Resistive random-access memory (ReRAM)-based in-memory computing (IMC) systems offer significant advantages for efficient neural network inference. However, these systems are vulnerable to stuck-at faults (SAFs), which degrade inference accuracy-a challenge that becomes more pronounced in multilevel cell (MLC) configurations. A conventional fault mitigation technique, array-wise weight remapping (AWR), addresses SAFs but incurs significant hardware overhead. To overcome these limitations, we propose pseudo-array-wise weight remapping (PAWR), a novel method that integrates mux-wise weight remapping (MWR) and mux group remapping (MGR) to achieve costefficient fault tolerance. Experimental results demonstrate that even at a high 20% SAF rate, PAWR achieves accuracies within 0.7% of AWR, while significantly reducing area overhead by 86.5% and energy overhead by 72.4% compared to AWR.

In this study, we address the challenge of low-rank model compression in the context of in-memory computing (IMC) architectures. Traditional pruning approaches, while effective in model size reduction, necessitate additional peripheral circuitry to manage complex dataflows and mitigate dislocation issues, leading to increased area and energy overheads. To circumvent these drawbacks, we propose leveraging low-rank compression techniques, which, unlike pruning, streamline the dataflow and seamlessly integrate with IMC architectures. However, low-rank compression presents its own set of challenges, namely i) suboptimal IMC array utilization and ii) compromised accuracy. To address these issues, we introduce a novel approach i) employing shift and duplicate kernel (SDK) mapping technique, which exploits idle IMC columns for parallel processing, and ii) group lowrank convolution, which mitigates the information imbalance in the decomposed matrices. Our experimental results demonstrate that our proposed method achieves up to 2.5× speedup or +20.9% accuracy boost over existing pruning techniques.

The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing (IMC) architectures faces significant challenges due to the mismatch between high-dimensional vectors and IMC array sizes, leading to inefficient memory utilization and increased computation cycles. This paper presents MEMHD, a Memory-Efficient Multi-centroid HDC framework designed to address these challenges. MEMHD introduces a clustering-based initialization method and quantization-aware iterative learning for multi-centroid associative memory. Through these approaches and its overall architecture, MEMHD achieves a significant reduction in memory requirements while maintaining or improving classification accuracy. Our approach achieves full utilization of IMC arrays and enables one-shot (or few-shot) associative search. Experimental results demonstrate that MEMHD outperforms state-of-the-art binary HDC models, achieving up to 13.69% higher accuracy with the same memory usage, or 13.25x more memory efficiency at the same accuracy level. Moreover, MEMHD reduces computation cycles by up to 80x and array usage by up to 71x compared to baseline IMC mapping methods when mapped to 128x128 IMC arrays, while significantly improving energy and computation cycle efficiency.

Compute-in-memory (CIM) is an efficient method for implementing deep neural networks (DNNs) but suffers from substantial overhead from analog-to-digital converters (ADCs), especially as ADC precision increases. Low-precision ADCs can reduce this overhead but introduce partial-sum quantization errors degrading accuracy. Additionally, low-bit weight constraints, imposed by cell limitations and the need for multiple cells for higher-bit weights, present further challenges. While fine-grained partial-sum quantization has been studied to lower ADC resolution effectively, weight granularity, which limits overall partial-sum quantized accuracy, remains underexplored. This work addresses these challenges by aligning weight and partial-sum quantization granularities at the column-wise level. Our method improves accuracy while maintaining dequantization overhead, simplifies training by removing two-stage processes, and ensures robustness to memory cell variations via independent column-wise scale factors. We also propose an open-source CIM-oriented convolution framework to handle fine-grained weights and partial-sums efficiently, incorporating a novel tiling method and group convolution. Experimental results on ResNet-20 (CIFAR-10, CIFAR-100) and ResNet-18 (ImageNet) show accuracy improvements of 0.99%, 2.69%, and 1.01%, respectively, compared to the best-performing related works. Additionally, variation analysis reveals the robustness of our method against memory cell variations. These findings highlight the effectiveness of our quantization scheme in enhancing accuracy and robustness while maintaining hardware efficiency in CIM-based DNN implementations. Our code is available at https://github.com/jiyoonkm/ColumnQuant.

