## 1 Introduction  
The Internet of Things (IoT) is a technology describing a network of resource-constrained cyber-physical systems. Through seamless connectivity, IoT aims to revolutionize the world with the use of heterogeneous smart devices [1, 2] deployed in areas such as energy, transportation, healthcare, agriculture, home and city life, to name a few [3, 4]. IoT creates such a system by automating processes with little or no human intervention. Specifically, the end goal of IoT is to increase efficiency in systems and provide an environment that positively improves various aspects of business and daily life [1]. However, to achieve this goal, IoT requires technical innovations in several fields ranging from sensor and communication networks to nanotechnology [5]. More so, IoT nodes are made up of sensors/actuators, computing resources to process collected data, and a network to communicate with other nodes or transmit collected data to a remote location for storage or further processing. Recent advancements in the development of different IoT technologies have led to the availability of cheaper sensors, robust processing resources, and ubiquitous network coverage, nevertheless, without the presence of improved cognitive capabilities, many noteworthy IoT applications may remain limited [5, 6]. Cognition is a term used to describe the mental activities of thinking, recollecting information, judging, drawing inference and problem-solving which are required for acquiring knowledge and understanding. A key area of IoT that would benefit from the integration of cognitive computing methodologies is data collection (DC). Data collection is one of the major reasons for the deployment of many IoT applications and it is a major energy-consuming task. Hence, recently, research efforts are channeled towards improving DC in IoT applications with an end goal to provide secure and energy-efficient DC processes for IoT systems. In achieving this, the concept of cognitive sensing (CS) was proposed for use in future IoT (FIoT) systems. It describes the special capability of nodes to intelligently perceive the world around them via the use of cognitive sensors. CS leverages data and other techniques to achieve the much-desired intelligent DC operations for IoT systems. Figure 1 outlines the IoT research focus areas over the last decade. The acceptance of artificial intelligence (AI) as a tool for improving systems has increased in recent times. This is evidenced in the integration of AI in technologies such as healthcare, data analysis, websites, security, etc. which have led to better and innovative systems. These new milestones motivate the research community to provide CS solutions that leverage AI tools and methodologies [7]. A survey of the literature reveals that there is a lack of a comprehensive study that reviews the contributions of AI specifically to CS solutions in the IoT domain. Table 1 summarizes some studies we identified to have reviewed the use of AI in other IoT related concepts. Thus, to highlight the contributions and further motivate new studies leveraging the capabilities of AI in CS solutions, our article provides a review of the state-of-the-art, potentials and challenges of AI in CS solutions. Specifically, we provide a classification for different CS solutions and AI techniques, followed by state-of-the-art approaches, and the different potentials of each classification. We identified some challenges in using AI techniques in CS and suggested the way forward. The organization of the paper is as follows: In Section 2, we present the classification of CS solutions and the surveyed AI techniques. Sections 3-6 presents the state-of-the-art and application areas of AI for each of the classification. Section 7 discusses the surveyed AI techniques and highlights contributions from the literature. The challenges and the way forward for AI in CS are discussed in Section 8 while the concluding remarks are presented in Section 9.