The continuous growth of interconnected objects in the Internet of things (IoT) raises a challenge to the wireless communication technology. Cognitive radio could make full use of the dynamic spectrum access and spectrum diversity over wide spectrum to alleviate the spectrum scarcity problem and satisfy the enormous connectivity demands in IoT, which has garnered significant attention over the last few years. This paper addresses the spectrum allocation problem with respect to both spectrum utilization and network throughput in the cognitiveradio- based IoT. On the one side, each link in a transmission path intends to improve the transmission performance on the assigned spectrum channel to maximize the end-to-end throughput. On the other side, these links share the same spectrum channel to concurrently transmit as much as possible to achieve the maximum spectrum utilization. In order to solve the problem, we propose a concurrent transmission model in the network which reveals the constraints of mutual interference and resource competition in links concurrent transmissions. Based on this model, we formulate the spectrum allocation plan for links as the chromosome (solution) in genetic algorithms. Then, we apply the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective spectrum allocation problem. Simulation results validate that the proposed strategy can search the optimal solutions efficiently and satisfy the requirements of spectrum allocation in various cases.

Video Delivery Networks (VDNs) are one of the key enablers for alleviating contradiction between demands increasing for online video and response time of users accessing video content. However, existed works mainly adopt either proactive or reactive delivery scheme, which leads to the problems of backbone traffic overloaded and delay increased. In these works, the impact of performance indicators, such as bandwidth, delay and personalized demand on delivery cost are not comprehensively taken into account. To overcome these problems, we propose a Cost Optimization for Coupled Video Delivery Model (CO-CVDM), which combines coupled proactive delivery and coupled reactive delivery method to minimize delivery cost in terms of bandwidth consumption, delay performance, personalized demand and construct a multicast delivery tree for the video delivery. The coupled delivery cost minimization problem under constraint conditions is formulated. Since this problem is NP-hard and is prohibitively difficult to solve, we develop a Heuristic Multicast Delivery Tree (HMDT) algorithm to provide more optimized approximate cost. Based on this, we extend CO-CVDM to distribute video in dynamic scenario. Specifically, with dynamic change of user’s requirement for video, the process of video delivery is truncated into a sequence of static graphs and the change of interest attenuation is modeled with human forgetting curve. Finally, numerical simulations are conducted to validate the advantage of CO-CVDM in terms of optimization delivery cost, and the delivery cost is inversely proportional to the attenuation of user interest.