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  Subjects -> ELECTRONICS (Total: 207 journals)
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IEEE Transactions on Services Computing
Journal Prestige (SJR): 0.87
Citation Impact (citeScore): 5
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1374
Published by IEEE Homepage  [228 journals]
  • A Correct-by-Construction Model for Verifying Transactional Composite
           Services Configuration

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      Authors: Imed Abbassi;Amel Mammar;Mohamed Graiet;
      Pages: 2511 - 2525
      Abstract: Reusability is a central concept of Web services as it allows for the construction of composite Web services at a lower cost/effort. Web services offer diverse functional capabilities (e.g., ticket purchase, hotel booking) and inherent transactional properties. However, due to the lack of an explicit and formal description of these functional and transactional perspectives, the correctness of the transactional reliability and functional properties cannot be verified. The composite Web service reliability is computed using a set of transactional requirements defined by designers throughout the Accepted Termination States (ATS) concept. The main objective of this article is to introduce a formal model of the Web service configuration and its correctness requirements that permit to ensure the correct Web service execution from functional and transactional points of view. For that purpose, we developed a Correct Configuration Model for Transactional Composite Services (CCM4TCS) using the Event-B method. This model is used to formally validate the consistency of composite Web service configuration’s properties and requirements. It allows also to check the correctness of ATS constraints that we use as reliability parameters. The correctness and the validation of our model are ensured by discharging proof obligations and by animating the specification using the ProB model-checker.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Decentralized Approach for Resource Discovery using Metadata Replication
           in Edge Networks

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      Authors: Ilir Murturi;Schahram Dustdar;
      Pages: 2526 - 2537
      Abstract: Recent advancements in distributed systems have enabled deploying low-latency edge applications (i.e., IoT applications) in proximity to the end-users, respectively, in edge networks. The stringent requirements combined with heterogeneous, resource-constrained and dynamic edge networks make the deployment process a challenging task. Besides that, the lack of resource discovery features make it particularly difficult to fully exploit available resources (i.e., computational, storage, and IoT resources) provided by low-powered edge devices. To that end, this article proposes a decentralized resource discovery mechanism that enables discovering resources in an automatic manner in edge networks. Through replicating resource descriptions (i.e., metadata), edge devices exchange information about available resources within their scope in a peer-to-peer manner. To handle the resource discovery complexity, we propose a solution to built edge networks as a flat model and enable edge devices to be organized in clusters. Our approach supports the system in coping with the dynamicity and uncertainty of edge networks. We discuss the architecture, processes of the approach, and the experiments we conducted on a testbed to validate its feasibility on resource-constrained edge networks.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Deep Reinforcement Learning Approach for Composing Moving IoT Services

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      Authors: Azadeh Ghari Neiat;Athman Bouguettaya;Mohammed Bahutair;
      Pages: 2538 - 2550
      Abstract: We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Traitor-Resistant and Dynamic Anonymous Communication Service for
           Cloud-Based VANETs

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      Authors: Huiying Hou;Jianting Ning;Yunlei Zhao;Robert H. Deng;
      Pages: 2551 - 2564
      Abstract: Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this article, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs, which supports several advantageous features. In TD-ACF, each vehicle is represented by a set of attributes instead of its real identity, and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved in this way. Meanwhile, TD-ACF supports two practical requirements in cloud-based VANETs: the revocation and the traceability of traitor. For the former, TD-ACF can force a vehicle to exit the communication network at any moment. We employ an efficient binary tree algorithm to reduce the size of key updates for revocation from the traditional linear to the logarithmic level. For the latter, we overcome the barrier of the one-to-many relationship between a vehicle and the shared set of attributes to support traitor tracing. In TD-ACF, unlike most existing schemes, the Semi-Trusted Cloud (STC) can directly capture and punish a traitor instead of querying all the records in the list of unrevoked vehicles. In addition, we solve the key escrow problem that plagues most existing attribute-based schemes. The theoretical analysis and experimental simulation show that the proposed scheme is feasible and effective.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Trust Model for SLA Negotiation Candidates Selection in a Dynamic IoT
           Environment

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      Authors: Fan Li;Gary White;Siobhán Clarke;
      Pages: 2565 - 2578
      Abstract: The Internet of Things envisions billions of physical devices connecting over the Internet to provide a near real-time view of the state of the world. These devices' capabilities can be abstracted as IoT services and provided on demand. To enable quality-aware service provision, Service Level Agreements (SLA) are widely used as legally binding contracts to obligate service providers to comply with a pre-negotiated Quality of Service (QoS). With a possible ever-increasing number of service providers in an IoT environment, multi-bilateral SLA negotiation is likely to be prohibitively time-consuming without an a-priori process to select trusted candidate providers with whom to negotiate. In this article, a trust model is proposed to identify trusted service providers in a dynamic IoT environment before attempting to negotiate an SLA. A trust credit that indicates both the SLA’s fulfillment and the possible negotiation success rate is derived based on historical information relating to a service’s previous negotiations and its monitored run-time performance. Indiscernibility analysis in Rough Set theory is used to predict the negotiation success rate, while Bayesian inference is applied to deduce the possibility of SLA violation according to the monitored data. The simulation results demonstrate the feasibility and efficiency of the proposed trust model.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Truthful Double Auction Mechanism for Multi-Resource Allocation in Crowd
           Sensing Systems

