Adeel Baig

Routing protocols in Delay Tolerant Networks - a survey

Delay Tolerant Networks (DTN) are a class of networks that lack continuous connectivity between nodes due to limited wireless radio coverage, widely scattered mobile nodes, constrained energy resources, high levels of interference or due to some other similar channel impairment. Examples of such networks are those operating in mobile networks or extreme terrestrial environments, or simply as planned networks in space. The term disruption-tolerant network is also occasionally used in place of DTN. Routing in DTN is challenging because of frequent and long duration periods of non-connectivity. Several routing protocols have been proposed with strategies ranging from flooding to opportunistic approaches. Due to the diversity of available strategies, there is a need to properly classify and evaluate their performance with various angles. In this paper, we study state of the art routing protocols and give a comparison between them with respect to the characteristic features and methodology involved. The pros and cons of each, their performance and open research issues are also discussed.

Spectrum-aware dynamic channel assignment in cognitive radio networks

During the past few years, cognitive radio networks (CRNs) have emerged as a solution for the problems created due to fixed spectrum allocation such as inefficient usage of licensed spectrum. CRNs aim at solving this problem by exploiting the spectrum holes (the spectrum not being used by primary radio nodes at a particular time) and allocating the spectrum dynamically. In this paper, we address the problem of dynamic channel assignment for cognitive radio users in multi-radio multichannel cognitive radio networks (MRMC-CRNs). We propose an efficient spectrum-aware dynamic channel assignment (SA-DCA) strategy for such networks. SA-DCA utilizes available channels and assigns them to multiple radio interfaces of cognitive radio nodes based on primary radio unoccupancy, minimum interference to primary radio nodes, maximum connectivity and minimum interference between cognitive radio nodes. We perform simulations in NS-2 and compare the performance of SA-DCA with two related strategies. Simulation results show that SA-DCA assigns channels efficiently and results in significantly reduced interference to primary radio nodes and increased packet delivery ratio in MRMC-CRNs.

Multicasting in cognitive radio networks

Multicasting is a fundamental networking primitive utilized by numerous applications. This also holds true for cognitive radio networks which have been proposed as a solution to the problems that emanate from the static non-adaptive features of classical wireless networks. A prime application of cognitive radio networks is dynamic spectrum access, which improves the efficiency of spectrum allocation by allowing a secondary network, comprising secondary users, to share spectrum licensed to a primary licensed network comprising primary users. Multicasting in cognitive radio networks is a challenging problem due to the dynamic nature of spectrum opportunities available to the secondary users. Various approaches, including those based on optimization theory, network coding, algorithms, have been proposed for performing efficient multicast in cognitive radio networks. In this paper, we provide a self-contained tutorial on algorithms and techniques useful for solving the multicast problem, and then provide a comprehensive survey of protocols that have been proposed for multicasting in cognitive radio networks. We conclude this paper by identifying open research questions and future research directions.

Prediction-based recovery from link outages in on-board mobile communication networks

In on-board mobile networks, such as those proposed for (and employed in) public transport vehicles, users are connected to a local network that attaches to the Internet via a mobile router and a wireless link. Central and coordinated management of mobility in a single router, rather than by each user device individually, has numerous advantages; however, it also means that link outages, e.g. due to signal degradation or handoff failure, may have an immediate impact on a potentially large number of connections. We argue that the advance knowledge of public transport routes, and their repetitive nature, allows a certain degree of prediction of impending link outages, which can be used to offset their catastrophic impact. Focusing on the TCP protocol and its extension known as Freeze-TCP, we study how the performance of the protocol depends on the outage prediction probability. In particular, we propose a Markov model of Freeze-TCP and, using simulations, show that it accurately predicts the performance improvement gained by outage prediction.

Unified channel assignment for unicast and broadcast traffic in Cognitive Radio Networks

The rising density of wireless devices, combined with the availability of a plethora of wireless web applications, has overcrowded the radio frequency spectrum. Majority of the present wireless radio spectrum is already licensed; however, studies have shown that the licensed spectrum is significantly underutilized. Cognitive Radio Networks (CRNs) are envisioned to utilize the radio spectrum more efficiently. CRNs may be required to handle both unicast and broadcast traffic, which makes the task of channel assignment in CRNs more challenging, as communication mode required for each type of traffic is different. Unicast traffic may suffer due to interference if the same channel is assigned to and used by neighboring nodes; on the other hand for broadcast traffic if a larger set of neighbors share a common channel, any particular node may exploit wireless broadcast advantage to communicate with a maximum number of neighbors in a single transmission. Existing channel assignment schemes either favor unicast or broadcast traffic only. In this paper a Unified Channel Assignment (UCA) algorithm is proposed which assigns channels according to their respective interference and connectivity parameters depending on the proportions of unicast and broadcast traffic in the network.

