Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

In channel-hopping (CH)-based distributed cognitive radio networks (CRNs), the time duration that secondary users (SUs) spend for establishing communication links is called access delay. To evaluate access delay, we propose an access delay model by jointly considering imperfect spectrum sensing and multi-channel multi-SU transmission, from the cross-layer perspective. The model considers two typical scenarios. The first scenario assumes that the SUs do not use contention scheme (CS) which indicates that the time slot is relatively shorter to just allow a transmission. The second scenario assumes that the SUs employ CS [i.e., modified Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) in this paper], which indicates that the time slot is long enough to regulate multiple transmissions. We then propose a bio-inspired algorithm for the first scenario and a self-adaptive step-length algorithm for the second scenario to search for the optimal values of spectrum sensing parameters. The theoretical analysis and simulation results validate the proposed access delay model and show that the proposed algorithms can reduce the most redundant computation. They also show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, we conduct a cost-benefit analysis to evaluate the performance of the two scenarios.

Dynamic malicious jamming of spectrum throughout the entire communication band is a major issue confronted by tactical communications. The conventional spectrum access scheme based on the central control node fails to satisfy the demand of tactical communications; it has a number of drawbacks including low battlefield survival rates, high computational complexity, large interactive communication overhead and slow reaction to dynamic jamming. In this paper, we propose a distributed multi-agent spectrum access strategy without the interactive communication overhead to evade dynamic jamming. Furthermore, A simplified Q reinforcement learning algorithm is applied to alleviate collisions of spectrum among nodes. Under the proposed strategy, all nodes can predict and evade dynamic jamming as well as avoid collisions of spectrum usage with other nodes. Simulation results verify the collision-avoidance learning algorithm and indicate that the proposed strategy outperforms the random spectrum access strategy.

In this paper, we propose an optimization approach on access delay by jointly modeling imperfect spectrum sensing and multi-channel multi-secondary user (SU) access contention, from a cross-layer perspective, for channel-hopping (CH) based cognitive radio networks (CRNs). Specifically, we first employ an absorbing Markov chain to model the multi-SU access contention process by combining the impacts of spectrum sensing and rendezvous; furthermore, we propose a methodology to derive the enclosed expression of rendezvous probability by analyzing the regularity and the periodicity of a CH rendezvous algorithm. We further analyze the access delay model and compute the optimal parameter combination (e.g., sensing duration and access contention duration) to build a communication link within minimum time duration Theoretical analysis and simulation results validate the proposed optimization approach and show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, the impact of the numbers of SUs and channels is also analyzed to study the relationship between CRN size and SUs' minimum access delay.

Cognitive radio (CR) has emerged as an advanced and promising technology to exploit the wireless spectrum opportunistically. In cognitive radio networks (CRNs), any pairwise communicating nodes are required to rendezvous on a commonly available channel prior to exchange information. In the earlier research, the most popular method is selecting a Common Control Channel (CCC) in CRNs to establish the rendezvous. However, employing a CCC has many problems such as the control channel saturation, vulnerability to jamming attacks, and inapplicability to dynamic network scenarios. Therefore, the blind rendezvous, which requires neither CCC nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait (SJ-RW) blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis, computer simulations and experiment with testbed have validated the proposed SJ-RW algorithm.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

This paper describes our demonstration of a self-adaptive network architecture reconfiguration technology in Cognitive Radio Networks (CRNs) under complex and vicious environment. The technology enables CRN to switch between three kinds of typical network architectures, i.e., centralized, ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switch can solve the problem of physical breakdown and invalid communication link in vicious network environment, and hence enhance the network robustness. We implement the technology on a CRN testbed consisting of GNU Radio and USRPs and verify its performance including switching time and throughput.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.

In order to improve the connectivity and survivability of Cognitive Radio Networks (CRNs) under complex and vicious communication environment, we propose a network architecture self-adaption technology. The technology enables CRN to switch between three kinds of architectures, i.e., centralized, single-hop ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switching can deal with physical breakdown and invalid communication link due to great distance or spectrum heterogeneity, and enhance the network robustness thereby. The work patterns of each architecture and the switching scheme between them were demonstrated, and theoretical switching time was also calculated. Moreover, a testbed based on GNU Radio and USRPs was set up to test its performance including switching time and throughput. Testing results prove the effectiveness of the technology.