The advent of the Internet of Things has generated loads of data from the devices that are now connected to the Internet. While the majority of the data corresponds to measurements done by these devices, there is a second type of information (the metadata) that provides information about the devices themselves. Most of this metadata is still underused when used at all. On the other hand, the graphical user interfaces that allow operating and/or monitoring the connected devices from a computer or smartphone, are usually programmed from zero. However, the metadata that describes the main properties of the devices (i.e., inputs, outputs, precision, range, etc.) can be used along with smart web design techniques to automatically create these interfaces. This article proposes a framework to achieve this, and presents an application example consisting of an online lab of a servo-motor.

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.

In this paper, we present SpiderRadio, a software defined cognitive radio (CR) prototype for dynamic spectrum access (DSA) networking. The medium access control (MAC) layer of SpiderRadio is implemented in software on top of commodity IEEE 802.11a/b/g hardware. However, the proposed architecture and implementation are applicable to other spectrum bands as well. We also present a dynamic spectrum sensing methodology for primary incumbent detection. The proposed method is based on observing the PHY errors, received signal strength and statistical model building. For coordination among radio nodes, synchronization and fast channel switching, we present new communication protocols, design extended management frame structure and modify the hardware abstraction layer. Several fundamental tradeoffs (e.g., complexity versus network performance) to be considered during a dynamic spectrum access radio network prototype implementation are also discussed in detail. To demonstrate the practical capabilities of the proposed SpiderRadio prototype, we also present various testbed experimental measurement results.

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.

The authors present a distributed channel acquisition mechanism in dynamic spectrum access (DSA) networks. A novel graph MAximum SPECTrum packing algorithm (MASPECT) is proposed for a system with N secondary networks. Each secondary network makes use only of the local topology information to resolve contentions during channel access. The proposed algorithm also adapts easily to topological changes. Most approaches to channel acquisition use either centralised graph colouring or distributed approaches that result in under utilisation of spectrum. The authors show that the proposed MASPECT algorithm results in improvements by up to one order of magnitude in the spectrum utilisation for the secondary networks and results in Jain's fairness index of about 0.9. The authors further present a modified probabilistic heuristic, PMASPECT, that improves the termination time of the algorithm by up to two orders of magnitude. The authors also study the impact of the MASPECT algorithm on each individual secondary networks in the system in the presence of primary activity. For each secondary network, the proposed algorithm results in up to two orders of magnitude of reduction in the call blocking dropping probabilities in the presence of primary activity.

We present a game theoretic power control of overlay/overt communications to maximize the goodput (effective throughput of error-free bits) of multi-terminal covert timing channels. Most approaches in the literature on covert timing channels discuss capacities of the timing channels but do not study how the overlay communication can be controlled to maximize the goodput of covert timing channels. We study the factors of the overlay communication that affect the goodput of each timing channel in a multi-terminal covert timing network. We show that the goodput of the covert timing channel can be enhanced by increasing the rate of overlay transmission and by game theoretic power control of overlay communication. We finally extend the game theoretic power control to maximize the goodput of each covert timing channel in a multi-terminal covert timing network by maximizing the asymptotic spectral efficiency of the overlay communication.

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.

Secondary users sharing primary users' spectrum is modeled as a graphical game. Users located in random graphs and a regular lattice are considered. Secondary users are assumed to differentiate the ``quality" of the primary spectrum while interacting within their local neighborhood to minimize interference and congestion. The learning algorithm is also shown to be effective in punishing malicious users that violate spectrum etiquettes. An equivalence between spectrum sharing neighborhood interaction and the spin-glass model in statistical physics is established. A distributed exponential learning algorithm is used to arrive at an evolutionary stable solution to the game. Some theoretical properties of the system are studied and simulation results are presented to illustrate price of anarchy, convergence of the learning algorithm and asymptotic invariance of the system performance with respect to spectrum quality.

