International Journal of Distributed Sensor Networks | 2021
Editorial
Abstract
The importance of sensor networks and the integration of signal processing have increased as a consequence of the growth of complex Internet-of-Things (IoT), distributed and wireless network applications, for commercial, medical, context-aware, and industrial domains, among others. The complexity, heterogeneity, and dynamicity of some sensor networks demand new intelligent solutions for data aggregation, integration, and management. Artificial intelligence helps in these sensor network applications to respond to the abovementioned challenges. In consideration of these issues, this Special Collection offered a platform for researchers to publish recent and original works in different topics, focusing on intelligent systems for sensor networks. Several researchers from different parts of the world submitted their papers. Hence, after a rigorous review process, we accept only seven papers for this collection. An overview of the key contributions of each paper is presented as follows. Medical and healthcare services based on IoT need a high degree of autonomy. Developing an efficient sensing method of physical body data is challenging given that they are highly diverse and dynamic, especially on the cloud computing system for artificial intelligence computation. The first paper titled ‘‘An edge cloudbased body data sensing architecture for artificial intelligence computation’’ presents an edge cloud–based body data sensing architecture with the purpose of analyzing physical body data on an edge cloud computing system. The authors, Kim and Lim, aim to identify relationships between the person’s activities and health conditions with a framework, which efficiently aggregates and processes sensor data. They address the important challenges of providing real-time service and mobility and evaluated the effectiveness of their architecture for activity recognition based on body sensor data. In other domains like face recognition, there are also important challenges to enhance these systems in an uncontrolled environment. The second paper contributed by Yu et al. entitled ‘‘A novel framework for face recognition using robust local representation-based classification’’ addresses the problem of how to enhance the performance of sparse representation classificationbased face recognition systems in an unconstrained environment. They adopted a three-dimensional (3D)based frontalization on the aligned downsampling local binary pattern feature to deal with the uncontrolled environments effectively. An optimized projection/sensing matrix is also designed in order to reduce the complexity and prevent overfitting problem. Wireless sensor networks (WSNs) may also change dynamically due to external or internal factors. Therefore, an energy-efficient data aggregation method is needed to enhance wireless sensor nodes’ lifetime and quality of service. With this regard, the third paper ‘‘A review on the applications of multiagent systems in wireless sensor networks,’’ Derakhshan and Yousefi review recent simulated approaches and real-time applications of multiagent systems (MASs) in WSN. First, they offer an overview on simulated and real-time approaches using MAS in WSN. The key challenges, efficiency factors, limitations, and future directions are provided. According to the authors, data aggregation is one of the most important challenges in applications using MAS in WSN. They propose a framework for energy-efficient and secure data aggregation of this kind of applications. Yang et al. present the fourth paper ‘‘Research on shore-based intelligent vessel support system based on multi-source navigation sensors simulation.’’ In this work, the authors propose an efficient simulation model for multi-source navigation sensors. They developed a virtual intelligent vessel as platform of a shorebased support system for remote monitoring of the autonomous navigation system. The virtual intelligent vessel platform is developed and tested with the vessel ‘‘Chang Shan Hai’’ and proved to be effective. In the fifth paper ‘‘On the adaptability of ensemble methods for distributed classification systems: a comparative analysis,’’ Villaverde et al. propose a two-stage classifier ensemble architecture composed of various