This paper addresses two critical challenges in analog In-Memory Computing (IMC) systems that limit their scalability and deployability: the computational unreliability caused by stuck-at faults (SAFs) and the high compilation overhead of existing fault-mitigation algorithms, namely Fault-Free (FF). To overcome these limitations, we first propose a novel multi-bit weight representation technique, termed row-column hybrid grouping, which generalizes conventional column grouping by introducing redundancy across both rows and columns. This structural redundancy enhances fault tolerance and can be effectively combined with existing fault-mitigation solutions. Second, we design a compiler pipeline that reformulates the fault-aware weight decomposition problem as an Integer Linear Programming (ILP) task, enabling fast and scalable compilation through off-the-shelf solvers. Further acceleration is achieved through theoretical insights that identify fault patterns amenable to trivial solutions, significantly reducing computation. Experimental results on convolutional networks and small language models demonstrate the effectiveness of our approach, achieving up to 8%p improvement in accuracy, 150 × faster compilation, and 2 × energy efficiency gain compared to existing baselines.

The deployment of deep neural networks on edge devices is a challenging task due to the increasing complexity of state-of-the-art models, requiring efforts to reduce model size and inference latency. Recent studies explore models operating at diverse quantization settings to find the optimal point that balances computational efficiency and accuracy. Truncation, an effective approach for achieving lower bit precision mapping, enables a single model to adapt to various hardware platforms with little to no cost. However, formulating a training scheme for deep neural networks to withstand the associated errors introduced by truncation remains a challenge, as the current quantization-aware training schemes are not designed for the truncation process. We propose TruncQuant, a novel truncation-ready training scheme allowing flexible bit precision through bit-shifting in runtime. We achieve this by aligning TruncQuant with the output of the truncation process, demonstrating strong robustness across bit-width settings, and offering an easily implementable training scheme within existing quantization-aware frameworks. Our code is released at https://github.com/a2jinhee/TruncQuant.

Multi-bit quantization networks enable flexible deployment of deep neural networks by supporting multiple precision levels within a single model. However, existing approaches suffer from significant training overhead as full-dataset updates are repeated for each supported bit-width, resulting in a cost that scales linearly with the number of precisions. Additionally, extra fine-tuning stages are often required to support additional or intermediate precision options, further compounding the overall training burden. To address this issue, we propose two techniques that greatly reduce the training overhead without compromising model utility: (i) Weight bias correction enables shared batch normalization and eliminates the need for fine-tuning by neutralizing quantization-induced bias across bit-widths and aligning activation distributions; and (ii) Bit-wise coreset sampling strategy allows each child model to train on a compact, informative subset selected via gradient-based importance scores by exploiting the implicit knowledge transfer phenomenon. Experiments on CIFAR-10/100, TinyImageNet, and ImageNet-1K with both ResNet and ViT architectures demonstrate that our method achieves competitive or superior accuracy while reducing training time up to 7.88×.

BLE beacon networks are widely adopted for IoT and smart city applications, but they are susceptible to security threats, including piggybacking and spoofing attacks. These attacks can infringe on or even jeopardize the profitability of network owners. To address this issue, BLE beacon packets are often encrypted or updated periodically, which requires frequent synchronization between individual beacons and a centralized server, consuming significant resources. We propose a novel crowd-assisted, secure BLE identifier (ID) updating framework that significantly reduces network overhead. The proposed framework quantifies mobile crowdsourcing participant presence to dynamically adjust the pool size of the required beacon IDs. Our experiments, which factor in real-life user presence information, prove the practicality of our framework, which reduced the network resource consumption by up to 90%.

