Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

More and more recent studies highlight the crucial role of the human microbiome in maintaining health, while modern advancements in metagenomic sequencing technologies have been accumulating data that are associated with human diseases. Although metagenomic data offer rich, multifaceted information, including taxonomic and functional abundance profiles, their full potential remains underutilized, as most approaches rely only on one type of information to discover and understand their related correlations with respect to disease occurrences. To address this limitation, we propose a multistage fusion tabular transformer architecture (MSFT-Transformer), aiming to effectively integrate various types of high-dimensional tabular information extracted from metagenomic data. Its multistage fusion strategy consists of three modules: a fusion-aware feature extraction module in the early stage to improve the extracted information from inputs, an alignment-enhanced fusion module in the mid stage to enforce the retainment of desired information in cross-modal learning, and an integrated feature decision layer in the late stage to incorporate desired cross-modal information. We conduct extensive experiments to evaluate the performance of MSFT-Transformer over state-of-the-art models on five standard datasets. Our results indicate that MSFT-Transformer provides stable performance gains with reduced computational costs. An ablation study illustrates the contributions of all three models compared with a reference multistage fusion transformer without these novel strategies. The result analysis implies the significant potential of the proposed model in future disease prediction with metagenomic data.

Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management.

The outbreak of COVID-19 has resulted in an "infodemic" that has encouraged the propagation of misinformation about COVID-19 and cure methods which, in turn, could negatively affect the adoption of recommended public health measures in the larger population. In this paper, we provide a new multimodal (consisting of images, text and temporal information) labeled dataset containing news articles and tweets on the COVID-19 vaccine. We collected 2,593 news articles from 80 publishers for one year between Feb 16th 2020 to May 8th 2021 and 24184 Twitter posts (collected between April 17th 2021 to May 8th 2021). We combine ratings from two news media ranking sites: Medias Bias Chart and Media Bias/Fact Check (MBFC) to classify the news dataset into two levels of credibility: reliable and unreliable. The combination of two filters allows for higher precision of labeling. We also propose a stance detection mechanism to annotate tweets into three levels of credibility: reliable, unreliable and inconclusive. We provide several statistics as well as other analytics like, publisher distribution, publication date distribution, topic analysis, etc. We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset. We find that the proposed architecture has an F-Score of 0.919 and accuracy of 0.882 for fake news detection. Furthermore, we provide benchmark performance for misinformation detection on tweet dataset. This new multimodal dataset can be used in research on COVID-19 vaccine, including misinformation detection, influence of fake COVID-19 vaccine information, etc.

Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.

We address the problem of estimating the source of a rumor in large-scale social networks. Previous works studying this problem have mainly focused on graph models with deterministic and homogenous internode relationship strengths. However, internode relationship strengths in real social networks are random. We model this uncertainty by using random, nonhomogenous edge weights on the underlying social network graph. We propose a novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms. We also propose a novel method to select these sensor nodes. We evaluate our algorithm using a large data set from Twitter and Sina Weibo. Real-world time series data are used to model the uncertainty in social relationship strengths. Simulations show that the proposed algorithm can determine the actual source within two hops, 69%-80% of the time, when the diameter of the networks varies between 7 and 13. Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality. Finally, we demonstrate that our two-stage algorithm outperforms the alternative algorithm in terms of the accuracy of localizing the source.

The well documented growth in mobile traffic is mainly driven by increasingly sophisticated smart phone applications. Simultaneously, user preference for lighter phones has resulted in more battery power constrained hand-helds that offload computations to resource intensive cloud. This second trend exacerbates the bandwidth crunch often experienced over wireless networks. Our idea (joint cognitive offloading and scheduling) is to use dynamic spectrum access and management concepts from wireless networking to effect computation offloading and scheduling solutions that achieves near optimal trade-offs between the mobile device and wireless resources. We use all radio available interfaces (e.g. WiFi, LTE) in multi-RAT enabled devices to schedule appropriate components of the application to run either on the mobile device or on the resource-rich cloud, while staying adaptive to the conditions of the wireless network.

The advent of 5G networking technologies has increased the expectations from mobile devices, in that, more sophisticated, computationally intense applications are expected to be delivered on the mobile device which are themselves getting smaller and sleeker. This predicates a need for offloading computationally intense parts of the applications to a resource strong cloud. Parallely, in the wireless networking world, the trend has shifted to multi-radio (as opposed to multi-channel) enabled communications. In this paper, we provide a comprehensive computation offloading solution that uses the multiple radio links available for associated data transfer, optimally. Our contributions include: a comprehensive model for the energy consumption from the perspective of the mobile device; the formulation of the joint optimization problem to minimize the energy consumed as well as allocating the associated data transfer optimally through the available radio links and an iterative algorithm that converges to a locally optimal solution. Simulations on an HTC phone, running a 14-component application and using the Amazon EC2 as the cloud, show that the solution obtained through the iterative algorithm consumes only 3% more energy than the optimal solution (obtained via exhaustive search).

Internet users can be classified as two types: (a) active users-actively contribute to blogs, publish opinions, write comments in Youtube, tweet messages, etc. and (b) passive consumers who only consume Internet information without contributing to it. While the majority of current social media research deals with active user analysis, there is very little work in understanding the dynamics of passive consumers and their influence. Our global scale Internet measurement of user access patterns of a diverse set of Internet media services indicates conclusively that majority of consumers are passive. In this paper, we develop a spatio-temporal mathematical model and the corresponding stochastic analysis to understand the passive consumer dynamics. Both discrete and continuous time analysis are presented. We also show how the analysis can be used to identify spatial points of influence, i.e., spatial locations that have maximum expertise or influence on a topic. The analysis takes into account, the initial level of consumption at each spatial location and the influence of different passive consumers at different geographic locations on each other. The effect of information noise is taken into account to derive fundamental limits of passive information consumption. Theoretical results are verified using real Internet measurement data. The large scale data have been made available for other researchers.

