1,645 research outputs found
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure
A multi-node energy prediction approach combined with optimum prediction interval for RF powered WSNs
Energy prediction plays a vital role in designing an efficient power management system for any environmentally powered Wireless Sensor Networks (WSNs). Most of the Moving Average (MA)-based energy prediction methods depend on past energy readings of the concerned node to predict its future energy availability. However, in case of RF powered WSNs the harvesting history of the main node along with neighbouring nodes can also be used to develop a more robust prediction technique. In this paper, we propose a Multi-Node energy prediction method for Radio Frequency Energy Harvesting (RF-EH) WSNs, which predicts the future energy availability by taking into account harvesting history of all nodes surrounding the main node. We analyse the effective distance for prediction and also develop a mathematical model to compute the optimum value of prediction interval, which has a major effect in prediction accuracy and system design, considering energy neutrality. Results show that Multi-Node prediction is less sensitive to prediction interval while inheriting the advantages of MA techniques. Also, nodes located at a larger distance were utilized less for prediction, and as the prediction interval increased, the utilization of more distant nodes decreased. Furthermore, we also establish a linear relation between the prediction interval and the energy threshold limit
Pible: Battery-Free Mote for Perpetual Indoor BLE Applications
Smart building applications require a large-scale deployment of sensors
distributed across the environment. Recent innovations in smart environments
are driven by wireless networked sensors as they are easy to deploy. However,
replacing these batteries at scale is a non-trivial, labor-intensive task.
Energy harvesting has emerged as a potential solution to avoid battery
replacement but requires compromises such as application specific design,
simplified communication protocol or reduced quality of service. We explore the
design space of battery-free sensor nodes using commercial off the shelf
components, and present Pible: a Perpetual Indoor BLE sensor node that
leverages ambient light and can support numerous smart building applications.
We analyze node-lifetime, quality of service and light availability trade-offs
and present a predictive algorithm that adapts to changing lighting conditions
to maximize node lifetime and application quality of service. Using a 20 node,
15-day deployment in a real building under varying lighting conditions, we show
feasible applications that can be implemented using Pible and the boundary
conditions under which they can fail.Comment: 4 pages, 4 figures, BuildSys '18: Conference on Systems for Built
Environments, November 7--8, 2018, Shenzen, Chin
Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks
The battery-less Internet of Things (IoT) devices are a key element in the
sustainable green initiative for the next-generation wireless networks. These
battery-free devices use the ambient energy, harvested from the environment.
The energy harvesting environment is dynamic and causes intermittent task
execution. The harvested energy is stored in small capacitors and it is
challenging to assure the application task execution. The main goal is to
provide a mechanism to aggregate the sensor data and provide a sustainable
application support in the distributed battery-less IoT network. We model the
distributed IoT network system consisting of many battery-free IoT sensor
hardware modules and heterogeneous IoT applications that are being supported in
the device-edge-cloud continuum. The applications require sensor data from a
distributed set of battery-less hardware modules and there is provision of
joint control over the module actuators. We propose an application-aware task
and energy manager (ATEM) for the IoT devices and a vector-synchronization
based data aggregator (VSDA). The ATEM is supported by device-level federated
energy harvesting and system-level energy-aware heterogeneous application
management. In our proposed framework the data aggregator forecasts the
available power from the ambient energy harvester using long-short-term-memory
(LSTM) model and sets the device profile as well as the application task rates
accordingly. Our proposed scheme meets the heterogeneous application
requirements with negligible overhead; reduces the data loss and packet delay;
increases the hardware component availability; and makes the components
available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure
On the impact of mobility on battery-less RF energy harvesting system performance
The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage
E2BNAR: Energy Efficient Backup Node Assisted Routing for Wireless Sensor Networks
In Wireless Sensor Networks (WSNs), each sensor node can only use so much power before recharging. If energy is depleted too quickly, nodes will fail one by one, bringing down the network as a whole. To this end, a design is needed to reduce the burden on the sensor nodes' power supplies while extending the network's useful life. This paper proposes a new approach, called Energy Efficient Backup Node Assisted Routing, to accomplish this (E2BNAR). Each primary node in the network has a group of backup nodes to ensure the network continues functioning. Assuming that the sensor nodes are capable of energy harvesting, E2BNAR finds the best backup node by analyzing the statistical relationship between energy harvesting and consumption rates. Periodically, residual energy is used to analyze the current energy consumption rate. When evaluating performance, several different indicators are taken into account. These include the Packet Delivery Ratio, Throughput, Average Energy Consumption, and Number of Awakened Sensor Nodes. Through analysis and experimentation in several settings, the proposed method's efficacy has been established
Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.
The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome.
This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd
Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks
Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O( N 2 ) linear programming problems for N -node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions. </jats:p
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