Internet of Things (IoT) networks are increasingly used for edge data analytics, i.e., collecting and analyzing data at the network edge. However, the IoT devices are typically resource-constrained and cannot support fast and accurate execution of such tasks, while the involvement of distant cloud servers is often impractical and entails huge communication overheads. To address this problem, we develop a framework for enabling the devices to collaboratively execute their tasks, exploiting their proximity and resource complementarity. Our mechanism relies on an auction-based algorithm that optimizes the execution accuracy and delay for all tasks, without requiring information about the performance priorities of nodes. The algorithm yields the optimal task – node assignment, and the necessary reimbursements for ensuring the devices’ cooperation, by using the auction for dual subgradient evaluations. We further extend this mechanism for multi-stage analytics and explain how it can be implemented in a decentralized fashion, namely without an auctioneer. We conduct a battery of testbed experiments with representative data analytic applications that show gains both in accuracy and delay compared to heuristic and greedy policies, and verify the minimal computation and communication overheads of our solution.

中文翻译：

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物联网数据分析的协同边缘计算

物联网 (IoT) 网络越来越多地用于边缘数据分析，即在网络边缘收集和分析数据。然而，物联网设备通常资源受限，无法支持此类任务的快速准确执行，而远程云服务器的参与通常不切实际，并且需要巨大的通信开销。为了解决这个问题，我们开发了一个框架，使设备能够协同执行任务，利用它们的邻近性和资源互补性。我们的机制依赖于基于拍卖的算法，该算法优化所有任务的执行准确性和延迟，而无需有关节点性能优先级的信息。该算法产生最佳任务——节点分配，以及确保设备合作的必要补偿，通过使用拍卖进行双重次梯度评估。我们进一步扩展了多阶段分析的这种机制，并解释了它如何以分散的方式实施，即没有拍卖人。我们对具有代表性的数据分析应用程序进行了一系列测试台实验，与启发式和贪婪策略相比，这些应用程序在准确性和延迟方面均有所提高，并验证了我们解决方案的最小计算和通信开销。