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      Authors: Xi Liu;Jun Liu;
      Pages: 2579 - 2590
      Abstract: As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • A Utility Game Driven QoS Optimization for Cloud Services

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      Authors: Yan Wang;Jian-Tao Zhou;Xiaoyu Song;
      Pages: 2591 - 2603
      Abstract: Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Achieving Efficient and Privacy-Preserving Set Containment Search Over
           Encrypted Data

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      Authors: Yandong Zheng;Rongxing Lu;Yunguo Guan;Jun Shao;Hui Zhu;
      Pages: 2604 - 2618
      Abstract: Set containment search, which aims to retrieve all set records containing a specific query set, has received considerable attention. Meanwhile, due to the dramatic growth of data, data owners tend to outsource their data to the cloud and deploy the cloud server to offer the set containment search services. However, as the cloud server is not fully trustable and the data may be sensitive, a straightforward strategy for the data owners is to encrypt the data before outsourcing them. Although the encryption technique can preserve data privacy, it inevitably hinders the functionality of set containment search. Many existing studies on the set containment search over outsourced data still suffer from the search efficiency and security issues. In this article, aiming at the above issues, we propose an efficient and privacy-preserving set containment search scheme. Specifically, we first deploy an asymmetric scalar-product-preserving encryption technique to design a set containment/intersection encryption (SCIE-Enc) scheme. Then, we build a radix tree to represent the set records. Based on the radix tree and SCIE-Enc construction, we present our scheme that can achieve efficient set containment search while preserving the privacy of set records, query sets, and query results, as indicated in our security analysis and performance evaluation.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Aggregated Capability Assessment (AgCA) For CAIQ Enabled Cross-cloud
           Federation

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      Authors: Usama Ahmed;Imran Raza;Omer F. Rana;Syed Asad Hussain;
      Pages: 2619 - 2632
      Abstract: Cross-Cloud Federation (CCF) enables resource exchange among multiple, heterogeneous Cloud Service Providers (CSPs) to support the composition of services (workflow) hosted by different providers. CCF participation can either be fixed, or the types of services that can be used are limited to reduce the potential risk of service failure or secure access. Although many service selection approaches have been proposed in literature for cloud computing, their applicability to CCF i.e., cloud-to-cloud interaction, has not been adequately investigated. A key component of this cloud-to-cloud paradigm involves assessing the combined capability of contributing participants within a federation and their connectivity. A novel Aggregated Capability Assessment (AgCA) approach based on using the Consensus Assessment Initiative Questionnaire from Cloud Security Alliance is proposed for CCF. The proposed mechanism is implemented as a component of a centralized broker to enhance the quality of the selection process for participants within a federation. Our experimental results show that AgCA is a useful tool for partner selection in a dynamic, heterogeneous and multilevel cloud federation.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Comment on “A Lightweight Auditing Service for Shared Data With Secure
           User Revocation in Cloud Storage”

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      Authors: Jinyong Chang;Bilin Shao;Yanyan Ji;Genqing Bian;
      Pages: 2633 - 2634
      Abstract: Recently, Rabaninejad et al. (2019) proposed an excellent auditing protocol for shared data (CoRPA, for short) [IEEE Trans. Ser. Comp.,
      DOI 10.1109/TSC.2019.2919627], which has many better properties, like the identity-privacy, collusion resistant, efficient user revocation and supporting dynamic update etc. In addition, they also presented the detailed security analysis for CoRPA and described the reduction from the soundness of CoRPA to discrete logarithm assumption. However, in this article, we analyze their original security reduction (to discrete logarithm) and find out that it is incorrect and misleading. That is, the soundness of CoRPA cannot be obtained from the discrete logarithm assumption. Now, we give a new proof for their CoRPA based on the square-CDH assumption, which is also used by them to prove the security of homomorphic proxy re-signature scheme. We also hope the new security proof will provide theoretical guarantee when using CoRPA in practical scenes.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Constrained App Data Caching Over Edge Server Graphs in Edge Computing
           Environment

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      Authors: Xiaoyu Xia;Feifei Chen;John Grundy;Mohamed Abdelrazek;Hai Jin;Qiang He;
      Pages: 2635 - 2647
      Abstract: In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider’s perspective, caching app data on edge servers can ensure low latency in its users’ data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this article, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider’s perspective and prove its $mathcal {NP}$NP-hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set. The results demonstrate that they significantly outperform four representative approaches.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Cost-Friendly Differential Privacy of Smart Meters Using Energy Storage
           and Harvesting Devices

