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Applications of Data Dissemination inWireless Networks
In vehicular ad-hoc networks, an interesting application is to disseminate emergency messages to the specific area while guaranteeing all relevant vehicles receive the emergency message, such that people can change their routes to destination in time. In this way, people can avoid getting into a traffic jam. In this context, an analysis of emergency message dissemination in vehicular networks is done in [40]. In [41], a fast and reliable emergency message dissemination mechanism was proposed to disseminate emergency message in VANETs. In addition, the authors discussed how to solve the broadcast storm problem, achieving low dissemination delay, and providing a high reliability in freeway scenario [41].Data dissemination in wireless sensor networks has been widely studied in the literature.
InWSNs, data dissemination is generally performed from sensor nodes to a static sink. This data could be an emergency message such as a fire alarm, and it must be transmitted fastly and reliably towards the sink. Note that in emergency situations, the sink could move, e.g., a fire fighter roaming in the area or an Unattended Aerial Vehicle (UAV). For e.g., the authors in [42] proposed data dissemination protocol for emergency message transmission in mobile multi-sink WSNs. In [43], the authors proposed density-based proactive data dissemination protocol, Deep, for wireless sensor networks with uncontrolled sink mobility. Similarly, a proactive data dissemination approach, called Supple, for data gathering in self-organized Wireless Sensor Networks is proposed in [44]. Supple effectively distributes and stores monitored data in WSNs such that it can be later sent to or retrieved by a sink. Epidemic dissemination has huge potential, enabling, for instance, a wide range of mobile ad-hoc communication and social networking applications, supported entirely through opportunistic contacts in the physical world. For instance, the authors in [45] improved the understanding of data dissemination in opportunistic mobile ad hoc networks. In fact, their work is a first step in studying the impact of social behaviour of users on information dissemination. Another application where epidemic dissemination could be used is WSNs.
Directed diffusion [46] is one such example, where interest propagation is done through flooding. At the following, we discuss the classification of broadcasting protocols.
Data Dissemination in Multi-Channel Environment
In this section, our goal is to briefly discuss some data dissemination protocols for multichannel environment.
In [53], the authors proposed two protocols, McSynch and McTorrent, for data dissemination in multi-channel wireless sensor networks. McTorrent achieves end-to-end data dissemination in less time than the single channel protocols, while McSynch can substantially reduce the time required for a cluster-wide synchronization. In [54], the authors analyzed the performance limits of data dissemination with multi-channel, single radio sensors under random packet loss. The authors showed that, for an arbitrary topology, the problem of minimizing the expected delay of data dissemination can be treated as a stochastic shortest path problem. Broadcasting on multiple access channels by deterministic distributed protocols are studied by authors in [55]. The authors compared the packet latency of deterministic protocols and backoff-type randomized protocols.Broadcasting protocols for multi-channel wireless networks in the presence of adversary attacks are proposed in [56]. The authors used network coding for data dissemination in order to reduce the impact of suck adversary attacks on dissemination performance and derived the optimum number of channels that nodes have to access in order to minimize the reception delay. A power saving data dissemination architecture for mobile clients’ units in multi-channel environment is proposed in [57]. The authors proposed a concurrency control technique suitable for the multi-channel dissemination-based architectural model.
A data scheduling algorithm over multiple channels in mobile computing environment is proposed in [58]. The authors formulated the average expected delay of multiple channels considering data items’ access frequencies, variable length, and different bandwidth of each channel. In cognitive radio networks, very less work has been done on data dissemination. For e.g., in [59], the authors investigated the distribution and limits of information dissemination latency and speed in cognitive radio networks. Hereafter, we discuss the challenges of data dissemination in cognitive radio networks.
Challenges of Data Dissemination in Cognitive Radio Networks
Robust data dissemination is a challenge in cognitive radio networks due to its intrinsic properties, such as:
• the availability of multiple-channels i.e., CR nodes have more than one channel in the available channel set. Available channel set is the set of channels eligible by CR nodes for any communication.
• the diversity in the number of available channels i.e., CR nodes have diverse set of available channels in the available channel set.
