Adaptive resource allocation for cognitive wireless ad hoc networks

Widespread use of resource constrained wireless ad hoc networks requires careful management of the network resources in order to maximize the utilization. In cognitive wireless networks, resources such as spectrum, energy, communication links/paths, time, space, modulation scheme, have to be managed to maintain quality of service (QoS). Therefore in the first paper, a distributed dynamic channel allocation scheme is proposed for multi-channel wireless ad hoc networks with single-radio nodes. The proposed learning scheme adapts the probabilities of selecting each channel as a function of the error in the performance index at each step. Due to frequent changes in topology and flow traffic over time, wireless ad hoc networks require a dynamic routing protocol that adapts to the changes of the network while allocating network resources. In the second paper, approximate dynamic programming (ADP) techniques are utilized to find dynamic routes, while solving discrete-time Hamilton-Jacobi-Bellman (HJB) equation forward-in-time for route cost. The third paper extends the dynamic routing to multi-channel multi-interface networks which are affected by channel uncertainties and fading channels. By the addition of optimization techniques through load balancing over multiple paths and multiple wireless channels, utilization of wireless channels throughout the network is enhanced. Next in the fourth paper, a decentralized game theoretic approach for resource allocation of the primary and secondary users in a cognitive radio networks is proposed. The priorities of the networks are incorporated in the utility and potential functions which are in turn used for resource allocation. The proposed game can be extended to a game among multiple co-existing networks, each with different priority levels --Abstract, page iv

Spectrum and transmission range aware clustering for cognitive radio ad hoc networks

Cognitive radio network (CRN) is a promising technology to overcome the problem of spectrum shortage by enabling the unlicensed users to access the underutilization spectrum bands in an opportunistic manner. On the other hand, the hardness of establishing a fixed infrastructure in specific situations such as disaster recovery, and battlefield communication imposes the network to have an ad hoc structure. Thus, the emerging of Cognitive Radio Ad Hoc Network (CRAHN) has accordingly become imperative. However, the practical implementation of CRAHN faced many challenges such as control channel establishment and the scalability problems. Clustering that divides the network into virtual groups is a reliable solution to handle these issues. However, previous clustering methods for CRAHNs seem to be impractical due to issues regarding the high number of constructed clusters and unfair load distribution among the clusters. Additionally, the homogeneous channel model was considered in the previous work despite channel heterogeneity is the CRN features. This thesis addressed these issues by proposing two clustering schemes, where the heterogeneous channel is considered in the clustering process. First, a distributed clustering algorithm called Spectrum and Transmission Range Aware Clustering (STRAC) which exploits the heterogeneous channel concept is proposed. Here, a novel cluster head selection function is formulated. An analytical model is derived to validate the STRAC outcomes. Second, in order to improve the bandwidth utilization, a Load Balanced Spectrum and Transmission Range Aware Clustering (LB-STRAC) is proposed. This algorithm jointly considers the channel heterogeneity and load balancing concepts. Simulation results show that on average, STRAC reduces the number of constructed clusters up to 51% compared to conventional clustering technique, Spectrum Opportunity based Clustering (SOC). In addition, STRAC significantly reduces the one-member cluster ratio and re-affiliation ratio in comparison to non-heterogeneity channel consideration schemes. LB-STRAC further improved the clustering performance by outperforming STRAC in terms of uniformity and equality of the traffic load distribution among all clusters with fair spectrum allocation. Moreover, LB-STRAC has been shown to be very effective in improving the bandwidth utilization. For equal traffic load scenario, LB-STRAC on average improves the bandwidth utilization by 24.3% compared to STRAC. Additionally, for varied traffic load scenario, LB-STRAC improves the bandwidth utilization by 31.9% and 25.4% on average compared with STRAC for non-uniform slot allocation and for uniform slot allocation respectively. Thus, LB-STRAC is highly recommended for multi-source scenarios such as continuous monitoring applications or situation awareness applications