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      Authors: Mohammad Belayet Hossain;Iynkaran Natgunanathan;Yong Xiang;Yushu Zhang;
      Pages: 2648 - 2657
      Abstract: Cost-friendly differential privacy (CDP) of smart meters can be preserved by an appropriate charging and discharging mechanism that uses rechargeable batteries (RBs) to generate Laplace distributed random noise. However, the existing CDP methods have several issues. First, the maximum discharge rate of an RB requires to vary with the maximal consumption of houses. Second, the probability of an RB to charge/discharge depends on the demand, regardless of the state-of-charge (SoC) of an RB. Third, in extreme SoC (near-empty or almost fully charged) of an RB, no noise added to the demand. To overcome these, we propose a mechanism in which a novel probability density function is designed to generate near Laplace distributed random noise. We also utilize a renewable energy source with small storage in cascade with an RB to enhance performance. Both theoretical analysis and simulations are performed to demonstrate the effectiveness of our proposed method.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Data Access Control in Cloud Computing: Flexible and Receiver Extendable

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      Authors: Jianchang Lai;Fuchun Guo;Willy Susilo;Xinyi Huang;Peng Jiang;Futai Zhang;
      Pages: 2658 - 2670
      Abstract: Broadcast encryption provides a promising technique of data access control for specified users in cloud computing. A data uploader can generate a ciphertext for a set of chosen users such that only the intended users are able to access the data. However, with the rapidly increasing of collaboration between users, it is desired to extend the receiver set to grant decryption right for more users. The existing broadcast encryption systems cannot support receiver extension. In this article, we for the first time take this problem into consideration and give a solution. We take the merits of identity-based cryptosystem and propose a notion of EIBBE: a flexible data access control with receiver extendable for cloud computing based on broadcast encryption. It allows the authorized user to extend the receiver set $S$S stated in the IBBE ciphertext by adding a new receiver set $S^{prime }$S' without re-encryption. Both the users in $S$S and $S^{prime }$S' can access the data successfully. Moreover, the data uploader determines the maximum number of extended receivers. We then give a concrete construction of EIB-E and provide a rigorous security analysis of our proposed scheme. Finally, we demonstrate the scheme's efficiency and feasibility.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Demand-Driven Deep Reinforcement Learning for Scalable Fog and Service
           Placement

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      Authors: Hani Sami;Azzam Mourad;Hadi Otrok;Jamal Bentahar;
      Pages: 2671 - 2684
      Abstract: The increasing number of Internet of Things (IoT) devices necessitates the need for a more substantial fog computing infrastructure to support the users’ demand for services. In this context, the placement problem consists of selecting fog resources and mapping services to these resources. This problem is particularly challenging due to the dynamic changes in both users’ demand and available fog resources. Existing solutions utilize on-demand fog formation and periodic container placement using heuristics due to the NP-hardness of the problem. Unfortunately, constant updates of services are time consuming in terms of environment setup, especially when required services and available fog nodes are changing. Therefore, due to the need for fast and proactive service updates to meet users’ demand, and the complexity of the container placement problem, we propose in this article a Deep Reinforcement Learning (DRL) solution, named Intelligent Fog and Service Placement (IFSP), to perform instantaneous placement decisions proactively. By proactively, we mean making placement decisions before demands occur. The DRL-based IFSP is developed through a scalable Markov Decision Process (MDP) design. To address the long learning time for DRL to converge, and the high volume of errors needed to explore, we also propose a novel end-to-end architecture utilizing a service scheduler and a bootstrapper. on the cloud. Our scheduler and bootstrapper perform offline learning on users’ demand recorded in server logs. Through experiments and simulations performed on the NASA server logs and Google Cluster Trace datasets, we explore the ability of IFSP to perform efficient placement and overcome the above mentioned DRL limitations. We also show the ability of IFSP to adapt to changes in the environment and improve the Quality of Service (QoS) compared to state-of-the-art-heuristic and DRL solutions.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Drone-as-a-Service Composition Under Uncertainty

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      Authors: Ali Hamdi;Flora D. Salim;Du Yong Kim;Azadeh Ghari Neiat;Athman Bouguettaya;
      Pages: 2685 - 2698
      Abstract: We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose an uncertainty-aware DaaS route-planning algorithm that selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. A spatiotemporal DaaS composer first selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. A predictive DaaS composer then utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Dynamic User Allocation in Stochastic Mobile Edge Computing Systems

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      Authors: Phu Lai;Qiang He;Xiaoyu Xia;Feifei Chen;Mohamed Abdelrazek;John Grundy;John Hosking;Yun Yang;
      Pages: 2699 - 2712
      Abstract: Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Edge-Based Runtime Verification for the Internet of Things

