SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

“…[126] Placement optimisation problem using thee Cuckoo optimisation algorithm. [127] Optimal controller placement using the Cuckoo optimisation algorithm.…”

Scalability Enhancement on Software Defined Industrial Wireless Sensor Networks Over TSCH

“…In [26] SDN and a clustering algorithm are used to simulate a network of 1000 nodes, in this case using unrestricted nodes, to observe the effects on the SDN controller of this number of nodes in terms of CPU and memory. The position of the SDN controller and the sink nodes also has an effect on the QoS parameters, as analyzed by the authors of [27], in which the results have shown that it is an optimization problem, due to the congestion of the nodes near the sink. These approximations have also been used to modify the TSCH schedules [16], improving the agility with which these plans are modified, including cases in which mobile nodes are used, extending network capacity and therefore scalability by having more free radio resources available.…”

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

“…[114] Placement optimisation problem using thee Cuckoo optimisation algorithm. [115] Optimal controller placement using the Cuckoo optimisation algorithm. [116] Two approaches for optimal placement were discussed: k-means for local controllers and k-centre for global controller.…”