A truthful double auction for device-to-device communications in cellular networks

Data traffic in cellular networks has dramatically increased in recent years as the emergence of various new wireless applications, which imposes an immediate requirement for large network capacity. Although many efforts have been made to enhance wireless channel capacity, they are far from solving the network capacity enhancement problem. Device-to-Device (D2D) communication is recently proposed as a promising technique to increase network capacity. However, most existing work on D2D communications focuses on optimizing throughput or energy efficiency, without considering economic issues. In this paper, we propose a truthful double auction for D2D communications (TAD) in multi-cell cellular networks for trading resources in frequencytime domain, where cellular users with D2D communication capability act as sellers, and other users waiting to access the network act as buyers. Both intra-cell and inter-cell D2D sellers are accommodated in TAD while the competitive space in each cell is extensively exploited to achieve a high auction efficiency. With a sophisticated seller-buyer matching, winner determination and pricing, TAD guarantees individual rationality, budget balance, and truthfulness. Furthermore, we extend our TAD design to handle a more general case that each seller and buyer ask/bid multiple resource units. Extensive simulation results show that TAD can achieve truthfulness as well as high performance in terms of seller/buyer sanctification ratio, auctioneer profit and network throughput.

Blind channel estimation and signal retrieving for MIMO relay systems

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

Robust time-spread echo watermarking using characteristics of host signals

A robust audio watermarking method based on the time-spread (TS) echo hiding scheme is proposed. Compared with existing TS watermarking methods, the approach is more robust as it exploits the characteristics of host signals in the encoding stage. Theoretical analysis and simulation examples demonstrate the effectiveness and advantages of the method.

Rank-based Image watermarking method with high embedding capacity and robustness

This paper presents a novel rank-based method for image watermarking. In the watermark embedding process, the host image is divided into blocks, followed by the 2-D discrete cosine transform (DCT). For each image block, a secret key is employed to randomly select a set of DCT coefficients suitable for watermark embedding. Watermark bits are inserted into an image block by modifying the set of DCT coefficients using a rank-based embedding rule. In the watermark detection process, the corresponding detection matrices are formed from the received image using the secret key. Afterward, the watermark bits are extracted by checking the ranks of the detection matrices. Since the proposed watermarking method only uses two DCT coefficients to hide one watermark bit, it can achieve very high embedding capacity. Moreover, our method is free of host signal interference. This desired feature and the usage of an error buffer in watermark embedding result in high robustness against attacks. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

Quantification of the finger functional range via explicit descriptions of reachable subspaces

In recent times, the finger flexibility assessment by means of reachable space is considered as an effective tool to describe the range of motion of the hand. Existing approaches numerically compute the reachable space using forward kinematics such as exhaustive scanning or Monte Carlo methods. In this paper, we provide explicit formulas mathematically determining the reachable space boundary. Green's theorem is used to deduce the corresponding capacity formula for the size of the reachable space as opposed to an implicit numerical solution. Using this new mechanism, we accurately quantify and compare the reachable space of different subjects in order to effectively compare the functionality of the fingers. We evaluate the performance of our proposed method against the kinematic feed-forward (KFF) approach in calculating the reachable space. The execution time to capture the reachable space is significantly less than that for the standard KFF method. The computational cost for quantifying the reachable space capacity is significantly improved due to explicit capacity formulas resulting from the abstract form of boundary descriptions of the reachable space, unique to the proposed approach.

Enhanced playback of automated service emulation models using entropy analysis

Service virtualisation is a supporting tool for DevOps to generate interactive service models of dependency systems on which a system-under-test relies. These service models allow applications under development to be continuously tested against production-like conditions. Generating these virtual service models requires expert knowledge of the service protocol, which may not always be available. However, service models may be generated automatically from network traces. Previous work has used the Needleman-Wunsch algorithm to select a response from the service model to play back for a live request. We propose an extension of the Needleman-Wunsch algorithm, which uses entropy analysis to automatically detect the critical matching fields for selecting a response. Empirical tests against four enterprise protocols demonstrate that entropy weighted matching can improve response accuracy.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Software-defined carrier wireless networks

The telecommunication industry is entering a new era. The increased traffic demands imposed by the huge number of always-on connections require a quantum leap in the field of enabling techniques. Furthermore, subscribers expect ever increasing quality of experience with its joys and wonders, while network operators and service providers aim for cost-efficient networks. These requirements require a revolutionary change in the telecommunications industry, as shown by the success of virtualization in the IT industry, which is now driving the deployment and expansion of cloud computing. Telecommunications providers are currently rethinking their network architecture from one consisting of a multitude of black boxes with specialized network hardware and software to a new architecture consisting of “white box” hardware running a multitude of specialized network software. This network software may be data plane software providing network functions virtualization (NVF) or control plane software providing centralized network management — software defined networking (SDN). It is expected that these architectural changes will permeate networks as wide ranging in size as the Internet core networks, to metro networks, to enterprise networks and as wide ranging in functionality as converged packet-optical networks, to wireless core networks, to wireless radio access networks.

Supporting adaptive tour with high level petri nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.

Mobile health (mHealth) biomedical imaging paradigm

Technology assisted methods for medical diagnosis and biomedical health monitoring are rapidly shifting from classical invasive methods to handheld-based non-invasive approaches. Biomedical imagining is one of the most prominent practices of non-invasive mechanisms in medical applications. This paper considers the medical imaging schemes for Mobile Health (mHealth) applications and studies the feasibility of future mobile systems for accommodating image informatics capabilities.

Integration of electric vehicles for load frequency output feedback H ∞ control of smart grids

This study considers a novel application of electric vehicles (EVs) to quickly help reheated thermal turbine units to provide the stability fluctuated by load demands. A mathematical model of a power system with EVs is first derived. This model contains the dynamic interactions of EVs and multiple network-induced time delays. Then, a dynamic output feedback H∞ controller for load frequency control of power systems with multiple time delays in the control input is proposed. To address the multiple time delays issue, a refined Jensen-based inequality, which encompasses the Jensen inequality, is used to derive less conservative synthesis conditions in terms of tractable linear matrix inequalities. A procedure is given to parameterise an output feedback controller to guarantee stability and H∞ performance of the closed-loop system. Extensive simulations are conducted to validate the proposed control method.

Achieving simple, secure and efficient hierarchical access control in cloud computing

Access control is an indispensable security component of cloud computing, and hierarchical access control is of particular interest since in practice one is entitled to different access privileges. This paper presents a hierarchical key assignment scheme based on linear-geometry as the solution of flexible and fine-grained hierarchical access control in cloud computing. In our scheme, the encryption key of each class in the hierarchy is associated with a private vector and a public vector, and the inner product of the private vector of an ancestor class and the public vector of its descendant class can be used to derive the encryption key of that descendant class. The proposed scheme belongs to direct access schemes on hierarchical access control, namely each class at a higher level in the hierarchy can directly derive the encryption key of its descendant class without the need of iterative computation. In addition to this basic hierarchical key derivation, we also give a dynamic key management mechanism to efficiently address potential changes in the hierarchy. Our scheme only needs light computations over finite field and provides strong key indistinguishability under the assumption of pseudorandom functions. Furthermore, the simulation shows that our scheme has an optimized trade-off between computation consumption and storage space.

On the race of worms and patches: modeling the spread of information in wireless sensor networks

Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.