Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

Abstract

Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

“…Finding the exact number of controllers required for a specific network topology using GA and GRASP algorithms. [114] Placement optimisation problem using thee Cuckoo optimisation algorithm. [115] Optimal controller placement using the Cuckoo optimisation algorithm.…”

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

“…In [20], the authors use reinforcement learning to maximize the sensing quality of the sensor nodes, then perform duty cycle based on the available energy, and use the collected energy to make the nodes continuously adapt to the changing environment. With the same purpose of prolonging lifetime of the IWSNs, the paper [21] expresses the position distribution of sensors as an optimization problem and then proposes a cuckoo algorithm to solve the problem, thereby obtaining the optimal position of sensor nodes. In [22], the authors use Q-learning technology and linear regression function to design a MAC protocol.…”

“…In [23], the authors consider the asymmetry of the asynchronous duty cycle, obtain the upper and lower limits of the node's duty cycle, and use block design to establish the duty cycle model. The paper [21] uses empirical data to find the noise relationship between the maximum discovery time and the duty cycle by analyzing the error of the proposed model,…”

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

“…Some approaches are basing the fog operation on 5G technology [46], [47], while other approaches (e.g., [48]) try to optimally localize the virtual machines in the fog network. Additionally, there exits a large body of research focused on multi-sink localization in WSNs [22], [23], [26], [27], [29], [49]- [53]. In one of the studies, the sink placement problem was considered as an optimization problem with an energy saving objective function.…”

“…In one of the studies, the sink placement problem was considered as an optimization problem with an energy saving objective function. For example, an optimal solution was proposed in [22], where different heuristics were used based on Cuckoo [23], Local search [26], Bactria [27], and Backbone information [29]. However, energy saving in WSNs may also depend on the used routing algorithms.…”