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Opportunistic Spectrum Utilization by Cognitive Radio Networks: Challenges and Solutions
Title
Opportunistic Spectrum Utilization by Cognitive Radio Networks: Challenges and Solutions.
Creator
Amjad, Muhammad Faisal, Zou, Changchun, Bassiouni, Mostafa, Turgut, Damla, Wang, Chung-Ching, University of Central Florida
Abstract / Description
Cognitive Radio Network (CRN) is an emerging paradigm that makes use of Dynamic Spectrum Access (DSA) to communicate opportunistically, in the un-licensed Industrial, Scientific and Medical bands or frequency bands otherwise licensed to incumbent users such as TV broadcast. Interest in the development of CRNs is because of severe under-utilization of spectrum bands by the incumbent Primary Users (PUs) that have the license to use them coupled with an ever-increasing demand for unlicensed...
Show moreCognitive Radio Network (CRN) is an emerging paradigm that makes use of Dynamic Spectrum Access (DSA) to communicate opportunistically, in the un-licensed Industrial, Scientific and Medical bands or frequency bands otherwise licensed to incumbent users such as TV broadcast. Interest in the development of CRNs is because of severe under-utilization of spectrum bands by the incumbent Primary Users (PUs) that have the license to use them coupled with an ever-increasing demand for unlicensed spectrum for a variety of new mobile and wireless applications. The essence of Cognitive Radio (CR) operation is the cooperative and opportunistic utilization of licensed spectrum bands by the Secondary Users (SUs) that collectively form the CRN without causing any interference to PUs' communications.CRN operation is characterized by factors such as network-wide quiet periods for cooperative spectrum sensing, opportunistic/dynamic spectrum access and non-deterministic operation of PUs. These factors can have a devastating impact on the overall throughput and can significantly increase the control overheads. Therefore, to support the same level of QoS as traditional wireless access technologies, very closer interaction is required between layers of the protocol stack.Opportunistic spectrum utilization without causing interference to the PUs is only possible if the SUs periodically sense the spectrum for the presence of PUs' signal. To minimize the effects of hardware capabilities, terrain features and PUs' transmission ranges, DSA is undertaken in a collaborative manner where SUs periodically carry out spectrum sensing in their respective geographical locations. Collaborative spectrum sensing has numerous security loopholes and canbe favorable to malicious nodes in the network that may exploit vulnerabilities associated with DSA such as launching a spectrum sensing data falsification (SSDF) attack. Some CRN standards such as the IEEE 802.22 wireless regional area network employ a two-stage quiet period mechanism based on a mandatory Fast Sensing and an optional Fine Sensing stage for DSA. This arrangement is meant to strike a balance between the conflicting goals of proper protection of incumbent PUs' signals and optimum QoS for SUs so that only as much time is spent for spectrum sensing as needed. Malicious nodes in the CRN however, can take advantage of the two-stage spectrum sensing mechanism to launch smart denial of service (DoS) jamming attacks on CRNs during the fast sensing stage.Coexistence protocols enable collocated CRNs to contend for and share the available spectrum. However, most coexistence protocols do not take into consideration the fact that channels of the available spectrum can be heterogeneous in the sense that they can vary in their characteristics and quality such as SNR or bandwidth. Without any mechanism to enforce fairness in accessing varying quality channels, ensuring coexistence with minimal contention and efficient spectrum utilization for CRNs is likely to become a very difficult task.The cooperative and opportunistic nature of communication has many challenges associated with CRNs' operation. In view of the challenges described above, this dissertation presents solutions including cross-layer approaches, reputation system, optimization and game theoretic approaches to handle (1) degradation in TCP's throughput resulting from packet losses and disruptions in spectrum availability due non-deterministic use of spectrum by the PUs (2) presence of malicious SUs in the CRN that may launch various attacks on CRNs' includingSSDF and jamming and (3) sharing of heterogeneous spectrum resources among collocated CRNs without a centralized mechanism to enforce cooperation among otherwise non-cooperative CRNs
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Date Issued
2015
Identifier
CFE0005571, ucf:50249
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005571
Bootstrapping Cognitive Radio Networks
Title
Bootstrapping Cognitive Radio Networks.
