In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.

中文翻译：

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ThriftyEdge：适用于智能物联网应用的资源高效型边缘计算


在本文中，我们为新兴的智能物联网应用提出了一种资源高效边缘计算的新范式，例如用于精准农业、电子医疗和智能家居的飞行自组织网络。我们设计了一种资源高效的边缘计算方案，以便智能物联网设备用户可以通过在本地设备、附近的辅助设备和附近的边缘云之间进行适当的任务卸载来很好地支持其计算密集型任务。与现有的移动计算卸载研究不同，我们探索了资源效率的新视角，并设计了一种高效的计算卸载机制，该机制由延迟感知任务图划分算法和最佳虚拟机选择方法组成，以最小化智能物联网设备的边缘资源占用，同时满足其QoS要求。性能评估证实了所提出的资源高效边缘计算方案的有效性和优越性能。