With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem in-creasing dramatically, well-designed indoor light energy harvesting solutions are needed. The first step towards this development is to determine the harvestable energy in real indoor environ-ments. But the harvestable energy varying over time with nature (spectra) and intensity of the light multi-sources, lighting data must be collected for sufficiently long periods. Besides, for a real implementation on-site, studies must be able to be carried out simultaneously in several places to determine locations with the highest energy harvesting potential. In this context, this manuscript presents a very low-cost prototype based on commercial photodiodes (rather than very expensive spectrometers), which measures only a very rudimentary number of spectral data. Thanks to a classification supervised machine learning from Matlab, in which an algorithm learns to classify new observations, and thanks to a simple principle of the superposition approximation model de-veloped for flexible GaAs solar cells, our harvestable energy estimation error is less than 5 percents after more than 2 weeks of observation. To measure this error, the data collected leading to an estimate of the harvestable energy is compared to what has been experimentally harvested in a real IoT system Li-ion battery and compared to what has been estimated using an expensive spectrometer during the same period. Our prototype should allow the development and the massive deploy-ment of a new generation of low cost indoor light energy harvesting sensors for future reliable indoor energy harvesters.
Indoor beacon system is a cornerstone of internet of things. Until now, most of the research effort has focused on achieving various applications based on beacon communication. However, the power consumption on beacon devices becomes the bottleneck for the large-scale deployments of indoor networks. On one hand, the size of beacon devices is required to be small enough for easy deployment, which further limits the size of battery. On the other hand, replacing the batteries of the beacon devices could lead to high maintenance costs. It is important to provide them with full energy autonomy. To …
Leveraging unmanned aerial vehicle (UAV) is convenient to collect data from ground sensor. However, in the presence of unknown urban environment, the data collection is subject to the blockage of urban buildings. In this paper, considering the urban environment during flight, we propose dynamic adaptive modulation and height control for UAV-sensor data harvesting in urban areas. In each time slot, the modulation format and flight height are selected based on current system states, with the aim of minimizing the expected transmission energy of sensor under data volume and flight height constrai…
We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), tasked with detecting a known signal in uncorrelated Gaussian noises. Each sensor can harvest randomly arriving energy and store it in a finite-size battery. Sensors communicate directly with the FC over orthogonal fading channels. Each sensor adapts its transmit symbol power, such that the larger its stored energy and its quantized channel gain are, the higher its transmit symbol power is. To strike a balance between energy harvesting and energy consumption, we formulate two constrained opti…
Wireless Sensor Networks (WSNs) are extensively used in monitoring applications such as humidity and temperature sensing in smart buildings, industrial automation, and predicting crop health. Sensor nodes are deployed in remote places to sense the data information from the environment and to transmit the sensing data to the Base Station (BS). When a sensor is drained of energy, it can no longer achieve its role without a substituted source of energy. However, limited energy in a sensor's battery prevents the long-term process in such applications. In addition, replacing the sensors' batteries …
In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light…
The Internet of Things (IoT) has important applications in our daily lives including health and fitness tracking, environmental monitoring and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resulting from their finite energy availability. A promising solution is to harvest energy from environmental sources, such as solar, kinetic, thermal and radio frequency, for perpetual and continuous operation of IoT sensor nodes. In addition to energy generation, recently energy harvesters have been used for context detection, eliminating the need for additional…
We address the two fundamental problems of spatial field reconstruction and sensor selection in het- erogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (eg. wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We account …
We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), tasked with solving a binary distributed detection problem. Sensors communicate directly with the FC over orthogonal fading channels. Each sensor can harvest randomly arriving energy and store it in a battery. Also, it knows its quantized channel state information (CSI), acquired via a limited feedback channel from the FC. We propose a transmit power control strategy such that the J-divergence based detection metric is maximized, subject to an average transmit power per sensor constraint. The…
We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received s…
We consider a wireless sensor network, consisting of K heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting and storing energy for communication with the FC. For energy efficiency, a sensor transmits only if the sensor test statistic exceeds a local threshold θk, its channel gain exceeds a minimum threshold, and its battery state can afford the transmission. Our proposed transmission model at each sensor is motivated by the channel inversion power control strategy in the wireless communication c…
Digital technologies have made it possible to deploy visual sensor nodes capable of detecting motion events in the coverage area cost-effectively. However, background subtraction, as a widely used approach, remains an intractable task due to its inability to achieve competitive accuracy and reduced computation cost simultaneously. In this paper, an effective background subtraction approach, namely NeSe, for tiny energy-harvested sensors is proposed leveraging non-volatile memory (NVM). Using the developed software/hardware method, the accuracy and efficiency of event detection can be adjusted …
The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors…
Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control systems. We consider an IoT status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users send requests to the edge node where a cache contains the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send a status update or retrieves the aged measurement from the cache. We aim at finding …
University of Oulu（奥卢大学）；Linköping University（林雪平大学）
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-te…
The time average expected age of information (AoI) is studied for status updates sent from an energy-harvesting transmitter with a finite-capacity battery. The optimal scheduling policy is first studied under different feedback mechanisms when the channel and energy harvesting statistics are known. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the status update policy in real time. The effectiveness of the proposed methods is verified through numerical results.