Creator
Horine, Brent, Turgut, Damla, Wei, Lei, Boloni, Ladislau, Sukthankar, Gita, Garibay, Ivan, University of Central Florida
Abstract / Description
Cognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees...
Show moreCognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees of freedom, and little shared knowledge. The second is how radio nodes can negotiate for spectrum access with incomplete information.To address the first problem, we present our Frequency Parallel Blind Link Rendezvous algorithm. This approach, designed for recent generations of digital front-ends, implicitly shares vague information about spectrum occupancy early in the process, speeding the progress towards a solution. Furthermore, it operates in the frequency domain, facilitating a parallel channel rendezvous. Finally, it operates without a control channel and can rendezvous anywhere in the operating band. We present simulations and analysis on the false alarm rate for both a feature detector and a cross-correlation detector. We compare our results to the conventional frequency hopping sequence rendezvous techniques.To address the second problem, we model the network as a multi-agent system and negotiate by exchanging proposals, augmented with arguments. These arguments include information about priority status and the existence of other nodes. We show in a variety of network topologies that this process leads to solutions not otherwise apparent to individual nodes, and achieves superior network throughput, request satisfaction, and total number of connections, compared to our baselines. The agents independently formulate proposals based upon communication desires, evaluate these proposals based upon capacity constraints, create arguments in response to proposal rejections, and re-evaluate proposals based upon received arguments. We present our negotiation rules, messages, and protocol and demonstrate how they interoperate in a simulation environment.
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Date Issued
2012
Identifier
CFE0004546, ucf:49240
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004546
Spectrum Map and its Application in Cognitive Radio Networks
Title
Spectrum Map and its Application in Cognitive Radio Networks.
Creator
Debroy, Saptarshi, Chatterjee, Mainak, Bassiouni, Mostafa, Zou, Changchun, Jha, Sumit, Catbas, Necati, University of Central Florida
Abstract / Description
Recent measurements on radio spectrum usage have revealed the abundance of underutilizedbands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access. Cognitive radio based secondary networks thatutilize such unused spectrum holes in the licensed band, have been proposed as a possible solution to the spectrum crisis. The idea is to detect times when a particular licensed band is unused and use it for transmission without causing...
Show moreRecent measurements on radio spectrum usage have revealed the abundance of underutilizedbands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access. Cognitive radio based secondary networks thatutilize such unused spectrum holes in the licensed band, have been proposed as a possible solution to the spectrum crisis. The idea is to detect times when a particular licensed band is unused and use it for transmission without causing interference to the licensed user. We argue that prior knowledge about occupancy of such bands and the corresponding achievable performance metrics can potentially help secondary networks to devise effective strategiesto improve utilization.In this work, we use Shepard's method of interpolation to create a spectrum mapthat provides a spatial distribution of spectrum usage over a region of interest. It is achieved by intelligently fusing the spectrum usage reports shared by the secondary nodes at various locations. The obtained spectrum map is a continuous and differentiable 2-dimension distribution function in space. With the spectrum usage distribution known, we show how different radio spectrum and network performance metrics like channel capacity, secondary network throughput, spectral efficiency, and bit error rate can be estimated. We show the applicability of the spectrum map in solving the intra-cell channel allocation problem incentralized cognitive radio networks, such as IEEE 802.22. We propose a channel allocationscheme where the base station allocates interference free channels to the consumer premise equipments (CPE) using the spectrum map that it creates by fusing the spectrum usage information shared by some CPEs. The most suitable CPEs for information sharing arechosen on a dynamic basis using an iterative clustering algorithm. Next, we present a contention based media access control (MAC) protocol for distributed cognitive radio network. The unlicensed secondary users contend among themselves over a common control channel. Winners of the contention get to access the available channels ensuring high utilization and minimum collision with primary incumbent. Last, we propose a multi-channel, multi-hop routing protocol with secondary transmission power control. The spectrum map, created and maintained by a set of sensors, acts as the basis of finding the best route for every source destination pair. The proposed routing protocol ensures primary receiver protection and maximizes achievable link capacity.Through simulation experiments we show the correctness of the prediction model and how it can be used by secondary networks for strategic positioning of secondary transmitter-receiver pairs and selecting the best candidate channels. The simulation model mimics realistic distribution of TV stations for urban and non-urban areas. Results validate the nature and accuracy of estimation, prediction of performance metrics, and efficiency of the allocation process in an IEEE 802.22 network. Results for the proposed MAC protocol show high channel utilization with primary quality of service degradation within a tolerable limit. Performance evaluation of the proposed routing scheme reveals that it ensures primary receiver protection through secondary power control and maximizes route capacity.
