Source of support: NSF CAREER (2019--2024) 

As a key technology driving the Internet-of-Things (IoT), Low-Power Wide-Area Networks (LPWANs) are evolving to overcome the range limits and scalability challenges in traditional wireless sensor networks. With the support from NSF and through collaboration with Microsoft Research, we have developed a highly scalable LPWAN architecture – called SNOW (Sensor Network Over White Spaces)- by designing sensor networks to operate over the TV white spaces. White spaces refer to the allocated but locally unused TV channels, and can be used by unlicensed devices, according to the FCC in the US. Compared to the existing LPWAN technologies, SNOW offers much higher scalability and energy efficiency and takes the advantages of freely available TV white spaces.

 SNOW is the first design of a highly scalable low power and long range wireless sensor network over the TV white spaces. At the heart of its design is a Distributed implementation of Orthogonal Frequency Division Multiplexing (OFDM), called D-OFDM. The base station splits the wide white space spectrum into narrowband orthogonal subcarriers allowing D-OFDM to carry parallel data streams to/from the distributed nodes from/to the base station. Each sensor uses only one narrow-band radio. The base station uses two wide-band radios, one for transmission and the other for reception, allowing transmission and reception in parallel. Each radio of the base station and a sensor is half-duplex and equipped with a single antenna. SNOW supports reliable, concurrent, and asynchronous receptions with one single-antenna radio and multiple concurrent data transmissions with the other single-antenna radio. This is achieved through a new physical layer design by adopting D-OFDM for multiple access in both directions and through a lightweight MAC protocol. While OFDM has been embraced for multiple access in various wireless broadband and cellular technologies recently, its adoption in low power, low data rate, narrowband, and sensor network design is novel. Taking the advantage of low data rate and short payloads, we adopt OFDM in SNOW through a much simpler and energy-efficient design.

In SNOW, a subcarrier bandwidth can be chosen as low as 100kHz, 200kHz, 400kHz depending on the packet size and expected bit rate. Using a subcarrier, a sensor node can have several kilometers of transmission range at 0dBm. Using a single base station, SNOW has the capability of supporting millions of devices. As an added advantage, it can use fragmented spectrum of white spaces. We implemented SNOW on two hardware platforms -- USRP using GNU radio and TI CC1310. CC1310 is a tiny, cheap (<$30), and commercially off-the-shelf (COTS) device with a programmable PHY. We implemented SNOW with CC1310 (using CC-ANTENNA-DK2 antenna)  as a node  and the BS is a USRP210 with a laptop. Experiments through deployments in multiple geographical areas as well as large-scale simulations demonstrated that SNOW drastically enhances the scalability of sensor network and outperforms existing techniques in terms of scalability, energy, and latency. Both analytical and experimental study hint that SNOW will be one of the most scalable and the key technologies to drive the IoT. The design of SNOW is available here.

This YouTube video shows a short demonstration of SNOW: https://youtu.be/y7Q932A24Zc

This project will design and implement an LPWAN architecture and complete protocol stack based on SNOW to support scalable integration, coexistence, mobility, and time-sensitive communication as follows. It will implement the proposed protocols on TI CC1310 (as SNOW nodes) and also on universal software radio peripheral devices. The protocols will be evaluated through experiments in on our LPWAN testbeds. 

SNOW Testbed:

Currently we have a temporary SNOW testbed of 43 nodes for experimental purposes that co-locates with our permanent wireless sensor network (WSN) testbed at Wayne State University. Specifically, we have 43 SNOW nodes of which 34 are based on CC1310 and 9 are based on USRP which are co-located with our WSN testbed as shown in the following figure. Currently all SNOW nodes are powered through USB from Laptop and the laptops are connected with those of WSN testbed through department Ethernet. The entire testbed is located in Maccabees Building at Wayne state University. 

Link to the open-source implementation of SNOW PHY: https://github.com/snowlab12/gr-snow

Personnel:

Abusayeed Saifullah (PI)

Mahbubur Rahman (former graduate student)

Dali Ismail (former graduate student)

Sezana Fahmida  (graduate student) 

Mohammad Pivezhandi (graduate student)

Collaborator:

Ranveer Chandra (Microsoft Research and Microsoft Azure Global)

Jie Liu (Microsoft Research, formerly)

Publications:

Abusayeed Saifullah*, Mahbubur Rahman*, Dali Ismail, Chenyang Lu, Jie Liu, and Ranveer Chandra, “Low-Power Wide-Area Network over White Spaces”, in ACM/IEEE Transactions on Networking; Vol. 26, No. 4. pp. 1893--1906; 2018. * First co-author.

