Computing in the network (COIN) exploits the sparce computing power of network nodes to offload applications' computations. This paradigm benefits computation-demanding applications, such as source separation for acoustic anomaly detection. However, wider adoption of COIN has not occurred due to intertwined challenges. The monolithic design of the source separation algorithms and the lack of a flexible transport layer in COIN hinders its exploitation. This article presents network joint independent component analysis (NJICA), leveraging COIN to recover original acoustic sources from a mixture of raw sensory signals. NJICA redesigns the monolithic algorithm for source separation into a distributed one to unleash the offloading capability to an arbitrary number of network nodes. Furthermore, NJICA develops a message-based transport layer that allows aggregating application data at network nodes and differentiating message types. Extensive evaluations of the practical implementation of NJICA using a realistic dataset shows that NJICA significantly reduces both the computation and service latencies.

中文翻译：

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计算与网络的结合：用于数据密集型盲源分离的 COIN 感知卸载


网络计算（COIN）利用网络节点的稀疏计算能力来卸载应用程序的计算。这种范例有利于计算要求较高的应用，例如用于声学异常检测的源分离。然而，由于相互交织的挑战，COIN 尚未得到更广泛的采用。 COIN 中源分离算法的整体设计以及缺乏灵活的传输层阻碍了其利用。本文介绍了网络联合独立分量分析 (NJICA)，利用 COIN 从原始感官信号的混合物中恢复原始声源。 NJICA 将用于源分离的整体算法重新设计为分布式算法，以释放到任意数量的网络节点的卸载能力。此外，NJICA 开发了基于消息的传输层，允许在网络节点聚合应用程序数据并区分消息类型。使用真实数据集对 NJICA 的实际实施进行的广泛评估表明，NJICA 显着降低了计算和服务延迟。