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Date Issued
2014
Identifier
CFE0005324, ucf:50515
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005324
AN ECONOMIC FRAMEWORK FOR RESOURCE MANAGEMENT AND PRICING IN WIRELESS NETWORKS WITH COMPETITIVE SERVICE PROVIDERS
Title
AN ECONOMIC FRAMEWORK FOR RESOURCE MANAGEMENT AND PRICING IN WIRELESS NETWORKS WITH COMPETITIVE SERVICE PROVIDERS.
Creator
SENGUPTA, SHAMIK, Chatterjee, Mainak, University of Central Florida
Abstract / Description
A paradigm shift from static spectrum allocation to dynamic spectrum access (DSA) is becoming a reality due to the recent advances in cognitive radio, wide band spectrum sensing, and network aware real--time spectrum access. It is believed that DSA will allow wireless service providers (WSPs) the opportunity to dynamically access spectrum bands as and when they need it. Moreover, due to the presence of multiple WSPs in a region, it is anticipated that dynamic service pricing would be offered...
Show moreA paradigm shift from static spectrum allocation to dynamic spectrum access (DSA) is becoming a reality due to the recent advances in cognitive radio, wide band spectrum sensing, and network aware real--time spectrum access. It is believed that DSA will allow wireless service providers (WSPs) the opportunity to dynamically access spectrum bands as and when they need it. Moreover, due to the presence of multiple WSPs in a region, it is anticipated that dynamic service pricing would be offered that will allow the end-users to move from long-term service contracts to more flexible short-term service models. In this research, we develop a unified economic framework to analyze the trading system comprising two components: i) spectrum owner--WSPs interactions with regard to dynamic spectrum allocation, and ii) WSP--end-users interactions with regard to dynamic service pricing. For spectrum owner--WSPs interaction, we investigate various auction mechanisms for finding bidding strategies of WSPs and revenue generated by the spectrum owner. We show that sequential bidding provides better result than the concurrent bidding when WSPs are constrained to at most single unit allocation. On the other hand, when the bidders request for multiple units, (i.e., they are not restricted by allocation constraints) synchronous auction mechanism proves to be beneficial than asynchronous auctions. In this regard, we propose a winner determination sealed-bid knapsack auction mechanism that dynamically allocates spectrum to the WSPs based on their bids. As far as dynamic service pricing is concerned, we use game theory to capture the conflict of interest between WSPs and end--users, both of whom try to maximize their respective net utilities. We deviate from the traditional per--service static pricing towards a more dynamic model where the WSPs might change the price of a service almost on a session by session basis. Users, on the other hand, have the freedom to choose their WSP based on the price offered. It is found that in such a greedy and non-cooperative behavioral game model, it is in the best interest of the WSPs to adhere to a price threshold which is a consequence of a price (Nash) equilibrium. We conducted extensive simulation experiments, the results of which show that the proposed auction model entices WSPs to participate in the auction, makes optimal use of the common spectrum pool, and avoids collusion among WSPs. We also demonstrate how pricing can be used as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services.
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Date Issued
2007
Identifier
CFE0001848, ucf:47364
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001848
SPECTRUM SHARING AND SERVICE PRICING IN DYNAMIC SPECTRUM ACCESS NETWORKS
Title
SPECTRUM SHARING AND SERVICE PRICING IN DYNAMIC SPECTRUM ACCESS NETWORKS.