Processing-in-memory (PIM) architectures have been highlighted as one of the most viable options for faster and more power-efficient computation. Paired with a convolutional weight mapping scheme, PIM arrays can accelerate various deep convolutional neural networks (CNNs) and the applications that adopt them. Recently, shift and duplicate kernel (SDK) convolutional weight mapping scheme was proposed, achieving up to 50% throughput improvement over the prior arts. However, the traditional pattern-based pruning methods, which were adopted for row-skipping and computing cycle reduction, are not optimal for the latest SDK mapping due to the loss of structural regularity caused by the shifted and duplicated kernels. To address this challenge, we propose kernel shape control (KERNTROL), a method where kernel shapes are controlled depending on their mapped columns with the purpose of fostering a structural regularity that is favorable in achieving a high row-skipping ratio and model accuracy. Instead of permanently pruning the weights, KERNTROL with an empty mask (KERNTROL-M) temporarily omits them in the underutilized row using a utilization threshold, thereby preserving important weight elements. However, a significant portion of the memory cells is still underutilized where the threshold is not enforced. To overcome this, we extend KERNTROL-M into KERNTROL with compensatory weights (KERNTROL-C). By populating idle cells with compensatory weights, KERNTROL-C can offset the accuracy drop from weight omission. In comparison to pattern-based pruning approaches, KERNTROL-C achieves simultaneous improvements of up to 36.4% improvement in the compression rate and 5% in model accuracy with up to 100% array utilization.

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 sensor information-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%.

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. A few recent works tackled the challenge by adjusting the operating configuration subject to the nearby user occupancy in a reactive manner. However, previous works failed to adapt to dynamically changing user occupancy behaviors due to the lack of a prediction mechanism. Such shortcomings lead to a shorter lifetime and longer packet arrival delays. To overcome this limitation, a novel neural network architecture that makes accurate and timely user occupancy prediction is introduced. The proposed network learns partial correlation of the time-series data and attention score for robust prediction. The predictions are then utilized to adaptively change the operation configurations to maximize the lifetime and minimize the packet arrival delay. To the best of our knowledge, this is the first work to leverage time-series prediction to optimize the performance of a BLE beacon. The effectiveness of the proposed learning methods is verified by comprehensive simulations with real-life data. The results demonstrate that the proposed method can extend a beacon lifetime by 50% more than the existing reactive approach. Moreover, the packet arrival delays are also reduced by up to 40%.

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, researchers proposed solar-powered designs, equipped with rechargeable energy storage such as a supercapacitor. However, accurately monitoring the energy status - an essential step for device maintenance - has shown to be a major concern. Existing energy status monitoring methods, which are either crowd-assisted or require on-site data collection, suffer from severe losses of energy status information. This paper presents an energy status recovery framework with support vector regression (SVR) to address this issue. The proposed framework leverages recurrence training of SVR with lost energy status information to capture features from discharge behavior, achieving high accuracy while minimizing training and prediction time. Multiple real-life BLE beacon energy level records are evaluated to demonstrate that our proposed framework can recover the energy information with at least 98% accuracy under a data loss rate of up to 99%.

Processing-in-memory (PIM) architectures have been highlighted as one of the viable solutions for faster and more power-efficient convolutional neural networks (CNNs) inference. Recently, shift and duplicate kernel (SDK) convolutional weight mapping scheme was proposed, achieving up to 50% through-put improvement over the prior arts. However, the traditional pattern-based pruning methods, which were adopted for row-skipping and computing cycle reduction, are not optimal for the latest SDK mapping due to structural irregularity caused by the shifted and duplicated kernels. To address this issue, we propose a method called kernel shape control (KERNTROL) that aims to promote structural regularity for achieving a high row-skipping ratio and model accuracy. Instead of pruning certain weight elements permanently, KERNTROL controls the kernel shapes through the omission of certain weights based on their mapped columns. In comparison to the latest pattern-based pruning approaches, KERNTROL achieves up to 36.4% improvement in the compression rate, and 38.6% in array utilization with maintaining the original model accuracy.

The ReRAM-based neuromorphic computing system (NCS) has been widely used as an energy-efficient platform for deep neural network (DNN) acceleration. However, ReRAM commonly suffers from stuck-at-fault (SAF), resulting in permanent device failure. SAF tolerance is an essential task to ensure the reliability of the system by minimizing the DNN inference accuracy degradation. Since hardware-based solutions incur additional overhead and power consumption, it is necessary to seek a solution that can be executed offline to mitigate the impact of SAF. In this work, we propose a decomposition-aware column re-mapping (DCR) for SAF tolerance in analog ReRAM arrays (RAs). Our DCR consists of the column re-mapping technique combined with fault-aware weight decomposition and an advanced sensitivity metric. As a result, it generates a final weight map optimized for the fault map. Our DCR achieves only about 1% loss of inference accuracy on CIFAR-10 and CIFAR-100 for the analog RAs with the SAF rate of 2% and 1%, respectively, without any hardware-based solution or re-training.