This paper solves the optimal flow management problem in cognitive radio (CR) networks with multiple self-interested CRs and multiple self interested servers with multiple radio interfaces. We formulate a novel flow management problem in which the cognitive nodes compete to minimize the total delay over all interfaces while at the same time the servers compete to maximize their individual profit. We model this problem as a leader-follower game and propose a dynamic linear pricing scheme designed to achieve optimal flow allocation. We propose an iterative algorithm to solve the game and analyze the criteria for convergence to the unique Nash Equilibrium. The messaging required to implement this algorithm is minimal thereby making it suitable for distributed implementations. Numerical simulations demonstrate significant improvement in terms of average total delay for cognitive nodes in comparison with alternative algorithms. Simulation results show that when quality of service in terms of average total delay is fixed, our algorithm improves the capacity of the network by 40% for the maximum allowable throughput demand.

In wireless communication systems users compete for communication opportunities through a medium access control protocol. Previous research has shown that selfish behavior in medium access games could lead to inefficient and unfair resource allocation. We introduce a new notion of reciprocity in a medium access game and derive the corresponding Fairness Nash equilibrium. Further, using mechanism design we show that this type of reciprocity can remove unfair/inefficient equilibrium solutions.

Information consumers in online social networks receive information from multiple information providers, which results in confusion. The amount of confusion depends on three main factors-(a) attributes of the source, (b) characteristics of the consumer and (c) trust relation between the information provider and the consumer. While information confusion has been qualitatively observed in social networks, no quantitative model or analysis was presented. We present the first quantitative model to analyze confusion in the presence of multiple information providers. We address the following fundamental issues-(i) What is a good model for confusion? (ii) How does the quality of information degrade due to confusion? (iii) What are good strategies for the information providers to control the power or the intensity with which the information is transmitted? The scenario is modeled as a non-cooperative game with pricing, whose Nash equilibrium provides the solution to the questions posed above. We use data from Twitter (e.g., on full body scan in airports) and diabetes outreach networks to illustrate the analysis. We use the solution of the non-cooperative game to study the confusion levels of consumers, in terms of the aggressiveness and passiveness of the information providers. Results indicate that confusion levels are high in in networks in which all information providers are equally trusted. In networks where information providers are unequally trusted, the confusion levels are moderate.

Cognitive radio (CR) enabled dynamic spectrum access (DSA) networks are designed to detect and opportunistically utilise the unused or under-utilised spectrum bands. However, due to the open paradigm of CR networks and lack of proactive security protocols, the DSA networks are vulnerable to various denial-of-service threats. The authors propose a game-theoretic framework to study the primary user emulation attack (PUEA) on CR nodes. A non-cooperative dynamic multistage game between the secondary nodes and the adversaries generating the PUEA is formulated. The pure-strategy and mixed-strategy Nash equilibria for the secondary user and malicious attacker are investigated. Moreover, a novel belief updating system is proposed for the secondary user to learn the state of the primary user as the game evolves. Simulation results demonstrate that the proposed belief updating system achieves better performance than other models for the secondary user in terms of greater payoff, lower probability of missing primary user and better robustness to the inaccurate estimation of the primary user's state.

Primary user emulation attack (PUEA) is a denial of service (DoS) attack unique to dynamic spectrum access (DSA) networks. While there have been studies in the literature to detect and mitigate PUEA, the impact of PUEA on the performance of secondary networks has seldom been studied. In this paper, we analyze how PUEA affects call dropping and delay in secondary networks carrying both real-time traffic and non-real-time traffic. Numerical results indicate that PUEA can increase the number of dropped calls by up to two orders of magnitude and can increase the mean delay by up to a factor larger than two. We then evaluate the performance of secondary networks that deploy the protocols which we proposed previously to mitigate PUEA. Our protocols reduce the number of dropped calls by up to one order of magnitude. Our protocols are also shown to exhibit almost the same delay performance as that of a system with no PUEA, for low malicious traffic load. When malicious traffic load is high, our protocols provide an improvement on the delay performance by up to 54%.

This paper proposes an active steganalysis method for quantization index modulation (QIM)-based steganography. The proposed nonparametric steganalysis method uses irregularity (or randomness) in the test image to distinguish between the cover image and the stego image. We have shown that plain quantization (quantization without message embedding) induces regularity in the resulting quantized object, whereas message embedding using QIM increases irregularity in the resulting QIM-stego. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test image. The QIM-stego image is then analyzed to estimate secret message length. To this end, the QIM codebook is estimated from the QIM-stego image using first-order statistics of the image coefficients in the embedding domain. The estimated codebook is then used to estimate secret message. Simulation results show that the proposed scheme can successfully estimate the hidden message from the QIM-stego with very low decoding error probability. For a given cover object the decoding error probability depends on embedding rate and decreases monotonically, approaching zero as the embedding rate approaches one.

