Kooperativna evolucija za kvalitetno pružanje usluga u paradigmi Interneta stvari

To facilitate the automation process in the Internet of Things, the research issue of distinguishing prospective services out of many “similar” services, and identifying needed services w.r.t the criteria of Quality of Service (QoS), becomes very important. To address this aim, we propose heuristic optimization, as a robust and efficient approach for solving complex real world problems. Accordingly, this paper devises a cooperative evolution approach for service composition under the restrictions of QoS. A series of effective strategies are presented for this problem, which include an enhanced local best first strategy and a global best strategy that introduces perturbations. Simulation traces collected from real measurements are used for evaluating the proposed algorithms under different service composition scales that indicate that the proposed cooperative evolution approach conducts highly efficient search with stability and rapid convergence. The proposed algorithm also makes a well-designed trade-off between the population diversity and the selection pressure when the service compositions occur on a large scale.Kako bi se automatizirali procesi u internetu stvati, nužno je rezlikovati bitne usluge u moru sličnih kao i identificirati potrebne usluge u pogledu kvalitete usluge (QoS). Kako bi doskočili ovome problemu prdlaže se heuristička optimizacija kao robustan i efikasan način rješavajne kompleksnih problema. Nadalje, u članku je predložen postupak kooperativne evolucije za slaganje usluga uz ograničenja u pogledu kvalutete usluge. Predstavljen je niz efektivnih strategija za spomenuti problem uključujući strategije najboljeg prvog i najboljeg globalnog koje unose perturbacije u polazni problem. Simulacijski rezultati kao i stvarni podatci su korišteni u svrhu evaluacije prodloženog algoritma kako bi se osigurala efikasna pretraga uz stabilnost i brzu konvergenciju. Predloženi algoritam tako.er vodi računa o odnosu izme.u različitosti populacije i selekcijskog pritiska kada je potrebno osigurati slaganje usluga na velikoj skali

Contributions to degree structures

The investigation of computably enumerable degrees has led to the deep understanding of degree structures and the development of various construction techniques. This thesis is mainly concerned with the cupping and capping properties of computably enumerable degrees. In Chapter 1, we give an introduction to the fundamentals of computability theory, and notations used through the thesis. In Chapter 2, we study the only-high cuppable degrees, which was recently found by Greenberg, Ng and Wu, we prove that such degrees can be plus-cupping. This result refutes a claim of Li and Y. Wang, which says that every plus-cupping degree is 3-plus-cupping. In Chapter 3, we study the locally noncappable degrees, and we prove that for any nonzero incomplete c.e. degree a, there exist two incomparable c.e. degrees c, d > a witnessing that a is locally noncappable, and the supremum of c and d is high. This result implies that both classes of the plus-cuppping degrees and the nonbounding c.e. degrees do not form an ideal, which was proved by Li and Zhao by two separate constructions. Chapter 4 is devoted to the study of the infima of n-c.e. degrees. Kaddah proved that there are n-c.e. degrees a, b, c and an (n+1)-c.e. degree x such that a is the infimum of b and c in the n-c.e. degrees, but not in the (n+1)-c.e. degrees, as a < x < b, c. We will prove that such 4-tuples occur densely in the c.e. degrees. This result immediately implies that the isolated (n+1)-c.e. degrees are dense in the c.e. degrees, which was first proved by LaForte.DOCTOR OF PHILOSOPHY (SPMS

A Blockchain-Empowered Arbitrable Multimedia Data Auditing Scheme in IoT Cloud Computing

As increasing clients tend to outsource massive multimedia data generated by Internet of Things (IoT) devices to the cloud, data auditing is becoming crucial, as it enables clients to verify the integrity of their outsourcing data. However, most existing data auditing schemes cannot guarantee 100% data integrity and cannot meet the security requirement of practical multimedia services. Moreover, the lack of fair arbitration leads to clients not receiving compensation in a timely manner when the outsourced data is corrupted by the cloud service provider (CSP). In this work, we propose an arbitrable data auditing scheme based on the blockchain. In our scheme, clients usually only need to conduct private audits, and public auditing by a smart contract is triggered only when verification fails in private auditing. This hybrid auditing design enables clients to save audit fees and receive compensation automatically and in a timely manner when the outsourced data are corrupted by the CSP. In addition, by applying the deterministic checking technique based on a bilinear map accumulator, our scheme can guarantee 100% data integrity. Furthermore, our scheme can prevent fraudulent claims when clients apply for compensation from the CSP. We analyze the security strengths and complete the prototype’s implementation. The experimental results show that our blockchain-based data auditing scheme is secure, efficient, and practical