• the primary radio activity i.e., channels are occupied by the PR nodes and are only available to CR nodes for transmission when they are idle. In fact, the spatiotemporal utilization of spectrum by PR nodes (i.e. primary radio nodes’ activity) adds another challenge to data dissemination. As a consequence, the number of available channels to CR nodes changes with time and location leading to the diversity in the number of available channel set. Because of PR’s activity, the usability of the channels by CR nodes becomes uncertain.
Classification of Channel Selection Strategies in CRNs
Recently, a lot of channel selection strategies have been proposed for cognitive radio networks [60–70]. These channel selection strategies are designed to achieve different performance goals, for instance, optimization of throughput, delay, etc. Besides achieving these goals, each channel selection strategy has a nature, according to its reaction with the appearance of PR nodes on the CR communicating channel. Therefore, channel selection strategies can be classified into three categories by nature: (1) proactive (predictive), (2) threshold based, and (3) reactive. From the algorithmic perspective, channel selection strategies can be classified into centralized and distributed. The classification of channel selection strategies in cognitive radio networks is shown in Fig. 2.2. Table 2.1 compares different channel selection strategies for cognitive radio networks and their features. In the following, we discuss each classification in detail.
Goals of Channel Selection Strategies
Channel selection strategies have been used to achieve different goals, e.g., load balancing, throughput maximization, channel switching delay minimization etc. Authors in [60] proposed a channel selection strategy to satisfy the traffic demands of Access Points. Throughput maximization is another goal and several channel selection strategies were proposed for throughput maximization [63, 68, 70, 72, 73]. In [68], the authors determined the transmission schedule of the CR nodes in order to improve the network throughput. In [70], The authors improved the throughput of the CR users in the TV broadcast network. In fact, the authors proposed a predictive channel selection scheme to maximize spectrum utilization and minimize disruptions to PR nodes. They considered a single-hop network in which CR nodes coordinate with the TV receiver to collect information regarding PR activity. Two opportunistic channel selection schemes, CSS-MCRA and CSS-MHRA, are proposed in [72]. In CSS-MCRA, the goal was to maximize the throughput while minimize the collision rate. In CSS-MHRA, the goal was to maximize the throughput while minimizing the handoff rate. CSS-MCRA and CSS-MHRA both considered single user and are predictive in nature.
Load balancing is another important goal of channel selection strategies [74,75]. In [74], the authors proposed a channel and power allocation scheme for CR networks. The objective was to maximize the sum data rate of all CRs. They considered the availability of a centralized authority, which monitors the PR activity and assign channels to CR nodes. Sensing-based and probability-based spectrum decision schemes are proposed in [75] to distribute the load of CR nodes to multiple channels. The authors derived the optimal number of candidate channels for sensing-based scheme and the optimal channel selection probability for probability-based spectrum decision scheme. The objective of both schemes was to minimize the overall system time of the CR users.
Channel Selection Strategies from the Communication Perspective
From the communication perspective, channel selection strategies can be classified into centralized and distributed. In [99], a comparison between centralized and distributed approaches for spectrum management is provided.
• Centralized Channel Selection Strategies: In centralized channel selection strategies, a centralized entity is present, which helps CR nodes in their channel selection decision, e.g., [100–102]. The authors in [103] investigated different steps for the development of centralized algorithms for different radio networks. They discussed the current interests of regulators, technical requirements, and the possible schemes for dynamic spectrum allocation. In [60], the authors proposed an efficient spectrum allocation architecture that adapts to dynamic traffic demands but they considered a single-hop scenario of Access Points (APs) in Wi-Fi networks. An approach that uses noncontinuous unoccupied band to create a high throughput link is discussed in [65].
In [68], the authors proposed a threshold-based channel sharing scheme between CR nodes. Their algorithm is designed for source-destination pairs and is specially designed for single hop communication. In their paper, the authors assumed that all the PRs are active all the time and no idle channel is available to CR nodes for their communication. A centralized channel allocation scheme for IEEE 802.22 standard is proposed in [104]. The proposed channel allocation scheme allocates the channel based upon three rules: (1) maximum throughput rule, (2) utility fairness rule, and (3) time fairness rule. The authors in [105] proposed an opportunistic channel selection scheme for IEEE 802.11-based wireless mesh networks. In this channel selection scheme, an Access Point (AP) is required to connect the nodes to the Internet via mesh router.
