Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

In this paper, we study the multi-task cooperation problem for unmanned aerial vehicle (UAV) swarms, where the UAV energy consumption is taken into consideration during location scheduling and task implementation. One task may need the cooperation of several UAVs with specific capabilities. To avoid unreasonable task allocation, we quantify the mission properties of UAVs and task areas. We comprehensively consider the overlapping and complementary relationship of the UAV’s task types, so that UAVs can form corresponding collective execution tasks according to the task attributes. Based on the coalition game theory, we model the distributed task assignment problem of UAVs as a coalition formation game (CFG). We propose a task allocation algorithm, and then prove that it can achieve the joint optimization of energy and task completion by decision-making of UAVs in finite iterations. With the equilibrium properties of coalition formation in UAV networks, we further optimize the position of UAVs to minimize the network energy consumption. Simulation results verify that the proposed method can reduce the flight loss with high task completion degree.

For the converged use of LTE, WLAN and Visible Light Communication (VLC) in indoor scenarios, fine-grained and intelligent network selection is essential for ensuring high user quality of experience. To tackle the challenges associated with dynamic environments and complicated service requirements, we propose a context-aware solution for indoor network selection. Specifically, three-level contextual information is revealed and exploited in both the utility and algorithm designs. In particular, the contextual information about the asymmetric downlink-uplink features of network performance is used to design a fine-grained utility model. A context-aware learning algorithm sensitive to traffic type-location-time information is proposed. The timelocation dependent periodic changing rule of load statistical distributions is further used to realize efficient online network selection via knowledge transfer. The simulation results show that the proposed algorithm can achieve much better performance with faster convergence speed than traditional reinforcement learning.

Radio resource management (RRM) is crucial for improving resource utilization in heterogeneous wireless networks. Existing work attempts to exploit the network diversity to gain throughput improvement for users, which, however, neglects the impact of user demand on RRM. Armed with the idea that the ultimate goal of communications is to serve users with personalized demand, we introduce another dimension of potential performance gain, user demand diversity gain. This gain derives from the elaborate matching between user demand and radio resource, which can not be directly attained in existing throughput-centric optimization due to users' blindness in maximizing throughput. Aiming at obtaining this gain, we propose the user demand-centric optimization, where users seek to maximize quality of experience (QoE), instead of throughput. This shift enables us to propose a novel game formulation, QoE game. We derive the condition on the existence of the QoE equilibrium, validate the user demand diversity gain and propose a distributed QoE equilibrium learning algorithm. Finally, a cloud assisted learning framework is proposed to accommodate the learning algorithm with significantly reduced cost. Simulation results validate the existence of user demand diversity gain and the effectiveness of the proposed learning algorithm in improving the system efficiency and QoE fairness.

In this article, we investigate self-organizing optimization for cognitive small cells (CSCs), which have the ability to sense the environment, learn from historical information, make intelligent decisions, and adjust their operational parameters. By exploring the inherent features, some fundamental challenges for self-organizing optimization in CSCs are presented and discussed. Specifically, the dense and random deployment of CSCs brings about some new challenges in terms of scalability and adaptation; furthermore, the uncertain, dynamic, and incomplete information constraints also impose some new challenges in terms of convergence and robustness. For providing better service to users and improving resource utilization, four requirements for self-organizing optimization in CSCs are presented and discussed. Following the attractive fact that the decisions in game-theoretic models are exactly coincident with those in self-organizing optimization (i.e., distributed and autonomous), we establish a framework of gametheoretic solutions for self-organizing optimization in CSCs and propose some featured game models. Specifically, their basic models are presented, some examples are discussed, and future research directions are given.

We focus on the network selection problem in heterogeneous wireless networks. Many traditional approaches select the best network according to quality of service (QoS)-related criteria, which neglects diverse user demands. We aim to select networks maximizing the quality of experience (QoE) of users. When the availability and dynamics of network state information (NSI) are considered, most of the existing approaches cannot make effective selection decisions since they are vulnerable to the uncertainty in NSI. To address this issue, we introduce the idea of online learning for network selection. In this paper, we formulate the network selection problem as a continuous-time multiarmed bandit (CT-MAB) problem. A traffic-aware online network selection (ONES) algorithm is designed to match typical traffic types of users with respective optimal networks in terms of QoE. Moreover, we found that the correlation among multiple traffic network selections can be exploited to improve the learning capability. This motivates us to propose another two more efficient algorithms: the decoupled ONES (D-ONES) algorithm and the virtual multiplexing ONES (VM-ONES) algorithm. Simulation results demonstrate that our ONES algorithms attain around 10% gain in QoE reward rate over nonlearning-based algorithms and learning-based algorithms without QoE considerations.