We present a new approach to ubiquitous sensing for indoor applications, using high-efficiency and low-cost indoor perovksite photovoltaic cells as external power sources for backscatter sensors. We demonstrate wide-bandgap perovskite photovoltaic cells for indoor light energy harvesting with the 1.63eV and 1.84 eV devices demonstrate efficiencies of 21% and 18.5% respectively under indoor compact fluorescent lighting, with a champion open-circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW/cm2. Subsequently, we demonstrate a wireless temperature sensor self-powered …
The concept of sensor networks (WSNs) has become an important component of the recently proposed cyber-physical systems (CPSs) and Internet of Things which can connect the physical world with embedded software systems. Energy- harvesting (EH) as an enabling technology applied in SNs can heavily reduce the installation and maintenance cost as well as increase system life, flexibility, and scalability as the EH is subject to the important "energy interruption" problem where system tasks can be significantly disrupted by the EH outputs. This problem not only affects the system modeling but also r…
This paper proposes a received signal strength (RSS) based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy…
Intensive research on energy harvested sensor nodes with traditional battery powered devices has been driven by the challenges in achieving the stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seaso…
We consider an energy harvesting sensor that is sending measurement updates regarding some physical phenomenon to a destination. The sensor relies on energy harvested from nature to measure and send its updates, and is equipped with a battery of finite size to collect its harvested energy. The energy harvesting process is Poisson with unit rate, and arrives in amounts that fully recharge the battery. Our setting is online in the sense that the times of energy arrivals are revealed causally to the sensor after the energy is harvested; only the statistics of the arrival process is known a priori…
This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing st…
Capillary wireless sensor networks devoted to air quality monitoring have provided vital information on dangerous air conditions. In adopting the environmentally generated energy as the fundamental energy source the main challenge is the implementation of capillary networks rather than replacing the batteries on a set period of times that leads to functional dilemma of devices management and high costs. In this paper we present a battery-less, self-governing, multi-parametric sensing platform for air quality monitoring that harvests environment energy for long run. Furthermore study on sensor …
There is an increasing demand for intelligent processing on emerging ultra-low-power internet of things (IoT) devices, and recent works have shown substantial efficiency boosts by executing inference tasks directly on the IoT device (node) rather than merely transmitting sensor data. However, the computation and power demands of Deep Neural Network (DNN)-based inference pose significant challenges for nodes in an energy-harvesting wireless sensor network (EH-WSN). Moreover, these tasks often require responses from multiple physically distributed EH sensor nodes, which imposes crucial system op…
Detecting Plant VOC Traces Using Indoor Air Quality Sensors
Department of Systems and Information Engineering, link lab, University of Virginia（系统与信息工程系，link实验室，弗吉尼亚大学）；Department of Computer Science, University of Virginia（计算机科学系，弗吉尼亚大学）；Department of Electrical and Computer Engineering, University of Virginia（电气与计算机工程系，弗吉尼亚大学）；Department of Biology, University of Virginia（生物学系，弗吉尼亚大学）；Department of Civil and Environmental Engineering, University of Virginia（土木与环境工程系，弗吉尼亚大学）
We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node. More precisely, the control decisions are performed by relying only on the battery level knowledge captured from the last received status update packet. Upon receiving on-demand requests for fresh information from the users, the edge node uses the available information to decide whether to command the sensor to send a status update or to retrieve the most recently received measurement from the cache…
The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfa…
We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users' requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subjec…
A status updating system is considered in which data from multiple sources are sampled by an energy harvesting sensor and transmitted to a remote destination through an erasure channel. The goal is to deliver status updates of all sources in a timely manner, such that the cumulative long-term average age-of-information (AoI) is minimized. The AoI for each source is defined as the time elapsed since the generation time of the latest successful status update received at the destination from that source. Transmissions are subject to energy availability, which arrives in units according to a Poiss…
Vehicle electronic systems currently utilize wired networks for power delivery (from the main battery) and communication (e.g., LIN, CAN, FlexRay) between nodes. Wired networks cannot practically accommodate nodes in moving parts (e.g., tires) and with the increasing functional complexity in vehicles, they require kilometer-long harnesses, significantly increasing fuel consumption and manufacturing and design costs. As an alternative, energy harvesting intra-vehicular wireless sensor networks (IVWSN) can accommodate nodes in all locations and they obviate the need for wiring, significantly low…
