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.

Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models. Methods: EMG data were collected from the legs of 120 healthy individuals and 120 stroke patients during gait. Four statistical features were extracted from 16 EMG signals and trained on seven ML-based models. The accuracy of the validation and test datasets was also examined. Results: The model with the best performance was Random Forest. Among the 16 EMG signals, the average and maximum values of the muscle activities involved in knee extension (i.e., vastus medialis and rectus femoris) contributed significantly to the predictions. Conclusion: The results of this study confirmed that the simple processing and feature extraction of EMG signals effectively contributed to the accuracy of ML-based models. Routine use of EMG data collected in clinical environments is expected to provide benefits in terms of stroke prevention and rehabilitation.

In MQTT, a retained message allows new subscribers to receive the latest status update without waiting for the publisher to send a new message. However, because the retained message is delivered regardless of when it was originally published, it can become outdated, leading to potential issues such as production inefficiency. To address this challenge, this paper proposes an Age-of-Information (AoI)-aware retained message policy in MQTT-based IoT networks (ARMY). In ARMY, the broker evaluates AoI (i.e., freshness) of the retained message before delivering it to new subscribers. Specifically, if the retained message is outdated, the broker requests an updated message from the publisher before forwarding it to the subscribers, thereby reducing the AoI. This approach is particularly beneficial in scenarios where real-time data is crucial, such as industrial control systems. However, this process involves additional signaling overhead, so an optimal policy balancing AoI and signaling overhead is necessary. We formulate a Markov Decision Process (MDP) model and determine the optimal policy using Q-learning (QL). Simulation results show that ARMY significantly improves the average reward and AoI satisfaction ratio without incurring significant additional signaling costs compared to the comparison schemes across various settings.

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

KSII Transactions on Internet and Information Systems TIIS Paper Details

The age of information (AoI) has emerged as a performance metric that quantifies the freshness of information delivered over communication networks. Unlike conventional metrics such as delay, packet loss, and throughput, AoI measures the time elapsed since the generation of the most recently received update, which makes it well-suited for real-time applications in which decisions must rely on the latest information. This paper presents a structured survey of AoI research with a particular focus on unmanned aerial vehicle (UAV)-enabled networks. The survey is conducted as a structured narrative review, in which peer-reviewed studies published from 2011 to 2026 are selected according to explicit inclusion and exclusion criteria and organized along the three levels of theory, optimization methodology, and application. We first introduce the basic concept of AoI together with nonlinear and generalized penalty functions and review queueing theory and the Markov decision process (MDP) as the analytical foundations of AoI analysis. We then classify AoI optimization approaches into queueing theory-based and learning-based methods, compare the two classes in terms of their assumptions, optimality guarantees, scalability, and computational cost, and identify from the reviewed studies the conditions under which each is preferable. Building on these foundations, we review AoI optimization in energy harvesting and vehicle-to-everything (V2X) systems as two representative application domains and then examine UAV-enabled networks in depth, organizing the studies into trajectory and scheduling optimization, multi-UAV cooperation, and wireless power transfer, and discussing when each category is the appropriate design choice. UAV-enabled networks combine the energy constraints of energy harvesting systems with the time-critical requirements of V2X communications while adding a spatial optimization dimension, which makes AoI a central design metric for UAV-aided data collection and aerial sensing. Finally, we discuss open challenges and future research directions driven by emerging technologies such as sixth-generation (6G) mobile networks, integrated sensing and communication, and artificial intelligence.