peradeniya. His research interests include cooperative relay networks, full-duplex communications, multiple-input multiple-output systems, and energy harvesting communications. He is an Editor of IEEE TCOM and IEEE TGCN, and served as an editor for IEEE TWC and IEEE CL. He was a recipient of the IEEE Communications Society Leonard G. Abraham Prize in 2017.

 

Ioannis KrikidisIoannis Krikidis (S'03-M'07-SM'12-F'19) received the diploma in Computer Engineering from the Computer Engineering and Informatics Department (CEID) of the University of Patras, Greece, in 2000, and the M.Sc and Ph.D degrees from Ecole Nationale Superieure des Telecommunications (ENST), Paris, France, in 2001 and 2005, respectively, all in electrical engineering. He is currently an Associate Professor at the Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus. His current research interests include wireless communications, cooperative networks, 5G communication systems, wireless powered communications, and secrecy communications. He has been recognized by the Web os Science as a Highly Cited Researcher for 2017, 2018, and 2019. He is an IEEE Fellow and he has received the prestigious ERC consolidator grant.

Part of this work was supported by the Research Promotion Foundation, Cyprus, under the project EXCELLENCE/0918/0377 (PRIME).