Creator
Brahma, Swastik, Chatterjee, Mainak, University of Central Florida
Abstract / Description
Traditionally, radio spectrum has been statically allocated to wireless service providers (WSPs). Regulators, like FCC, give wireless service providers exclusive long term licenses for using specific range of frequencies in particular geographic areas. Moreover, restrictions are imposed on the technologies to be used and the services to be provided. The lack of flexibility in static spectrum allocation constrains the ability to make use of new technologies and the ability to redeploy the...
Show moreTraditionally, radio spectrum has been statically allocated to wireless service providers (WSPs). Regulators, like FCC, give wireless service providers exclusive long term licenses for using specific range of frequencies in particular geographic areas. Moreover, restrictions are imposed on the technologies to be used and the services to be provided. The lack of flexibility in static spectrum allocation constrains the ability to make use of new technologies and the ability to redeploy the spectrum to higher valued uses, thereby resulting in inefficient spectrum utilization [23, 38, 42, 62, 67]. These limitations have motivated a paradigm shift from static spectrum allocation towards a more 'liberalized' notion of spectrum management in which secondary users can borrow idle spectrum from primary spectrum licensees, without causing harmful interference to the latter- a notion commonly referred to as dynamic spectrum access (DSA) or open spectrum access ,. Cognitive radio [30, 47], empowered by Software Defined Radio (SDR), is poised to promote the efficient use of spectrum by adopting this open spectrum approach. In this dissertation, we first address the problem of dynamic channel (spectrum) access by a set of cognitive radio enabled nodes, where each node acting in a selfish manner tries to access and use as many channels as possible, subject to the interference constraints. We model the dynamic channel access problem as a modified Rubinstein-Stahl bargaining game. In our model, each node negotiates with the other nodes to obtain an agreeable sharing rule of the available channels, such that, no two interfering nodes use the same channel. We solve the bargaining game by finding Subgame Perfect Nash Equilibrium (SPNE) strategies of the nodes. First, we consider finite horizon version of the bargaining game and investigate its SPNE strategies that allow each node to maximize its utility against the other nodes (opponents). We then extend these results to the infinite horizon bargaining game. Furthermore, we identify Pareto optimal equilibria of the game for improving spectrum utilization. The bargaining solution ensures that no node is starved of channels. The spectrum that a secondary node acquires comes to it at a cost. Thus it becomes important to study the 'end system' perspective of such a cost, by focusing on its implications. Specifically, we consider the problem of incentivizing nodes to provide the service of routing using the acquired spectrum. In this problem, each secondary node having a certain capacity incurs a cost for routing traffic through it. Secondary nodes will not have an incentive to relay traffic unless they are compensated for the costs they incur in forwarding traffic. We propose a path auction scheme in which each secondary node announces its cost and capacity to the routing mechanism, both of which are considered as private information known only to the node. We design a route selection mechanism and a pricing function that can induce nodes to reveal their cost and capacity honestly (making our auction truthful), while minimizing the payment that needs to be given to the nodes (making our auction optimal). By considering capacity constraint of the nodes, we explicitly support multiple path routing. For deploying our path auction based routing mechanism in DSA networks, we provide polynomial time algorithms to find the optimal route over which traffic should be routed and to compute the payment that each node should receive. All our proposed algorithms have been evaluated via extensive simulation experiments. These results help to validate our design philosophy and also illustrate the effectiveness of our solution approach.
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Date Issued
2011
Identifier
CFE0004049, ucf:49125
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004049
Compressive Sensing and Recovery of Structured Sparse Signals
Title
Compressive Sensing and Recovery of Structured Sparse Signals.
Creator
Shahrasbi, Behzad, Rahnavard, Nazanin, Vosoughi, Azadeh, Wei, Lei, Atia, George, Pensky, Marianna, University of Central Florida
Abstract / Description
In the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller...
