Cooperative relaying in cognitive radio networks offers time and space diversity and thereby, provides an effective technique to improve spectrum utilization. Most of the existing research has been focused on adopting this technique in single hop communication which may not fully exploit the benefits of cooperative transmissions. Recently, multi-hop cooperative relaying has been explored in routing path formation between primary transmitter-receiver pairs. In this approach, each user handles one cooperation request at a time and takes part in at most one routing path, which makes the model unscalable and limits the benefits of cooperation. Also, primary users dictate the cooperation terms with no or limited involvement from the participating secondary users. This model, therefore, cannot capture the dynamics when both the primary and secondary users require to make cooperation decisions considering the tradeoffs of multiple offers. As a result, the essence of multi-hop cooperative relaying cannot be realized from the existing approach and calls for further investigation. In this work, we consider a network of coexisting primary and secondary users in which their intention to improve throughput via mutual cooperation is formulated as an overlapping coalition formation game. Based on the analysis of the game, we devise mcRoute, a distributed multi-hop coalition based cooperative routing and scheduling algorithm that forms stable coalitions satisfying users' mutual interest. A primary user constructs its routing path in the form of a coalition with secondary users relaying its packet. A secondary user takes part in one or more coalitions by relaying corresponding primary packets and accessing their channels for its own transmission. Finally, we analyze the performance of the algorithm through extensive numerical simulations.

The allocation of underutilized spectrum from primary users to secondary users in real time is likely the most promising avenue for advancing efficiency of spectrum use given the ever-increasing demand for transmission. Research in this area has focused on auctions to facilitate the distribution of spectrum, inducing truthful reporting by participants. However, most research has assumed a static or partially dynamic setting. These approaches are unable to capture that spectrum becomes available at random intervals as primary users' needs vary across time; and, similarly, secondary users' needs vary over time. Moreover, frequently there is flexibility regarding the time of transmission - with some transmissions being more urgent and time-sensitive than others. Therefore, existing research cannot be directly applied to such auction environments involving users with variable transmission deadlines, while preserving efficiency and truthfulness. In this work, we design SOADE, a strategyproof online auction mechanism in dynamic environments that considers dynamic arrival of bidders with varying transmission deadlines and dynamic availability of spectrum. SOADE builds on a priority function that determines the rank of a bidder of winning spectrum at an auction considering its valuation, deadline, and uncertainty associated with dynamic arrival of bidders and spectrum availability. We analytically prove that SOADE is truthful and ex ante and ex post individually rational. Finally, we perform numerical simulation to demonstrate the efficiency of SOADE and show that it improves auction revenue compared to prior work.

Online spectrum auctions will play a pivotal role in restructuring the existing spectrum allocation system given growing spectrum demand from users with diverse requirements. The research in this field mainly concentrates on designing truthful mechanisms in a static or partially dynamic setting. This setting, however, does not capture the fact that spectrum becomes available according to primary users' activities, and the reserve price for this unused spectrum differs from one primary user to another. The number of secondary users with spectrum demand, their transmission deadlines, and their valuations for their transmissions also vary in time and space. Existing research cannot ensure truthful reporting of participants in presence of such diversity in demand and supply. In this work, we consider such a dynamic secondary market and present two spectrum allocation and pricing algorithms. The first algorithm requires distribution knowledge of bidders and sellers, and assesses the winning probability and payment amount of participants based on a priority function. The second algorithm determines the payment amount by exploring the interference relationships between bidders, their transmission deadlines and valuations without any distribution knowledge. We analytically prove that both these algorithms satisfy three desired auction properties - truthfulness, individual rationality, and budget balance. Finally, we present simulation results to analyze the performance of these two algorithms under different auction settings.

Cooperative relaying in cognitive radio networks offers time and space diversity and thereby, provides a useful technique to improve spectrum utilization. Most of the existing research has adopted this technique in single hop communication which may not fully exploit the benefits of cooperative transmissions. So, the essence of multi-hop cooperative relaying cannot be realized from the existing research and needs further investigation. In this work, we consider a network of coexisting primary and secondary users in which their intention to improve throughput via mutual cooperation is formulated as an overlapping coalition formation game. Based on the analysis of the game, we devise mcRoute, a distributed multi-hop coalition based cooperative routing and scheduling algorithm that forms stable coalitions satisfying users' mutual interest. A primary user constructs its routing path in the form of a coalition with secondary users relaying its packet. A secondary user takes part in one or more coalitions by relaying corresponding primary packets and accessing their channels for its own transmission. Finally, we analyze the performance of the algorithm through extensive numerical simulations.