We address a unique security threat that arises due to channel fragmentation (or aggregation or bonding) in dynamic spectrum access (DSA) based IEEE 802.22 networks. Typically, channel fragmentation, aggregation and bonding have been studied in the literature as a means to enhance the spectrum utilization. However, the loss of orthogonality between the spectrum bands due to channel fragmentation, aggregation or bonding can be exploited by malicious attackers to cause a cognitive service disruption. We present an analysis of such a threat. We determine the optimal transmit powers a malicious attacker transmits on each fragment, so as to create maximum service disruption. Numerical results indicate that a malicious attacker can cause up to about 16% loss in the capacity of the system as a consequence of fragmentation. Detailed analysis is presented for channel fragmentation and can be easily applied to channel aggregation and bonding. To the best of our knowledge,this is the first analysis of such cognitive service disruption threats due to fragmentation.

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.

Efficient routing is a fundamental issue in multi-hop wireless ad hoc networks. In this paper, we study the limitation of traditional routing structure in multi-hop wireless ad hoc networks due to (a) the layered structure of a wireless protocol stack and (b) the lack of coordination between medium access control (MAC) and routing protocols. These limitations result in long processing delays in a relay/forwarding node. In order to alleviate these issues, we propose a solution based on cross-layer MAC design, which improves the coordination between MAC and routing layers using an idea we call ``virtual link". The virtual link idea was implemented and tested in an ad hoc wireless network testbed. Experimental results show that the proposed cross-layer design significantly improves the performance in terms of reduced round trip time (RTT), reduced processing time in the intermediate relay/forwarding nodes and increased throughput compared to a legacy architecture.

Spectrum sensing is one of the critical features in cognitive radio based dynamic spectrum access networking. In this paper, we discuss a new spectrum sensing technique for primary incumbent detection. The proposed method is based on observing the PHY errors, received signal strength and n-moving window averaging of the observed measurement. The sensing parameters are dynamically optimized based on the operating radio environment. This sensing method is implemented in SpiderRadio, a cognitive radio testbed based on off-the-shelf IEEE 802.11 devices. Experimental results show that the proposed technique results in very low sensing delay and failure probability.

We discuss malicious interference based denial of service (DoS) attacks in multi-band covert timing networks using an adversarial game theoretic approach. A covert timing network operating on a set of multiple spectrum bands is considered. Each band has an associated utility which represents the critical nature of the covert data transmitted in the band. A malicious attacker wishes to cause a DoS attack by sensing and creating malicious interference on some or all of the bands. The covert timing network deploys camouflaging resources to appropriately defend the spectrum bands. A two tier game theoretic approach is proposed to model this scenario. The first tier of the game is the sensing game in which, the covert timing network determines the amount of camouflaging resources to be deployed in each band and the malicious attacker determines the optimal sensing resources to be deployed in each band. In the second tier of the game, the malicious attacker determines the optimal transmit powers on each spectral band it chooses to attack. We prove the existence of Nash equilibriums for the games. We compare the performance of our proposed game theoretic mechanism with that of other well known heuristic mechanisms and demonstrate the effectiveness of the proposed approach.

We study the optimum location of an eavesdropper from a secrecy capacity perspective in multiterminal networks with power control. We determine the logical location of an eavesdropper (represented by the channel gain from all the transmitters) which 1) results in zero secrecy capacity for maximum number of users in the network and 2) results in zero secrecy capacity for the bottle-neck links. We then analyze the asymptotic secrecy capacity of the system and the asymptotic behavior of the optimum logical location of the eavesdropper. Results indicate that power control can make eavesdropping more difficult as it results in infeasible locations for the eavesdropper. Power control is also shown to provide scenarios which can result in positive asymptotic secrecy capacity for all the users.

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%.

