Cognitive Radio Mobile Ad Hoc Networks


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1. Introduction

In cognitive radio networks, secondary users exchange control information to utilize the available channels efficiently, to maintain connectivity, to negotiate for data communication such as sender-receiver handshakes, for neighbor discovery etc. This task is not trivial in cognitive radio networks due to the dynamic nature of network environment. Generally, this problem is tackled by using two famous approaches.

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The first one is the use of common control channel CCC and the second one is using channel hopping a. However, it may not be feasible in cognitive radio networks as the available channels, including control channel, are dynamically changing according to primary user activities.

Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks

Channel hopping approaches can tolerate the failure of network due to primary user activities. But it causes significant amount of channel access delay which is known as time to rendezvous TTR. In this paper, we propose a hybrid protocol of these two mechanisms. This hybrid protocol can maintain connectivity and it can guarantee the secondary users to be able to exchange necessary control information in dynamic environment.

Recent research activities about cognitive radio CR are mainly focusing on opportunistic spectrum access and spectrum utilization. However, CR technology will have significant impacts on upper layer performance such as topology control and routing in wireless networks, especially in mobile ad hoc networks MANETs. Since the spectrum availability is affected by primary user activities and the mobility of cognitive users, cognitive routing is required to be forward looking rather than reactive.

To this end, a topology control and routing framework is presented in this chapter, where cognitive routing is enabled by topology control. In the framework, topology control serves as a middleware and a cross-layer module residing between routing and CR module.


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  6. Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey.

Prediction techniques can be used to construct a smart network topology, which provisions cognition capability to routing. Particularly, we present a distributed prediction-based cognitive topology control PCTC scheme to demonstrate the framework and verify its feasibility. This chapter presents analysis for delays for both multihop cognitive radio networks and single-hop cognitive radio networks. For multihop cognitive radio networks, we analyze the amount of time that a packet spends to travel over the intermittent relaying links over multiple relaying hops and characterize it with the metric called information propagation speed.

Optimal relaying node placement strategies are derived to maximize information propagation speed. For single-hop cognitive radio networks, we will analyze how delay is affected by multiple cognitive radio design options, including the number of channels to be aggregated, the duration of transmission, the channel separation constraint on channel aggregation, and the time needed for spectrum sensing and protocol handshake. How these different options may affect the delay under different secondary and primary user traffic loads is revealed.

Methods for computing optimal cognitive radio design and operation strategy are derived. Cognitive radio CR has been proposed as a promising solution to improve connectivity, self-adaptability, and efficiency of spectrum usage. When used in video applications, user-perceived video quality experienced by secondary users is a very important performance metric to evaluate the effectiveness of CR technologies.

However, most current research only considers spectrum utilization and effectiveness at MAC and PHY layers, ignoring the system performance of upper layers. Therefore, in this chapter we aim to improve the user experience of secondary users for wireless video services over cognitive radio networks.

We propose a quality-driven cross-layer optimized system to maximize the expected user-perceived video quality at the receiver end, under the constraint of packet delay bound. By formulating network functions such as encoder behavior, cognitive MAC scheduling, transmission, as well as modulation and coding into a distortion-delay optimization framework, important system parameters residing in different network layers are jointly optimized in a systematic way to achieve the best user-perceived video quality for secondary users in cognitive radio networks.

The performance enhancement of the proposed system is evaluated through extensive experiments based on H. The proposed platform is based on a heterogeneous multihop cluster-based vehicular network, where a vehicular node can choose to play the role of a gateway or a client. The gateway nodes communicate directly with a roadside base station through a WiMAX link. The client nodes connect to the gateways through WiFi links. Traffic from client nodes are relayed by the gateways to a roadside base station.

The vehicular nodes are the self-interest i. This distributed decision-making framework, which enables the vehicular nodes with cognitive capability, is modeled and analyzed using game theory. Also, a Q-learning algorithm is used in vehicular nodes to provide the cognitive capability to learn and adapt their decision. Dynamics of Q-learning algorithm can be modeled as an evolutionary game.

Legacy health care monitoring systems demand a great amount of resources such as health care personnel and medical equipments. This increases the cost of health care making it unaffordable to the majority of our society. This chapter introduces an architecture and design of a health care automation network. The health care automation network can be implemented in hospitals or in senior communities.