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      Authors: Christos Tsigkanos;Marcello M. Bersani;Pantelis A. Frangoudis;Schahram Dustdar;
      Pages: 2713 - 2727
      Abstract: Complex distributed systems such as the ones induced by Internet of Things (IoT) deployments, are expected to operate in compliance to their requirements. This can be checked by inspecting events flowing throughout the system, typically originating from end-devices and reflecting arbitrary actions, changes in state or sensing. Such events typically reflect the behavior of the overall IoT system – they may indicate executions which satisfy or violate its requirements. This article presents a service-based software architecture and technical framework supporting runtime verification for widely deployed, volatile IoT systems. At the lowest level, systems we consider are comprised of resource-constrained devices connected over wide area networks generating events. In our approach, monitors are deployed on edge components, receiving events originating from end-devices or other edge nodes. Temporal logic properties expressing desired requirements are then evaluated on each edge monitor in a runtime fashion. The system exhibits decentralization since evaluation occurs locally on edge nodes, and verdicts possibly affecting satisfaction of properties on other edge nodes are propagated accordingly. This reduces dependence on cloud infrastructures for IoT data collection and centralized processing. We illustrate how specification and runtime verification can be achieved in practice on a characteristic case study of smart parking. Finally, we demonstrate the feasibility of our design over a testbed instantiation, whereupon we evaluate performance and capacity limits of different hardware classes under monitoring workloads of varying intensity using state-of-the-art LPWAN technology.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Efficient and Anonymous Authentication for Healthcare Service With Cloud
           Based WBANs

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      Authors: Xu Yang;Xun Yi;Surya Nepal;Ibrahim Khalil;Xinyi Huang;Jian Shen;
      Pages: 2728 - 2741
      Abstract: As a promising technology in the development of human healthcare services, the wireless body area networks (WBANs) technology has attracted widespread attention in recent years from both industry and academia. However, due to the sensitiveness of the medical system and the capability limitation of the wearable devices, security, privacy, and efficiency of the healthcare services in WBANs are remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. In this article, we firstly provide a review and cryptanalysis on the state-of-the-art authentication scheme. In order to meet the challenges and address the drawbacks in previous works, we then propose a new efficient and anonymous authentication scheme for cloud based WBANs. Through the security analysis, we show that our scheme could overcome the weaknesses in previous schemes and meet all the security requirements. Besides, we show the advantages of the proposed scheme through performance evaluation in terms of functionality features, computation overhead, communication overhead and storage overhead, which shows our scheme is more appropriate for practical applications on healthcare services.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Efficient Privacy-Preserving Similarity Range Query With Quadsector Tree
           in eHealthcare

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      Authors: Yandong Zheng;Rongxing Lu;Yunguo Guan;Jun Shao;Hui Zhu;
      Pages: 2742 - 2754
      Abstract: As a consequence of advance in the Internet of Things (IoT) and big data technology, smart eHealthcare has emerged and greatly enabled patients to enjoy high-quality healthcare services in disease prediction, clinical decision making and healthcare surveillance. Meanwhile, in order to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to a powerful cloud and deploy the cloud server to manage the data. However, since the healthcare data usually contain some sensitive information and also the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them to the cloud. Unfortunately, data encryption inevitably hinders some advanced applications of the data like the similarity range query in cloud. Although many studies on similarity range query over encrypted data have been reported, most of them still have some limitations in security, efficiency and practicality. Aiming at this challenge, in this article, we propose a new efficient privacy-preserving similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove it is selectively secure. Then, we introduce a Quadsector tree to represent the data, and employ a filtration condition to design an efficient algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis indicates that our proposed EPSim scheme is really secure. In addition, extensive performance evaluations are conducted, and the results also demonstrate it is efficient and practical.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • EHRChain: A Blockchain-Based EHR System Using Attribute-Based and
           Homomorphic Cryptosystem

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      Authors: Fengqi Li;Kemeng Liu;Lupeng Zhang;Sikai Huang;Qiufan Wu;
      Pages: 2755 - 2765
      Abstract: There is an urgent need to solve the problems of secure storage, reliable sharing, access control and privacy protection in medical industry. In this paper, we propose EHRChain, a blockchain-based EHR system using attribute-based and homomorphic cryptosystem to solve the above problems. First, we design a medical record storage scheme to realize secure high capacity medical data storage and reliable sharing based on blockchain technology and IPFS. Second, we propose an improved cryptographic primitive called SHDPCPC-CP-ABE. Our SHDPCPC-CP-ABE realizes the functions of semi-policy hiding and dynamic permission changing based on partial ciphertext simultaneously. Furthermore, our program achieves the neutrality of the subject of judicial identification in medical disputes and fine-grained access control of medical data. Third, our system applies an additive homomorphic cryptosystem, Paillier cryptosystem with optimized parameters on patients’ privacy protection during the process of the medical insurance claim. After analysis and experiment, we have proved that the SHDPCPC-CP-ABE is indistinguishable under chosen plaintext attack and takes one third of the time of CP-ABE when changing access policy. Our system has higher performance than other EHR systems based on blockchain.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Energy-minimized Scheduling of Real-time Parallel Workflows on
           Heterogeneous Distributed Computing Systems