In this paper, we present the research of a foreign currency investment framework involving the prediction of the foreign currency exchange rates and the portfolio optimization under certain constrains. We adopt two machine learning methods, support vector machines (SVMs) and neural networks (NNs), as well as the traditional moving average method, to predict the exchange rates for three foreign currencies including Australia Dollars (AUD), European Euro (EUR), and Swiss Francs (CHF). Based on these forecastings, we choose two out of the three currencies listed above and build a portfolio by adopting multi-objective portfolio optimization techniques by maximizing the return and minimizing the risk. Karush-Kuhn-Tucker (KKT) theorem guarantees that the optimal portfolio is reachable. Simulation results show that the optimal portforlio investment can achieve superior return performance compared with three single currency investment benchmarks.

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.

The cognitive radio based IEEE 802.22 wireless regional area network (WRAN) is designed to operate in the under-utilized TV bands by detecting and avoiding primary TV transmission bands in a timely manner. Such networks, deployed by competing wireless service providers, would have to self-coexist by accessing different parts of the available spectrum in a distributed manner. Obviously, the goal of every network is to acquire a clear spectrum chunk free of interference from other IEEE 802.22 networks so as to satisfy the QoS of the services delivered to the end-users. In this paper, we study the distributed WRAN self-coexistence problem from a minority game theoretic perspective. We model the spectrum band switching game where the networks try to minimize their cost in finding a clear band. We propose a mixed strategy that the competing networks must adhere to in order to achieve the Nash equilibrium. Simulation experiments have also been conducted and results corroborate with the theoretical analysis.

With the explosive growth of wireless multimedia applications over the wireless Internet in recent years, the demand for radio spectral resources has increased significantly. In order to meet the quality of service, delay, and large bandwidth requirements, various techniques such as source and channel coding, distributed streaming, multicast etc. have been considered. In this paper, we propose a technique for distributed multimedia transmission over the secondary user network, which makes use of opportunistic spectrum access with the help of cognitive radios. We use digital fountain codes to distribute the multimedia content over unused spectrum and also to compensate for the loss incurred due to primary user interference. Primary user traffic is modelled as a Poisson process. We develop the techniques to select appropriate channels and study the trade-offs between link reliability, spectral efficiency and coding overhead. Simulation results are presented for the secondary spectrum access model.

With the advent of cognitive radios, secondary usage of spectrum on opportunistic basis, in the licensed band, is becoming practical. In this type of opportunistic spectrum access, secondary users may loose access due to the sudden arrival of a primary user who owns the spectrum. In order to ensure the link reliability and to compensate for the loss, some spectrally efficient mechanisms are required. In this paper we consider the spectrum pooling model for secondary spectrum access for delay-constrained applications. Along with this we study the application of Luby Transform (LT) codes to compensate the secondary user for the loss incurred due to primary user interference. We study the trade-offs between spectral efficiency, link reliability and overhead due to LT coding. Primary user traffic model is used to parametrize the trade- offs. Simulation results are presented for the secondary spectrum access model.

Economics of security is a nascent research area. In this paper we identify buyer-level, buyer-seller level and seller-level economic problems. Some open questions are identified and their implications are discussed.

We study a buffer that implements the Random Early Detect/Discard (RED) mechanism to cope with congestion, and offers service differentiation by proposing a finite number of slopes at different prices for the RED probability. As a characteristic, the smaller the slope, the better the resulting QoS. Users are sensitive to their average throughput and to the price they pay. Since the study of the noncooperative game played is rendered difficult by the discrete nature of the strategy sets, and since it is not likely that users have a perfect knowledge of the game but only know their experienced utility, we introduce a decentralized learning algorithm to progressively reach a Nash equilibrium over time. We examine the effect of prices on the final game outcomes.

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.

We explore the price dynamics in a competitive market consisting of spectrum agile network service providers and users. Here, multiple self interested spectrum providers operating with different technologies and costs compete for potential customers. Different buyers or consumers may evaluate the same seller differently depending on their applications, operating technologies and locations. Two different buyer populations, the quality-sensitive and the price-sensitive are investigated, and the resulting collective price dynamics are studied using a combination of analysis and simulations. Various scenarios are considered regarding the nature and accuracy of information available to the sellers. A myopically optimal strategy is studied when full information is available, while a stochastic learning based strategy is considered when the information is limited. Cooperating groups may be formed among the sellers which will in-turn influence the group profit for those participants. Free riding phenomenon is observed under certain circumstances

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

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.