We address a unique security vulnerability in long term evolution (LTE) advanced and high speed packet access (HSPA+) wireless networks due to carrier/channel bonding. This vulnerability is shown to result in various amounts of service disruption based on the radio network parameters and the user locations. Typically, channel bonding have been perceived as a means to enhance the bandwidth and throughput for the users. However, this could also result in the loss of orthogonality between the bonded spectrum bands. We show that this leads to a security vulnerability that can be exploited by an attacker to cause service disruption. In this case, the attacker need not even operate in the same bands as the user, to be effective. We present an analysis to compare the loss in throughput caused by the vulnerability due to channel bonding in advanced LTE and HSPA+ networks. Results indicate that channel bonding is susceptible to about 70% loss of throughput in LTE networks and about 11-15% in HSPA+ networks compared to systems with no bonding. Also, users farther away from the base station suffer larger throughput degradation due to channel bonding in LTE networks, while it causes larger degradation in throughput for near users in HSPA+ networks. To the best of our knowledge, this is the first attempt to identify and analyze a significant security vulnerability in LTE and HSPA+ networks.

In this paper, we propose a computationally-efficient data hiding method for images which achieves Cachin's security criterion: zero Kullback- Liebler(KL) divergence. In order to preserve the statistical properties of the cover medium, we change the order of pixels rather than modify their values to embed the hidden message. We then subject the proposed stego method to a higher-order statistics based universal steganalysis algorithm and a new learning based steganalysis that we propose specifically for this hiding algorithm. Experimental results show that our proposed method can prevent statistical detection, when the embedding rate is smaller than or equal to 10%, which is higher than those of other existing data hiding methods. Hence the proposed method is safe even in a practical sense against a steganalysis method designed specifically against this stego method.

Efficient and fair resource allocation in cognitive radio (CR) networks under strict constraints imposed by the primary network is a challenging problem for underlay dynamic spectrum access (DSA) networks. Due to the distributed nature of secondary networks, traditional approaches are usually computationally inefficient in providing fair resource management while meeting severe constraints imposed by the primary network. In this paper we consider the scenario of homogeneous CDMA-based DSA network with one main primary receive-transmit pair and an arbitrary number of secondary users (SUs). Unlike the prior works, we consider the case where SUs can adaptively adjust their transmit directions besides transmit power according to available channel information. For such a scenario, we formulate the joint power allocation and phase control problem and find the optimal solution to maximize the total rate for the secondary network subject to interference and power constraints. Our method has a linear computational complexity and guarantees fairness among SUs. Numerical analysis show that introducing the phase control alleviates the restriction of strict interference constraint while keeping the efficiency of power allocation.

In this paper, we investigate a new type of denialof- service attack in dynamic spectrum access networks - Sybilenabled attack. In this attack, the attacker not only launches the primary user emulation (PUE) attacks but also creates and infiltrates multiple Sybil identities to compromise the decision making process of the secondary network via Byzantine attacks. We implement this attack in our cognitive radio testbed to show its feasibility and attack impact. We further analyze the optimal attack strategy from the perspective of the malicious attacker, i.e., the optimal allocation of Sybil interfaces for different attacks, to maximize the impact on the secondary network. The attack models are analyzed under two different scenarios: with and without a reputation system in the network fusion center. Numerical analysis and simulations are conducted to solve the optimal attack strategy and demonstrate the impact of attacks on the secondary network.

In this paper, we investigate the parochialism in dynamic spectrum access networks and its effect on spectrum resource competition and self-coexistence among cognitive radio secondary users. We assume that some greedy secondary users in the network form a parochial community in private and try to maximize their own utilities without concern for the interests of other secondary users outside the community. A noncooperative game is formulated first to analyze the equilibrium strategy of secondary users without any parochial community in the network. In the scenario where there is a parochial community, we analyze the expected payoff for the insider secondary users who deviate from the Nash equilibrium and derive the optimal strategy for them. Through numerical analysis, we see how the optimal strategies for the secondary users inside the parochial community in different circumstances deviate from Nash equilibrium. Simulation results demonstrate that being a part of the parochial community, the secondary users can obtain more utility than the outsider secondary users and the system will change from an equilibrium to an unfair situation.

In this paper, we investigate Sybil attacks on spectrum allocation in distributed dynamic spectrum access (DSA) networks. Using IEEE 802.11 devices as secondary nodes, we demonstrate the feasibility of mounting Sybil attacks in the cognitive radio testbed, in which the malicious node poses as multiple normal secondary nodes with different identities in order to steal more spectrum bands. We also show the impact of the attack through an example and simulation results. A defense strategy using the statistics of beacon intervals is also proposed. Through experimental results, we show the effectiveness of this defense mechanism when there is no interference from external sources as well as in the presence of interference.

We study the dynamics of social networks in terms of population growth and control of user behavior. Most of the current research in social networks focus on static analysis through graph theoretic models to represent the networks or focus on modeling the traffic. Here, we study the cost of collaborative vs individualistic behavior of users in order to grow their network size in a social network. Each user incurs a cost (monetary or emotional) for collaboration. We formulate the behavior of the users as a non-linear optimization problem with a cost. The objective function of the optimization problem is obtained using a stochastic analysis of population growth in social networks, based on the first-passage time of a birth-death process. The stochastic model is validated by comparison with real data obtained from Twitter Results indicate that a homogeneous social network (in which users have similar characteristics) will be individualistic. However, heterogeneous social networks (users with different characteristics) exhibit a threshold effect, i.e., there is a minimum cost, below which the network is as collaborative as desired and a maximum cost above which the network is individualistic as required. To the best of our knowledge, this is one of the first analysis of dynamics of user behavior and temporal population growth in social networks.