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks

Vehicular Dynamic Spectrum Access (VDSA) combines the advantages of dynamic spectrum access to achieve higher spectrum efficiency and the special mobility pattern of vehicle fleets. This dissertation presents several noval contributions with respect to vehicular communications, especially vehicle-to-vehicle communications. Starting from a system engineering aspect, this dissertation will present several promising future directions for vehicle communications, taking into consideration both the theoretical and practical aspects of wireless communication deployment. This dissertation starts with presenting a feasibility analysis using queueing theory to model and estimate the performance of VDSA within a TV whitespace environment. The analytical tool uses spectrum measurement data and vehicle density to find upper bounds of several performance metrics for a VDSA scenario in TVWS. Then, a framework for optimizing VDSA via artificial intelligence and learning, as well as simulation testbeds that reflect realistic spectrum sharing scenarios between vehicle networks and heterogeneous wireless networks including wireless local area networks and wireless regional area networks. Detailed experimental results justify the testbed for emulating a mobile dynamic spectrum access environment composed of heterogeneous networks with four dimensional mutual interference. Vehicular cooperative communication is the other proposed technique that combines the cooperative communication technology and vehicle platooning, an emerging concept that is expected to both increase highway utilization and enhance both driver experience and safety. This dissertation will focus on the coexistence of multiple vehicle groups in shared spectrum, where intra-group cooperation and inter-group competition are investigated in the aspect of channel access. Finally, a testbed implementation VDSA is presented and a few applications are developed within a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system

Cross layer routing and scheduling for multi-channel Wimax mesh networks

Broadband wireless networks are becoming increasingly popular due to their fast and inexpensive deployment and their capabilities of providing flexible and ubiquitous Internet access. Due to the limitation of shared resources in wireless mesh network such as bandwidth, spatial reuse is introduced for concurrent transmissions. The simultaneous transmissions face many challenges regarding interference on the ongoing transmission. To maximize the network performance of mesh networks in terms of spatial reuse, it is essential to consider a cross-layer for resource allocation in different layers such as the routing network layer, the scheduling resource allocation Media Access Control (MAC) layer and physical layer. Therefore, this thesis focuses on improving the spatial reuse for resource allocation mechanism including routing tree construction by taking into consideration the reliable path, channel assignment and scheduling algorithms. Firstly, a Fuzzy based Constructed Routing Tree (FLCRT) is proposed to incorporate fuzzy logic with routing to enable cognitive capability in packet forwarding for uplink or downlink communication. Secondly, the link-aware routing path is proposed to satisfy the connection lifetime and better routing stability for successful requirements of transmission using multi sponsor node technique. Then, a better understanding of reliability analysis is pursued in the context of homogeneous wireless network. Ultimately, heuristic resource allocation including channel assignment and centralized scheduling algorithms are proposed based on the cellular learning automata to enhance the number of concurrent transmissions in the network by efficiently reusing the spectrum spatially. The attempt of heuristic resource allocation algorithms is to find the maximal number of nodes that could transmit data concurrently. The numerical and simulation results show that FLCRT, Learning Automata Heuristic Channel Assignment (LAHCA), and Learning Automata Heuristic Centralized Scheduling (LAHCS) perform better in terms of scheduling length, channel utilization ratio, and average transmission delay as compared with the existing approaches. The proposed FLCRT scheme with respect to the number of subscriber station (SS) nodes performs better in decreasing the scheduling length, average transmission delay, and channel utilization ratio by 38%, 19%, and 38% compared with Interference-Load-Aware routing. LAHCA algorithm improves the number of channels in comparison with random selection algorithm by 8%. LAHCS algorithm using multi channels proposed by LAHCA can reduce the scheduling time, average transmission delay as well as enhance channel utilization ratio versus number of SS nodes by 7%, 8%, and 6% respectively compared with Nearest algorithm in higher traffic demands

Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V