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.

One of the reasons for the limitation of bandwidth in current generation wireless networks is the spectrum policy of the Federal Communications Commission (FCC). But, with the spectrum policy reform, open spectrum wireless networks, and spectrum agile radios are set to drive next general wireless networks. In this paper, we investigate continuous-time Markov models for dynamic spectrum access in open spectrum wireless networks. Both queueing and no queueing cases are considered. Analytical results are derived based on the Markov models. A random access protocol is proposed that is shown to achieve airtime fairness. A distributed version of this protocol that uses only local information is also proposed based on homo egualis anthropological model. Inequality aversion by the radio systems to achieve fairness is captured by this model. These protocols are then extended to spectrum agile radios. Extensive simulation results are presented to compare the performances of fixed versus agile radios.

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.

The security of multimedia data transmitted over wireless networks is of increased interest. Encryption mechanisms securely transmit multimedia data over insecure networks. A major issue that has received very little attention so far is that the very same properties that gives ciphers (encryption mechanisms) their cryptographic strength make them sensitive to channel errors as well. In addition, this would enhance the error propagation inherent in compressed data. Therefore provision of security for multimedia transmission over wireless channel results in throughput loss. Nevertheless this lost throughput is traded for increased security. To our knowledge there has been no substantial effort to optimize this tradeoff. Opportunistic encryption proposed in this work is a way to optimize the tradeoff between security offered and the throughput lost due to a cipher. We show that opportunistic encryption methods that adapt to channel variations will lead to an overall increase in the system performance. Two broad scenarios based on channel knowledge are considered, (a) exact channel state information up to a finite time horizon is known and (b) only the average signal-to-noise ratio (SNR) is known. Proposed opportunistic encryption framework is found to achieve significant gains in throughput compared to fixed block length encryption methods for a wide range of average SNR values. We have shown that applying opportunistic encryption on JPEG compressed image results in a better quality of received image and improved security compared to fixed block length encryption.

Wireless multimedia services currently suffer due to limited bandwidth. One of the reasons for this limitation was the Federal Communications Commission's (FCC) spectrum regulation policy. But, with the advantage of open spectrum wireless networking the bandwidth constraint is minimized. In this paper, a Markov model is presented and used to analyze the unlicensed band access. Then a random access scheme is proposed which provisions the existing etiquettes with more efficiency and fairness. Later a homo egualis society model based access scheme is built to implement the random access scheme in a distributed fashion. Simulations are performed to demonstrate the accuracy of this Markov model and to show the efficiency and fairness of the proposed schemes.

In this paper, we discuss a cross-layer design for joint user scheduling and adaptive rate control for downlink wireless transmission. We take a stochastic learning-based approach to achieve this. The scheduling is performed at the medium access control (MAC) layer, whereas the rate selection takes place at the physical/link (PHY/LINK) layer. These two components residing in the two layers exchange information to ensure that user defined rate requests are satisfied by the right combination of transmission schedules and rate selections. The method is highly efficient for low mobility applications with mobile speeds in the order of a few kilometers per hour. While simple to implement, this technique requires no explicit channel estimation phase. The only feedback used are the single bit ACK/NACK signal indicating the correct reception/failure of the packet. As shown in the convergence theorems, the algorithm achieves optimal performance in "stationary" channels. With slowly varying channels, the rate selection algorithm sees a "quasi-stationary" channel and adaptively converges to an optimal solution. Simulations performed using a third-generation wireless system, namely, high-speed downlink packet access (HSDPA) validate the theoretical results.

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.

