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IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual “objects”, these things which are equipped with “sensing devices” also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses’ burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device–object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices’ data and demonstrating the ability to recover the missing bounding box.

One of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. The sensing ranges of sensors can be unit disks or non-unit disks. We present polynomial-time algorithms, in terms of the number of sensors, that can be easily translated to distributed protocols. The result is a generalization of some earlier results where only k = 1 is assumed. Applications of the result include determining insufficiently covered areas in a sensor network, enhancing fault-tolerant capability in hostile regions, and conserving energies of redundant sensors in a randomly deployed network. Our solutions can be easily translated to distributed protocols to solve the coverage problem. [PUBLICATION ABSTRACT]

The incidence and prevalence of dialysis in Taiwan are high compared to other regions. Consequently, mitigating chronic kidney disease (CKD) and the worsening of kidney function have emerged as critical healthcare priorities in Taiwan. Heat stress is known to be a significant risk factor for CKD and kidney function impairment. However, differences in the impact of heat stress between males and females remains unexplored. We conducted this retrospective cross-sectional analysis using data from the Taiwan Biobank (TWB), incorporating records of the wet bulb globe temperature (WBGT) during midday (11 AM–2 PM) and working hours (8 AM–5 PM) periods based on the participants’ residential address. Average 1-, 3-, and 5-year WBGT values prior to the survey year were calculated and analyzed using a geospatial artificial intelligence-based ensemble mixed spatial model, covering the period from 2010 to 2020. A total of 114,483 participants from the TWB were included in this study, of whom 35.9% were male and 1053 had impaired kidney function (defined as estimated glomerular filtration rate < 60 ml/min/1.73 m 2 ). Multivariable analysis revealed that in the male participants, during the midday period, the 1-, 3-, and 5-year average WBGT values per 1 ℃ increase were significantly positively associated with eGFR < 60 ml/min/1.73 m 2 (odds ratio [OR], 1.096, 95% confidence interval [CI] = 1.002–1.199, p  = 0.044 for 1 year; OR, 1.093, 95% CI = 1.000–1.196, p  = 0.005 for 3 years; OR, 1.094, 95% CI = 1.002–1.195, p  = 0.045 for 5 years). However, significant associations were not found for the working hours period. In the female participants, during the midday period, the 1-, 3-, and 5-year average WBGT values per 1 ℃ increase were significantly negatively associated with eGFR < 60 ml/min/1.73 m 2 (OR, 0.872, 95% CI = 0.778–0.976, p  = 0.018 for 1 year; OR, 0.874, 95% CI = 0.780–0.978, p  = 0.019 for 3 years; OR, 0.875, 95% CI = 0.784–0.977, p  = 0.018 for 5 years). In addition, during the working hours period, the 1-, 3-, and 5-year average WBGT values per 1 ℃ increase were also significantly negatively associated with eGFR < 60 ml/min/1.73 m 2 (OR, 0.856, 95% CI = 0.774–0.946, p  = 0.002 for 1 year; OR, 0.856, 95% CI = 0.774–0.948, p  = 0.003 for 3 years; OR, 0.853, 95% CI = 0.772–0.943, p  = 0.002 for 5 years). In conclusion, our results revealed that increased WBGT was associated with impaired kidney function in males, whereas increased WBGT was associated with a protective effect against impaired kidney function in females. Further studies are needed to elucidate the exact mechanisms underlying these sex-specific differences.

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.

Broadcasting is a common operation in a network to resolve many issues. In a mobile ad hoc network (MANET) in particular, due to host mobility, such operations are expected to be executed more frequently (such as finding a route to a particular host, paging a particular host, and sending an alarm signal). Because radio signals are likely to overlap with others in a geographical area, a straightforward broadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision, to which we call the broadcast storm problem. In this paper, we identify this problem by showing how serious it is through analyses and simulations. We propose several schemes to reduce redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem. Simulation results are presented, which show different levels of improvement over the basic flooding approach. [PUBLICATION ABSTRACT]

When mining frequent itemsets (abbr. FIs) from dense datasets, it usually produces too many itemsets and results in the mining task to suffer from a very long execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a compact and lossless representation of FI. Mining FCIs can not only reduce the execution time and memory usage, but also reserve the complete information of FIs derived from FCIs. Although many studies have been proposed with various efficient methods for mining FCIs, few of them have developed algorithms for efficiently deriving FIs from FCIs. In this work, we propose two efficient algorithms named DFI-List and DFI-Growth for efficiently deriving FIs from FCIs. The both algorithms adopt depth-first search and divide-and-conquer methodology to derive all the FIs. DFI-List efficiently derives all the FIs with a vertical index structure called Cid List. DFI-Growth compresses the information of FCIs into tree structures and applies pattern-growth strategy to derive FIs from the trees. Empirical experiments show that DFI-List is the most efficient and scalable algorithm on the dense datasets. For example, when the minimum support threshold is set to 50% on the Chess dataset, DFI-List runs faster than LevelWise (Pasquier et al. Inf Syst 24(1): 25-46, 1999b) over 100 times. As for DFI-Growth, it is the most stable and memory efficient algorithm on the sparse datasets. Both DFI-Growth and DFI-List are superior to the state-of-the-art algorithm (Pasquier et al. Inf Syst 24(1): 25-46, 199b) in terms of execution time.

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

This paper investigates the power mode management problem for an IEEE 802.11-based mobile ad hoc network (MANET) that allows mobile hosts to tune to the power-saving (PS) mode. There are two major issues that need to be addressed in this problem: (a) wakeup prediction and (b) neighbor discovery. The former is to deliver buffered packets to a PS host at the right time when its radio is turned on. The latter is to monitor the environment change under a mobile environment. One costly, and not scalable, solution is to time-synchronize all hosts. Another possibility is to design asynchronous protocols as proposed by Tseng et al. in [25]. In this paper, we adopt the latter approach and correlate this problem to the quorum system concept. We identify a rotation closure property for quorum systems. It is shown that any quorum system that satisfies this property can be translated to an asynchronous power-saving protocol for MANETs. Thus, the result bridges the classical quorum system design problem in the area of distributed systems to the power mode management problem in the area of mobile ad hoc networks. We derive a lower bound for quorum sizes for any quorum system that satisfies the rotation closure property. We identify a group of quorum systems that are optimal or near optimal in terms of quorum sizes, which can be translated to efficient asynchronous power-saving protocols. We also propose a new e-torus quorum system, which can be translated to an adaptive protocol that allows designers to trade hosts' neighbor sensibility for power efficiency. Simulation experiments are conducted to evaluate and compare the proposed protocols. [PUBLICATION ABSTRACT]