The cognitive radio enabled IEEE 802.22 wireless regional area network (WRAN) is designed to opportunistically utilize the unused or under-utilized TV bands. However, due to the open nature of cognitive radio networks and lack of proactive security protocols, the IEEE 802.22 networks are vulnerable to various denial-of-service (DoS) threats. In this paper, we study coordinated DoS attacks on IEEE 802.22 networks from the malicious users' perspective. We consider both one-stage and a multi-stage cases of the problem. In the one-stage scenario, we formulate a cooperative game among the malicious nodes and derive the optimal decision strategy for the them. In the multi-stage case, we propose a discrete-time Markov chain model for the dynamic behavior of both malicious nodes and the 802.22 secondary networks. Simulation and numerical results demonstrate that in the one-stage case, the coordinated attack achieves 10-15% improvement compared to the non-cooperative attack from the perspective of malicious nodes, and, in the multi-stage case, there exists an optimal number of malicious nodes that maximize the net payoff under the steady state.

We propose a spectrum decision protocol resilient to primary user emulation attacks (PUEA) in dynamic spectrum access networks. PUEA is a type of denial-of-service attack that can severely interfere with the spectrum sensing process and unfairly deprive legitimate secondary users of spectrum access. In this paper, we present a robust spectrum decision protocol that can mitigate PUEA using individual spectrum decisions made by secondary nodes in the network. In order to enable each secondary node to make an individual spectrum decision to detect PUEA, we first characterize the received power at good secondary user. This is done by using a flexible log-normal sum approximation. The received power thus characterized is used to determine the probability of successful PUEA on each secondary user, which is used to develop the proposed protocol. Simulation results demonstrate that the proposed protocol can significantly reduce the probability of successful PUEA under Byzantine attacks (i.e., when the malicious users intentionally provide false spectrum decisions), while still following the spectrum evacuation etiquette.

Primary user emulation attack (PUEA) is a denial of service (DoS) attack unique to dynamic spectrum access (DSA) networks. While there have been studies in the literature to detect and mitigate PUEA, the impact of PUEA on the call blocking and call dropping probabilities of users in secondary networks has not been studied. We present the first analysis to study the impact of PUEA on the secondary users in terms of call blocking and call dropping. We propose a three dimensional continuous time Markov chain (3D-CTMC) to model the channel occupancy in DSA networks. We use the 3D-CTMC to determine the call blocking and call dropping probabilities for secondary users. We validate our analysis with simulations. Results indicate that while PUEA does not affect the call blocking probability, it can increase the call dropping probability of secondary users by more than one order of magnitude. We also evaluate the call dropping performance of some of our previously proposed protocols to mitigate PUEA. We show that our protocols can improve the call dropping performance by about 40% for high traffic loads of malicious users and can provide almost the same performance as that of a system with no PUEA, when the malicious user traffic is low.

The concept of dynamic spectrum access (DSA) enables the licensed spectrum to be traded in an open market where the unlicensed users can freely buy and use the available licensed spectrum bands. However, like in the other traditional commodity markets, spectrum trading is inevitably accompanied by various competitions and challenges. In this paper, we study an important business competition activity - price war in the DSA market. A non-cooperative pricing game is formulated to model the contention among multiple wireless spectrum providers for higher market share and revenues. We calculate the Pareto optimal pricing strategies for all providers and analyze the motivations behind the price war. The potential responses to the price war are in-depth discussed. Numerical results demonstrate the efficiency of the Pareto optimal strategy for the game and the impact of the price war to all participants.

The cognitive radio enabled IEEE 802.22 wireless regional area network (WRAN) is designed to opportunistically utilize the unused or under-utilized TV bands. However, due to the lack of proactive security protocols and proper interaction policies among the secondary networks themselves, the IEEE 802.22 networks are vulnerable to various denial-of-service (DoS) threats. In this paper, we study the impact of coordinated DoS attacks on IEEE 802.22 networks from the malicious nodes' perspective. Assuming that multiple malicious nodes will launch coordinated attacks, we formulate a cooperative game among the malicious nodes. The expression of the net payoff is derived and the optimal decision strategy for the malicious nodes is obtained numerically. Simulation results demonstrate that the coordinated attack approach can enhance as high as 10-15% more net payoff for the malicious nodes than the uncoordinated attack.

In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and support vector machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2%.

The key idea of dynamic spectrum access (DSA) networks is to allow the secondary, unlicensed users to detect and use unused portions of the spectrum (white spaces) opportunistically. The two main constraints in the design of DSA networks is to make sure that this opportunistic access is done without any disruption of service to the primary users and without any modifications to the primaries themselves. Most architectures and protocols for DSA networks in the literature assume that all parties are honest and that there are no attackers. Recently (IEEE ICC, CogNet 2008) we demonstrated the failure of this approach by showing that an attacker can manipulate messages to convince the parties involved in the protocol to make incorrect spectrum decisions. In this paper, we consider spectrum decision protocols in clustered infrastructure-based dynamic spectrum access networks where the spectrum decision in each cluster is coordinated by some central authority. We propose an efficient and provably secure protocol that can be used to protect the spectrum decision process against a malicious adversary.

In this paper, we present an analytical model as well as a practical mechanism to detect denial of service (DoS) attacks on secondary users in dynamic spectrum access (DSA) networks. In particular, we analyze primary user emulation attacks (PUEA) in cognitive radio networks without using any location information and therefore can do away with dedicated sensor networks. We present an analysis using Fenton's approximation and Wald's sequential probability ratio test (WSPRT) to detect PUEA. Simulation results demonstrate that it is possible to keep the probability of success of PUEA low, while still keeping the probability of missing the return of the primary low as well.