Computational power optimization is crucial in the battery power limited secure wireless mobile networks. Therefore, in this paper, we (a) introduce a hardware/software set-up to measure and model the battery power consumption of different encryption algorithms through real-life experimentation and (b) compute optimal power (number of rounds) allocation for encrypting packets such that the constraints on power and security are met. We present the results for three block ciphers: DES, IDEA, and COST though the same analysis can be extended to the other ciphers such as AES, RC4, etc. A new measure called "vulnerability" that quantifies the success of linear cryptanalysis attack is proposed and its relationship with the power consumption is explored. Two mathematical optimization problems are then posed: (a) compute the optimal power allocation to encrypt each packet such that the vulnerability is minimized subject to a total power constraint and (b) to compute the optimal number of encryption rounds for each packet such that a total power constraint is met. The differences in these two formulations are presented. A closed form solution to the first problem is derived while the second optimization formulation is posed as an integer program and is solved numerically. Several numerical results are also provided.

In this paper, we analyze the performance of on-line image transmission algorithms designed to minimize wastage of battery energy and bandwidth over bursty wireless channels. These algorithms estimate the wireless channel state in an online fashion and adapt accordingly. Theoretical basis for these algorithms are presented using competitive analysis. Computational and transmission energy consumption factors are taken into account. Experimental results show that some of the proposed algorithms can result in significant performance improvement.

A joint channel adaptive rate control and randomized scheduling algorithm based on learning automata (LA) K. S. Narendra et al., (1989)is presented. The scheduling is performed at the medium access control (MAC) layer whereas the rate selection takes place at the physical/link (PHY/LINK) layer. The two components residing in the two layers exchange minimal amount of information and adaptively achieve the best throughput and desired quality of service (QoS) in terms of average transmission rates in the prevailing channel conditions. Scheduling is carried out by a LA of continuous reward penalty variate, and a discrete pursuit reward inaction (DPRI) type B. J. Oommen et al., (1990) is used for adaptive rate selection. While simple to implement, this technique requires no explicit channel estimation phase. The only feedback required are the single bit ACK signal indicating the correct reception of packets. As shown in the convergence theorems, the algorithm achieves optimal performance in "stationary'" channels. With slowly varying channels, the MCS selection algorithm sees a "quasistationary" channel and adaptively converges to the optimality. Simulation results are provided for parameters as per to HSDPA standard.

Distributed power control is an important issue in wireless networks. Recently, due to the bursty nature of data communication, packet switching is used in cellular systems. In addition, majority of previous power control algorithm assume that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and QUALCOMM's proposal to the IS-95 standard use a finite number of discretized power levels. These motivate the need to investigate solutions for distributed discrete power control for bursty transmission. Therefore, we propose a probabilistic power adaptation algorithm and analyze its theoretical properties along with the numerical behavior for bursty transmission. We approximate the discrete power control iterations by an equivalent ordinary differential equation (ODE) to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium may exist are also studied. Experimental results are presented for several cases and the impact of data burstiness on the proposed algorithm is also concerned.

Suppose the information hider has a set of information hiding schemes to be employed in the presence of a set of steganalysis detectors. Let the information hider have only an incomplete information about the steganalysis detectors. That is, suppose the probability of the jth steganalysis detector would be used is p/sub j/, j = 1,2...n, then the information hider only knows the ordering of these probabilities, say, p/sub 1//spl ges/p/sub 2//spl ges/...p/sub n/. Under this circumstance how can the information hider choose the optimal embedding strategy? This is the question addressed in this paper. Theoretical analysis and some numerical results are presented.

