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Value-Based Caching in Information-Centric Wireless Body Area Networks

机译:以信息为中心的无线人体局域网中基于值的缓存

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We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures.
机译:我们提出了一种基于感知信息价值(VoI)策略的弹性缓存替换方法。为了在由于隔离的网络内节点(碎片)和苛刻的操作条件而导致源不可用时解析和获取内容,我们采用了内容缓存方法。我们的方法取决于感官无线人体局域网(WBAN)中的四个功能参数。这四个参数是:基于定期请求的数据寿命,按需请求的流行程度,通信干扰成本以及要求传感器节点以活动模式运行以捕获感测到的读数的持续时间。这些参数一起考虑,可以为高速缓存的数据分配一个值,以将最有价值的信息长时间保留在高速缓存中。值越高,将数据保留在缓存中的持续时间越长。这种缓存策略为WBAN中最有价值和最难检索的数据提供了显着的可用性。进行广泛的仿真以将提出的方案与文献中的其他重要缓存方案进行比较,同时改变WBAN中的关键方面(例如,数据受欢迎程度,缓存大小,发布者负载,连接度以及节点故障的严重概率)。这些仿真结果表明,基于提议的基于VoI的方法是一种有效的工具,可用于在破坏性和挑战性场景(例如WBAN中遇到的场景)中检索缓存的内容,因为即使在经历了严重的严峻考验之后,它也可以长时间检索内容网络内节点故障。

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