In this paper we study the complexity of spectrum assignment problems in cognitive radio networks (CRNs) in the presence of several constraints. Although optimal spectrum assignment for secondary transmissions in CRNs is generally believed to be NP complete, the impact of fairness and link quality constraints on the hardness of the problem is not well studied. In this paper we show that when a minimum quality constraint is imposed on secondary transmissions, the spectrum assignment problem can be solved in polynomial time. However, such assignments may not guarantee fairness. We also show that when fairness is desired, even in the presence of quality constraints spectrum assignment problems remain NP complete. We then propose a tree pruning based algorithm to solve distance constrained spectrum assignment problem. We also discuss some heuristic techniques to solve fair distance constrained spectrum assignment problems in polynomial time.

Encryption is one of the fundamental technologies that is used in digital rights management. Unlike ordinary computer applications, multimedia applications generate large amounts of data that has to be processed in real time. So, a number of encryption schemes for multimedia applications have been proposed in recent years. We analyze the following proposed methods for multimedia encryption: key-based multiple Huffman tables (MHT), arithmetic coding with key-based interval splitting (KSAC), and randomized arithmetic coding (RAC). Our analysis shows that MHT and KSAC are vulnerable to low complexity known- and/or chosen-plaintext attacks. Although we do not provide any attacks on RAC, we point out some disadvantages of RAC over the classical compress-then-encrypt approach.

Abstract Traditional automatic repeat request (ARQ) protocols are channel unaware. That is, they react to channel errors by simply retransmitting erroneous packets and do not proactively decide whether or not to transmit a packet in a given slot based on past channel conditions. Clearly, ARQ protocols operating in this mode are not energy efficient. For example, continuously retransmitting erroneous packets when the wireless channel is in deep fade would cause significant wastage of transmission energy. In this paper, we present a stochastic learning automaton‐based wireless channel state aware ARQ protocol. The learning automaton learns to predict and track the time‐varying wireless channel conditions based on past observations. A Markov chain model for the channel state transitions is used. No a priori knowledge about the state transition probabilities is required by this predictor. Stochastic convergence of the learning algorithm is proved. The proposed ARQ protocol utilizes the predictions to compute transmission/retransmission policies in an online fashion. No pilot (training) symbols are used by the protocol for channel state prediction thereby avoiding any energy wastage due to the transmission of these symbols. Simulation results show that depending on the channel memory significant energy savings can be attained when compared with standard ARQ protocols. We also discuss the transmission energy versus delay trade‐off. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract — Over the past few years there has been a growing demand for radio resources and at the same time these resources are under utilized due to static spectrum allocation techniques. Dynamic spectrum access (DSA) has been thought of as a solution that would satisfy both the growing demand for radio resources and to efficiently utilize the spectrum. The radio devices that have the capability to dynamically sense the spectrum and access the under utilized bands are called cognitive radios (CR). There are two broad classes of users in CR, the primary user is a licensed user of a particular radio frequency band and the secondary users are unlicensed users who cognitively operate without causing harmful interference to the primary user. In this paper we consider a denial attack on centralized DSA networks where a malicious secondary user masquerades as a primary user and effectively shuts off access to all other secondary users. Note that this problem is unique to CR due to the distinction between primary and secondary users. We propose a public key cryptography based primary user iden-tification mechanism that prevents malicious secondary users from masquerading as primary users. We show that the proposed identification mechanism and the associated key management are computationally light weight. We also discuss some advantages and limitations of the proposed identification mechanism.

In this paper we propose an optimal joint source channel decoder (JSCD) for two mutually correlated sources each having memory that can be represented by first order Markov processes, and are transmitted through channels with memory representing slow fading wireless channels. We use the additive Markov channel (AMC) model and multiple access source codes (MASC)

In this paper we present a study of the effects of data hiding on the power spectra of digital images. Several imperceptible data hiding techniques have been proposed that provide strong visual security and robustness. Although imperceptible to the human visual system, the hidden data affects the natural qualities of the image, such as the image power spectrum. In this study, we classify a large image database into a number of categories. For each category, we calculate the slope of the power spectra for the marked and unmarked images. We note that in the case of spatial data hiding the average slope of the power spectra of marked images is 54.93% higher compared to that of the unmarked images. Also in the cases of transform domain data hiding we note that the average slope of the power spectra of the images marked using a discrete cosine (wavelet) transform (DC(W)T) based technique is higher by 9.12% (38.39%). We also test a commercially available data hiding software namely Digimarc Corp.'s MyPictureMarc 2005 VI.0. In this case the average power spectra of the marked images is 35.99% higher. Hence we see that the proposed scheme is a tool for universal steganalysis with varying degrees of success depending on the type of embedding.

We propose a joint encryption and compression (JEC) scheme with emphasis on application to video data. The proposed JEC scheme uses the philosophy of distributed source coding with side information to reduce the complexity of the compression process and at the same time uses cryptographic principles to ensure that security is built into the scheme. The joint distributed compression and encryption is achieved using a special class of codes called high-diffusion (HD) codes that were proposed recently in the context of joint error correction and encryption. By using the duality between channel codes and Slepian-Wolf coding, we construct a joint compression and encryption scheme that uses these codes in the diffusion layer. We adapt this cipher to MJPEG2000 with the inclusion of minimal amount of joint processing of video frames at the encoder.

The 19 papers in this special issue focus on cross-layer optimized wireless multimedia communications. The papers are organized into four sections: quality of service support for wireless networks; system architecture for multimedia over wireless networks; resource allocation in wireless multimedia communications, and multimedia coding and scheduling issues in wireless networks.

