Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

This study empirically analyzes factors affecting the adoption and recommendation intentions for next-generation electronic authentication services based on a convergent model of the value-based adoption model (VAM), protection motivation theory (PMT), and theory of planned behavior (TPB). As a result of the analyses, perceived benefits and perceived sacrifices (based on the VAM), perceived threats (based on the PMT), and adoption motives (based on the TPB) are found to significantly impact the adoption and recommendation intentions. The verification results demonstrate that all factors (and sub-factors) — perceived benefits (perceived usefulness and enjoyment), perceived sacrifices (technicality and perceived fee), perceived threats (perceived susceptibility and severity), and adoption motives (adoption attitude, subjective norm, and perceived behavior control) — have a significant effect on adoption intentions. Further, all variables and sub-factors, except for technicality in perceived sacrifice, have a significant effect on recommendation intentions. This study provides theoretical and practical guidelines that are helpful for future development, dissemination, and promotion strategies of new electronic authentication technologies demanded and expected by most financial consumers. At the same time, it verifies the validity and rationality of the convergent model based on the three theories, thereby presenting an in-depth and expanded methodology for theoretical model application.

As the number of Internet of Things (IoT) sensors increases and their deployment becomes denser, power management for IoT sensor networks becomes more important. In most IoT sensor networks, one cluster head (CH) collects data from a large number of sensors and forwards them to backbone networks. Thus, managing CH’s queue condition is crucial in order to extend the network’s lifespan or satisfy quality of service (QoS) requirements. Meanwhile, reducing age of information (AoI), a metric describing how fresh information is, has become one of the most important metrics, from simple data such as temperature and humidity to more complex data that must be timely, such as vehicle information and road dynamics. However, it is shown that AoI may heavily fluctuate depending on the medium access control protocol. In this paper, we propose a deep reinforcement learning-based AoI-aware low-power active queue management for IoT sensor networks. To this end, we formulate a Markov decision process model in which the CH can select one of the actions including forward, flush, or leave buffered data from associated cluster member nodes. Extensive simulations show that compared with traditional queue management methods, our queue management method can reduce the power consumption of CH while trying not to exceed the AoI value threshold, thereby enabling IoT sensor networks to be stable while ensuring satisfactory QoS.

Fog node (FN) close to Internet of Things (IoT) devices can be exploited to offload the computing task of IoT devices. However, when lots of tasks are simultaneously offloaded from multiple IoT devices, FN can be overloaded. To mitigate this problem, we introduce a DYSON: DelaYed offloading System with Opportunistic fog Node (OFN) such as vehicles and mobile phones which can opportunistically reduce the load of static FN thanks to their mobility. In DYSON, to maximize the offloading effect of OFN, when IoT device cannot use OFN, it delays the task offloading with the expectation of future contacts of OFN. To assess the performance of DYSON, we develop an analytic model for the opportunistic offloading probability that the task can be offloaded to OFN. Based on the analytic model, we derive the optimal delay timer to maximize the opportunistic offloading probability while maintaining the probability that the task cannot be processed within the deadline below a target probability. Extensive simulation results are provided to show the superiority of DYSON.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Due to the dynamic mobility feature, the proactive flow rule cache method has become one promising solution in software-defined networking (SDN)-based access networks to reduce the number of flow rule installation procedures between the forwarding nodes and SDN controller. However, since there is a flow rule cache limit for the forwarding node, an efficient flow rule cache strategy is required. To address this challenge, this paper proposes the mobility-aware hybrid flow rule cache scheme. Based on the comparison between the delay requirement of the incoming flow and the response delay of the controller, the proposed scheme decides to install the flow rule either proactively or reactively for the target candidate forwarding nodes. To find the optimal number of proactive flow rules considering the flow rule cache limits, an integer linear programming (ILP) problem is formulated and solved using the heuristic method. Extensive simulation results demonstrate that the proposed scheme outperforms the existing schemes in terms of the flow table utilization ratio, flow rule installation delay, and flow rules hit ratio under various settings.