With the enrichment of types and numbers of sensing terminals, more and more devices such as mobile phones, smart wearables, mobile robots, drones have appeared in our life, enabling the development of Mobile Crowd Sensing (MCS) technology. MCS systems has gradually changed from isomorphic sensing to heterogeneous collaborative sensing, and finally evolved into a heterogeneous multi-source sensing mode of the fusion of humans, machines and objects (things). However, state-of-the-art systems/frameworks do not well support efficient interactions and Heterogeneous Crowd Agents (HCA) management in Heterogeneous MCS (H-MCS) systems. With this in mind, this article aims at two major gaps: Efficient interaction and collaboration of HCA, automated modeling and flexible management of HCA. To deal with the challenges, we design an ontology-based interaction and management middleware (CrowdManager). Three core modules that constitute the middleware: HCA Information Extraction and Representation (HER), Ontology-based HCA Construction & Management (OCM), and Communication and Interaction Module (CIM) are well demonstrated. Extensive comparative evalution suggests that our approach not only brings rich and efficient HCA management and interactive functions to H-MCS systems, but also reduces communication time and various resource occupancy rates by more than 50%.

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms ($\mathbb {CAP}$) have appeared. In the mean time,$\mathbb {CAP}$-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However,$\mathbb {CAP}$urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types; thus, requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including task resolution and assignment framework (TRAF), integrated resource management (IRM), and task result quality optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further revealsTRObrings enormous improvement in efficiency and a reduction in energy consumption.