Wireless network security based on encryption is widely prevalent at this time. However, encryption techniques do not take into account wireless network characteristics such as random bit errors due to noise and burst errors due to fading. We note that the avalanche effect that makes a block cipher secure also causes them to be sensitive to bit errors. This results in a fundamental trade-off between security and throughput in encryption based wireless security.1 Further, if there is an adversary with a certain attack strength present in the wireless network, we see an additional twist to the security-throughput trade-off issue. In this paper, we propose a framework called opportunistic encryption that uses channel opportunities (acceptable signal to noise ratio) to maximize the throughput subject to desired security constraints. To illustrate this framework and compare it with some current approaches, this paper presents the following: 1) mathematical models to capture the security-throughput trade-off, 2) adversary models and their effects, 3) joint optimization of encryption and modulation (single and multirate), 4) the use of forward error correcting (FEC) codes to protect encrypted packets from bit errors, and 5) simulation results for Rijndael cipher. We observe that opportunistic encryption produces significant improvement in the performance compared to traditional approaches.

Spectrum is one of the most precious radio resources. With the increasing demand for wireless communication, efficiently using the spectrum resource has become an essential issue. With the Federal Communications Commission's (FCC) spectrum policy reform, secondary spectrum sharing has gained increasing interest. One of the policy reforms introduces the concept of an interference temperature - the total allowable interference in a spectral band. This means that secondary users can use different transmit powers as long as the sum of these power is less than the interference threshold. In this paper, we study two problems in secondary spectrum access with minimum signal to interference noise ratio (quality of service (QoS)) guarantee under an interference temperature constraint. First, when all the secondary links can be supported, a nonlinear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The nonlinear optimization is solved efficiently using geometric programming techniques. The second problem we address is, when not all the secondary links can be supported with their QoS requirement, it is desirable to have the spectrum access opportunity proportional to the user priority if they belong to different priority classes. In this context, we formulate an operator problem which takes the priority issues into consideration. To solve this problem, first, we propose a centralized reduced complexity search algorithm to find the optimal solution. Then, in order to solve this problem distributively, we define a secondary spectrum sharing potential game. The Nash equilibria of this potential game are investigated. The efficiency of the Nash equilibria solutions are characterized. It is shown that distributed sequential play and an algorithm based on stochastic learning attain the equilibrium solutions. Finally, the performances are examined through simulations

In many applications, data hiding can be viewed as a tradeoff between capacity, robustness (against attacks), and embedding induced distortion. In this paper, we consider a fourth parameter: the security of the hidden information. Specifically, we propose a hash-based randomized embedding algorithm (HRE) that increases the security of the hidden data. We then optimize this algorithm against JPEG attacks. We derive a mathematical expression for the security of our algorithm, using which we show that the security of our algorithm can be increased independent of capacity, robustness, and embedding induced distortion. The maximum security depends only on the length of the key sequence, which is limited only by the size of the host image. Using a joint security and capacity measure, we show that the proposed scheme performs better than current secure quantization based data hiding schemes. We also derive the optimal value of distortion compensation factor of the HRE algorithm against JPEG compression attack. Experimental results show that the operating points achieved by the proposed scheme are 7 dB better than current blind data hiding schemes against the JPEG attack

Abstract — We study the quality of service (QoS) issue in sec-ondary spectrum sharing subject to an interference temperature constraint. A non-linear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The non-linear optimization is solved efficiently using geometric programming techniques. When not all the secondary links can be supported with their QoS requirement, a reduced complexity searching algorithm is introduced to find the optimal subset of links which contains the maximum number of links with both QoS and interference temperature constraints satisfied. We also defined a secondary spectrum sharing potential game. The Nash equilibria of this potential game are reached by distributed sequential play. The efficiency of the Nash equilibria solutions is characterized. Finally, the performances of both the reduced complexity algorithm and the sequential play are examined through simulations. I.

The current encryption standard for wireless networks recommend using the AES cipher in the counter (CTR) mode for confidentiality and in the cipher block chaining (CBC) mode for authentication. In the counter mode, a 128 bit counter is encrypted using the AES into 128 bit keystream which is then XORed with 128 bits of plaintext before transmission. This operation is repeated for the entire frame and results in heavy energy consumption for larger frames. In this paper, we propose a novel cipher called High Diffusion (HD) cipher that securely expands a given 128 bit counter value to a larger 288 bit keystream during encryption, thus reducing the number of encryptions per frame compared to the AES. We show that the HD cipher is as secure as the AES under differential, linear cryptanalysis and the Square attack. Using an experimental set up consisting of a laptop with 1.8 GHz Pentium 4 processor and an Intrinsyc CerfCube with 233 MHz ARM processor we measure the energy consumption of both the AES and the HD cipher encryption operation. We observe that using HD cipher instead of AES for encryption will result in about 40% saving in energy consumption on both the laptop and the CerfCube. We also observe that energy efficiency due to HD cipher is significant for larger frame lengths.

We study the problem of dynamic spectrum access by secondary users with minimum signal to interference noise ratio (quality of service (QoS)) and interference temperature constraints. A non-linear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The non-linear optimization is solved efficiently using geometric programming techniques. When not all the secondary links can be supported with their QoS requirement, a reduced complexity search algorithm is introduced to find the optimal subset of allowable links. Secondary users may belong to different priority classes. Accessing opportunities should be proportional to priorities. Therefore, we defined a secondary spectrum sharing potential game which takes these priority classes into consideration. The Nash equilibria of this potential game are reached by distributed sequential play. The efficiency of the Nash equilibria solutions are characterized. Finally, the performances of both the reduced complexity algorithm and the sequential play are examined through simulations.

