Sustainability

• IEEE TAFE
• 2023

Greenhouse farming is a trending practice to secure food production in desert environments. Such a practice often requires sensing systems to monitor the greenhouse microclimate. However, traditional monitoring systems are often limited by their feature size, energy consumption, and maintenance cost. To address these issues, this article introduces a luXSensing beacon—an energy harvesting sensing device empowered with Bluetooth communication technology to perform continuous environmental sensing. To enable long-lasting or even batteryless operation of the sensing device, we propose a novel and generic design methodology to suggest minimum energy harvesting hardware requirements, namely the photovoltaic panel’s area and supercapacitor’s size for energy storage. In addition, a lifetime model is also proposed to calculate the extended lifetime of a hybrid energy harvesting device if it is equipped with a backup battery. Based on the proposed methodology, a prototype system is developed, deployed, and tested in a desert greenhouse. The luXSensing beacon demonstrated its capability of monitoring air temperature and illuminance continuously in a 24/7 manner. The comparative compactness and low-energy consumption of the system are advantageous not only to its deployment in greenhouses but also to the reduction of energy budget and the maintenance cost of greenhouse farming.

• IEEE TMC
• 2023

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications that involve a plethora of sensing tasks. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. Multiple works have attempted to extend the lifetime via energy harvesting hardware, adaptive advertising interval by user existence-aware operation, and energy-efficient routing schemes. However, few attempts were made to reduce the energy consumption related to sensing tasks. In light of this shortcoming, a sensing-aware framework is proposed to adjust the sensing task interval adaptively based on the predicted portion of changes of the sensor measurements. Furthermore, to estimate the impact of varying sensing task intervals on the amount of sensed information, a model that correlates energy and amount of information is proposed. The sensor portion of changes is predicted with a novel neural network, coined oracle-interpreter network, that significantly reduces the energy consumption while upkeeping a good prediction accuracy by leveraging two independent neural networks tailored for feature extraction and prediction tasks. The effectiveness of the proposed framework is verified by comprehensive simulations based on real-life data. The results demonstrate that the proposed framework can effectively reduce the energy consumption involved in sensing tasks up to 30%, machine learning tasks up to 60%, and finally, extend the lifetime up to 75%.

• VTC
• 2021

As Internet of things (IoT) infrastructures such as BLE beacon networks are gaining more attention, excessive battery consumption and subsequent maintenance operations have proven to be crucial drawbacks. As part of the green IoT trend, light energy harvesting BLE beacons have been proposed in the literature to reduce energy consumption from batteries and extend their lifetime. However, these devices do not consider adjusting the angle of the solar panel, which prevents them from maximizing their energy harvesting capability by adapting to various indoor lighting conditions. Furthermore, an algorithm to compute the angle to optimize the energy harvesting capability has not yet been investigated for small energy harvesting IoT devices. To address such issues, our paper first proposes a model of lighting conditions in an environment with multiple light sources, and based on the proposed model, we present an algorithm to compute the optimal angle of the solar panel that would maximize the harvested energy. Finally, we prototype an energy harvesting BLE beacon with an adjustable solar panel angle and conduct real-life experiments in three different locations with varying lighting conditions. The experiments prove that the proposed design can accelerate the energy storage charging rate by up to 570%.

• WCNC
• 2021

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications due to the proliferation of Bluetooth-enabled devices. However, the BLE beacon network usually suffers from high maintenance costs due to the short battery lifetime. A recent work proposed duty-cycling BLE advertising interval subject to the detected existence of a user; if a user is detected, the beacon operates in a shorter advertising interval, otherwise operates in a longer advertising interval to reduce its energy consumption. However, since such reactive approach operates based on hardcoded advertising intervals for the two scenarios, the energy-efficiency is bound to be sub-optimal. To overcome this limitation, this paper proposes to predict the user traffic condition thereby adapting the optimal advertising interval based on the predicted user traffic condition. To this end, we introduce a novel neural network architecture that learns partial correlation and leverages attention mechanism to make accurate predictions with minimum prediction lag. The effectiveness of the proposed learning methods is verified by comprehensive simulations. It is proved that the proposed method can extend a beacon lifetime by at least 200% more than the state-of-the-art techniques.

• IEEE TMC
• 2020

Bluetooth low energy (BLE) beacon networks are one common infrastructure for IoT and smart city applications because of their scalability and affordability, as well as the proliferation of Bluetooth-enabled devices. However, BLE beacon networks suffer from short battery lifetime, which induces additional maintenance costs. In this paper, we propose a novel user existence-aware BLE beacon firmware, User-B, that extends BLE beacon lifetime by changing its operating configuration. Leveraging scan response and request features of Bluetooth Core Specifications, a mechanism for the detection of nearby user smartphones is proposed. Furthermore, we present an energy consumption model of the proposed firmware, along with an optimization problem for finding the optimal configuration that minimizes the overall energy consumption and overhead induced by switching delay. Last but not least, we introduce a prototype of the User-B firmware and demonstrate experiments. Through the experiments, we prove that the User-B firmware can extend a beacon’s lifetime up to 250 percent under low user-existence frequency and high energy demand application conditions.

