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1. Distributed Virtual Time-Based Synchronization for Simulation of Cyber-Physical Systems

   https://doi.org/10.1145/3446237  

   Hannon, Christopher ; Yan, Jiaqi ; Jin, Dong ( April 2021 , ACM Transactions on Modeling and Computer Simulation)

   null (Ed.)

   Our world today increasingly relies on the orchestration of digital and physical systems to ensure the successful operations of many complex and critical infrastructures. Simulation-based testbeds are useful tools for engineering those cyber-physical systems and evaluating their efficiency, security, and resilience. In this article, we present a cyber-physical system testing platform combining distributed physical computing and networking hardware and simulation models. A core component is the distributed virtual time system that enables the efficient synchronization of virtual clocks among distributed embedded Linux devices. Virtual clocks also enable high-fidelity experimentation by interrupting real and emulated cyber-physical applications to inject offline simulation data. We design and implement two modes of the distributed virtual time: periodic mode for scheduling repetitive events like sensor device measurements, and dynamic mode for on-demand interrupt-based synchronization. We also analyze the performance of both approaches to synchronization including overhead, accuracy, and error introduced from each approach. By interconnecting the embedded devices’ general purpose IO pins, they can coordinate and synchronize with low overhead, under 50 microseconds for eight processes across four embedded Linux devices. Finally, we demonstrate the usability of our testbed and the differences between both approaches in a power grid control application. 

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2. Wake-up radio-based data forwarding for green wireless networks

   https://doi.org/10.1016/j.comcom.2020.05.046  

   Koutsandria, G. ; DiValerio, V. ; Spenza, D. ; Basagni, S. ; Petrioli, C. ( April 2020 , Computer communications)

   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. 

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3. An architecture for IoT clock synchronization

   https://doi.org/10.1145/3277593.3277606  

   Mani, Sathiya Kumaran ; Durairajan, Ramakrishnan ; Barford, Paul ; Sommers, Joel ( October 2018 , 8th International Conference on Internet of Things (IoT 2018))

   In this paper, we describe an architecture for clock synchronization in IoT devices that is designed to be scalable, flexibly accommodate diverse hardware, and maintain tight synchronization over a range of operating conditions. We begin by examining clock drift on two standard IoT prototyping platforms. We observe clock drift on the order of seconds over relatively short time periods, as well as poor clock rate stability, each of which make standard synchronization protocols ineffective. To address this problem, we develop a synchronization system, which includes a lightweight client, a new packet exchange protocol called SPoT and a scalable reference server. We evaluate the efficacy of our system over a range of configurations, operating conditions and target platforms. We find that SPoT performs synchronization 22x and 17x more accurately than MQTT and SNTP, respectively, at high noise levels, and maintains a clock accuracy of within ∼15ms at various noise levels. Finally, we report on the scalability of our server implementation through microbenchmark and wide area experiments, which show that our system can scale to support large numbers of clients efficiently. 

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4. Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands

   https://doi.org/10.1145/3470452  

   Shi, Junyang ; Mu, Di ; Sha, Mo ( February 2022 , ACM Transactions on Sensor Networks)

   null (Ed.)

   Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands. 

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5. TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

   https://doi.org/10.1145/3534594  

   Saha, Swapnil Sayan ; Sandha, Sandeep Singh ; Garcia, Luis Antonio ; Srivastava, Mani ( July 2022 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31× to 134× with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ±0.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models. 

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