Parallel and Edge Computing With Artificial Intelligence for Sensor Networks: Second Edition

Dear Colleagues,
As the key components of the Internet of Things (IoT), sensors of disparate types and modalities have been deployed almost everywhere, including home appliances, personal portable devices, factory pipelines, autonomous vehicles, transportation infrastructures, human bodies, country boarders, battlefields, farms, etc. The ubiquitous deployment of such sensors leads to the formation of various sensor networks, which have found numerous successful applications in industry, agriculture, healthcare, homeland security, smart city, smart manufacturing, etc.

Over the years, sensors have become smarter, smaller, cheaper, and more powerful than ever before, which now makes it possible to achieve quality through quantity. Consequently, widespread deployment of sensors could generate colossal amounts of data on a regular basis, which must be transferred, integrated, and analyzed for decision making and decision support. The processing of sensor data at large scales typically requires the use of massive resources and big data frameworks for parallel computing at data centers on cloud networks.

Edge computing, on the other hand, pushes computing to the edge of the network to relieve the resource pressure on cloud-based data centers and reduce the amount of data that needs to be transferred over networks. Processing data at or close to the data source could bring significant performance improvements, such as better response time of sensor network applications with a drastic reduction in energy consumption for sustainable operations. This new computing paradigm has attracted more attention due to the increasing number of resources made available on edge devices.

Either on the cloud or at the edge, artificial intelligence (AI) can be fused into every step of the data collection and processing workflow. Particularly, a plethora of machine-learning-based approaches have been employed to determine sensor deployment and data routing, model and predict application performance, optimize resource allocation and utilization, reduce energy consumption, etc.

This Special Issue focuses on discussions and insights into the latest advancements and technologies on parallel and edge computing with artificial intelligence in support of sensor network applications in all domains. We welcome novel and original contributions with a broad range of problems and methods on the theory, design, implementation, and evaluation of sensor-network-based computing solutions and AI-enabled approaches such as machine learning and deep learning.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Computation- and data-intensive sensor network applications;
• Cloud computing for sensor data processing;
• Big data technologies, frameworks, systems, and platforms for parallel computing;
• Distributed computation and data management in sensor network applications;
• Edge computing in sensor networks;
• Energy-efficient computation, communication, and caching at the edge;
• Edge intelligence in energy-efficient Internet of Things;
• Edge and fog computing for smart environments;
• Mobile edge computing for smart environments;
• Optimization, control, and automation of edge and cloud computing;
• Computing continuum from clouds to edges;
• Sustainable and smart edge systems;
• Resource management in edge and cloud computing;
• Artificial intelligence and machine learning for sensor network applications;
• Edge intelligence in smart home, smart buildings, and smart cities;
• Parallel and edge computing for smart manufacturing;
• Collaborative Internet of Vehicles for intelligent transportation;
• Artificial-intelligence-enabled sensor-based surveillance, monitoring, and tracking;
• Sensor network applications in agriculture, healthcare, homeland security, etc.;
• Novel applications, experiences, and field trials with parallel and edge computing.

Prof. Dr. Chase Wu
Dr. Celimuge Wu
Dr. Kihyeon Kwon
Guest Editors

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• sensor networks
• parallel computing
• edge computing
• artificial intelligence

• Parallel and Edge Computing with Artificial Intelligence for Sensor Network in Sensors (11 articles)