In this paper, we study the cooperation model to design and develop a system that improves the reception rate of delay sensitive packets in realtime applications. While making a cooperation decision, a user with real-time traffic must consider the queue size, packet deadline, the traffic rate, channel condition. Existing cooperation models do not take these factor into consideration and therefore, are not applicable to the improvement of reliability of real-time traffic. We formulate the cooperation model using a Markov decision process (MDP) which is NP-Hard. The MDP-based optimization problem of a single pair of primary and secondary user is analyzed to find the optimum transmission decision in different states of the network. Based on the findings of a single pair case, we develop a distributed cooperation algorithm where a user (primary or secondary) quantifies the immediate and future impact of cooperation and takes its decision accordingly. Extensive simulation is performed to evaluate the performance of the proposed cooperation algorithm which reveals its efficacy compared to the non-cooperative scheme.

Dynamic spectrum access through Cognitive Radio Networks (CRNs) and exploiting multiple radios on a single node are two different well accepted techniques for enhancing network performance. However, simultaneous usage of both the techniques, i.e., augmenting dynamic spectrum access with multiple radios, is yet to be investigated in the literature. Therefore, in this paper, we investigate simultaneous usage of both the techniques. In our investigation, we perform rigorous ns-2 simulation. Simulation results reveal a key finding - augmenting spectrum harvesting with multiple radios makes throughput worse, however, can improve delay. Besides, as the simulation results do not reveal micro-level aspects of the improved delay performance, we perform mathematical modeling of the delay to do so. Further, we present numerical results based on the models to demonstrate the micro-level aspects.

IEEE 802.22 is the first standard based on the concept of cognitive radio. It recommends collaborative spectrum sensing to avoid the unreliability of individual spectrum sensing while detecting primary user signals. However, it opens an opportunity for attackers to exploit the decision making process by sending false reports. In this paper, we address security issues regarding distributed node sensing in the 802.22 standard and discuss how attackers can modify or manipulate their sensing result independently or collaboratively. This problem is commonly known as spectrum sensing data falsification (SSDF) attack or Byzantine attack. To counter the different attacking strategies, we propose a reputation based clustering algorithm that does not require prior knowledge of attacker distribution or complete identification of malicious users. We provide an extensive probabilistic analysis of the performance of the algorithm. We compare the performance of our algorithm against existing approaches across a wide range of attacking scenarios. Our proposed algorithm displays a significantly reduced error rate in decision making in comparison to current methods. It also identifies a large portion of the attacking nodes and greatly minimizes the false detection rate of honest nodes.

In this paper, we exploit mutual cooperation between primary and secondary users to reduce delay in packet transmission in a cognitive network. More formally, we present a delay optimization problem based on the cooperation model and show that the problem is NP-Hard. The mutual benefit through cooperation is analyzed using a time dependent priority queueing system. Analytical simulation results are presented to validate the proposed cooperation model.

In this paper, we study the distributed channel selection problem in a cognitive network. We consider a time varying channel environment where secondary users do not have any prior knowledge of primary transmission and independently learn about existing channels without any explicit communication with other users. To solve this problem, we address the impact of switching between channels during the learning process that is mostly ignored in literature. We propose a scalable distributed block learning algorithm that minimizes the switching cost, adapts to time varying channel conditions, and achieves logarithmic regret. Simulation results show that our algorithm performs significantly better than the existing ones.

In this paper, we address the channel selection problem with switching cost and propose a distributed learning approach that minimizes the sum regret while ensuring quick convergence to an optimal solution and logarithmic regret. Our algorithm is adaptive in the sense that it adapts to the changing idle status of channels and achieves logarithmic regret even in a dynamic environment. The experimental result shows that our algorithm outperforms the existing algorithm in terms of regret, scalability and channel switching cost.

Vehicular Communication Networks and Systems (VCNS) and Intelligent Transportation Systems (ITS) are one of the most attractive and challenging topics in recent days since a well efficient protocol for vehicular communication can facilitate the reduction of traffic congestion and can provide us with many more promising applications. In this paper, we propose a protocol for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. As one of the challenging parts of this paper, we present an experimental testbed in which two major applications of V2I & V2V communication (i.e. traffic congestion detection and emergency warning) is implemented. Based on careful analysis, we also calculate some key system parameters which reflect the efficiency of the protocol in different applications.