Table of contents :
1 Introduction
1.1 Cognitive Radio Networks
1.1.1 Architecture
1.1.2 Open Issues
1.1.3 CRNs Standards
1.2 Problem Statement
1.3 Contributions of the thesis
1.3.1 Proposed Solutions
1.3.2 Methodology
1.4 Outline of Thesis
2 Data Dissemination and Channel Selection in CRNs
2.1 Applications of Data Dissemination in Wireless Networks
2.2 Classification of Broadcasting Protocols
2.3 Data Dissemination in Multi-Channel Environment
2.4 Challenges of Data Dissemination in Cognitive Radio Networks
2.5 Classification of Channel Selection Strategies in CRNs
2.5.1 Goals of Channel Selection Strategies
2.5.2 Nature of Channel Selection Strategies
2.5.3 Channel Selection Strategies from the Communication Perspective .
2.6 Conclusion
3 SURF: Channel Selection Strategy for Data Dissemination
3.1 System Model and Assumptions
3.2 Channel Selection Strategy SURF
3.3 Primary Radio Unoccupancy
3.3.1 Wrong Prediction of Channel Availability
3.4 Cognitive Radio Occupancy
3.5 Simulation Environment
3.5.1 Implementation Setup
3.5.2 Performance Metrics
3.5.3 Simulation Environment
3.6 SURF Parameters Evaluation
3.6.1 Tries in SURF
3.6.2 Impact of Varying Neighborhood Density on SURF
3.6.3 PR Utilization of the Selected Channel
3.7 SURF Comparison
3.7.1 Protection to Primary Radio Nodes
3.7.2 Robust Data Dissemination
3.7.3 Tuning of Sender and Receiver
3.7.4 Packet Ratio
3.8 Conclusion
4 Impact of Primary Radio Nodes Activity on Channel Selection Strategies
4.1 Introduction
4.2 Channel Selection Strategies
4.3 Primary Radio Nodes Activity Pattern
4.4 Performance Analysis
4.5 Improvements regarding SURF
4.6 Conclusion and Future Work
5 Applicability of SURF
5.1 1st Application: General Context of Internet Access Framework
5.1.1 Related Work
5.1.2 An Internet Access Framework for Future Cognitive Radio Networks
5.1.2.1 Architecture
5.1.2.2 Working Principle
5.1.3 Deployment and Connectivity: Issues and Challenges
5.1.3.1 Network Deployment and Connectivity
5.1.3.2 Infrastructure Discovery
5.1.3.3 Inter-network Coordination
5.1.4 Channel Selection Strategy SURF for CR Devices and CMRs
5.2 2nd Application: General Context of Channel Bonding
5.2.1 System Model and Assumptions
5.2.2 Spectrum Characterization
5.2.3 Channel Bonding Criteria
5.2.4 Discussion
5.3 Conclusion
6 Conclusion and Future Work
6.1 Summary of Contributions
6.2 Future Research
6.2.1 Channel Activity Models of a PR Network
6.2.2 Exploitation of Real Traces of PR Activity
6.2.3 Improvements in SURF considering PR activities’ study
6.2.4 Channel Bonding in Cognitive Radio Networks
6.2.5 Spontaneous CR deployments
A Thesis Publications
B NS-2 Contributed Code
B.1 NS-2 Modifications
C Version Fran¸caise
C.1 R´eseaux Radio Cognitifs
C.1.1 Architecture
C.2 Probl´ematique
C.3 Contributions de la th`ese
C.3.1 Solutions propos´ees
C.3.2 M´ethodologie
C.4 Aper¸cu de la th`ese
C.5 1`ere Partie: SURF M´ethode de s´election de fr´equences
C.5.1 Comparaison de SURF
C.5.1.1 Protection de noeuds radio primaire
C.5.1.2 Diffusion Fiable des Donn´ees
C.6 2`eme Partie: Impact de l’activit´e des noeuds PR
C.7 3`eme Partie: L’applicabilit´e de SURF
C.7.1 1`ere Application: architecture d’acc`es `a Internet
C.7.2 2`eme Application: Agr´egation de fr´equences
C.8 Conclusion
References