Show moreIn the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller dimensionality in an appropriate domain. CS taps into this principle and dramatically reduces the number of samples that is required to be captured to avoid any distortion in the information content of the data. This reduction in the required number of samples enables many new applications that were previously infeasible using classical sampling techniques.Most CS-based approaches take advantage of the inherent low-dimensionality in many datasets. They try to determine a sparse representation of the data, in an appropriately chosen basis using only a few significant elements. These approaches make no extra assumptions regarding possible relationships among the significant elements of that basis. In this dissertation, different ways of incorporating the knowledge about such relationships are integrated into the data sampling and the processing schemes.We first consider the recovery of temporally correlated sparse signals and show that using the time correlation model. The recovery performance can be significantly improved. Next, we modify the sampling process of sparse signals to incorporate the signal structure in a more efficient way. In the image processing application, we show that exploiting the structure information in both signal sampling and signal recovery improves the efficiency of the algorithm. In addition, we show that region-of-interest information can be included in the CS sampling and recovery steps to provide a much better quality for the region-of-interest area compared the rest of the image or video. In spectrum sensing applications, CS can dramatically improve the sensing efficiency by facilitating the coordination among spectrum sensors. A cluster-based spectrum sensing with coordination among spectrum sensors is proposed for geographically disperse cognitive radio networks. Further, CS has been exploited in this problem for simultaneous sensing and localization. Having access to this information dramatically facilitates the implementation of advanced communication technologies as required by 5G communication networks.
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Date Issued
2015
Identifier
CFE0006392, ucf:51509
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006392
Reliable Spectrum Hole Detection in Spectrum-Heterogeneous Mobile Cognitive Radio Networks via Sequential Bayesian Non-parametric Clustering
Title
Reliable Spectrum Hole Detection in Spectrum-Heterogeneous Mobile Cognitive Radio Networks via Sequential Bayesian Non-parametric Clustering.
Creator
Zaeemzadeh, Alireza, Rahnavard, Nazanin, Vosoughi, Azadeh, Qi, GuoJun, University of Central Florida
Abstract / Description
In this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and...
Show moreIn this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and frequency domains, by proposing a low-cost and practical framework. Spectrum-heterogeneous networks are the networks in which different sensors experience different spectrum opportunities. Thus, the sensing data from sensors cannot be combined to reach consensus and to detect the spectrum opportunities. Moreover, unreliable data, caused by noise or malicious attacks, will deteriorate the performance of the decision-making process. The problem becomes even more challenging when the locations of the sensors are unknown. In this work, a probabilistic model is proposed to cluster the sensors based on their readings, not requiring any knowledge of location of the sensors. The complexity of the model, which is the number of clusters, is automatically inferred from the sensing data. The processing node, also referred to as the base station or the fusion center, infers the probability distributions of cluster memberships, channel availabilities, and devices' reliability in an online manner. After receiving each chunk of sensing data, the probability distributions are updated, without requiring to repeat the computations on previous sensing data. All the update rules are derived mathematically, by employing Bayesian data analysis techniques and variational inference.Furthermore, the inferred probability distributions are employed to assign unique spectrum opportunities to each of the sensors. To avoid interference among the sensors, physically adjacent devices should not utilize the same channels. However, since the location of the devices is not known, cluster membership information is used as a measure of adjacency. This is based on the assumption that the measurements of the devices are spatially correlated. Thus, adjacent devices, which experience similar spectrum opportunities, belong to the same cluster. Then, the problem is mapped into a energy minimization problem and solved via graph cuts. The goal of the proposed graph-theory-based method is to assign each device an available channel, while avoiding interference among neighboring devices. The numerical simulations illustrates the effectiveness of the proposed methods, compared to the existing frameworks.
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Date Issued
2017
Identifier
CFE0006963, ucf:51639
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006963
Quantifying Trust and Reputation for Defense against Adversaries in Multi-Channel Dynamic Spectrum Access Networks
Title
Quantifying Trust and Reputation for Defense against Adversaries in Multi-Channel Dynamic Spectrum Access Networks.
Creator
Bhattacharjee, Shameek, Chatterjee, Mainak, Guha, Ratan, Zou, Changchun, Turgut, Damla, Catbas, Necati, University of Central Florida
Abstract / Description
Dynamic spectrum access enabled by cognitive radio networks are envisioned to drivethe next generation wireless networks that can increase spectrum utility by opportunisticallyaccessing unused spectrum. Due to the policy constraint that there could be no interferenceto the primary (licensed) users, secondary cognitive radios have to continuously sense forprimary transmissions. Typically, sensing reports from multiple cognitive radios are fusedas stand-alone observations are prone to errors...