Mahbubur Rahman and Abusayeed Saifullah; “Integrating Multiple Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage”; In IEEE/ACM Transactions on Networking; Vol. 28, No. 1, pp. 413–426; 2020

Sezana Fahmida, Venkata Modekurthy, Mahbubur Rahman, Abusayeed Saifullah, Marco Brocanelli; “Long-Lived LoRa: Prolonging the Lifetime of a LoRa Network”; Accepted to appear in ICNP ’20 (The 28th IEEE International Conference on Network Protocols); pp. 1–12; 2020. 

Mahbubur Rahman, Dali Ismail, Prashant Modekurthy, Abusayeed Saifullah; “LPWAN in the TV White Spaces: A practical implementation and deployment experiences”; In ACM Transactions on Embedded Computing Systems; Accepted; pp. 1 – 25; 2021.

Venkata Modekurthy, Dali Ismail, Mahbubur Rahman, and Abusayeed Saifullah; “Low-latency in-band integration of multiple low-power wide-area networks”, In IEEE RTAS '21 (The 27th IEEE Real-Time and Embedded Technology and Applications Symposium); CPS-IoT Week 2021; pp. 1--13; 2021. 

Dali Ismail and Abusayeed Saifullah, "Mobility in low-power wide-area networks over White Spaces", in ACM EWSN '21 (The 18th International Conference on Embedded Wireless Systems and Networks); pp. 1–12; 2021.

Sezana Fahmida, Dali Ismail, Venkata Modekurthy, Aakriti Jain, and Abusayeed Saifullah; “Real-Time Communication over LoRa Networks”; In ACM/IEEE IoTDI ’22 (The ACM/IEEE Conf. on Internet-of-Things Design and Implementation); CPS-IoT Week 2022; pp. 14–27; 2022.

Sezana Fahmida, Venkata Modekurthy, Mahbubur Rahman, and Abusayeed Saifullah; “Handling Coexistence of LPWANs through Embedded Reinforcement Learning”; In ACM/IEEE IoTDI ’23 (The ACM/IEEE Conf. on Internet-of-Things Design and Implementation); pp. 410–423; CPS-IoT Week 2023; 2023.

Mahbubur Rahman and Abusayeed Saifullah, “Boosting reliability and energy-efficiency in indoor LoRa”; In ACM/IEEE IoTDI ’23 (The ACM/IEEE Conf. on Internet-of-Things Design and Implementation); pp. 396 –409; CPS-IoT Week 2023; 2023.

Mahbubur Rahman and Abusayeed Saifullah, “T-IoT: Transparent and Tamper-Proof Event Ordering in the Internet of Things Platforms”, 13 pages; In IEEE Internet of Things Journal; 2022.

Venkata Modekurthy, Mahbubur Rahman, and Abusayeed Saifullah; “Towards Mixed Criticality Industrial Wireless Sensor-Actuator Network”; In IoST-5G&B ’23) The 4th International Workshop on Recent Trends of Internet of Softwarized Things; pp. 1–6; 2023.  Best Paper Award.

Qaisar Bashir, Mohammad Pivezhandi, and Abusayeed Saifullah; “Energy- and Temperature-Aware Scheduling: From Theory to an Implementation on Intel Processor”; in ICESS ’22 (the 18th IEEE International Conference on Embedded Software and Systems); pp. 1–9; 2022.

Ashik Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Modekurthy, and Abusayeed Saifullah; “Precise Scheduling of DAG Tasks with Dynamic Power Management”; In ECRTS ’23 (The 35th Euromicro Conference on Real-Time Systems); Article No. 8; pp. 8:1--8:24; Leibniz International Proceedings in Informatics; 2023.

Educational activities, Outreach and other broader impact outcomes: 

We have added various LPWANs including LoRa and SNOW design in the course on Computer Networking that is offered in each Fall for both undergraduate and graduate students. Through this topic, both undergraduate and graduate students enrolled in this course will learn about LPWANs that are not typically covered in a standard networking course.  Besides, some research results of this project such as the design of SNOW, real-time communication protocol for LPWAN, and coexistence handling of LPWANs based on machine learning has been added to the graduate course on Advanced Topics on Computer Networks. We are also currently working to commercialize SNOW. 

 For questions, please contact Prof. Abusayeed Saifullah: saifullah@wayne.edu