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1. Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information

   Yu, Hao; Neely, Michael J (January 2018, PROC. IEEE INFOCOM 2018)

   This paper considers utility optimal power control for energy harvesting wireless devices with a finite capacity battery. The distribution information of the underlying wireless environment and harvestable energy is unknown and only out- dated system state information is known at the device controller. This scenario shares similarity with Lyapunov opportunistic optimization and online learning but is different from both. By a novel combination of Zinkevich’s online gradient learning technique and the drift-plus-penalty technique from Lyapunov opportunistic optimization, this paper proposes a learning-aided algorithm that achieves utility within O(epsilon) of the optimal, for any desired epsilon > 0, by using a battery with an O(1/epsilon) capacity. The proposed algorithm has low complexity and makes power investment decisions based on system history, without requiring knowledge of the system state or its probability distribution. 

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2. Bregman-style Online Convex Optimization with EnergyHarvesting Constraints

   https://doi.org/10.1145/3428337  

   Asgari, Kamiar; Neely, Michael J. (November 2020, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   null (Ed.)

   This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve $$O(\sqrtT )$$ regret by using a battery with an $$O(\sqrtT )$$ capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from $$O(\sqrtn )$$ to $$O(\sqrtłog(n) )$$. The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component. 

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3. A Converse Result on Convergence Time for Opportunistic Wireless Scheduling

   Neely, Michael J. (January 2020, IEEE INFOCOM---International Conference on Computer Communications)

   This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multi-access and broadcast systems. Every time slot an access point observes the current channel states and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of O(log(T)/T ), where T is the time the algorithm has been running. An existing O(1/T ) converse is known. The current paper improves the converse to O(log(T)/T), which matches the known achievability result. The proof uses a reduction from the opportunistic scheduling problem to a Bernoulli estimation problem. Along the way, it refines a result on Bernoulli estimation. 

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4. LS-AODV: An Energy Balancing Routing Algorithm For Mobile Ad Hoc Networks

   https://doi.org/10.1109/IEMCON53756.2021.9623217  

   Lane, Cameron; Smith, Calvin Jarrod; Wang, Nan (October 2021, IEEE IEMCON 2021)

   Battery-powered computing solutions have grown in importance and utility across a wide range of applications in the technology industry, including both consumer and industrial uses. Devices that are not attached to a stable and constant power source must ensure that all power consumption is minimized while necessary computation and communications are performed. WiFi networking is ubiquitous in modern devices, and thus the power consumption necessary to transmit data is of utmost concern for these battery powered devices. The Ad hoc OnDemand Distance Vector (AODV) routing algorithm is a widely adopted and adapted routing system for path finding in wireless networks. AODV’s original implementation did not include power consumption as a consideration for route determinations. The Energy Aware AODV (EA-AODV) algorithm was an attempt to account for energy conservation by varying broadcast power and choosing paths with distance between nodes as a consideration in routing. Lightning Strike AODV (LS-AODV) described in this paper is a proposed routing algorithm that further accounts for energy consumption in wireless networking by balancing energy in a network. Quality of service is maintained while energy levels are increased through networks using the LS-AODV algorithm. 

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5. 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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