This network can leverage the existing infrastructure and reduce the cost of implementation. Research challenges in development of cognitive radio health care automation network are also discussed. Ziqian Dong, Shamik Sengupta, S. This scenario involves the end-user traveling in a public transportation system and requesting multimedia services to the operator. We reached the conclusion that the CRRM enables to increase the system throughput when the load thresholds are set to 0. We have also sought the most efficient way to manage routing packets inside the Wi-Fi network.

Finally, we discuss the challenges that need to be addressed in order to materialise the envisaged cognitive radio scenario in public transportation. In this chapter, we present an application of the Cognitive Networking paradigm to the design and development of autonomous Cognitive Access Point CogAP for Wi-Fi hotspots and home wireless networks.

In [ 28 ], several routing metrics are introduced and combined. The routing metrics attempt to capture the end-to-end performance of SUs and the interference to PUs. It focuses on actual end-to-end delay, and thus, it is suitable for delay-sensitive applications.

For a more realistic routing metric, one possible candidate is the metric based on interference with PUs. Therefore, the interference-based routing metric is seen to be favorable and promising for implementation in CRAHNs. This condition sets a limitation on network resources. Thus, they should manage their packet transmissions efficiently, possibly by reducing the number of control packet transmissions and focusing on data packet transmissions.

On-demand protocols do not require periodic control packet exchanges, but they broadcast and unicast control packets in the route discovery and route maintenance procedures for active routes. In other words, by eliminating periodic control packet exchanges, on-demand protocols reduce the number of packet transmissions required by SUs. They discover multiple paths during the route discovery. The effectiveness of adding control packet transmissions against the channel availability should be considered.

The available spectrum for SUs may be different from node to node. Most of the routing protocols address the spectrum diversity by jointly selecting path and channel and by considering channel switching events. ARDC [ 33 ] adopts graph modeling to adapt to dynamic changes in the network topology efficiently. There is a wide range of reconfigurability supported by SUs with their cognitive radio devices, that is, transmission power, modulation scheme, coding rate, etc.

CRAHNs: Cognitive radio ad hoc networks

These reconfigurability options can be combined with the routing decision. For example, by adjusting the transmission power and modulation scheme, the SUs might change the communication technique from overlay to underlay, without switching to another channel.

By considering the reconfigurability of SUs, various options for routing decision are possible. When considering a network with mobile node deployment, energy conservation is inevitable since mobile nodes are often battery-powered.


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In CRAHNs, energy conservation of nodes is crucial because they have some extra particular tasks compared to noncognitive radio network. In the previous discussion, it has been pointed out that routing in CRAHNs requires further complexities; those are novel routing metric, learning module, and route maintenance. However, above all, the routing protocol should be as simple as possible to conserve energy. The works referred to in this paper fail to pay attention to this issue, except AiSorp and anti-intermittence routing, both of which increase the routing lifetime, and local coordination-based routing that focuses on load balancing.

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However, local coordination-based routing requires additional control packet exchanges for its workload evaluation, while it does not study the energy consumption of doing so. Nevertheless, for all referred works, since the routing protocols are on-demand based, they support simple energy conservation by setting up the route only when it is needed.

To preserve network lifetime, analyzing the energy consumption of additional tasks given to the nodes by the routing protocol is encouraged. The nodes have to perform extra task to attach the spectrum-related information as well as to send longer packets. Those additional activities are energy consuming. Therefore, node-burdening tasks should be avoided if the performance is not fairly improved. The basic categories of services are bandwidth, latency, jitter, and packet loss [ 34 ].

The applications might only need one of the services or a combination of them. However, in the referred works, we could not find any routing protocol with QoS support. Even though providing QoS requires additional computation, it is advantageous for SUs, especially when there are various kinds of application traffic with different service requests. By defining QoS, spectrum management becomes more efficient.

For example, given a set of available spectrum band, there are two kinds of applications run by a SU: data transfer and voice communication. The routing protocol with QoS support could recognize the application service demands and would choose the path with the lowest loss for data transfer, lowest end-to-end delay, and lowest jitter for voice communication. Without QoS support, the routing protocol would assign the path and spectrum based solely on its routing metric and might fail to satisfy the application requirements.

