ON THE LOCATION-AWARE COOPERATIVE SPECTRUM SENSING IN URBAN ENVIRONMENT

Spectrum sensing is a key enabling technology for cognitive radio networks (CRNs). The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive radio users without interfering with the operations of the licensed network. Spectrum sensing decisions can lead to erroneous sensing with low performance due to fading, shadowing and other interferences caused by either terrain inconsistency or dense urban structure. In order to improve spectrum sensing decisions, in this paper a cooperative spectrum sensing scheme is proposed. The propagation conditions such as the variance and intensity of terrain and urban structure between two points with respect to signal propagation are taken into consideration. We have also derived the optimum fusion rule which accounts for location reliability of secondary users (SUs). The analytical results show that the proposed scheme slightly outperforms the conventional cooperative spectrum sensing approaches

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Energy-efficient spectrum sensing approaches for cognitive radio systems

Designing an energy efficient cooperative spectrum sensing for cognitive radio network is our main research objective in this dissertation. Two different approaches are employed to achieve the goal, clustering and minimizing the number of participating cognitive radio users in the cooperative process. First, using clustering technique, a multilevel hierarchical cluster-based structure spectrum sensing algorithm has been proposed to tackle the balance between cooperation gain and cost by combining two different fusion rules and exploiting the tree structure of the cluster. The algorithm considerably minimizes the reporting overhead while satisfying the detection requirements. Second, based on reducing the number of participating cognitive radio users, primary user protection is considered to develop an energy efficient algorithm for cluster-based cooperative spectrum sensing system. An iterative algorithm with low complexity has been proposed to design energy efficient spectrum sensing for cluster-based cooperative systems. Simulation results show that the proposed algorithm can significantly minimize the number of contributing of cognitive radio users in the collaboration process and can compromise the performance gain and the incurred overhead. Moreover, a variable sensing window size is also considered to propose three novel strategies for energy efficient centralized cooperative spectrum sensing system using the three hard decision fusion rules. The results show that strategies remarkably increase the energy efficiency of the cooperative system; furthermore, it is shown optimality of k out of N rule over other two hard decision fusion rules. Finally, joint optimization of transmission power and sensing time for a single cognitive radio is considered. An iterative algorithm with low computational requirements has been proposed to jointly optimize power and sensing time to maximize the energy efficiency metric. Computer results have shown that the proposed algorithm outperforms those existing works in the literature

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research

Radio Resource Management for Unmanned Aerial Vehicle Assisted Wireless Communications and Networking

In recent years, employing unmanned aerial vehicles (UAVs) as aerial communication platforms or users is envisioned as a promising solution to enhance the performance of the existing wireless communication systems. However, applying UAVs for information technology applications also introduces many new challenges. This thesis focuses on the UAV-assisted wireless communication and networking, and aims to address the challenges through exploiting and designing efficient radio resource management methods. Specifically, four research topics are studied in this thesis. Firstly, to address the constraint of network heterogeneity and leverage the benefits of diversity of UAVs, a hierarchical air-ground heterogeneous network architecture enabled by software defined networking is proposed, which integrates both high and low altitude platforms into conventional terrestrial networks to provide additional capacity enhancement and expand the coverage of current network systems. Secondly, to address the constraint of link disconnection and guarantee the reliable communications among UAVs as aerial user equipment to perform sensing tasks, a robust resource allocation scheme is designed while taking into account the dynamic features and different requirements for different UAV transmission connections. Thirdly, to address the constraint of privacy and security threat and motivate the spectrum sharing between cellular and UAV operators, a blockchain-based secure spectrum trading framework is constructed where mobile network operators and UAV operators can share spectrum in a distributed and trusted environment based on blockchain technology to protect users' privacy and data security. Fourthly, to address the constraint of low endurance of UAV and prolong its flight time as an aerial base station for delivering communication coverage in a disaster area, an energy efficiency maximization problem jointly optimizing user association, UAV's transmission power and trajectory is studied in which laser charging is exploited to supply sustainable energy to enable the UAV to operate in the sky for a long time