Convolutional weight mapping plays a stapling role in facilitating convolution operations on Processing-in-memory (PIM) architecture which is, at its essence, a matrix-vector multiplication (MVM) accelerator. Despite its importance, convolutional mapping methods are under-studied and existing mapping methods fail to exploit the sparse and redundant characteristics of heavily quantized convolutional weights, leading to low array utilization and ineffectual computations. To address these issues, this paper proposes a novel weight-aware activation mapping method where activations are mapped onto the memory cells instead of the weights. The proposed method significantly reduces the number of computing cycles by skipping zero-valued weights and merging those PIM array rows with the same weight values. Experimental results on ResNet-18 demonstrate that the proposed weight-aware activation mapping can achieve up to 90% energy saving and latency reduction compared to the conventional approaches.

Processing-in-memory (PIM) architecture is becoming a promising candidate for convolutional neural network (CNN) inference. A recent weight mapping method called shift and duplicate kernel (SDK) improves the utilization by the deployment of shifting the same kernels into idle columns. However, this method inevitably generates idle cells with an irregular distribution, which limits reducing the size of the weight matrix. To effectively compress the weight matrix in the PIM array, prior works have introduced a row-wise pruning scheme, one of the structured weight pruning schemes, that aims to skip the operation on a row by zeroing out all weight in the specific row (we call it row-skipping). However, due to the deployment of shifting kernels, SDK mapping complicates zeroing out all the weight in the same row. To address this issue, we propose pruning-aided row-skipping (PAIRS) that effectively reduces the number of rows of convolutional weights that are mapped with SDK mapping. By pairing the SDK mapping-aware pruning pattern design and row-wise pruning, PAIRS achieves a higher row-skipping ratio. In comparison to pruning methods, PAIRS achieves up to$1.95\times$rows skipped and$4\times$higher compression rate with similar or even better inference accuracy.

A sensor network is an underpinning infrastructure that enables various future IoT applications, such as precision agriculture, smart farm, and greenhouse monitoring. However, these sensor devices often suffer from short-lived battery lifetime that incurs frequent maintenance operation. Although there have been a few attempts to smartly reduce the power consumption associated with communication tasks of the sensors, very few have addressed the power consumption of sensing tasks. In light of this shortcoming, we propose an information-aware sensing framework that adaptively adjusts the sensing interval for energy-saving operations based on the learned behavior of the sensor data. To prove the effectiveness of the proposed framework, we have deployed four BLE beacons equipped with luminosity and temperature sensors to collect real-life data from a desert greenhouse, which is then used to train and evaluate our proposed framework. Additionally, we have implemented the proposed framework on a commodity BLE beacon device to validate the energy-saving performance of the proposed framework. The results demonstrate that the proposed framework can effectively reduce the energy consumption involved in sensing tasks by 30% and extend the battery lifetime by up to 75%.

With growing demands for Internet of Things (IoT) applications, BLE beacon networks are rapidly being adopted. Periodic battery replacement operations and onsite maintenance are required to ensure continuous and reliable service. These operations are labor intensive and resource exhaustive. Therefore, Bluetooth gateways/mobile devices are often employed to monitor/collect the energy status. However, the gateways consume a considerable amount of network requests, and the user existence influences the data collection, thus the data accuracy, based on mobile devices. Reducing the number of energy status reports and maintaining the high accuracy of the energy status monitoring service is essential to catalyze a generic adoption of beacon networks and IoT infrastructure of similar nature in more businesses and real-life applications. In this article, we proposed a novel energy status monitoring framework that will dynamically change the energy status report interval based on the discharging rate of the battery, thereby reducing the total number of network requests and maintaining the required accuracy of energy status. The proposed framework identifies the BLE beacons with similar battery discharging rates, suggests a dynamic report interval, and leverages this information to reduce the number of energy status reports. We have experimented with real-life BLE beacon energy status data for 50 days to demonstrate that we could reduce the total number of network requests up to 70% while retaining 99% estimation accuracy.

