Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN).

The expected widespread usage of Internet-of-Things (IoT) devices and the associated diversity of applications will place extra pressure on network resources including bandwidth availability. In networking within IoT, spectrum scarcity requires the consideration of the adaptive cognitive radio (CR) technology to achieve interference-free and on-demand IoT solutions for a number of applications. However, CR networks share the same security weaknesses of legacy wireless networks. Jamming is considered a common attack, where jammers can attack networks in a proactive or reactive approach. In this paper, we study the channel assignment problem under both proactive and reactive jamming attacks. Specifically, we propose a novel probabilistic-based channel assignment mechanism that aims at minimizing the invalidity ratio of CR packet transmissions subject to delay constraints. The proposed scheme exploits the statistical information of licensed primary users activities, fading conditions and jamming attacks over idle channels to provide communicating CR IoT devices with the most secured channels of lowest invalidity ratios. Simulation results show that our proposed security, availability, and quality-aware channel assignment algorithm can significantly improve network performance.

Due to their distinctive features, unmanned aerial vehicles (UAVs) have been recently exploited to support a wide range of applications. The features include low maintenance cost, compact size, and excellent capability of maneuvering. In particular, UAVs have the potential capabilities to support different technologies such as Internet-of-things (IoT) devices, sensors, cameras, and systems, thus, performing civilian, target tracking, industrial, and military applications. Specifically, target tracking has been recently configured as one of the most attractive applications of UAVs. With this, UAVs estimate and detect the behavior or locate a moving or a stationary item. Accordingly, several research efforts have been conducted to investigate the promising capabilities of UAVs in target-tracking missions, including indoor and outdoor tracking missions. This paper surveys UAV-based target tracking and monitoring for indoor and outdoor environments, where the deployment scenarios of such UAV-based systems are characterized and investigated. Furthermore, we discuss a set of practical design challenges of UAV-based target tracking systems, and thus, we provide a set of potential solutions to deal with these challenges. Specifically, we present a set of recent enabling technologies that might be integrated into UAV target tracking systems, including machine learning (ML), cloud computing, and emerging fifth-generation (5G) technologies. We also demonstrate a use-case scenario in which ML is used to facilitate indoor target tracking and monitoring. Finally, future research directions are outlined that can help in improving the efficiency of the UAV-based target-tracking systems.

Densely crowded environments such as stadiums and metro stations have shown shortcomings when users request data and services simultaneously. This is due to the excessive amount of requested and generated traffic from the user side. Based on the wide availability of user smart-mobile devices, and noting their technological advancements, devices are not being categorized only as data/service requesters anymore, but are readily being transformed to data/ service providing network-side tools. In essence, to offload some of the workload burden from the cloud, data can be either fully or partially replicated to edge and mobile devices for faster and more efficient data access in such dense environments. Moreover, densely crowded environments provide an opportunity to deliver, in a timely manner, through node collaboration, enriched user-specific services using the replicated data and device-specific capabilities. In this article, we first highlight the challenges that arise in densely crowded environments in terms of data/service management and delivery. Then we show how data replication and service composition are considered promising solutions for data and service management in densely crowded environments. Specifically, we describe how to replicate data from the cloud to the edge, and then to mobile devices to provide faster data access for users. We also discuss how services can be composed in crowded environments using service-specific overlays. We conclude the article with most of the open research areas that remain to be investigated.

The cognitive radio (CR) paradigm calls for open spectrum access according to a predetermined etiquette. Under this paradigm, CR nodes access the spectrum opportunistically by continuously monitoring the operating channels. A key challenge in this domain is how the nodes in a CR network (CRN) cooperate to access the medium in order to maximize the CRN throughput. Typical multichannel MAC protocols assume that frequency channels are adjacent and that there are no constraints on the transmission power. However, a CRN may operate over a wide range of frequencies, and a power mask is often enforced on the transmission of a CR user to avoid corrupting the transmissions of spectrum-licensed primary-radio (PR) users. To avoid unnecessary blocking of CR transmissions, we propose a novel distance-dependent MAC protocol for CRNs. Our protocol, called DDMAC, attempts to maximize the CRN throughput. It uses a novel probabilistic channel assignment mechanism that exploits the dependence between the signal's attenuation model and the transmission distance while considering the traffic profile. DDMAC allows a pair of CR users to communicate on a channel that may not be optimal from one user's perspective, but that allows more concurrent transmissions to take place, especially under moderate and high traffic loads. Simulation results indicate that, compared to typical multichannel CSMA-based protocols, DDMAC reduces the blocking rate of CR requests by up to 30%, which consequently improves the network throughput.