Construction of Dual Optimal Bidirectional Double-Loop Networks for Optimal Routing

Bidirectional double-loop networks (BDLNs) are widely used in computer networks for their simplicity, symmetry and scalability. One common way to improve their performance is to decrease the diameter and average distance. Attempts have been made to find BDLNs with minimal diameters; however, such BDLNs will not necessarily have the minimum average distance. In this paper, we construct dual optimal BDLNs with minimum diameters and average distances using an efficient method based on coordinate embedding and transforming. First, we get the lower bounds of both the diameter and average distance by embedding a BDLN into Cartesian coordinates. Then, we construct tight optimal BDLNs that provide the aforementioned lower bounds based on an embedding graph. On the basis of node distribution regularity in tight optimal BDLNs, we construct dual optimal BDLNs with minimum diameters and average distances for any number of nodes. Finally, we present on-demand optimal message routing algorithms for the dual optimal BDLNs that we have constructed. The presented algorithms do not require routing tables and are efficient, requiring little computation

A Blockchain-Empowered Arbitrable Multimedia Data Auditing Scheme in IoT Cloud Computing

As increasing clients tend to outsource massive multimedia data generated by Internet of Things (IoT) devices to the cloud, data auditing is becoming crucial, as it enables clients to verify the integrity of their outsourcing data. However, most existing data auditing schemes cannot guarantee 100% data integrity and cannot meet the security requirement of practical multimedia services. Moreover, the lack of fair arbitration leads to clients not receiving compensation in a timely manner when the outsourced data is corrupted by the cloud service provider (CSP). In this work, we propose an arbitrable data auditing scheme based on the blockchain. In our scheme, clients usually only need to conduct private audits, and public auditing by a smart contract is triggered only when verification fails in private auditing. This hybrid auditing design enables clients to save audit fees and receive compensation automatically and in a timely manner when the outsourced data are corrupted by the CSP. In addition, by applying the deterministic checking technique based on a bilinear map accumulator, our scheme can guarantee 100% data integrity. Furthermore, our scheme can prevent fraudulent claims when clients apply for compensation from the CSP. We analyze the security strengths and complete the prototype&rsquo;s implementation. The experimental results show that our blockchain-based data auditing scheme is secure, efficient, and practical

Construction of Dual Optimal Bidirectional Double-Loop Networks for Optimal Routing

Bidirectional double-loop networks (BDLNs) are widely used in computer networks for their simplicity, symmetry and scalability. One common way to improve their performance is to decrease the diameter and average distance. Attempts have been made to find BDLNs with minimal diameters; however, such BDLNs will not necessarily have the minimum average distance. In this paper, we construct dual optimal BDLNs with minimum diameters and average distances using an efficient method based on coordinate embedding and transforming. First, we get the lower bounds of both the diameter and average distance by embedding a BDLN into Cartesian coordinates. Then, we construct tight optimal BDLNs that provide the aforementioned lower bounds based on an embedding graph. On the basis of node distribution regularity in tight optimal BDLNs, we construct dual optimal BDLNs with minimum diameters and average distances for any number of nodes. Finally, we present on-demand optimal message routing algorithms for the dual optimal BDLNs that we have constructed. The presented algorithms do not require routing tables and are efficient, requiring little computation

Vehicular Ad Hoc Networks: Architectures, Research Issues, Methodologies, Challenges, and Trends

Vehicular ad hoc networks (VANETs) have been quite a hot research area in the last few years. Due to their unique characteristics such as high dynamic topology and predictable mobility, VANETs attract so much attention of both academia and industry. In this paper, we provide an overview of the main aspects of VANETs from a research perspective. This paper starts with the basic architecture of networks, then discusses three popular research issues and general research methods, and ends up with the analysis on challenges and future trends of VANETs

Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace

Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality

Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Massive sensing data are generated continuously in the Internet of Things. How to organize and how to query the big sensing data are big challenges for intelligent applications. This paper studies the organization of big sensing data with event-linked network ( ELN ) model, where events are regarded as primary units for organizing data and links are used to represent the semantic associations among events. Several different types of queries on the event-linked network are also explored, which are different from queries on traditional relational database. We use an instance of smart home to show the effectiveness and efficiency of organization and query approaches based on the event-linked network