Title: TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds
Wireless protocol design for IoT networks is an active area of research which has seen significant interest and developments in recent years. The research community is however handicapped by the lack of a flexible, easily deployable platform for prototyping IoT endpoints that would allow for ground up protocol development and investigation of how such protocols perform at scale. We introduce tinySDR, the first software-defined radio platform tailored to the needs of power-constrained IoT endpoints. TinySDR provides a standalone, fully programmable low power software-defined radio solution that can be duty cycled for battery operation like a real IoT endpoint, and more importantly, can be programmed over the air to allow for large scale deployment. We present extensive evaluation of our platform showing it consumes as little as 30 uW of power in sleep mode, which is 10,000x lower than existing SDR platforms. We present two case studies by implementing LoRa and BLE beacons on the platform and achieve sensitivities of -126 dBm and -94 dBm respectively while consuming 11% and 3% of the FPGA resources. Finally, using tinySDR, we explore the research question of whether an IoT device can demodulate concurrent LoRa transmissions in real-time, within its power and computing constraints. more »« less
Shahid, Muhammad Osama; Krishnaswamy, Bhuvana
(, Proceedings of the ACM on Networking)
Adaptive Data Rate (ADR) is used by multi-channel LoRaWANs to meet the demanding capacity needs of LoRa networks. The network server running ADR in each channel determines the optimum data rate and assigns the appropriate spreading factor for each LoRa device to maximize the network throughput. This in turn requires the gateway to be capable of receiving LoRa packets of all possible spreading factors. Existing gateways achieve this by using multiple RF front ends, increasing the overall cost and complexity. In this work, we propose BYOG (Bring Your Own Gateway), a LoRaWAN receiver that can receive and decode 10 channels simultaneously in real-time. Towards this pipeline, we develop self-dechirping, an SF-agnostic packet detection algorithm that also detects the spreading factor of the packet. This computationally lightweight algorithm can be implemented on any general-purpose software-defined radio, bringing down the cost and ease of LoRaWAN gateway implementations. BYOG will enable research and development in LoRaWAN ADR. Using experimental, real-world datasets, we show that the proposed algorithm can detect the spreading factor accurately and operate over a wide range of SNRs using three different SDRs (RTL-SDR, HackRF One, USRP B210). BYOG performs as well as a high-end LoRaWAN gateway in terms of network throughput.
Gutterman, Craig; Minakhmetov, Artur; Yu, Jiakai; Chen, Tingjun; Zhu, Shengxiang; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar; Kilper, Dan
(, ACM SIGCOMM 2019 Workshop on Optical Systems Design)
ABSTRACT The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multiuser advanced wireless testbed that is being deployed in New York City [1]. Open APIs and programmability across all the technology components and protocol layers in COSMOS will enable researchers to explore 5G technologies in a real world environment. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies as well as experimentation directly in the optical physical layer. A paper on the COSMOS optical architecture was previously presented in [2]. In this talk, we briefly introduce COSMOS’ optical x-haul network with SDN control, and its integration with the software-defined radio (SDR) and mobile edge cloud.
Sub-terahertz (THz) wireless communication links require low-SWaP (size, weight, and power) software defined radio (SDR) modems to achieve efficient and reliable data transmission. This research presents the design, development, and experimentation of an SDR system operating in the 135-150 GHz frequency range, utilizing simple I/Q modulation techniques such as differential binary phase shift keying (DBPSK). The system integrates advanced components, including Virginia Diodes (VDI) 110-170 GHz compact upconverter (CCU) and compact downconverter (CCD), high-gain lens horn antennas from Anteral (40 dBi), and the Xilinx RF-SoC ZCU-111 for real time DSP. A 500 MHz IF is implemented on RF-SoC with baseband bandwidth 64 MHz and data rate 64 Mbps via DBPSK modulation. For 20 dBm transmit power at 147 GHz, the nearfield SNR was measured to be 55 dB at 1m lens-to-lens separation for a baseband of 64 MHz. Simulation models of propagation predict 64 Mbps is possibly viable at up to 2 km in a point-to-point connection for a BER of < 10−3. The SDR was realized on the Xilinx PYNQ platform, offering a user-friendly interface while being adaptable to high data rate applications. This digital design is particularly suited for deployment in scenarios such as vehicle-to-vehicle communication, backhaul networks, and data center level interconnects. The research explored challenges related to synchronization, signal integrity, and environmental sensitivity, which are critical for maintaining reliable communication in a 147 GHz channel. A real-time text messaging application demonstrated correct operation of the PYNQ modem.
Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption ∗ Corresponding author. E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria). https://doi.org/10.1016/j.comcom.2020.05.046 (sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12]. This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a ABSTRACT This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively.
Chen, L.; Hu, W.; Jamieson, K.; Chen, X.; Fang, D.; Gummeson, J.
(, Proceedings of the USENIX NSDI Symposium)
Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoT network as LLAMA, a Low-power Lattice of Actuated Metasurface Antennas, designed for the pervasively used 2.4 GHz ISM band. We optimize LLAMA’s metasurface design for both low transmission loss and low cost, to facilitate deployment at scale. We then build an end-to-end system that actuates the metasurface structure to optimize for link performance in real time. Our experimental prototype-based evaluation demonstrates gains in link power of up to 15 dBm, and wireless capacity improvements of 100 and 180 Kbit/s/Hz in through-surface and surface-reflective scenarios, respectively, attributable to the polarization rotation properties of LLAMA’s metasurface.
@article{osti_10114060,
place = {Country unknown/Code not available},
title = {TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds},
url = {https://par.nsf.gov/biblio/10114060},
abstractNote = {Wireless protocol design for IoT networks is an active area of research which has seen significant interest and developments in recent years. The research community is however handicapped by the lack of a flexible, easily deployable platform for prototyping IoT endpoints that would allow for ground up protocol development and investigation of how such protocols perform at scale. We introduce tinySDR, the first software-defined radio platform tailored to the needs of power-constrained IoT endpoints. TinySDR provides a standalone, fully programmable low power software-defined radio solution that can be duty cycled for battery operation like a real IoT endpoint, and more importantly, can be programmed over the air to allow for large scale deployment. We present extensive evaluation of our platform showing it consumes as little as 30 uW of power in sleep mode, which is 10,000x lower than existing SDR platforms. We present two case studies by implementing LoRa and BLE beacons on the platform and achieve sensitivities of -126 dBm and -94 dBm respectively while consuming 11% and 3% of the FPGA resources. Finally, using tinySDR, we explore the research question of whether an IoT device can demodulate concurrent LoRa transmissions in real-time, within its power and computing constraints.},
journal = {arXiv.org > eess > arXiv:1907.02063},
author = {Hessar, Mehrdad and Najafi, Ali and Iyer, Vikram and Gollakota, Shyamnath},
}
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