MIF: Optimizing Information Freshness in Intermittently Connected Sensor Networks
MIF: Optimizing Information Freshness in Intermittently Connected Sensor Networks
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DOI：
10.1145/3491315.3491338
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Howard Luu;Hung L. Ngo;Bin Tang;M. Beheshti
中科院分区：
文献类型：
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作者：
Howard Luu;Hung L. Ngo;Bin Tang;M. Beheshti
我们研究如何在间歇连接的传感器网络（ICSNs）最大限度地提高信息的新鲜度。ICSN是部署在具有挑战性的环境（例如，水下探索）。由于环境的不可访问性，ICSN中新生成的数据必须在上传机会（例如，自主水下航行器（AUV））变得可用。如何准确量化并有效实现ICSN中存储的信息的新鲜度提出了新的挑战。我们提出了一个算法框架，被称为MIF：最大化的信息新鲜度，以最大限度地提高新鲜度的数据包存储在ICSN，同时在这个过程中产生的能量成本最小。首先，我们制定了一个整数线性规划（ILP）问题，以解决最佳的MIF。然后，我们提出了一个更有效的时间贪婪算法。最后，仿真结果表明，我们的算法实现了信息新鲜度的ICSN在不同的网络参数，而招致最小的能量消耗。
We study how to maximize information freshness in intermittently connected sensor networks (ICSNs). ICSNs are emerging sensing applications and systems that are deployed in challenging environments (e.g., underwater exploration). Due to the inaccessibility of the environments, the newly generated data in ICSNs must be stored inside the network before uploading opportunities (e.g., autonomous underwater vehicles (AUVs)) become available. How to accurately quantify and effectively achieve the freshness of information stored in ICSNs pose a new challenge. We propose an algorithmic framework, referred to as MIF: maximization of information freshness, to maximize the freshness of data packets stored in ICSNs while incurring a minimum amount of energy cost in this process. We first formulate an integer linear programming (ILP) problem to solve MIF optimally. We then propose a more time-efficient greedy algorithm. Finally, simulation results show that our algorithms achieve information freshness for ICSNs under different network parameters while incurring minimum energy consumptions.