Quantifying the Multiple Cognitive Radio Interfaces Advantage

In recent times, wireless communications has established itself as a popular access technology due to the user preference for the flexibility of untethered communication. The single biggest problem that still impedes broader uptake of wireless technology is scarceness of wireless capacity. The lack of wireless capacity scaling is primarily due to two factors: firstly, wireless interference that limits a wireless channel to only possible transmission at any given time, and secondly, the current radio spectrum management scheme based on licensing frequency spectrum which is known to be very inefficient. Two well-known techniques that address parts of our considered problem space in wireless networks include: 1) cognitive radio networks (CR) or dynamic-spectrum-access (DSA) networks that utilize programmable software defined radios to address the wireless standards interoperability problem, and 2) multi-radiomulti-channel (MRMC) technology, which addresses the wireless scalability problem, in which each node is equipped with multiple radio interfaces (that can tune to any one of the available orthogonal channel) to allow multiple overlapping transmissions. In this work, we aim to investigate the benefits of a hybrid of these approaches: an approach that call C-MRMC technology. In C-MRMC wireless networks, each node is equipped with multiple cognitive radio interfaces. We investigate in our work the potential improvement in performance (which we gauge in metrics such as throughput, packet delivery ratio) gained by such an approach through extensive simulations. Our results demonstrate that having such an approach is viable and can lead to significant performance gains.

VoIP and Tracking Capacity over WiFi Networks

Wireless VoIP is becoming an increasingly important application in recent years. This fact, coupled with the increasing interest in location based services, strongly suggest that tracking of wireless VoIP clients will become a widely deployed feature in emerging wireless applications. In this paper, we evaluate the capacity of WiFi networks carrying VoIP and tracking sessions for our novel architectures developed specifically to support such applications. We first present an upper bound on a maximum number of users who can be supported by the proposed architecture for tracking only applications via simulations and experiments. We then vary the transmission frequency of tracking data and observe that it has a significant impact on the tracking capacity. Finally, we utilize one of our other locations tracking architecture that is designed for wireless VoIP to investigate how the transmission frequency affects the capacity of combined VoIP and tracking sessions. We develop an insight that at high packetization intervals (e.g. 60 ms) of VoIP traffic, there is a 30% decrease in combined VoIP and tracking capacity in comparison to VoIP only capacity. We further evaluate the capacity of proposed architecture using UDP (User Datagram Protocol) and DCCP (Datagram Congestion Control Protocol) in the presence of TCP (Transmission Control Protocol) traffic. Our studies suggest that compared to UDP, DCCP not only improves the combined VoIP and tracking capacity but also enables TCP to get a reasonable bandwidth share.

Learning automata based multipath multicasting in cognitive radio networks

Cognitive radio networks (CRNs) have emerged as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows a secondary network comprising of secondary users (SUs) to coexist with a primary network comprising of licensed primary users (PUs) subject to the condition that SUs do not cause any interference to the primary network. Since it is necessary for SUs to avoid any interference to the primary network, PU activity precludes attempts of SUs to access the licensed spectrum and forces frequent channel switching for SUs. This dynamic nature of CRNs, coupled with the possibility that an SU may not share a common channel with all its neighbors, makes the task of multicast routing especially challenging. In this work, we have proposed a novel multipath on-demand multicast routing protocol for CRNs. The approach of multipath routing, although commonly used in unicast routing, has not been explored for multicasting earlier. Motivated by the fact that CRNs have highly dynamic conditions, whose parameters are often unknown, the multicast routing problem is modeled in the reinforcement learning based framework of learning automata. Simulation results demonstrate that the approach of multipath multicasting is feasible, with our proposed protocol showing a superior performance to a baseline state-of-the-art CRN multicasting protocol.

A game-theoretic spectrum allocation framework for mixed unicast and broadcast traffic profile in cognitive radio networks

In this paper, we present a game theoretic framework for spectrum allocation in distributed cognitive radio networks containing both unicast and broadcast traffic. Our proposed scheme aims to minimize broadcast latency for broadcast traffic and minimize interference and access contention for both types of traffic. We develop a utility function that ensures that both objectives are met yielding a higher network throughput. Our proposed spectrum allocation game is also formulated as a potential game and is guaranteed to converge to a Nash equilibrium if the sequential best response dynamics is followed. A proof of concept of the proposed algorithm has been implemented on the Orbit radio testbed and the results verify the convergence of the potential game. Our simulation and experimental results also reveal that the choice of utility function improves the average network throughput for a mixed traffic profile.

Passive Analysis of Web Traffic Characteristics for Estimating Quality of Experience

Detecting users quality of experience is a method which deals with estimating how a user perceives the network performance. Although users behavior is very complex, only analyzing a large amount of operational network data enables us to effectively detect the users satisfaction from the network. One important parameter to detect users behavior is users interruption of the TCP connections in case of bad performance. In this paper we take passive network traffic measurements at the TCP flow level to monitor the effect of packet loss on Web traffic characteristics, such as TCP connection duration, size, and inter-arrival time of connections. From these traffic characteristics correlation with end user behavior (interruptions) is analyzed. Different trends are identified in the duration and size of connections with increasing loss rates. We explain and confirm the reasons for the different trends with different parameters. We discuss whether the end user behavior, such as interrupting connections, be identified from these traffic characteristics. We also want to know whether it provides us reasons for a connection being small or large, both in duration and size, due to users behavior. A precise way of estimating packet losses for passive measurements is devised, giving a lower bound on the total packet losses in a TCP connection.