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      Authors: Biao Hu;Zhengcai Cao;MengChu Zhou;
      Pages: 2766 - 2779
      Abstract: Today's large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-recognized that energy consumption accounts for a large part of a computing system's total cost, and timeliness and reliability are two important service indicators. This work studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Enhancing Investigative Pattern Detection via Inexact Matching and Graph
           Databases

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      Authors: Shashika R. Muramudalige;Benjamin W. K. Hung;Anura P. Jayasumana;Indrakshi Ray;Jytte Klausen;
      Pages: 2780 - 2794
      Abstract: Tracking individuals or groups based on their hidden and/or emergent behaviors is an indispensable task in homeland security, mental health evaluation, and consumer analytics. On-line and off-line communication patterns, behavior profiles and social relationships form complex dynamic evolving knowledge graphs. Investigative search involves capturing and mining such large-scale knowledge graphs for emergent profiles of interest. While graph databases facilitate efficient and scalable operations on complex heterogeneous graphs, dealing with incomplete, missing and/or inconsistent information and need for adaptive querying pose major challenges. We address these by proposing an inexact graph pattern matching method, which is implemented in a graph database with a scoring mechanism that helps identify hidden behavioral patterns. PINGS (Procedures for INvestigative Graph Search), a graph database library of procedures for investigative graph search is presented. Results presented demonstrate the capability of detecting individuals/groups meeting query criteria as well as the iterative query performance in graph databases. We evaluate our approach on three datasets: a synthetically generated radicalization dataset, a publicly available patient’s ICU hospitalization stays dataset, and a crime dataset. These varied datasets demonstrate the wide-range applicability and the enhanced effectiveness of observing suspicious or latent trends in investigative domains.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Fair Outsourcing Polynomial Computation Based on the Blockchain

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      Authors: Yunguo Guan;Hui Zheng;Jun Shao;Rongxing Lu;Guiyi Wei;
      Pages: 2795 - 2808
      Abstract: Due to the big data blowout from the Internet of Things and the rapid development of cloud computing, outsourcing computation has received considerable attention in recent years. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis and machine learning. However, none of those schemes are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and the worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, in this article, we propose a new outsourcing polynomial computation scheme by employing a variant of Horner’s method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and the worker if the result from the worker can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we also implement a prototype of our proposal and run it on an Ethereum test net. The extensive experimental results demonstrate that our proposal is efficient in terms of computational cost.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Generalized Nesterov's Acceleration-Incorporated, Non-Negative and
           Adaptive Latent Factor Analysis

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      Authors: Xin Luo;Yue Zhou;Zhigang Liu;Lun Hu;MengChu Zhou;
      Pages: 2809 - 2823
      Abstract: A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service-oriented applications. However, its convergence rate is slow. To address this issue, this study proposes a Generalized Nesterov's acceleration-incorporated, Non-negative and Adaptive Latent Factor (GNALF) model. It results from a) incorporating a generalized Nesterov's accelerated gradient (NAG) method into an SLF-NMU algorithm, thereby achieving an NAG-incorporated and element-oriented non-negative (NEN) algorithm to perform efficient parameter update; and b) making its regularization and acceleration parameters self-adaptive via incorporating the principle of a particle swarm optimization algorithm into the training process, thereby implementing a highly adaptive and practical model. Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate without the need of hyper-parameter tuning, making its computational efficiency significantly higher than state-of-the-art models. Meanwhile, such efficiency gain does not result in accuracy loss, since its prediction accuracy is comparable with its peers. Hence, it can better serve practical service applications with real-time demands.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Hierarchical Scheduling Mechanisms in Multi-Level Fog Computing

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      Authors: Maycon Leone Maciel Peixoto;Thiago A. L. Genez;Luiz F. Bittencourt;
      Pages: 2824 - 2837
      Abstract: Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as micro data centers, cloudlets, or fog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application’s latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Incentive Mechanism Design for Truth Discovery in Crowdsourcing With
           Copiers

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      Authors: Lingyun Jiang;Xiaofu Niu;Jia Xu;Dejun Yang;Lijie Xu;
      Pages: 2838 - 2853
      Abstract: Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot deal with the crowdsourcing with copiers, who copy a part (or all) of data from other workers. This article aims at designing crowdsourcing incentive mechanism for truth discovery of textual answers with copiers. We formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery and design an three-stage incentive mechanism. In contextual embedding and clustering stage, we construct and cluster the content vector representations of textual crowdsourced answers at the semantic level. In truth discovery stage, we estimate the truth for each task based on the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery methods show prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Indirect Revocable KP-ABE With Revocation Undoing Resistance