Currently, most quantization based data hiding algorithms are built assuming specific distributions of attacks, such as additive white Gaussian noise (AWGN), uniform, noise, and so on. In this paper, we prove that the worst case additive attack for quantization based data hiding is a 3-delta function. We derive the expression for the probability of error (Pe) in terms of distortion compensation factor, alpha, and the attack distribution. By maximizing Pewith respect to the attack distribution, we get the optimal placement of the 3-delta function. We then experimentally verify that the 3-delta function is indeed the worst case attack for quantization based data hiding

Minimizing power consumption is crucial in battery power-limited secure wireless mobile networks. In this paper, we (a) introduce a hardware/software set-up to measure the battery power consumption of encryption algorithms through real-life experimentation, (b) based on the profiled data, propose mathematical models to capture the relationships between power consumption and security, and (c) formulate and solve security maximization subject to power constraints. Numerical results are presented to illustrate the gains that can be achieved in using solutions of the proposed security maximization problems subject to power constraints.

Summary form only given. Most current distributed joint source-channel coding (DJSCC) work focus on sources without memory, like i.i.d sources. We consider a general scenario where the sources are not only correlated with each other but also have memory. In particular, we design a constrained distributed joint source-channel decoder (C-DJSCD) for the optimal variable length multiple access source code (MASC) encoded sources for the symmetric distributed coding scenario. The proposed decoder is based on maximum a posteriori probability (MAP) estimation.

In this paper, we propose a novel discrete cosine transform (DCT) based spread spectrum data-hiding algorithm that provides statistical security. Unlike other spread spectrum based data hiding algorithms, the proposed algorithm does not introduce a low-pass filtering effect in the stego image's histogram, thereby making the presence of hidden data statistically undetectable. The proposed algorithm does not compromise on robustness or capacity to achieve this goal. When compared to the generic block DCT based data-hiding scheme, the proposed algorithm provides a 41% reduction in the relative entropy between the probability mass functions of the host and stego images, thus improving the statistical security of the system. The proposed algorithm is robust against a variety of image manipulating attacks such as noise addition, filtering, blurring, sharpening, JPEG compression etc

In this paper we propose a new secure quantization based data embedding algorithm and analyze the security, capacity, robustness and embedding induced distortion of this scheme. Using a joint security, robustness and capacity measure, we show that the proposed scheme provides an enhancement in security in comparison to current secure quantization based data hiding schemes. We derive a mathematic expression for the security of our algorithm. Using this metric, we show that the security of our algorithm can be increased independent of these parameters: capacity, robustness and embedding induced distortion. The maximum security depends only on the length of key sequence, which is limited only by the size of the host image

This paper presents a critical analysis of some of the current steganalysis methodologies. The pros and cons of these methods are discussed from statistical and usability perspectives. It is concluded that no single strategy works best. Depending on the amount of statistical information available at hand, a proper choice has to be made.

Most digital data hiding schemes are sensitive to simple geometric attacks namely rotation, cropping, and scaling. Of these, rotational attacks possess the capability to not only change each pixel value but also dislocate the image pixels in a circular fashion, thus creating a synchronization issue for any linear data hiding algorithm. So far, Fourier Mellin transform (FMT) based algorithms have been considered a standard for rotation invariant data hiding. This method causes some difficulties in implementation due to unstable log-polar mapping, iterative inversion of the interpolation and aliasing effects on the FMT magnitude and phase spectra. In this paper, we propose a data hiding algorithm that is based on the Zernike moment transform (ZMT), accompanied with an odd-even quantizer based embedding scheme to defeat the effects of rotation and cropping attacks. We note that this algorithm is robust to a combination of rotation and other popular attacks. The proposed algorithm has good embedding capacity and very low induced distortion. Experimental results over a range of rotational attacks (from 0/spl deg/ to 360/spl deg/) show a recovery rate (of the embedded bits) of 97% or greater.

Most quantizer based data hiding schemes use uniform quantizer, which is not optimal if the host signal is not uniformly distributed. In this paper, we design a quantizer that is not only pdf-matched but also more suited to embedding than the Linde-Buzo-Gray (Lloyd-Max) algorithm for vector (scalar) quantizer design. Experimental results shows that the proposed scheme provides better trade-offs between robustness to attacks, embedding induced distortion and embedding capacity than a simple pdf-matched scheme. The proposed algorithm also shows about 3 dB improvements in embedding distortion over other popular quantizer based embedding algorithms.

Delivering multimedia content over the network pose several challenges that include higher bandwidth and sensitivity to packet losses resulting due to congestion and/or transmission errors. Multiple description coding (MDC) is one of the source coding approaches to alleviate the problems of packet loss in a network since MDC splits the source information into several descriptions which can then be transmitted over several paths in the network. Parallel delivery of descriptions over multiple paths should guarantee better quality of transmission. In this paper we investigate the performance of a generalized MDC scheme over multi-path and single path scenarios and compare its performance to a layered single description (SDC) scheme. In the multi-path case, multimedia content is delivered via k-shortest paths that are selected on the basis of the packet loss probability of overall links in that path. Our experimental results show that MDC scheme always outperforms the SDC scheme in terms of PSNR quality for both single and multi-path transmissions. The improvement varies from 7 dB at (5% packet loss) to 13 dB at (20% packet loss) for the single path and by 3 dB to 9 dB respectively for the multi-path transmission.

