Spectrum Sensing as a Service (SSaS) has recently emerged as a promising solution to realize dynamic spectrum access networks (aka, cognitive radio networks (CRNs)). This paradigm relies on a dedicated infrastructure wireless sensor network, operated by a spectrum sensing service provider S³P entity, for spectrum monitoring. Wireless users which are interested in knowing the status of a given spectrum band make a request to the S³P to perform that monitoring on its behalf. In this work, we study the problem of optimizing the selection of sensors needed to serve a given request such that the total cost (consumed energy) is minimized. We consider a spatial and temporal scheduling problem, in which, the energy levels at sensors, the maximum transmission range of secondary user (SUs), and the required monitoring time are taken into account. The problem is formulated as an Integer Linear Program (ILP). Then a sub-optimal greedy algorithm is proposed with two variations. Thorough evaluation shows that the proposed algorithm performs very well with respect to the optimal solution. Finally, results illustrate that the first algorithm variation is better in terms of energy cost, and the second variation allows for serving requests with a lower number of nodes.

频谱感知即服务（SSaS）最近已成为实现动态频谱接入网络（又称为认知无线电网络（CRN））的有希望的解决方案。该范例依赖于频谱监测服务提供商S ³ P实体运营的专用基础设施无线传感器网络，以进行频谱监视。有兴趣了解给定频段状态的无线用户向S ³发出请求P代表它执行监视。在这项工作中，我们研究优化满足给定请求所需的传感器选择的问题，以使总成本（消耗的能量）最小化。我们考虑一个空间和时间调度问题，其中考虑了传感器的能量水平，次要用户（SU）的最大传输范围以及所需的监视时间。该问题被表述为整数线性程序（ILP）。然后提出了具有两个变体的次优贪婪算法。全面的评估表明，相对于最优解，该算法的性能非常好。最后，结果说明，第一种算法的变体在能源成本方面更好，而第二种变体允许为节点数量较少的请求提供服务。