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a b s t r a c tMobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always colocated to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users' social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild. H. Flores et al. / Pervasive and Mobile Computing () effort of applications running on the device in an opportunistic manner [7][8][9][10][11][12][13][14]. Simply put, computational offloading is a technique where a resource constrained device, e.g., CPU, battery, storage, outsources the processing of a task to a more powerful machine. In this process, the device weighs during runtime the effort to execute an application and calculates whether the cost of outsourcing a task from the application is less than the actual effort to process the task on its own. The cost of outsourcing the task is calculated by taking into consideration multiple parameters of the system [15], e.g., network latency, processing intensity of the code, surrogate capabilities, among others. Computational offloading systems can be categorized into three different classes, namely (i) cloudlets [16], (ii) remote cloud [5] and (iii) device-to-device (D2D) [17]. Each system defines a particular opportunistic criteria to estimate the effort to offload. A mobile device that uses an offloading system, detects opportunities to offload when an application is executed. Thus, the augmentation of the mobile resources with external infrastructure is temporal as long as the criteria is fulfilled. By outsourcing a task, overall the mobile device consumes less resources, and in some cases, even the response time of the application is accelerated [5,18].While different systems deal with the temporal resource augmentation of the mobile device in specific ways, the opportunistic moments provided by each offloading system are sporadic as each criteria need to meet many ...

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Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.