Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

In recent years, rapidly increasing Internet-scale services are deployed in production data centers, which causes a huge amount of energy consumption and environment problem. It is a challenge for these data centers to reduce energy consumption while satisfying the increasing performance requirement of these services. Servers in data centers are usually heterogeneous, which makes task scheduling process more sophisticated. This paper first analyzes and explores two types of heterogeneity from a publicly Google cluster trace. Based on analysis results, we model the task scheduling problem in a heterogeneous data center considering energy consumption and performance requirement, and present an energy-aware task scheduling algorithm based on greedy algorithm. This energy-aware algorithm can achieve local optimal according to our analysis.

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