Java-enabled wireless devices are preferred for various reasons. For example, users can dynamically download Java applications on demand. The dynamic download capability supports extensibility of the mobile client features and centralizes application maintenance at the server. Also, it enables service providers to customize features for the clients. In this work, we extend this client-server collaboration further by offloading some of the computations (i.e., method execution and dynamic compilation) normally performed by the mobile client to the resource-rich server in order to conserve energy consumed by the client in a wireless Java environment. In the proposed framework, the object serialization feature of Java is used to allow offloading of both method execution and bytecode-to-native code compilation to the server when executing a Java application. Our framework takes into account communication, computation, and compilation energies to decide where to compile and execute a method (locally or remotely), and how to execute it (using interpretation or just-in-time compilation with different levels of optimizations). As both computation and communication energies vary based on external conditions (such as the wireless channel state and user supplied inputs), our decision must be done dynamically when a method is invoked. Our experiments, using a set of Java applications executed on a simulation framework, reveal that the proposed techniques are very effective in conserving the energy of the mobile client.

We present a computationally simple stochastic learning control protocol framework for an adaptive energy efficient link layer protocol. A stochastic iterative technique is discussed that can produce soft channel state predictions and track slow/rapidly varying bursty, finite state wireless channels. No piori knowledge about the state transition probabilities is needed for this. Theoretical convergence of the proposed technique is shown. The proposed link layer protocol utilizes the channel state predictions from the stochastic learning algorithm while computing energy efficient transmission policies. This entire process is performed on-line with no pilot (training) symbols, etc., thereby improving the throughput and avoiding energy wastage due to pilot symbols. Simulation results show that up to 50% energy savings can be obtained for some channels when compared with a popular link layer protocol. Energy and delay can be trade-off efficiently using the proposed method.

Video communications using mobile wireless devices is a challenging task due to the limited capacity of batteries. Some of the key technologies that affect battery life are source compression, channel error control coding and radio transmission. In this paper, we propose several experimental profiling based mathematical models for energy consumption of MPEG4 video transmission. These models capture both the computational and transmission energy as a function of several MPEG4 parameters. Results presented in this paper show that the proposed models are fairly accurate.

In this paper, we present solutions to minimize wastage of battery power and bandwidth through the design of efficient transmission control strategies. We present several on-line transmission techniques. We analyze the proposed algorithms using competitive analysis techniques against an optimal off-line strategy, which has perfect knowledge of the channel conditions in future. We show that all of our techniques are highly competitive with a majority of them having a small constant-factor (at most 2) competitive ratio.

In broadband wireless applications, power consumption is one of the critical system parameters. It is important to achieve high spectral efficiency and power saving at the same time. For high spectral efficiency, adaptive M-QAM systems have been previously proposed, but the impact of the additional hardware components required to implement such systems have not been well studied. In this paper, we study the effect of adaptive M-QAM modems on hardware power consumption. This is followed by the description of a new architecture for low power ADC

We present a hybrid approach to image watermarking that exploits results from both information theory and perceptual studies, and we extend some of the results of our earlier paper (see Somasundaram, S. and Chandramouli, R., Proc. ISCAS, 2002). We use a waterfilling-type algorithm to establish a connection between information theoretic and perceptually based watermarking. An information theoretic capacity formula for a Gaussian signal over a Gaussian channel is used and some parameters of the corresponding waterfilling-type algorithm are chosen based on a perceptual criterion. Image sub-blocks to be watermarked are chosen based on the local statistics of each sub-block and the embedded watermark strength for each of these sub-blocks is computed based on a perceptual criterion. An iterative algorithm is proposed for this purpose. Experimental results are presented to discuss the trade-off between information theoretic capacity, robustness and perceptual distortion.

There have been many techniques for hiding messages in images in such a manner that the alterations made to the image are perceptually indiscernible. However, the question whether they result in images that are statistically indistinguishable from untampered images has not been adequately explored. We look at some specific image based steganography techniques and show that an observer can indeed distinguish between images carrying a hidden message and images which do not carry a message. We derive a closed form expression of the probability of detection and false alarm in terms of the number of bits that are hidden. This leads us to the notion of steganographic capacity, that is, how many bits can we hide in a message without causing statistically significant modifications? Our results are able to provide an upper bound on the this capacity. Our ongoing work relates to adaptive steganographic techniques that take explicit steps to foil the detection mechanisms. In this case we hope to show that the number of bits that can be embedded increases significantly.