• WCNC
• 2020

Bluetooth Low Energy (BLE) beacon networks are a popular infrastructure for IoT and smart city applications due to their scalability and affordability, as well as the proliferation of Bluetooth-enabled devices. However, BLE beacon networks suffer from short battery lifetime, inducing additional maintenance costs. Previous works have tackled this problem by proposing a more energy-efficient BLE beacon firmware that will change its operating configuration based on user existence information. However, previous efforts could not adapt to varying user traffic conditions and therefore was impractical. To address this issue, this paper proposes a novel neural network-driven framework, User-P, that extends beacon lifetime by changing its operating configuration by predicting user traffic conditions. Furthermore, the paper also presents a novel machine learning method tailored for user traffic prediction. Last but not least, the effectiveness of the proposed framework and methods are proven through a set of simulations. The simulation results show that the proposed framework can extend the beacon lifetime by 180% in comparison to that of the state-of-the-art techniques.

• WiMob
• 2019

Bluetooth low energy (BLE) beacon network is one of the most favored IoT infrastructures due to its flexibility and scalability. Monitoring and updating the battery statuses of the on-site BLE beacons is an essential task for reliable operation and timely maintenance of the infrastructure. However, unregulated frequent updates of the battery statuses result in stressing the beacon network management platform, possibly threatening the reliable operation of the infrastructure. Whereas too infrequent updates degrade the freshness and reliability of the updated information. Without a reliable estimation on battery status, management and timely battery replacement operation would be difficult. To address this issue, this paper presents an efficient update method of battery status for BLE beacon network that minimizes the stress on the management platform server. The proposed approach leverages the correlation in battery status information between certain beacons to reduce the number of necessary updates while retaining high accuracy. Necessary reference data estimation, reference data reliability checking, and error correction on the estimation are the three major components in the solution. An estimation model allows accurate estimation in the cold-start stage. Moreover, an error-correction model allows to check the reliability of reference data and make a correction on the estimated value.

• CPSCom
• 2019

Bluetooth Low Energy (BLE) beacons can be powered up with a small coin cell battery. The problem with battery-powered beacon is that frequent battery replacement is required. Such a battery replacement process can be very tedious considering the massive amount of already deployed beacons. While solar-powered beacons have emerged as an alternative to the battery-powered beacon, beacon deployment is challenging considering the very low ambient light energy available in indoor environments. This paper presents an innovate solar-powered beacon with an adjustable solar panel. In particular, we employ Markov Decision Process (MDP) to model the angle adjusting problem. The contribution of this paper is two-fold 1) the MDP formulation is based on the insight obtained from a series of preliminary experiments which unveil the relationship between the incident angle and the harvested power; 2) our experiment shows that the legacy Policy Iteration (PI) and Value Iteration (VI) algorithms achieve similar optimized decision-making by adjusting the angle of solar panels such that to quickly charge up the beacon when it is low in energy. This rapid charging time guarantees the sustainable operation of solar-powered beacons in indoor environments.

• WCNC
• 2019

Bluetooth Low Energy (BLE) beacon networks are one common infrastructure for IoT and smart city applications because of their scalability and affordability, as well as the proliferation of Bluetooth-enabled devices. However, BLE beacon networks suffer from short battery lifetime, which induces additional maintenance costs. In this paper, we propose a novel user existence-aware BLE beacon firmware, USTEA, that extends BLE beacon lifetime by changing its operating configuration. Leveraging scan response and request features of Bluetooth Core Specifications, a mechanism for the detection of nearby user smartphones is proposed. Furthermore, we present an energy consumption model of the proposed firmware, along with an optimization problem for finding the optimal configuration that minimizes the overall energy consumption and overhead induced by switching delay. Last but not least, we introduce a prototype of the USTEA firmware and demonstrate experiments. Through the experiments, we prove that the USTEA firmware can extend a beacon’s lifetime up to 250% under low user-existence frequency and high energy demand application conditions.

• IEEE IoTJ
• 2019

The maturing deployment of the Internet of Things is gradually realizing new smart applications that strongly leverage recent advances in proximity detection methods. To this end, Bluetooth low energy (BLE) beacons are one of the preferred candidates because of the widespread use of Bluetooth-enabled devices. However, traditional battery-powered BLE beacons suffer from a limited operation lifetime, inducing additional maintenance operations and costs. This paper addresses this issue by proposing design principles for an ambient light energy harvesting BLE beacon capable of perpetual operation in the indoor environment. The contributions made in this paper include 1) investigation and modeling of related hardware components, namely the BLE beacon, photovoltaic panel, and capacitor; 2) design principles for selecting hardware components subject to varying environmental conditions and application requirements; and 3) prototyping and field-tests to prove its practicality. Through multiple experiments, this paper proves that the design can operate perpetually under 40 lux light intensity, and can last over 17 h once fully charged.