Energy Harvesting Internet Of Things Research Articles

Navigating Boundaries in Quantifying Robustness: A DRL Expedition for Non-Linear Energy Harvesting IoT Networks

Navigating Boundaries in Quantifying Robustness: A DRL Expedition for Non-Linear Energy Harvesting IoT Networks

Energy Harvesting IoT Devices

Energy Harvesting IoT Devices

Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy-based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology’s potential for diverse geographical applications and compatibility with low-performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally-trained control strategies and can be successfully re-applied in other locations.

A dynamic programming schedule trading off quality and stability in task allocation for energy-neutral Internet of Things devices harvesting solar energy

Energy neutrality in an energy harvesting Internet of Things (IoT) device ensures continuous operation of the device by trading performance with energy consumption, and a way to achieve this is by adopting a task-based model. In this model, the device embeds several alternative tasks with different ratio energy-cost/quality and a scheduler that, depending on the current energy production and battery level, runs at any time the best task to maximize the performance while guaranteeing energy neutrality. In this context, this work proposes a novel scheduling algorithm that takes into account also the stability of the device, by minimizing the leaps of quality between two consecutive tasks in the scheduling. We show by simulation and by experiments on a low-power IoT platform that the proposed algorithm greatly improves the stability of the device with respect to the state-of-the-art algorithms, with a marginal worsening of the overall quality of the tasks executed.

On Virtualizing Targets Coverage in Energy Harvesting IoT Systems

On Virtualizing Targets Coverage in Energy Harvesting IoT Systems

Restoration-Aware Sleep Scheduling Framework in Energy Harvesting Internet of Things: A Deep Reinforcement Learning Approach

Restoration-Aware Sleep Scheduling Framework in Energy Harvesting Internet of Things: A Deep Reinforcement Learning Approach

State-Aware Timeliness in Energy Harvesting IoT Systems Monitoring a Markovian Source

State-Aware Timeliness in Energy Harvesting IoT Systems Monitoring a Markovian Source

Status Updating Under Partial Battery Knowledge in Energy Harvesting IoT Networks

Status Updating Under Partial Battery Knowledge in Energy Harvesting IoT Networks

Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas

Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas

VNF Scheduling and Sampling Rate Maximization in Energy Harvesting IoT Networks

VNF Scheduling and Sampling Rate Maximization in Energy Harvesting IoT Networks

LANTERN:Learning-Based Routing Policy for Reliable Energy-Harvesting IoT Networks

LANTERN:Learning-Based Routing Policy for Reliable Energy-Harvesting IoT Networks

Task offloading to edge cloud balancing utility and cost for energy harvesting Internet of Things

Task offloading from many Internet of Things (IoT) devices to a mobile edge computing (MEC) system, consisting of multiple collaborative edge servers (CES), results in reduced task execution delay as well as energy consumption. However, the exploitation of computation resources of CESs incurs additional costs. Existing works in the literature either focused on minimizing execution latency or energy consumption. In this paper, we have developed a task offloading policy that aims at making a trade-off between device utility and execution cost. The utility is defined as a function of task execution latency and energy consumption of energy-harvesting IoT devices. The task offloading problem is formulated as a subset selection problem that makes the desired trade-off. The offloading problem is proven to be NP-hard and thus we develop a meta-heuristic approach, namely SCOPE, inspired by Social Cognitive Optimization (SCO) to obtain the desired polynomial time execution. The results show its potency compared to the state-of-the-art methods in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.

Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

Wireless power transmission (WPT) is expected to play a crucial role in supporting the perpetual operations of Internet of Things (IoT) devices, thereby contributing significantly to IoT services. However, the development of efficient power allocation algorithms has remained a longstanding challenge. This paper addresses the aforementioned challenge by proposing a novel strategy, called energy poverty-based device selection (EPDS), in conjunction with energy beamforming, where orthogonal frequency bands are allocated to energy harvesting IoT devices (EHIs). To solve two power allocation problems, a logarithmic-based nonlinear energy harvesting model (NEHM) is introduced. The first problem tackled is the total received power maximization (TRPM), which is initially presented and, then, solved optimally in closed-form by incorporating Karush–Kuhn–Tucker (KKT) conditions with the modified water-filling algorithm. The second problem formulated is the common received power maximization (CRPM), which takes into account energy fairness considerations. To assess the proposed algorithms and gain insights into the effects of mobility, the mobility of EHIs is modeled as a one-dimensional random walk. Extensive numerical results are provided to validate the advantages of the proposed algorithms. Both the TRPM and CRPM algorithms exhibit exceptional performance in terms of total and minimum received energy, respectively. Furthermore, in comparison to round-robin scheduling, the EPDS demonstrates superior performance in terms of minimum received energy. This paper highlights the impact of the proposed energy harvesting (EH) model, demonstrating 12.68% and 3.69% higher values than the linear model for the minimum and total received energy, respectively.

Neighbor-Aware Distributed Task Offloading Algorithm in Energy-Harvesting Internet of Things

In the distributed task offloading system, the desired task completion time cannot be achieved when lots of mobile devices offload simultaneously the tasks with high complexity to a specific edge cloud. In this research, we present a neighbor-aware distributed task offloading algorithm (NA-DTOA) where the IoT device takes its energy status and the decisions of neighbor devices into account for the decisions on whether to process the task by itself and on which edge cloud is selected to offload the task. To decrease the task completion time while maintaining the average energy outage probability below the specified threshold, we design a constrained stochastic game model. To achieve the solution of the model, a best response dynamics-based algorithm is devised. The evaluation results reveal that, compared to a probability-based scheme, NA-DTOA reduces the average task completion time by almost 47 percent while ensuring a substantially low average energy outage probability of 0.03.