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      Authors: Marco Rasori;Pericle Perazzo;Gianluca Dini;Shucheng Yu;
      Pages: 2854 - 2868
      Abstract: Lately, many cloud-based applications proposed attribute-based encryption (ABE) as an all-in-one solution for achieving confidentiality and access control. Within this paradigm, data producers store the encrypted data on a semi-trusted cloud server, and users, holding decryption keys issued by a key authority, can decrypt data according to some access control policy. To be used in practical cases, any ABE scheme should implement a key revocation mechanism which assures that a compromised decryption key cannot be used anymore to decrypt data. Yu et al. (2010) introduced an ABE scheme with revocation capabilities that enjoys several unique advantages, such as reactivity and efficiency. In the scheme, the cloud server is entitled to update keys and ciphertexts in order to achieve revocation. Unfortunately, the cloud server retains the power to undo the revocation of a key (revocation undoing attack) so endangering confidentiality. In this article, we propose a revocable ABE scheme that still ensures the advantages of Yu et al.’s scheme, but it also resists to the revocation undoing attack. We formally prove the security of our scheme and show through simulations that the user experiences a slightly higher computational cost with respect to Yu et al.’s scheme.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • LYRIC: Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud

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      Authors: Jaydeep Das;Shreya Ghosh;Soumya K. Ghosh;Rajkumar Buyya;
      Pages: 2869 - 2882
      Abstract: With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namely LYRIC (deadLine and budget aware spatio-temporal querY pRocessing In Cloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11 percent, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by $approx$≈23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • MespaConfig: Memory-Sparing Configuration Auto-Tuning for Co-Located
           In-Memory Cluster Computing Jobs

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      Authors: Zan Zong;Lijie Wen;Xuming Hu;Rui Han;Chen Qian;Li Lin;
      Pages: 2883 - 2896
      Abstract: Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cluster, which causes dynamic cluster load conditions. According to our observation, the variation of cluster load reduces effectiveness of configuration tuning. Besides, as a common problem of cluster computing jobs, overestimation of resources also occurs during configuration tuning. It is challenging to efficiently find the optimal configuration in a shared cluster with the consideration of memory-sparing. In this article, we introduce MespaConfig, a job-level configuration optimizer for distributed in-memory computing jobs. Advancements of MespaConfig over previous work are features including memory-sparing and load-sensitive. We evaluate MespaConfig by 6 typical Spark programs under different load conditions. The evaluation results show that MespaConfig improves the performance of six typical programs by up to 12× compared with default configurations. MespaConfig also achieves at most 41 percent reduction of configuration memory usage and reduces the optimization time overhead by 10.8× compared with the state-of-the-art approach.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge
           Computing

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      Authors: Quyuan Luo;Changle Li;Tom H. Luan;Weisong Shi;
      Pages: 2897 - 2909
      Abstract: The development of autonomous driving poses significant demands on computing resource, which is challenging to resource-constrained vehicles. To alleviate the issue, Vehicular edge computing (VEC) has been developed to offload real-time computation tasks from vehicles. However, with multiple vehicles contending for the communication and computation resources at the same time for different applications, how to efficiently schedule the edge resources toward maximal system welfare represents a fundamental issue in VEC. This article aims to provide a detailed analysis on the delay and cost of computation offloading for VEC and minimize the delay and cost from the perspective of multi-objective optimization. Specifically, we first establish an offloading framework with communication and computation for VEC, where computation tasks with different requirements for computation capability are considered. To pursue a comprehensive performance improvement during computation offloading, we then formulate a multi-objective optimization problem to minimize both the delay and cost by jointly considering the offloading decision, allocation of communication and computation resources. By applying the game theoretic analysis, we propose a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem. Extensive simulation results verify that our proposed PSOCO outperforms counterparts. Based on the results, we also present a comprehensive analysis and discussion on the relationship between delay and cost among the Pareto-optimal solutions.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Multi-GPU Efficient Indexing For Maximizing Parallelism of High
           Dimensional Range Query Services

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      Authors: Mincheol Kim;Ling Liu;Wonik Choi;
      Pages: 2910 - 2924
      Abstract: Numerous research efforts have been proposed for efficient processing of range queries in high-dimensional space by either redesigning R-tree access structure for exploring massive parallelism on single GPU or exploring distributed framework of R-tree. However, none of the existing efforts explores the integration of the parallelization of the R-tree on a single GPU with a distributed framework for the R-tree. The problem of designing an efficient multi-GPU indexing method, which can effectively combine the parallelism maximization with distributed processing of the R-tree, remains an open challenge. In this article, we present a novel multi-GPU efficient parallel and distributed indexing method, called LBPG-tree. The rationale of the LBPG-tree is to combine the advantages of an instruction pipeline in CPU with the massive parallel processing potential of multiple GPUs by introducing two new optimization strategies: First, we exploit the GPU L2 cache for accelerating both index search and index node access on GPUs. Second, we further improve utilization of L2 cache on GPUs by compacting and sorting candidate nodes called Compact-and-Sort. Our experimental results show that the LBPG-tree outperforms G-tree, the previous representative GPU index method and effectively support multiple GPUs for providing efficient high dimensional range query service.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Multi-Tenant Intrusion Detection Framework as a Service for SaaS