Multiple description coding (MDC) schemes have become popular as robustness tools in packet switched networks where multiple paths with varying packet loss rates are available. In this paper we propose a new MDC scheme for video using 3D wavelet decomposition. The spatio-temporal coefficients are then grouped using either a random, uniform scheme or a hierarchical scheme. The descriptions are constructed by arranging a subset of these groups in two positions: primary and the secondary. The bit-allocation problem between the primary and secondary groups is formulated and solved using the generalized BFOS [E.A. Riskin, 1991] algorithm. The bits allocated to the secondary position may be zero, in which case, the description is said to be constructed with no redundancy. Experiments using bi-orthogonal wavelet for the spatial and Haar wavelet for the temporal decomposition, along with embedded block coding with optimal truncation for the different groups have shown significant performance for test video sequences. We expect that, for finite packet loss probability, the MDC scheme with redundancy does better than the scheme that does not have redundancy. In this paper, we also present a discussion of the differences between our work with the most recent 3D based video MDC scheme [Joohie Kim et al., 2002].

This paper investigates the use of joint source-channel coding for reliable transmission of MPEG-4 video over wireless channels. We present a maximum a posteriori probability (MAP) based, joint source-channel decoder (JSCD) for variable length coded intra macroblocks and inter macroblocks in a MPEG-4 encoded video stream. We then provide a comprehensive evaluation of this paradigm as an alternative method for achieving error resilience. In particular, our method is tested in conjunction with the MPEG-4 error resilience tools such as reversible variable length codes (RVLC), resynchronization markers, and data partitioning, with and without forward error controls (FECs) and, finally, without the use of RVLCs (only variable length codes). In addition, the JSCD algorithm is tested with and without the use of FECs [Reed-Solomon (RS) codes]. These tests show that the best performance at high channel bit-error rate is obtained when all error resilience tools are co-opted. The maximum improvement due to the proposed decoder when used without FEC is about 7 dB for RVLC and 8 dB for VLCs. These numbers are about 2.2 and 7.5 dB, respectively, when the MAP decoder is used in conjunction with the RS codes.

We propose an optimal joint source-channel maximum a posteriori probability decoder for variable-length encoded sources transmitted over a wireless channel, modeled as an additive-Markov channel. The state space introduced by the authors in a previous paper is used to take care of the unique challenges posed by variable-length codes. Simulations demonstrate, that this decoder performs substantially better than the standard Huffman decoder for a simple test source and is robust to inaccuracies in channel statistics estimates. The proposed algorithm also compares favorably to a standard forward error correction-based system.

Multiple description coding (MDC) schemes have become popular as robustness tools in packet switched networks where multiple paths with varying packet loss rates are available. The descriptions are designed so that the quality of the reconstructed image depends on the number of descriptions received and not on which descriptions are actually received. We propose a new MDC framework for the EBCOT algorithm used in the current JPEG 2000 standard for still image compression. The inherent block coding in EBCOT allows for more flexibility in the formation of basic groups that are then combined in different ways to form descriptions. The bit allocation problem (between the different groups in a description) is formulated and optimized for good rate-distortion performance. Optimization of the bit allocation between the primary and secondary positions in each description is obtained using the generalized BFOS algorithm. Experiments using the Kakadu software (for JPEG 2000) show that the performance of the proposed framework degrades gracefully with increase in the number of descriptions lost.

This paper proposes an optimal maximum a posteriori probability decoder for variable-length encoded sources over binary symmetric channels (BSC) that uses a novel state-space to deal with the problem of variable-length source codes in the decoder. This sequential, finite-delay, joint source-channel decoder delivers substantial improvements over the conventional decoder and also over a system that uses a standard forward error correcting code operating at the same over all bit rates. This decoder is also robust to inaccuracies in the estimation of channel statistics.

This paper proposes an optimal joint source-channel, maximum a posteriori, decoder for entropy coded Markov sources transmitted over noisy channels. We introduce the concept of incomplete and complete states to deal with the problem of variable length source codes in the decoder. The proposed decoder is sequential, thereby making the expected delay finite. When compared to the traditional decoder, the proposed decoder shows a maximum improvement, of about 4 dB in a modified signal to noise ratio and an improvement of 21.95% in percentage of bits that are received in an out of synchronization condition, for a simple test source.

Summary form only given. Entropy codes (e.g. Huffman codes) are often used to improve the rate-distortion performance of codecs for most sources. However, transmitting entropy coded sources over noisy channels can cause the encoder and decoder to lose synchronization, because the codes tend to be of variable length. Designing optimal decoders to deal with this problem is nontrivial since it is no longer optimal to process the data in fixed-length blocks, as is done with fixed-length codes. This paper deals with the design of an optimal decoder (MAPD), in the maximum a posteriori (MAP) sense, for an entropy coded memoryless source transmitted over a binary symmetric channel (BSC) with channel cross over probability /spl epsiv/. The MAP problem is cast in a dynamic programming framework and a Viterbi like implementation of the decoder is presented. At each stage the MAPD performs two operations: the metric-update and the merger-check operations. A stream of 40,000 samples of a zero mean, unit variance, Gaussian source, quantized with uniform, N-level quantizers was Huffman encoded and the resulting bit stream was transmitted over a BSC. Experiments were performed for values of N ranging from 128 to 1024 and for four different random error patterns, obtained using a random number generator. The results demonstrate that the MAPD performs better than the HD on an average, whenever /spl epsiv/ is comparable to the source probabilities. A maximum reduction of 2.94% in the bits that are out of synchronization, was achieved for the 1024 level quantizer.

A variable dimension vector quantizer (VDVQ) has codewords of unequal dimensions. Here, a trellis-based sequential optimal VDVQ encoding algorithm is proposed. Also, a VDVQ codebook design algorithm based on splitting a node with equal or reduced dimensions is proposed that does not require any codebook parameter to be prespecified unlike known schemes. The VDVQ system is shown to outperform a few known VQ systems for AR(1) sources.