Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaningless if IoT objects are not equipped with cognitive radio capability. Equipping IoT objects with CR capability has lead to a new research dimension of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We highlight potential applications of CR-based IoT systems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues, research challenges, and future direction for these CR-based IoT networks.

Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes are motivating a transformation of the conventional ways of generating electricity. Globally, there is a desire to rely more on renewable energy resources (RERs) for electricity generation. RERs reduce green house gas emissions and may have economic benefits, e.g., through applying demand side management with dynamic pricing so as to shift loads from fossil fuel-based generators to RERs. The electricity grid is presently evolving towards an intelligent grid, the so-called smart grid (SG). One of the major goals of the future SG is to move towards 100% electricity generation from RERs, i.e., towards a 100% renewable grid. However, the disparate, intermittent, and typically widely geographically distributed nature of RERs complicates the integration of RERs into the SG. Moreover, individual RERs have generally lower capacity than conventional fossil-fuel plants, and these RERs are based on a wide spectrum of different technologies. In this article, we give an overview of recent efforts that aim to integrate RERs into the SG. We outline the integration of RERs into the SG along with their supporting communication networks. We also discuss ongoing projects that seek to integrate RERs into the SG around the globe. Finally, we outline future research directions on integrating RERs into the SG.

To keep the services and applications of Intelligent Transportation System (ITS) stable and active, Vehicular Ad hoc Networks (VANETs) are considered as an essential building block to maintain and manage its features. A wide deployment of VANETs is possible only after addressing numerous research challenges. One of the most complicated issues consists in designing a routing strategy, taking into consideration several serious constraints, and especially in a network such as VANET. The severity of these issues would be increased significantly when a VANET is deployed over an urban area, where we distinguish the high mobility of nodes and existing obstructions (e.g., buildings, bridges, tunnels, etc.). In this paper, an efficient routing solution based on a flooding technique is conceived to make the data delivery more reliable and to guarantee robust paths. Vehicles can cooperate in ad hoc fashion with existing Unmanned Aerial Vehicles (UAVs). This kind of collaboration provides reliable routing paths and ensures alternative solutions in the case of path failures. Furthermore, a prediction technique is used to expect the expiration time of each discovered path. To limit the overhead over the network, all control packets are characterized by their static size making the originality of this work. Based on the simulation outputs, we discuss the performances of the proposed approach as compared with other dedicated previous schemes in terms of several metrics. The obtained results demonstrate that the hybrid communication between vehicles and UAVs based on the proposed flooding technique is perfectly suited to improve the data delivery process.

In collaborative wireless networks with low infrastructure, the presence of misbehaving nodes can have a negative impact on network performance. In particular, we are interested in dealing with this nasty presence in road safety applications, based on vehicular ad hoc networks (VANETs). In this paper, we consider as harmful the presence of malicious nodes, which spread false and forged data, and selfish nodes, which cooperate only for their own benefit. To deal with this, we propose a distributed trust model $(DTM^{2})$ , which is adapted from the job market signaling model. $DTM^{2} $ is based on allocating credits to nodes and securely managing these credits. To motivate selfish nodes to cooperate more, our solution establishes the cost of reception to access data, forcing them to earn credits. Moreover, to detect and exclude malicious nodes, $DTM^{2} $ requires the cost of sending, using signaling values inspired from economics and based on the node's behavior so that the more malicious a node is, the higher its sending cost, thus limiting their participation in the network. Similarly, rewards are given to nodes whose sent messages are considered truthful and that paid a sending cost considered correct. The latter is a guarantee for the receivers about the truthfulness of the message since, in the case of message refusal, the source node is not rewarded, despite its payment. We validated $DTM^{2} $ via a theoretical study using Markov chains and with a set of simulations in both urban and highway scenarios. Both theoretical and simulation results show that $DTM^{2} $ excludes from the network 100% of malicious nodes without causing any false-positive detection. Moreover, our solution guarantees a good ratio of reception, even in the presence of selfish nodes.