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1. Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems

   https://doi.org/10.24963/ijcai.2020/473  

   Mirzadeh, Seyed Iman; Ghasemzadeh, Hassan (July 2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   null (Ed.)

   With the recent advances in both machine learning and embedded systems research, the demand to deploy computational models for real-time execution on edge devices has increased substantially. Without deploying computational models on edge devices, the frequent transmission of sensor data to the cloud results in rapid battery draining due to the energy consumption of wireless data transmission. This rapid power dissipation leads to a considerable reduction in the battery lifetime of the system, therefore jeopardizing the real-world utility of smart devices. It is well-established that for difficult machine learning tasks, models with higher performance often require more computation power and thus are not power-efficient choices for deployment on edge devices. However, the trade-offs between performance and power consumption are not well studied. While numerous methods (e.g., model compression) have been developed to obtain an optimal model, these methods focus on improving the efficiency of a single model. In an entirely new direction, we introduce an effective method to find a combination of multiple models that are optimal in terms of power-efficiency and performance by solving an optimization problem in which both performance and power consumption are taken into account. Experimental results demonstrate that on the ImageNet dataset, we can achieve a 20% energy reduction with only 0.3% accuracy drop compared to Squeeze-and-Excitation Networks. Compared to a pruned convolutional neural network for human activity recognition, while consuming 1.7% less energy, our proposed policy achieves 1.3% higher accuracy. 

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2. A Power Efficiency Metric for Comparing Energy Consumption in Future Wireless Networks in the Millimeter Wave and Terahertz bands

   https://doi.org/10.1109/MWC.005.2200083  

   Kanhere, Ojas; Poddar, Hitesh; Xing, Yunchou; Shakya, Dipankar; Ju, Shihao; Rappaport, Theodore S. (December 2022, IEEE Wireless Communications)

   Future wireless cellular networks will utilize millimeter- wave and sub-THz frequencies and deploy small-cell base stations to achieve data rates on the order of hundreds of gigabits per second per user. The move to sub-THz frequencies will require attention to sustainability and reduction of power whenever possible to reduce the carbon footprint while maintaining adequate battery life for the massive number of resource-constrained devices to be deployed. This article analyzes power consumption of future wireless networks using a new metric, a figure of merit called the power waste factor (W), which shows promise for the study and development of “green G” — green technology for future wireless networks. Using W, power efficiency can be considered by quantifying the power wasted by all devices on a signal path in a cascade. We then show that the consumption efficiency factor (CEF), defined as the ratio of the maximum data rate achieved to the total power consumed, is a novel and powerful measure of power efficiency which shows that less energy per bit is expended as the cell size shrinks and carrier frequency and channel bandwidth increase. Our findings offer a standard approach to calculating and comparing power consumption and energy efficiency. 

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3. Energy-Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

   https://doi.org/10.1109/ICC40277.2020.9149186  

   Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid (June 2020, IEEE International Conference on Communications (ICC-20))

   We study a wireless ad-hoc sensor network (WASN) where N sensors gather data from the surrounding environment and transmit their sensed information to M fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs that minimizes a Lagrangian combination of sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd-like algorithm to optimize node deployment. Simulation results show that our proposed algorithm outperforms the existing deployment algorithms, on average. 

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4. Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

   https://doi.org/10.1145/3649221  

   Babatunde, Simeon; Alsubhi, Arwa; Hester, Josiah; Sorber, Jacob (May 2024, ACM Transactions on Sensor Networks)

   Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime. 

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5. 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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