Mobile cognitive radio ad hoc networks routing omnet projects

This survey paper presents a number of on-demand routing protocols for cognitive radio ad hoc networks. It turns out that routing protocols that modify AODV are the most popular ones. One of the reasons is because DSR route discovery may lead to unpredictable packet length, which is not suitable for intermittent connectivity environment of cognitive radio networks.

Looking at existing works and discussions on routing protocol design for CRAHNs, an appropriate routing protocol could be derived. Firstly, we recommend an adaptation of on-demand routing since its performance has been proven to suit mobile ad hoc networks, and it has the preferred properties suitable for CRAHNs. Then, a novel routing metric should be defined to include spectrum-related information in the routing mechanisms. In this way, the path selection consists of not only the selected path but also of the assigned spectrum.

One promising candidate is a metric that is based on interference level with PUs since it guarantees PU avoidance. The routing protocol should consider the network resource consumption by examining both the necessity of multiple paths and the addition of control packet exchanges. Moreover, the routing protocol should be aware of network heterogeneity by considering the reconfigurability of SUs as one of the routing options. To preserve energy, the routing protocol should be as simple yet effective as possible. The trade-off between energy consumption and additional node tasks should be evaluated, especially when the extra tasks are oriented to a single objective and not overall network performance improvement.

Finally, the routing protocol should consider QoS support, which would be beneficial to SUs. The authors wish to thank the editor and anonymous reviewers for their helpful comments on this paper. This work was supported in part by the research fund from Chosun University, This article is published under license to BioMed Central Ltd.

Review Open Access. On-demand routing protocols for cognitive radio ad hoc networks. Introduction Cognitive radios enable an adaptive approach in utilizing existing wireless spectrum. In [ 3 ], a PU avoidance scheme is inserted in the route setup procedure. There are two significant times when the SUs should select appropriate channels for their communication: one time is at the beginning of the data transmission, and the other time is at the route repair occasion. The channel availability information is obtained from the spectrum sensing mechanism on the physical layer or spectrum occupancy database, if any [ 4 ].

As stated earlier, to apply the routing protocols of other wireless networks to cognitive radio networks is not feasible due to their poor performance in dynamic spectrum environment. A common approach is by inserting spectrum-related information, such as spectrum opportunity SOP , channel usage list, etc. For example, the source node may insert its spectrum-related information on RREQ packets. When the intermediate nodes forward the RREQ packets, they also include their own spectrum-related information.

Then, usually the destination node gets to decide the spectrum band to be used for data transfer. It assigns the spectrum, encapsulates it in RREP, and sends it back to the source node. The routing protocols that adopt this method are as follows: spectrum-aware on-demand routing protocol SORP [ 13 ], delay-motivated on-demand routing protocol DORP [ 14 ], and weighted hop, spectrum awareness, and stability WHAT routing metric [ 15 ].

CRAHNs: Cognitive radio ad hoc networks - Semantic Scholar

However, most studies that add extra information to control packets did not evaluate the overhead. Some protocols propose solutions to specific problems such as the deafness problem, load balancing, and intermittent connectivity problem. Local coordination-based routing is also a continuation work of SORP with a local coordination scheme on intersecting nodes to perform load balancing.

In this protocol, once a node becomes an intersecting node to accommodate multiple data flows, the local coordination scheme is invoked. This scheme helps intersecting nodes to decide whether to perform flow accommodation or flow redirection based on workload evaluation includes additional control packet exchanges. When new flow 1 occurs, first it is established with node 1 and node 2 as intermediate nodes. These two intersecting nodes perform a local coordination scheme to find appropriate neighbors to redirect the flow.

The results are as follows: node 1 redirects flow 1 to neighboring node 3, and node 2 redirects flow 3 to neighboring node 4. Figure 3 Load balancing scheme in local coordination-based routing. Acknowledgments The authors wish to thank the editor and anonymous reviewers for their helpful comments on this paper.

Table of Contents

Networks , 50 13 Ad Hoc Networks , 7: IEEE Comm. Mag , 48 9 Ad Hoc Networks , 9 3 New Orleans; 25—26 February In Mobile Computing. Edited by: Imielinski T, Korth H.

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Cognitive Radio Mobile Ad Hoc Networks Cognitive Radio Mobile Ad Hoc Networks
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