Rendezvous is a critical step in Cognitive Radio Network (CRN) prior to transmission for establishing communication links between secondary users (SUs). Due to the long-term blocking, channel saturation, and scalability issues encountered by dedicated common control channels (CCC) in a distributed CRN, rendezvous is preferable on the available idle channels of the primary users (PUs). In fact, each SU is unaware of the other SUs’ available channel sets, and the blind rendezvous is performed through the channel hopping (CH) process. As a result, ensuring a rendezvous between two SUs in a finite period in an asynchronous environment remains a challenging problem. In this work, a disjoint difference set based CH (DDSCH) method is developed that ensures the highest degree of rendezvous in comparatively less time.

Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes.