Non-cooperative Environment Research Articles

Offload Decision Models and the Price of Anarchy in Mobile Cloud Application Ecosystems

With the maturity of technologies, such as HTML5 and JavaScript, and with the increasing popularity of cross-platform frameworks, such as Apache Cordova, mobile cloud computing as a new design paradigm of mobile application developments is becoming increasingly more accessible to developers. Following this trend, future on-device mobile application ecosystems will not only comprise a mixture of native and remote applications, but also include multiple hybrid mobile cloud applications. The resource competition in such ecosystems and its impact over the performance of mobile cloud applications has not yet been studied. In this paper, we study this competition from a game theoretical perspective and examine how it affects the behavior of mobile cloud applications. Three offload decision models of cooperative and non-cooperative nature are constructed and their efficiency compared. We present an extension to the classic load balancing game to model the offload behaviors within a non-cooperative environment. Mixed-strategy Nash equilibria are derived for the non-cooperative offload game with complete information, which further quantifies the price of anarchy in such ecosystems. We present simulation results that demonstrate the differences between each decision model’s efficiency. Our modeling approach facilitates further research in the design of the offload decision engines of mobile cloud applications. Our extension to the classic load balancing game broadens its applicability to real-life applications.

Modeling logistics service providers in a non-cooperative supply chain

This paper presents a mathematical model for the design of a non-cooperative supply chain where transportation activities are provided by logistics companies. We consider a four-layer network comprised of manufacturers, retailers, customers, and logistics service providers (LSPs). In this problem, manufacturers, retailers, and LSPs do not collaborate or engage in any type of bargaining strategy among one other. Instead, they compete to supply products to customers at demand markets, while each agent seeks to maximize his own profit. LSPs compete among themselves to provide logistics (transportation/warehousing) services to manufacturers. It is considered that manufacturers, LSPs, and retailers collaborate to maximize services. Normally this problem cannot be modeled as an optimization problem, so we use a variational inequality approach to formulate it. To the best of our knowledge, this important problem, which helps evaluate options and make decisions when logistics activities are provided by LSPs, has not been modeled in the literature and this constitutes our main contribution. The model determines the optimal level of production for each manufacturer, the flow of products between manufacturers and retailers, the flow of products to be handled by each logistics service provider and the flow of products between retailers and demand markets in a non-cooperative environment. Numerous experiments are presented and adapted from test examples that are available in the literature, while results and important findings are discussed.

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction.

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer’s utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

Visual pose measurement based on structured light for MAVs in non-cooperative environments

Visual pose measurement of a micro aerial vehicle (MAV) plays an extremely important role in the environments lack of artificial marks and GNSS signal. A visual pose measurement method based on cross structured light is proposed in this paper. The artificial marks are not necessary in the proposed method. Only the intersections of cross structured light and a segment line in object plane are need to be captured by camera. In the method, the coordinates of intersections in camera coordinate system are calculated by the cross structured light visual sensor model. According to the geometric constrained relationship between each intersection, the relative pose between camera coordinate system and object coordinate system can be obtained. The simulations which consider various factors that affect measurement accuracy and the real data experimental results show the method is effective and feasible.

Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

3D face reconstruction is an efficient method for pedestrian recognition in non-cooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. Visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.

Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the “Two-step” method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.

Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system

The capture of an eye image with the occlusion of spectacles in a non-cooperative environment compromises the accuracy in identifying a person in an iris recognition system. This is due to the obstruction of the iris by the frame which tends to produce an incorrect estimation of the initial center of the iris and the pupil during the iris segmentation process. In addition, it also causes incorrect localization of the upper eyelid during the process of iris segmentation and sometimes, the edges of the frame are wrongly identified as the edges of the upper eyelid. A frame detection method which involves the combination of two gradients, namely the Sobel operator and high pass filter, followed by fuzzy logic and the dilation operation of morphological processing is proposed to identify the frame on the basis of different frame factors in the capture of a distant eye image. In addition, a different color space is applied and only a single channel is used for the process of frame detection. The proposed frame detection method provides the highest frame detection rate compared to the other methods, with a detection rate of more than 80.0%. For the accuracy of the iris localization, upper eyelid localization and iris recognition system, the proposed method gives more than 96.5% accuracy compared to the other methods. The index of decidability showed that the proposed method gives more than 2.35 index compared to the existing methods.

The El Farol Problem Revisited

The so-called El Farol problem describes a prototypical situation of interacting agents making binary choices to participate in a non-cooperative environment or to stay by themselves and choosing an outside option. In a much cited paper Arthur (1994) argues that persistent non-converging sequences of rates of participation with permanent forecasting errors occur due to the non-existence of a prediction model for agents to forecast the attendance appropriately to induce stable rational expectations solutions. From this he concludes the need for agents to use boundedly rational rules. This note shows that in a large class of such models the failure of agents to find rational prediction rules which stabilize is not due to a non-existence of perfect rules, but rather to the failure of agents to identify the correct class of predictors from which the perfect ones can be chosen. What appears as a need to search for boundedly rational predictors originates from the non existence of stable confirming self-referential orbits induced by predictors selected from the wrong class. Specifically, it is shown that, within a specified class of the model and due to a structural non-convexity (or discontinuity), symmetric Nash equilibria of the associated static game may fail to exist generically depending on the utility level of the outside option. If they exist, they may induce the least desired outcome while, generically, asymmetric equilibria are uniquely determined by a positive maximal rate of attendance. The sequential setting turns the static game into a dynamic economic law of the Cobweb type for which there always exist nontrivial ǫ−perfect predictors implementing ǫ−perfect steady states as stable outcomes. If zero participation is a Nash equilibrium of the game there exists a unique perfect predictor implementing the trivial equilibrium as a stable steady state. In general, Nash equilibria of the one-shot game are among the ǫ−perfect foresight steady states of the dynamic model. If agents randomize over indifferent decisions the induced random Cobweb law together with recursive predictors becomes an iterated function system (IFS). There exist unbiased predictors with associated stable stationary solutions for appropriate randomizations supporting nonzero asymmetric equilibria which are not mixed Nash equilibria of the one-shot game. However, the least desired outcome remains as the unique stable stationary outcome for ǫ = 0 if it is a Nash equilibrium of the static game.

GTDM: A DTN Routing on Noncooperative Game Theory in a City Environment

The performance of delay tolerant networks (DTNs) can be influenced by movement model in different application environments. The existing routing algorithms of DTNs do not meet the current city environments due to the large differences in node densities, social characteristics, and limited energy. The key indicators of DTNs such as success delivery ratio, average delivery latency, network lifetime, and network overhead ratio can influence the performances of civil DTNs applications. Aiming to improve the key indicators of DTNs in city environments, this paper presents a fixed sink station based structure and a more proper routing algorithm named Game Theory Based Decision Making (GTDM). GTDM shows decision-making process for neighborhood selection and packet delivering strategy which is based on the noncooperative game theory method and city environment characteristics. GTDM performance is evaluated using numerical simulations under Working Day Movement (WDM) model and the results suggested that GTDM outperforms other traditional DTNs routing approaches, such as Epidemic and Prophet algorithms.

A low lighting or contrast ratio visible iris recognition using iso-contrast limited adaptive histogram equalization

Condition of eye images with a low lighting or low contrast ratio between the iris and pupil is one of the challenges for iris recognition in a non-cooperative environment and under visible wavelength illumination. Incorrect iris localization can affect the performance of the iris recognition system. Iso-contrast limited adaptive histogram equalization is proposed to overcome this challenge and increase the performance of iris localization. The eye image is partitioned into the contextual sub-region; then, the proposed method transfers the pixel intensity by referring to a local intensity histogram and a newly suggested cumulative distribution function. This research was tested on 1000 eye images from the UBIRIS.v2 dataset. The results showed that the proposed method performed better than existing methods when dealing with a low lighting or low contrast ratio between the iris and pupil in the eye image.

Distance-Aware Approach for Time-Constraint Wireless Data Querying Based on Game Theory

With the rapid development of time-critical information services and business-oriented applications, scalable mechanisms supporting time-constraint data querying generated by mobile clients that carry limited amount of virtual cash and battery energy are in a high demand. To jointly optimize mobile clients' expense and energy consumption in such a non-cooperative environment, based on game theory, we present an intelligent wireless data access scheme called EPGA to address the issue of energy and expense optimization. In EPGA, the shared data items among the mobile clients are seen as public resources, and the expense and energy consumed by selfish clients for sending request messages are formulated as their cost. By comparing the utilities received through sending the request message at different time intervals, EPGA can help each client independently make the strategy of sending the request message, so as to save the energy and expense without violating the time-constraint requirement of their queries. We conduct the symmetric Nash Equilibrium strategy for the clients to achieve fairness, and also compare EPGA with other schemes by experiments. The EPGA approach shows its effectiveness in joint optimization of energy and expense among the mobile clients, especially the ones far away from the data server.

A new methodology for multi-objective multidisciplinary design optimization problems based on game theory

The design of engineering systems often involves multiple disciplines and competing objectives, which requires coordination, information exchange and share amongst the disciplines. However, in practical design environments, designers have to make decisions in isolation due to organization barriers, time schedules and geographical constraints. This paper will propose a new approach for the multi-objective multidisciplinary design optimization (MDO) problems in non-cooperative environments based on gene expression programming (GEP) and Nash equilibrium in the game theory. In this approach, the GEP method is used as a surrogate to construct the approximate rational reaction sets (RRSs) in the Nash model. The effectiveness of the proposed method is demonstrated by the design of a thin-walled pressure vessel and the hull form parameter design of a small waterplane area twin hull (SWATH) ship. The results show that this approach can fully explore and provide the explicit functional relationship between the strategy of an isolated player and the control variables of the other players, thus able to obtain a better Nash equilibrium solution.

Performance Comparison of Blind Equalization Algorithms for Wireless Communication

Adaptive equalization is an accepted method to mitigate the Inter-Symbol Interference (ISI) in wireless communication. Frequently, adaptive algorithm must needs transmission of well-known training sequence to track the time varying characteristics of the channel and hence make the most of superfluous bandwidth. It is also not viable to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept used to track the time varying characteristics of the channel in the deficiency of training sequence. Nevertheless, it leads to slow convergence. In this paper, the performance of Sato algorithm and Godard based blind algorithm is compared for PAM signal.

Efficient iris segmentation method in unconstrained environments

Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time.

Performance Comparison of Adaptive and Blind Equalization Algorithms for Wireless Communication

Adaptive equalization is a well known method to minimize the Inter-Symbol Interference (ISI) in wireless communication. Often, adaptive algorithm requires transmission of known training sequence to track the time varying characteristics of the channel and hence utilizes additional bandwidth. It is also impractical to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept to track the time varying characteristics of the channel in the absence of training sequence. However, it leads to slow convergence. In this paper, we compare the performance of adaptive LMS algorithm and SATO based blind algorithm for PAM signal.

Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

Iris recognition is a promising method by which to accurately identify a person. During the iris recognition stage, the features of the iris are extracted, including the unique, individual texture of the iris. The ability to extract the texture of the iris in non-cooperative environments from eye images captured at different distances, containing reflections, and under visible wavelength illumination will lead to increased iris recognition performance. A method that combined multiscale sparse representation of local Radon transform was proposed to down sample a normalized iris into different lengths of scales and different orientations of angles to form an iris feature vector. This research was tested using 1000 eye images from the UBIRIS.v2 database. The results showed that the proposed method performed better than existing methods when dealing with iris images captured at different distances.

3D Reconstruction and Restoration Monitoring of Sculptural Artworks by a Multi-Sensor Framework

Nowadays, optical sensors are used to digitize sculptural artworks by exploiting various contactless technologies. Cultural Heritage applications may concern 3D reconstructions of sculptural shapes distinguished by small details distributed over large surfaces. These applications require robust multi-view procedures based on aligning several high resolution 3D measurements. In this paper, the integration of a 3D structured light scanner and a stereo photogrammetric sensor is proposed with the aim of reliably reconstructing large free form artworks. The structured light scanner provides high resolution range maps captured from different views. The stereo photogrammetric sensor measures the spatial location of each view by tracking a marker frame integral to the optical scanner. This procedure allows the computation of the rotation-translation matrix to transpose the range maps from local view coordinate systems to a unique global reference system defined by the stereo photogrammetric sensor. The artwork reconstructions can be further augmented by referring metadata related to restoration processes. In this paper, a methodology has been developed to map metadata to 3D models by capturing spatial references using a passive stereo-photogrammetric sensor. The multi-sensor framework has been experienced through the 3D reconstruction of a Statue of Hope located at the English Cemetery in Florence. This sculptural artwork has been a severe test due to the non-cooperative environment and the complex shape features distributed over a large surface.

Vendor–buyer inventory models with trade credit financing under both non-cooperative and integrated environments

Most researchers studied vendor–buyer supply chain inventory policies only from the perspective of an integrated model, which provides us the best cooperative solution. However, in reality, not many vendors and buyers are wholly integrated. Hence, it is necessary to study the optimal policies not only under an integrated environment but also under a non-cooperative environment. In this article, we develop a supply chain vendor–buyer inventory model with trade credit financing linked to order quantity. We then study the optimal policies for both the vendor and the buyer under a non-cooperative environment first, and then under a cooperative integrated situation. Further, we provide some numerical examples to illustrate the theoretical results, compare the differences between these two distinct solutions, and obtain some managerial insights. For example, in a cooperative environment, to reduce the total cost for both parties, the vendor should either provide a simple permissible delay without order quantity restriction or offer a long permissible delay linked order quantity. By contrast, in a non-cooperative environment, the vendor should provide a short permissible delay to reduce its total cost.

A 3D Collision Avoidance Strategy for UAVs in a Non-Cooperative Environment

This paper presents a feasible 3D collision avoidance approach for fixed-wing unmanned aerial vehicles (UAVs). The proposed strategy aims to achieve the desired relative bearing in the horizontal plane and relative elevation in the vertical plane so that the host aircraft is able to avoid collision with the intruder aircraft in 3D. The host aircraft will follow a desired trajectory in the collision avoidance course and resume the pre-arranged trajectory after collision is avoided. The approaching stopping condition is determined for the host aircraft to trigger an evasion maneuver to avoid collision in terms of measured heading. A switching controller is designed to achieve the spatial collision avoidance strategy. Simulation results demonstrate that the proposed approach can effectively avoid spatial collision, making it suitable for integration into flight control systems of UAVs.

Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time.