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With an increase in the number of devices involved in the Industrial Internet, effectively combining the characteristics of industrial scenarios with an edge computing methodology for computation-intensive applications poses a critical challenge. This paper proposes an integrated architecture that allows industrial devices to offload tasks to cloud or edge servers. An offloading problem is also formulated into an energy-cost (EC) minimization problem while satisfying the deadline constraint. To solve the optimization problem, two types of offloading algorithms, namely ASO and Pro-ITGO, are proposed based on the integrated architecture. The ASO algorithm is a lightweight linear programming algorithm that includes subdeadline allocation, topology sorting, and task offloading sub-algorithms. The Pro-ITGO algorithm is a group intelligence heuristic algorithm that is derived from the original ITGO algorithm adapting the offloading scenarios of the Industrial Internet. Experimental results demonstrate that compared with state-of-the-art heuristic algorithms, the proposed algorithms can effectively reduce the energy consumption of industrial devices and cloud computing costs. [ABSTRACT FROM AUTHOR]

Mobile edge clouds can offer delay-sensitive services to users by deploying storage and computing resources at the network edge. Considering resource-limited edge server, it is impossible to deploy all services on the edge clouds. Thus, many existing works have addressed the problem of arranging services on mobile edge clouds for better quality of services (QoS) to users. In terms of existing service placement strategies, the historical data of requesting services by users are collected to analyze. However, those historical data belong to users' sensitive information, without appropriate privacy preserving measures may hinder the implementation of traditional service arrangement strategies. Service placement with considering users' privacy and limited resources of mobile edge clouds, is an extremely urgent problem to be solved. In this paper, we propose a privacy-aware service placement (PSP) scheme to address the problem of service placement with privacy-awareness in the edge cloud system. The purpose of PSP mechanism is to protect users' privacy and provide better QoS to users when obtaining services from mobile edge clouds. Specifically, whether service placement on mobile edge clouds or not is modeled as a 0–1 problem. Then, a hybrid service placement algorithm is proposed that combines a centralized greedy algorithm and distributed federated learning. Compared with other optimization schemes, the simulation experiments show that PSP algorithm could not only protect users' privacy but also meet users' service demands through mobile edge clouds. [ABSTRACT FROM AUTHOR]

• Collective Opportunistic IoT Services are key abstractions for novel Internet- and Edge-of-Things applications. • IoT Modelling may consider entire collectives and various infrastructural elements as first-class entities. • Services implemented with Aggregate Computing can run in Edge or Cloudbased deployments, providing different pros and cons. Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving "collectives" of devices coordinating in a complex fashion to provide "global" services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex "Edge of Things" applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile crowdsensing applications, task allocation has emerged as a new problem to be solved. Many task allocation strategies have been proposed to select the proper participants to complete tasks. Those methods need participants to submit their real locations to the platform in order to realize the optimal task assignment. However, those traditional task allocation strategies have two weaknesses. First, centralized task allocations result in high computing and communications loads. Second, the exposure of real locations increases participants' concerns regarding location privacy. To address these problems, in this paper, we propose an optimal geo-indistinguishable task allocation (GITA) mechanism using mobile edge clouds. First, the new task that is received by the remote cloud is sent to the mobile edge cloud that is nearest to the task location. Then, the mobile edge clouds serve as distributed controllers to allocate the assigned tasks to the proper candidates. To protect the candidates' real locations, we utilize a geo-indistinguishable mechanism based on differential privacy to preserve location privacy. Specifically, we obfuscate the participants' real locations as disturbed locations, and realize the optimal task allocation based on these disturbed locations. Furthermore, we apply multiobjective mixed integer nonlinear optimization to solve this problem. Finally, extensive experimental results show that, compared with the traditional Laplace mechanism and another privacy-preserving task allocation strategy, the GITA mechanism that is proposed in this paper can decrease users' moving distances and raise the task completion rate. [ABSTRACT FROM AUTHOR]

• Fall detection systems may rely on Cloud and Edge computing architectures. • Fall detection can be performed with sensors embedded in smartphones. • Edge-based fall detection reduces data transmission and storage space consumption. • Smart IoT devices allow monitoring people during daily activities performed outdoor. Remote monitoring of older adults and detecting dangers in the state of human health have become essential elements of modern telemedicine. Falls are a frequent reason for deaths or post-traumatic complications in the elderly. Therefore, the early detection of falls can be crucial for the survival of a person or for providing necessary support. However, telemedicine data centers require scalable computing and storage resources for the growing number of monitored people. Dedicated approaches that allow for minimal data transmission of strictly interesting cases are also required. In this paper, we show a scalable architecture of a system that can monitor thousands of older adults, detect falls, and notify caregivers. Scalability tests that disclose requirements to enable large scale system operations were also performed. Moreover, we validated several Machine Learning models to evaluate their suitability in the detection process. Among the tested models, Boosted Decisions Trees resulted in the best classification performance. We also experimentally tested the detection of falls inside a Cloud-based data center and on an Edge IoT device. Results of tests on the device-to-cloud data transmission confirmed that significant reduction in the size of stored and transmitted data can be achieved while performing fall detection on the Edge. [ABSTRACT FROM AUTHOR]

• An automatic audio-visual emotion recognition system based on CNN was proposed. • The audio-visual features were fused using a cascaded deep sparse autoencoder. • Signals were distributed to different cloud servers using multi-secret sharing scheme. • Edge computing was introduced to minimize the burden of transmission. This paper proposes an edge-cloud based privacy-preserved automatic emotion recognition system in which Internet of Things (IoT) devices capture facial image and speech signals from a user before distribution to different edge clouds using a secret sharing scheme, ensuring the privacy of the user. Simple signal preprocessing is performed in the edge clouds and then the signals are transmitted to the core cloud. In the core cloud, a Convolutional Neural Network (CNN)-based, pre-trained model is applied to extract deep-learned features from image and speech signals. Following the CNN model, two deep sparse auto-encoders fuse the signal features and introduce an extended non-linearity to the features. Finally, a support vector machine classifies the input in a corresponding emotion class. Several experiments were performed using the proposed system on two publicly available databases, namely, the RML database and eNTERFACE'05 database. The highest recognition accuracies by the proposed system on the two databases were 82.3% and 87.6%, respectively. These accuracies are higher than those by other state-of-the-art systems. [ABSTRACT FROM AUTHOR]