Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data.

Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction.

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins.

An Ensemble Surrogate-Based Coevolutionary Algorithm for Solving Large-Scale Expensive Optimization Problems.

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for solving expensive optimization problems (EOPs) whose true evaluations are computationally or physically expensive. However, most existing SAEAs only focus on the problems with low dimensionality and they rarely consider solving large-scale EOPs (LSEOPs). To fill this research gap, this article proposes an ensemble surrogate-based coevolutionary optimizer for tackling LSEOPs.

RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems.

With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction.

Cognitive information measurements: A new perspective.

From a traditional point of view, the value of information does not change during transmission. The Shannon information theory considers information transmission as a statistical phenomenon for measuring the communication channel capacity. However, in modern communication systems, information is spontaneously embedded with a cognitive link during the transmission process, which requires a new measurement that can incorporate continuously changing information values.

Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay.

The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices.

Beacon-based opportunistic scheduling in wireless body area network.

Wireless Body Area Networks (WBANs) are one of the key technologies that support the development of digital health care, which has attracted increasing attention in recent years. Compared with general Wireless Sensor Networks (WSNs), WBANs have more stringent requirements on reliability and energy efficiency. Though WBANs are applied within limited transmission range, the on-body channel condition can be very challenging because of blocking or absorbing of signal.

Wireless body area network node localization using small-scale spatial information.

We present a new scheme to automatically identify the locations of wearable sensor nodes in a wireless body area network (WBAN). Instantaneous atmospheric air pressure readings are compared to map nodes in physical space. This enhancement enables unassisted sensor node placement, providing a practical solution to obtain and continuously monitor node locations without anchor nodes or beacons.

Cross-layer design for prompt and reliable transmissions over body area networks.

In this paper, we propose a cross-layer design to make ambulatory health monitoring via body area networks (BAN) more reliable and robust. The proposed design builds on our centralized body area network access scheme (CBAS), a receiver-initiated medium access control (MAC) scheme that improves the visibility of a BAN in a coexistent environment, where diverse networks with various physical and MAC protocols share the radio spectrum. The design enhances CBAS by incorporating a network layer scheme that improves the packet delivery ratio (PDR), while minimizing the need for multihop cooperative transmissions; thus, packet delay is less compromised to achieve higher PDRs.

Mobility support for health monitoring at home using wearable sensors.

We present a simple but effective handoff protocol that enables continuous monitoring of ambulatory patients at home by means of resource-limited sensors. Our proposed system implements a 2-tier network: one created by wearable sensors used for vital signs collection, and another by a point-to-point link established between the body sensor network coordinator device and a fixed access point (AP). Upon experiencing poor signal reception in the latter network tier when the patient moves, the AP may instruct the sensor network coordinator to forward vital signs data through one of the wearable sensor nodes acting as a temporary relay if the sensor-AP link has a stronger signal.