Resource Allocation and Pricing in Secondary Dynamic Spectrum Access Networks

The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there is no interference. Then, we propose a double auction-based spectrum allocation technique by considering multiple bids from SUs and heterogeneity of channels. We use virtual grouping of conflict-free buyers to transform multi-unit bids to single-unit bids. For routing, we propose a market-based model where the PUs determine the optimal price based on the demand for bandwidth by the SUs. Routes are determined through a series of price evaluations between message senders and forwarders. Also, we consider auction-based routing for two cases where buyers can bid for only one channel or they could bid for a combination of non-substitutable channels. For a centralized DSA, we propose two scheduling algorithms-- the first one focuses on maximizing the throughput and the second one focuses on fairness. We extend the scheduling algorithms to multi-channel environment. Expected throughput for every channel is computed by modelling channel state transitions using a discrete-time Markov chain. The state transition probabilities are calculated which occur at the frame/slot boundaries. All proposed algorithms are validated using simulation experiments with different network settings and their performance are studied

Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay. [ABSTRACT FROM PUBLISHER

Distributed and asynchronous data collection in cognitive radio networks with fairness consideration

Abstract-As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay

White Space Network Management: Spectrum Quanti cation, Spectrum Allocation and Network Design

Philosophiae Doctor - PhD (Computer Science)The unused spectrum in the television broadcasting frequency bands (so-called TV white spaces) can alleviate the spectrum crunch, and have potential to provide broadband connection to rural areas of countries in the developing world. Current research on TV white spaces focuses on how to detect them accurately, and how they can be shared or allocated to secondary devices. Therefore, the focus of this research is three-fold: to investigate a novel distributed framework, which does not use propagation models in detecting TV white spaces, and suitable for use in countries of the developing world; to investigate a suitable spectrum sharing mechanism for short-time leasing of the TV white spaces to secondary devices; and extend the research to investigate the design of a TV white space-ware network in TV white space frequencies

Spectrum Allocation Algorithms for Cognitive Radio Mesh Networks

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use licensed spectrum bands as long as their are no primary, licensed, users occupying the channel in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. This heterogeneity forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take into consideration the heterogeneity property and its effect on the network performance. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by channel heterogeneity. We proposed a scheduling algorithm that schedules multicast transmissions over both time and frequency and integrates that with the use of network coding. This algorithm achieves a significant gain, measured as the reduction in the total multicast time, as the simulation results prove. We also proposed a failure recovery algorithm that can adaptively adjust the schedule in response to temporary changes in channel availability. The last objective is minimizing the effect of channel switching on the end-to-end delay and network throughput. Channel switching can be a significant source of delay and bandwidth wastage, especially if the secondary users are utilizing a wide spectrum band. To address this issue, we proposed an on-demand multicast routing algorithm for cognitive radio mesh networks based on dynamic programming. The algorithm finds the best available route in terms of end-to-end delay, taking into consideration the switching latency at individual nodes and the transmission time on different channels. We also presented the extensibility of the proposed algorithm to different routing metric. Furthermore, a route recovery algorithm that takes into consideration the overhead of rerouting and the route cost was also proposed. The gain of these algorithms was proved by simulation

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Re-feedback: freedom with accountability for causing congestion in a connectionless internetwork

This dissertation concerns adding resource accountability to a simplex internetwork such as the Internet, with only necessary but sufficient constraint on freedom. That is, both freedom for applications to evolve new innovative behaviours while still responding responsibly to congestion; and freedom for network providers to structure their pricing in any way, including flat pricing. The big idea on which the research is built is a novel feedback arrangement termed ‘re-feedback’. A general form is defined, as well as a specific proposal (re-ECN) to alter the Internet protocol so that self-contained datagrams carry a metric of expected downstream congestion. Congestion is chosen because of its central economic role as the marginal cost of network usage. The aim is to ensure Internet resource allocation can be controlled either by local policies or by market selection (or indeed local lack of any control). The current Internet architecture is designed to only reveal path congestion to end-points, not networks. The collective actions of self-interested consumers and providers should drive Internet resource allocations towards maximisation of total social welfare. But without visibility of a cost-metric, network operators are violating the architecture to improve their customer’s experience. The resulting fight against the architecture is destroying the Internet’s simplicity and ability to evolve. Although accountability with freedom is the goal, the focus is the congestion metric, and whether an incentive system is possible that assures its integrity as it is passed between parties around the system, despite proposed attacks motivated by self-interest and malice. This dissertation defines the protocol and canonical examples of accountability mechanisms. Designs are all derived from carefully motivated principles. The resulting system is evaluated by analysis and simulation against the constraints and principles originally set. The mechanisms are proven to be agnostic to specific transport behaviours, but they could not be made flow-ID-oblivious

Resource-Efficient Wireless Systems for Emerging Wireless Networks

As the wireless medium has become the primary source of communication and Internet connectivity, and as devices and wireless technologies become more sophisticated and capable, there has been a surge in the capacity demands and complexity of applications that run over these wireless devices. To sustain the volume and QoE guarantees of the data generated, the opportunity and need to rethink wireless network design across all the layers of the protocol stack has firmly emerged as a solution to enable the timely and reliable delivery of data, while handling the inherent challenges of a crowded wireless medium, such as congestion, interference, and hidden terminals. The research work presented in this dissertation builds efficient solutions and protocols with a theoretical foundation to address the challenges that arise in rethinking wireless network design. Example challenges include managing the overhead associated with complex systems. My work particularly focuses on the opportunities and challenges of sophisticated technology and systems in emerging wireless networks. I target the main thrusts in the evolution of wireless networks that create significant opportunity to achieve higher theoretical capacity, and have direct implications on our day-to-day wireless interactions: from enabling multifold increase in capacity in wireless physical links, to developing medium access techniques to exploit the high speed links, and making the applications more bandwidth efficient. I build deployable, and resource-aware wireless systems that exploit higher bandwidths by leveraging and advancing diverse research areas such as theory, analysis, protocol design, and wireless networking. Specifically, I identify the erroneous assumptions and fundamental limitations of existing solutions in capturing the true and complex interactions between wireless devices and protocols. I use these insights to guide practical and efficient protocol design, followed by thorough analysis and evaluation in testbed implementations via prototypes and measurements. I show that my proposed solutions achieve significant performance gains, at minimum cost to overhead