In this paper, we propose an efficient deep neural network-based relay selection (DNS) scheme to evaluate and improve the end-to-end throughput in wireless-powered cognitive Internet-of-Things (IoT) networks. In this system, multiple energy harvesting (EH) relays are deployed randomly to assist data transmission from a source node to multiple users under practical nonlinearity of the EH circuits. We first design an incremental relaying protocol, where a selected user will request the help from relays if the direct transmission is not favorable. In such a protocol, we develop a deep neural network framework for relay selection and throughput prediction with high accuracy, less channel feedback amount, and short execution time. Simulation results show that the proposed DNS scheme achieves higher throughput than the conventional relay selection methods, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the proposed DNS scheme achieves the root-mean-square-error (RMSE) of 6.6×10-3 on the considered dataset, which exhibits the lowest RMSE as compared to the state-of-the-art machine learning approaches.

In this paper, we propose a novel deep reinforcement learning-based quality-of-service (QoS) routing protocol, namely DRQR, exploiting cross-layer design to establish efficient QoS (EQS) routes in cognitive radio mobile ad hoc networks. An EQS route is a route with minimum end-to-end (E2E) queuing delay subject to QoS constraints such as link stability, residual energy, number of hops and avoiding licensed channels of primary users. Particularly, we propose an NP-complete optimization problem which has a feasible solution as an EQS route. To tackle this problem, we design a new deep reinforcement learning model which supports the DRQR protocol to establish EQS routes in real time by offline training instead of online training like most of literature studies. Moreover, the DRQR protocol guarantees to have high system performance. A mathematical analysis of the E2E queuing delay with random waypoint mobility model also provides to verify simulation results. Numerical results show that the DRQR protocol outperforms state-of-the-art routing protocols in terms of routing delay, queuing delay, control overhead, PDR and energy consumption.

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.

In mobile ad-hoc networks (MANETs), nodes are mobility naturally which renders a great challenge in topology controls. Connected dominating set (CDS) has been considered as an efficient solution to solve such a problem by constructing a virtual backbone network to achieve the scalability and efficiency of wireless networks. In this paper, we propose an efficient connected dominating set clustering based routing protocol with dynamic channel selection (CRD) in multi-channel cognitive radio MANETs to obtain high packet delivery ratio (PDR), low control overheads, low delay and low energy consumption when node is moving in high speed. Moreover, CRD protocol can apply for Vehicular Ad Hoc Network, IoT and 5G. The proposed CRD protocol includes clustering and routing phases under multi-channel cognitive radio approach. In the clustering phase, we propose CDS selection, CDS size reduction and CDS based clustering algorithms to formulate clusters and provide a set of intermediate nodes which is latter used as a route searching space for the routing phase. In the routing phase, a sending channel based focus region selection (CFS) algorithm is adopted dynamically to establish an efficient route from a set of intermediate nodes that avoids the affected region of a primary user. The CRD routing protocol is then implemented in environmental conditions close to reality (OMNET++ platform) to demonstrate its superiority over the conventional AODV protocol in terms of PDR, control overhead, delay and energy consumption.

The frequent movement of nodes in cognitive radio mobile ad hoc networks (CRAHNs) causes challenges in scalability, stability, channel sensing and channel access problems that can be solved by using clustering technique. Game theory is a feasible approach to solve such problems by casting clustering problems as distributed optimization problems. The main contributions of this article are as follows. Firstly, we propose a minimum connected weighted inner edge spanning tree (MWIEST) game to find an approximate solution of a MWIEST problem in CRAHNs. In this game, a link-weight function of each link is designed based on a combination of link-stability and link-connectivity ratio functions. Secondly, we prove that the MWIEST game is an exact potential game that exists at least one Nash equilibrium ( NE ) point which is an approximate solution of the MWIEST problem. Besides, we also prove that best responses ( BR s) of the game converge to a NE in finite iterations. Thirdly, based on the MWIEST game, we propose four algorithms including the node information exchange (NIE), the best response selection (BRS), the intermediate nodes selection (INS) and the forming cluster (FC). Specifically, the algorithms NIE, BRS and INS provide a set of intermediate nodes (SetIN) which supports the FC algorithm to form clusters. Finally, we propose the game theory based clustering (GBC) protocol which is combination of the FC algorithm and the proposed cluster maintenance algorithm to construct high stable clusters supporting multicast routing in CRAHNs. Moreover, each obtained cluster includes most members having the same receiving channel which avoids the affected regions of licensed channels. For the performance evaluation, we implement the GBC protocol in OMNET++ platform to demonstrate its performance improvement over the state-of-the-art protocols in terms of network stability and control overheads.

In ad-hoc vehicle networks (VANETs), the random mobility causes the rapid network topology change, which leads to the challenge of the reliable data transmission. In this paper, we propose a hybrid-price auction-based secure routing (HPA-SR) protocol using advanced speed and cosine similarity-based (ASCS) clustering to establish a secure route to avoid sinkhole attacks and improve connectivity between nodes. The main features and contributions of the proposed HPA-SR protocol are as follows. First, the HPA-SR protocol is employed by the first- and second-price auctions to avoid sinkhole attacks. More specifically, using the Markov decision process (MDP), each node can select a kind of auction method to establish the secure route by avoiding the sinkhole attack. Second, the advanced speed cosine similarity clustering protocol that is considered as underlying structure is presented to improve the connectivity between nodes. The ASCS is constructed based on the cosine similarity and distance between nodes using the speed and direction of the nodes. The results of the performance show that the proposed HPA-SR protocol can establish the secure route avoiding the sinkhole attack while the proposed ASCS clustering can support the strong connectivity. Besides, the HPA-SR with ASCS protocol can show better performance than the benchmark protocol in terms of the routing delay, packet loss ratio, number of packet loss, and control overhead.