Timestomping 下基于年龄的缓存更新
Age-Based Cache Updating Under Timestomping
AI总结：
本文研究 timestomping 对手篡改缓存分组时间戳时的信息年龄优化问题，将其建模为在线学习博弈，给出竞争比下界及具有可证明保证的确定性更新算法。
摘要：
我们考虑一个由源端、缓存、用户和 timestomping 对手组成的时隙通信系统。时间范围由总共 $T$ 个时隙构成，其中源端在 $T_{1}$ 个时隙内直接向用户发送更新分组，并在 $T_{2}$ 个时隙内向缓存发送更新分组。我们考虑 $T_{1}\ll T_{2}$ 且 $T_{1}+T_{2}<T$，从而源端在两次连续缓存更新之间向用户发送一次。更新分组标记有与其在源端生成时间相对应的时间戳。所有节点的缓冲区大小均为一，并存储具有最新时间戳的分组，以最小化其信息年龄。在此设置下，我们考虑存在一个无感知对手，其完全控制缓存与用户之间的通信链路。对手操纵从缓存发往用户的外出分组的时间戳，目的是在用户节点造成陈旧性。在每个时隙，对手可以选择将缓存的分组转发给用户，同时把其时间戳改为当前时间 $t$，从而将旧分组重新包装为新鲜分组并误导用户接受它，或者选择保持空闲。用户将每个接收分组的时间戳与其拥有的最新分组进行比较，以保留更新鲜的分组并丢弃更陈旧的分组。如果用户在一个时隙中同时收到来自缓存和源端的更新分组，则来自源端的分组优先。源端的目标是设计一种算法以最小化用户处的平均年龄，而对手的目标是增加用户处的平均年龄。我们将该问题表述为在线学习设置，并给出该问题竞争比的一个基本下界。我们进一步提出一种确定性算法，并对其竞争比提供可证明的保证。
英文摘要：
We consider a slotted communication system consisting of a source, a cache, a user and a timestomping adversary. The time horizon consists of total $T$ time slots, such that the source transmits update packets to the user directly over $T_{1}$ time slots and to the cache over $T_{2}$ time slots. We consider $T_{1}\ll T_{2}$, $T_{1}+T_{2} < T$, such that the source transmits to the user once between two consecutive cache updates. Update packets are marked with timestamps corresponding to their generation times at the source. All nodes have a buffer size of one and store the packet with the latest timestamp to minimize their age of information. In this setting, we consider the presence of an oblivious adversary that fully controls the communication link between the cache and the user. The adversary manipulates the timestamps of outgoing packets from the cache to the user, with the goal of bringing staleness at the user node. At each time slot, the adversary can choose to either forward the cached packet to the user, after changing its timestamp to current time $t$, thereby rebranding an old packet as a fresh packet and misleading the user into accepting it, or stay idle. The user compares the timestamps of every received packet with the latest packet in its possession to keep the fresher one and discard the staler packet. If the user receives update packets from both cache and source in a time slot, then the packet from source prevails. The goal of the source is to design an algorithm to minimize the average age at the user, and the goal of the adversary is to increase the average age at the user. We formulate this problem in an online learning setting and provide a fundamental lower bound on the competitive ratio for this problem. We further propose a deterministic algorithm with a provable guarantee on its competitive ratio.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
