Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network’s state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously.

In this paper, we first formulate the controller placement problem as an multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.

由于无线传感器网络（WSN）的高度动态特性，软件定义网络（SDN）是一种有前途的技术，可通过提供逻辑上集中的控制平面来简化网络管理。使用多个SDN控制器来形成物理分布式SDN以实现容错，提高可扩展性和增强性能是一种常见的方法。尽管具有物理分布，但由于SDN背后的概念是在逻辑上集中网络管理，因此必须为所有控制器提供一致的网络状态视图。部署多个控制器会导致更高的同步和部署成本。由于网络性能和控制器间同步成本似乎是相互矛盾的目标，

在本文中，我们首先将控制器布置问题表述为多目标优化问题。在这方面，考虑了多个约束，包括可靠性，容错性，延迟方面的性能，同步开销和部署成本。此外，我们利用Cuckoo优化算法来解决优化问题，这是一种自然启发的基于人口的元启发式算法。该算法试图通过模仿某些杜鹃物种的卵寄生来寻找全局最优值。最后，为了评估我们提出的方法，我们将其与文献中现有的几种方法进行了比较。实验表明，在性能和同步成本方面，我们提出的方法大大优于现有方法，即模拟退火（SA）和量子退火（QA）。