Maximizing Sensing and Computation Rate in Ad Hoc Energy Harvesting IoT Networks

This article considers the collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this article outlines a mixed-integer linear program (MILP), which can be used to optimize the sensing time of sensor devices, offloading decision of each server, and the number of virtual machines (VMs) assigned to each device. Further, this article proposes a multiagent co-operative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach coupled with the Hungarian algorithm to assign VMs to devices. It allows servers to learn when to share their energy and VMs with neighbor servers using only noncausal energy and channel gain information. The simulation results show that the amount of processed data by co-operative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning is 93% that of MILP.

Automating and Optimizing Reliability-Driven Deployment in Energy-Harvesting IoT Networks

Recent years have witnessed a significant expansion in Internet-of-Things (IoT) applications. Although the battery energy availability can be improved with energy harvesting, the overall device reliability management has been overlooked in the existing literature. State-of-the-art reliability models of solar panels, electronics and rechargeable batteries show exponential dependence of failures on temperature. This work is the first to develop a comprehensive reliability deployment framework for energy-harvesting IoT networks, reflecting the non-negligible thermal stresses on each hardware component. Our framework improves the reliability on both pre-deployment and post-deployment stages. Prior to deployment, given the historical temperature and solar radiation of the region, we formulate a Mixed Integer Linear Program (MILP) to place the minimum number of nodes, while ensuring (i) full target coverage, (ii) complete connectivity, (iii) energy-neutral operation, and (iv) reliability constraints at each deployed node. We propose a polynomial-time heuristic, R-TSH, to approximate the optimal placement in large-scale deployments. While R-TSH optimizes long-term reliability, the prompt temperature or link quality differences from the historical patterns can significantly degrade device reliability after deployment. The post-deployment section of our design consists of a reliability-driven routing algorithm, AODV-Rel, that adapts to real-time environmental and link quality changes. Extensive analysis is done using a real-world dataset from the National Solar Radiation Database. Simulations in ns-3 show that R-TSH meets all reliability constraints even after 5 years of deployment as compared to the state of the art. In addition, it is 2000x faster than the optimal solution, while placing only 28% more nodes. AODV-Rel further extends the minimal operational lifetime by 1.5 and 2.8 months under temperature deviation and wireless interference.

Opportunistic Backscatter Communication Protocol Underlying Energy Harvesting IoT Networks

Opportunistic Backscatter Communication Protocol Underlying Energy Harvesting IoT Networks

Design of a High-Efficiency DC-DC Boost Converter for RF Energy Harvesting IoT Sensors.

In this paper, an optimal design of a high-efficiency DC-DC boost converter is proposed for RF energy harvesting Internet of Things (IoT) sensors. Since the output DC voltage of the RF-DC rectifier for RF energy harvesting varies considerably depending on the RF input power, the DC-DC boost converter following the RF-DC rectifier is required to achieve high power conversion efficiency (PCE) in a wide input voltage range. Therefore, based on the loss analysis and modeling of an inductor-based DC-DC boost converter, an optimal design method of design parameters, including inductance and peak inductor current, is proposed to obtain the maximum PCE by minimizing the total loss according to different input voltages in a wide input voltage range. A high-efficiency DC-DC boost converter for RF energy harvesting applications is designed using a 65 nm CMOS process. The modeled total losses agree well with the circuit simulation results and the proposed loss modeling results accurately predict the optimal design parameters to obtain the maximum PCE. Based on the proposed loss modeling, the optimally designed DC-DC boost converter achieves a power conversion efficiency of 96.5% at a low input voltage of 0.1 V and a peak efficiency of 98.4% at an input voltage of 0.4 V.

Complete Targets Coverage in Energy Harvesting Internet of Things With Ambient Backscatter

This article considers deriving set covers or the set of active nodes that are responsible for monitoring all targets in an Internet of Things (IoT) network. A key distinction to prior works is that this article considers sensor nodes aided by backscatter communications, which allow them to communicate with negligible energy cost using ambient radio frequency (RF) signals. The article contains three main novelties. First, it presents a mixed integer linear program (MILP) that can be used to compute the global optimal solution. Second, it also outlines a centralized greedy scheduling (CGS) algorithm that selects nodes based on their energy and the number of covered targets. Third, it presents a distributed greedy scheduling (DGS) algorithm that selects nodes according to their energy level. The simulation results show that equipping nodes with ambient backscattering capability increases the ratio of complete targets coverage by more than 100% as compared to existing techniques.

Near-Optimal Energy Management for Energy Harvesting IoT Devices Using Imitation Learning

Internet of Things (IoT) devices are becoming popular in a number of transformative applications, including smart health, digital agriculture, and wide area sensing. However, small battery capacities and the need for frequent battery replacements or recharging have hindered their widespread adoption. Energy harvesting (EH) and management present a promising opportunity to enable long-term recharge free operation of IoT devices. State-of-the-art energy management approaches employ dynamic optimization methods to manage the harvested energy. However, the dynamic optimization methods are typically computationally intensive and lead to significant energy overhead for energy-constrained IoT devices. In contrast, this article proposes an imitation learning (IL)-based energy management algorithm that provides the energy budget or allocation for each decision interval without the need for dynamic optimization. We present an efficient approach to design Oracle policies that optimize the energy allocation of the IoT device to enable self-powered operation while maximizing the utility to the application. Then, we leverage the Oracle policy to train an online policy that performs near-optimal energy allocation at runtime. Our experiments with solar EH data for six years from three locations show that the proposed IL policies achieve allocation that is, on average, within 2.5 J of the Oracle, while having an energy consumption overhead of 154 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> .