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, solar-powered designs were proposed, equipped with rechargeable energy storage such as a supercapacitor. However, energy status monitoring, which is essential for device maintenance, proved to be a major concern as the energy status of energy harvesting devices can change quickly due to charging and discharging behaviours. Existing energy status monitoring methods performed in a crowd-assisted manner or by demanding on-site data collections are accompanied by severe loss of energy status information. This paper presents an accurate energy status recovery framework with SVR to address this issue. The proposed framework leverages recurrent training of SVR with lost energy status information to capture features from discharge behaviour to achieve high accuracy while minimizing the training and prediction time. Multiple real-life BLE beacon energy level records are evaluated to demonstrate that our proposed framework can recover the energy information with at least 90% accuracy under a data loss rate of up to 99%.

One key feature of Bluetooth low energy (BLE) beacons is the received signal strength, which can be used to estimate the distance between any Bluetooth-compatible receiver (e.g., smartphone, tablet, etc.) and fixed deployed beacon. Although received signal strength (RSS) can be measured easily with commonly available smart devices, the measurements are unreliable, in which general estimation models are not robust to different hardware and settings for real deployed beacon networks. Furthermore, the lack of consideration for object mobility in these models undermines its practicality. Motivated by the above limitations, this article proposes a novel distance classifier, d-Classifier, to classify the distance with a feature vector constructed with features such as hardware type and deployment environment to improve the robustness. Moreover, comprehensive experiments related to mobility are conducted to study the relationship between packet receiving rate and estimation accuracy. Improved performance can be achieved by providing extra mobility information with a list of RSS values during estimation. The proposed classifier is validated with an extensive data set that includes over 200 k data collected from real beacon networks. Overall, our proposed d-Classifier achieves a significant performance gain,$>25\%$accuracy improvement, over its prior arts.

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.

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%.

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.

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.

This research explores reliability and timing issues of push notification from Bluetooth Low Energy (BLE) beacons in outdoor districts for smartphone-based interactive applications. First, beacon signals fully cover a targeted area. The second issue is that beacon infrastructure is designed for reliable performance against environmental properties. Finally, the experience quality of notification to smartphone or mobile devices takes high level even though users have mobility such as a driver in a car, a user riding a bicycle, a walking user, etc. For finding out base lines to achieve these features in interactive system design and practical deployment of smart applications based on smartphone-beacon interaction, we evaluate real performances of beacon running at outdoor places. We compare the novel notification model, named n-1 model, which merely copes with application variables with delivering content redundancy, to the typical 1-1 alignment mean of one notification with single content. The proposed model shows average 70 % improvement against those impediments.

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.

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.

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.

A BLE beacon is a small electronic device that has recently been proposed as a building block to construct an infrastructure supporting emerging smart applications. However, due to its simple communication protocol architecture, which broadcasts a static payload, a BLE beacon-based infrastructure is vulnerable to different types of abuses and attacks, in particular free-riding and device spoofing. Many beacon manufacturers propose dynamically randomizing beacon advertisement packets at the device firmware level as a solution. However, this approach is difficult to implement for already deployed beacon nodes as it requires a firmware update on each device. To alleviate these drawbacks, a crowd-assisted architecture for securing BLE beacons is proposed in this paper. A detailed architecture is presented along with experimental results and an implementation to demonstrate its feasibility. It is found that the beacon ID can be changed by user's mobile phone within a 20 m range with probability of almost 100% under both stationary and mobile conditions.

While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon's cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

The meeting of pervasive screens and smart devices has witnessed the birth of screen-smart device interaction (SSI), a key enabler to many novel interactive use cases. Most current surveys focus on direct human-screen interaction, and to the best of our knowledge, none have studied state-of-the-art SSI. This survey identifies three core elements of SSI and delivers a timely discussion on SSI oriented around the screen, the smart device, and the interaction modality. Two evaluation metrics (i.e., interaction latency and accuracy) have been adopted and refined to match the evaluation criterion of SSI. The bottlenecks that hinder the further advancement of the current SSI in connection with this metrics are studied. Last, future research challenges and opportunities are highlighted in the hope of inspiring continuous research efforts to realize the next generation of SSI.