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      Authors: Mohamed Yassin;Hakima Ould-Slimane;Chamseddine Talhi;Hanifa Boucheneb;
      Pages: 2925 - 2938
      Abstract: Information technology (IT) service providers are nowadays moving toward cloud computing. Software-as-a-service (SaaS) refers to cloud service-oriented web applications. As a result of computation outsourcing, a customer (tenant) can subscribe to a self-service SaaS and use it on a pay-per-use basis. To reduce resource costs, a single instance of SaaS serves multiple tenants (multi-tenancy). However, outsourcing and multi-tenancy bring about new security issues. Indeed, tenants lose control over the source code, databases and infrastructure and cannot deploy their own intrusion detection system (IDS). In this context, the provider must not only integrate their preferred IDS into a public cloud, but also protect the tenants according to their individual security requirements. We put forth a multi-tenant intrusion detection framework as a service for SaaS (MTIDaaS) to allow the provider to undertake such integration. Our MTIDaaS has been integrated and tested in a real public cloud environment. It provides security-as-a-service (SecaaS) for both provider and tenant with high levels of portability, flexibility and cost-effectiveness. The experimental results demonstrate that our MTIDaaS offers easy integration of IDS with little virtualization overhead and insignificant impact on HTTP response time.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Priority-Based Selection of Individuals in Memetic Algorithms for
           Distributed Data-Intensive Web Service Compositions

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      Authors: Soheila Sadeghiram;Hui Ma;Gang Chen;
      Pages: 2939 - 2953
      Abstract: In distributed computing, Web Service Composition (WSC) leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Memetic Algorithms (MAs) are promising for automatically composing numerous Web services to satisfy the above requirements. Data-intensive Web services focus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challenging Distributed Data-intensive Web Service Composition (DDWSC) problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Privacy-Preserving Reverse Nearest Neighbor Query Over Encrypted Spatial
           Data

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      Authors: Xiaoguo Li;Tao Xiang;Shangwei Guo;Hongwei Li;Yi Mu;
      Pages: 2954 - 2968
      Abstract: With the advent of cloud computing, it has become more and more popular to outsource various services to the cloud for releasing the burden of local data storage and maintenance. However, it may cause serious privacy problems because the cloud may be untrusted. In this article, we study the privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. First, we introduce the concept of reference-locked order-preserving encryption (RL-OPE) with its construction and security proof, which reveals less information than traditional order-preserving encryption (OPE). Then, we present a novel PPRNN scheme in static setting based on structured encryption (SE) and the proposed RL-OPE, called sPPRNN. After that, we design a generic method that extends a PPRNN scheme in static setting to the counterpart in dynamic setting, called dPPRNN. Furthermore, we present a thorough privacy analysis of our proposal. Finally, we demonstrate its efficiency and effectiveness for practical deployment through extensive experiments.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • RAP: A Light-Weight Privacy-Preserving Framework for
           Recommender Systems

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      Authors: Miao Hu;Di Wu;Run Wu;Zhenkai Shi;Min Chen;Yipeng Zhou;
      Pages: 2969 - 2981
      Abstract: In today's Internet, recommender systems play an indispensable role in helping users discover items of interests, such as products, books, movies and so on. However, a higher recommendation accuracy is commonly at the cost of more disclosure of user privacy. Thus, a wider adoption of recommender systems poses significant security and privacy concerns to users. In this article, we propose a light-weight privacy-preserving framework called RAP for recommender systems, which can protect user privacy while still ensuring a high recommendation accuracy. Instead of directly sending users’ private ratings to the recommender, users first conduct a local perturbation operation on private ratings, and then send the perturbed ratings to the recommender. The recommender can run recommendation algorithms directly over the perturbed ratings and return the results to users. Different from crypto-based methods, our perturbation and de-perturbation methods are linear operations. Thus, RAP is light-weight and highly efficient in privacy protection. To be more rigorous, we formally prove that the order of recommendation accuracy will not decrease when our RAP framework is applied to any MF (Matrix Factorization)-based recommender systems. We also derive the closed-form expression for the degree of privacy preservation of our framework. Finally, we conduct extensive evaluations using large-scale real-world datasets to verify the effectiveness of our RAP framework and compare with other baseline algorithms. The results show that our RAP framework can improve the degree of privacy preservation from zero to over 0.5 for the Movielens dataset and 4 for the Jester dataset, and still maintain the approaching level of recommendation accuracy.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Resource Management for Latency-Sensitive IoT Applications With
           Satisfiability