Show moreDynamic spectrum access enabled by cognitive radio networks are envisioned to drivethe next generation wireless networks that can increase spectrum utility by opportunisticallyaccessing unused spectrum. Due to the policy constraint that there could be no interferenceto the primary (licensed) users, secondary cognitive radios have to continuously sense forprimary transmissions. Typically, sensing reports from multiple cognitive radios are fusedas stand-alone observations are prone to errors due to wireless channel characteristics. Suchdependence on cooperative spectrum sensing is vulnerable to attacks such as SecondarySpectrum Data Falsification (SSDF) attacks when multiple malicious or selfish radios falsifythe spectrum reports. Hence, there is a need to quantify the trustworthiness of radios thatshare spectrum sensing reports and devise malicious node identification and robust fusionschemes that would lead to correct inference about spectrum usage.In this work, we propose an anomaly monitoring technique that can effectively cap-ture anomalies in the spectrum sensing reports shared by individual cognitive radios duringcooperative spectrum sensing in a multi-channel distributed network. Such anomalies areused as evidence to compute the trustworthiness of a radio by its neighbours. The proposedanomaly monitoring technique works for any density of malicious nodes and for any physicalenvironment. We propose an optimistic trust heuristic for a system with a normal risk attitude and show that it can be approximated as a beta distribution. For a more conservativesystem, we propose a multinomial Dirichlet distribution based conservative trust framework,where Josang's Belief model is used to resolve any uncertainty in information that mightarise during anomaly monitoring. Using a machine learning approach, we identify maliciousnodes with a high degree of certainty regardless of their aggressiveness and variations intro-duced by the pathloss environment. We also propose extensions to the anomaly monitoringtechnique that facilitate learning about strategies employed by malicious nodes and alsoutilize the misleading information they provide. We also devise strategies to defend against a collaborative SSDF attack that islaunched by a coalition of selfish nodes. Since, defense against such collaborative attacks isdifficult with popularly used voting based inference models or node centric isolation techniques, we propose a channel centric Bayesian inference approach that indicates how much the collective decision on a channels occupancy inference can be trusted. Based on the measured observations over time, we estimate the parameters of the hypothesis of anomalous andnon-anomalous events using a multinomial Bayesian based inference. We quantitatively define the trustworthiness of a channel inference as the difference between the posterior beliefsassociated with anomalous and non-anomalous events. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data.Subsequently, we propose robust fusion models which utilize the trusts of the nodes to improve the accuracy of the cooperative spectrum sensing decisions. In particular, we propose three fusion models: (i) optimistic trust based fusion, (ii) conservative trust based fusion, and (iii) inversion based fusion. The former two approaches exclude untrustworthy sensing reports for fusion, while the last approach utilizes misleading information. Allschemes are analyzed under various attack strategies. We propose an asymmetric weightedmoving average based trust management scheme that quickly identifies on-off SSDF attacks and prevents quick trust redemption when such nodes revert back to temporal honest behavior. We also provide insights on what attack strategies are more effective from the adversaries' perspective.Through extensive simulation experiments we show that the trust models are effective in identifying malicious nodes with a high degree of certainty under variety of network and radio conditions. We show high true negative detection rates even when multiple malicious nodes launch collaborative attacks which is an improvement over existing voting based exclusion and entropy divergence techniques. We also show that we are able to improve the accuracy of fusion decisions compared to other popular fusion techniques. Trust based fusion schemes show worst case decision error rates of 5% while inversion based fusion show 4% as opposed majority voting schemes that have 18% error rate. We also show that the proposed channel centric Bayesian inference based trust model is able to distinguish between attacked and non-attacked channels for both static and dynamic collaborative attacks. We are also able to show that attacked channels have significantly lower trust values than channels that are not(-) a metric that can be used by nodes to rank the quality of inference on channels.
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Date Issued
2015
Identifier
CFE0005764, ucf:50081
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005764