We consider a Sensing-as-a-Service (S2aaS) system consisting of a sensor, a set of users, and a sensor cloud service provider (SCSP). The sensor updates its content each time it captures a new measurement. The SCSP occasionally fetches the content from the sensor, caches the latest fetched version and broadcasts it on being requested by the users. The SCSP incurs content fetching costs while fetching and broadcasting the contents. The SCSP also incurs an age cost if users do not receive the most recent version of the content after requesting. We study a content fetching and broadcast problem, aiming to minimize the time-averaged content fetching and age costs. The problem can be framed as a Markov decision process but cannot be elegantly solved owing to its multi-dimensional state space and complex dynamics. To address this, we first obtain the optimal policy for the homogeneous case with all the users having the same request probability and age cost. We extend this algorithm for heterogeneous case but the complexity grows exponentially with the number of users. To tackle this, we propose a low complexity Whittle index based algorithm, which performs very close to the optimal. The complexity of the algorithm is linear in number of users and serves as a heuristic for both homogeneous and heterogeneous cases.

翻译：本文研究一种感知即服务系统，该系统由传感器、用户集合及传感器云服务提供商构成。传感器每次采集新测量数据时即更新其内容。传感器云服务提供商不定期从传感器获取内容，缓存最新获取的版本，并在用户请求时进行广播。传感器云服务提供商在获取与广播内容时会产生内容获取成本。若用户请求后未能接收到最新版本内容，传感器云服务提供商还需承担时效成本。我们研究内容获取与广播的优化问题，旨在最小化时间平均的内容获取成本与时效成本。该问题可建模为马尔可夫决策过程，但由于其多维状态空间与复杂动态特性，难以获得优雅解法。为此，我们首先针对所有用户具有相同请求概率与时效成本的同构场景求得最优策略。将该算法扩展至异构场景时，其复杂度随用户数量呈指数增长。为解决此问题，我们提出一种基于Whittle索引的低复杂度算法，其性能接近最优解。该算法复杂度与用户数量呈线性关系，可作为同构与异构场景的通用启发式方法。