In recent years, with the increasing demand for indoor positioning service, visible light indoor positioning based on image sensors has been widely studied. However, many researches only put forward the relevant localization algorithm and did not make a deep discussion on the principle of the visible light localization. In this paper, we make a deep discussion on the principle of the two-light positioning algorithm and the three-light positioning algorithm based on the image sensor, which includes how these positioning algorithms work and the errors analysis. Based on the discussion above, we …
A real-time status updating system is considered, in which an energy harvesting sensor is acquiring measurements regarding some physical phenomenon and sending them to a destination through an erasure channel. The setting is online, in which energy arrives in units according to a Poisson process with unit rate, with arrival times being revealed causally over time. Energy is saved in a unit-sized battery. The sensor is notified by the destination of whether updates were erased via feedback. Updates need to reach the destination successfully in a timely fashion, namely, such that the long term a…
In this paper, we consider a non-Bayesian sequential change detection based on the Cumulative Sum (CUSUM) algorithm employed by an energy harvesting sensor where the distributions before and after the change are assumed to be known. In a slotted discrete-time model, the sensor, exclusively powered by randomly available harvested energy, obtains a sample and computes the log-likelihood ratio of the two distributions if it has enough energy to sense and process a sample. If it does not have enough energy in a given slot, it waits until it harvests enough energy to perform the task in a future ti…
IITB-Monash Research Academy（IITB-莫纳什研究院）；IIT Bombay（印度理工学院孟买分校）；Monash University（莫纳什大学）
We study wireless power transmission by an energy source to multiple energy harvesting nodes with the aim to maximize the energy efficiency. The source transmits energy to the nodes using one of the available power levels in each time slot and the nodes transmit information back to the energy source using the harvested energy. The source does not have any channel state information and it only knows whether a received codeword from a given node was successfully decoded or not. With this limited information, the source has to learn the optimal power level that maximizes the energy efficiency of …
Robust and versatile localization techniques are key to the success of the next industrial revolution. Yet, it is uncertain which combination of sensors will be the most robust and valuable. Thus, we present a versatile and reproducible measurement system incorporating a manifold number of state-of-the art sensors to compare and fuse the raw input data. It is shown that some techniques show very good results on the same scenario and data-set, but fall apart on translating to a slightly different scenario. In general we show that the vanilla approach to fuse the raw data achieves reasonable res…
An energy harvesting sensor that is sending status updates to a destination through an erasure channel is considered, in which transmissions are prone to being erased with some probability $q$, independently from other transmissions. The sensor, however, is unaware of erasure events due to lack of feedback from the destination. Energy expenditure is normalized in the sense that one transmission consumes one unit of energy. The sensor is equipped with a unit-sized battery to save its incoming energy, which arrives according to a Poisson process of unit rate. The setting is online, in which ener…
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；US DEVCOM Army Research Laboratory（美国DEVCOM陆军研究实验室）；Amazon（亚马逊）
Institut Charles Delaunay（查尔斯·德洛奈研究所）；Université de Technologie de Troyes（特鲁瓦技术大学）；Université de Rouen（鲁昂大学）
Indoor localization has become an important issue for wireless sensor networks. This paper presents a zoning-based localization technique that uses WiFi signals and works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the sensor using an observation model based on statistical learning.
We investigate an energy-harvesting wireless sensor transmitting latency-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides whether or not to transmit the queue's head-of-line packet at each transmission opportunity such that the expected packet queuing delay is minimized given the available harvested energy. No prior knowledge of the stochastic processes that govern the channel, captured data, or harvest…
A sensor node that is sending measurement updates regarding some physical phenomenon to a destination is considered. The sensor relies on energy harvested from nature to transmit its updates, and is equipped with a finite $B$-sized battery to save its harvested energy. Energy recharges the battery incrementally in units, according to a Poisson process, and one update consumes one energy unit to reach the destination. The setting is online, where the energy arrival times are revealed causally after the energy is harvested. The goal is to update the destination in a timely manner, namely, such t…
An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is online in which the harvested energy is revealed to the sensor causally over time, and the goal is to design status update transmission policy such that the long term average age of information (AoI) is minimized. AoI is defined as the time elapsed since the latest u…
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion
College of Computing, Mohammed VI Polytechnic University（穆罕默德六世理工大学计算机学院）；Institut Supérieur d’Électronique de Paris（巴黎高等电子学院）；University of Oulu（奥卢大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