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      Authors: Cosmin Avasalcai;Christos Tsigkanos;Schahram Dustdar;
      Pages: 2982 - 2993
      Abstract: Satisfying the software requirements of emerging service-based Internet of Things (IoT) applications has become challenging for cloud-centric architectures, as applications demand fast response times and availability of computational resources closer to end-users. Meeting application demands must occur at runtime, facing uncertainty and in a decentralized manner, something that must be reflected in system deployment. We propose a decentralized resource management technique and accompanying technical framework for the deployment of service-based IoT applications at the edge. Faithful to services engineering, applications we consider are composed of interdependent tasks, which in the IoT setting may be concretized as containerized microservices or serverless functions. A deployment for an arbitrary application is found at runtime through satisfiability; the mapping produced is compliant with tasks’ individual resource requirements and latency constraints by construction. Our approach ensures seamless deployment at runtime, assuming no design-time knowledge of device resources or the current network topology. We evaluate the applicability and realizability of our technique over single-board computers as edge devices, particularly in the absence of cloud resources.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Resource Trading in Edge Computing-Enabled IoV: An Efficient Futures-Based
           Approach

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      Authors: Minghui Liwang;Ruitao Chen;Xianbin Wang;
      Pages: 2994 - 3007
      Abstract: Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (EC-IoV), where a forward contract is used to facilitate resource trading-related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties of the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings mutually beneficial utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Scalable Service-Driven Database-Enabled Wireless Network Virtualization
           for Robust RF Sharing

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      Authors: Abdulhamid Adebayo;Danda B. Rawat;
      Pages: 3008 - 3018
      Abstract: Wireless virtualization has emerged as one of the efficient ways to overcome numerous wireless networking challenges such as resource sharing by allowing multiple concurrent virtual wireless networks having run on shared physical wireless infrastructure. Spectrum sharing in wireless networks focuses on increasing the usage efficiency of radio frequency (RF) spectrum with its increasing demand. To reduce uncertainties that accompany the spectrum sharing process caused by RF sensing, database-based spectrum sharing allows opportunistic spectrum access through occupancy information query of a spectrum database. In this article, we propose a 2-level hierarchical spectrum allocation framework for multiple service-based virtual networks (VN) by leasing wireless resources from Wireless Infrastructure Providers (WIPs). The framework leverages the software defined network (SDN)-enabled virtualization where network parameters are configured based on the operating environment on the fly. This article focuses on the competition between multiple VNs to get best RF channels to serve their users while eliminating the need for one-to-one contracts between VNs and WIPs. We develop a mathematical model for the spectrum allocation scheme which allows heterogeneous network configurations with better economic benefit. Using network segments formed based on available sharable resources, the allocation scheme is localized which makes the scalability of this scheme possible. A trust-based security mechanism is also proposed for early detection and prevention of security attacks to maintain the availability attribute of the resource allocation process. Simulation results show that spectrum sharing is efficient, and assures service availability to perceived legitimate VN users.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Secure Outsourced Attribute-Based Sharing Framework for Lightweight
           Devices in Smart Health Systems

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      Authors: Leyou Zhang;Wenting You;Yi Mu;
      Pages: 3019 - 3030
      Abstract: The rapid evolution of the Internet of Things has led to the development of smart health. As a form of medical care that uses advanced Internet technology to realize better diagnosis and treatment of patients, smart health transitions medical services move toward real intelligence and greatly helps users. And in smart health, the secure sharing of personal health records (PHRs) is one of the main concerns of patients and medical personnel. Many attribute-based sharing models have been proposed to secure the sharing of PHRs, but there are still two problems to resolve. One is the potential disclosure of the patient data. The attribute-based model achieves flexible access control, but the access policies contain sensitive information of patients. The disclosure of the policy will lead to the leakage of data of the users. The other is the high computational and storage overhead, particularly in smart health systems with limited computing power. In this article, we present a Smart Health-Lightweight Fine-Grained Sharing (SH-LFGS) framework based on attribute-based encryption (ABE). It achieves a fully hidden access policy by adopting Viéte's formula. SH-LFGS introduces an online/offline mechanism in the PHR encryption phase and the outsourced verifiable decryption mechanism. Because the decrypting test requires only one bilinear pair operation, the SH-LFGS can achieve the task of lightweight computation. Analysis of the performance and security of the proposed model confirm its efficiency and security.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Service Selection With Package Bundles and Compatibility Constraints

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      Authors: Kaustabha Ray;Ansuman Banerjee;Swarup Kumar Mohalik;
      Pages: 3031 - 3046
      Abstract: With the rapid proliferation of strategic alliances between service providers, enterprises cooperate towards service quality improvement and provide lower cost service bundles. This article presents a novel solution to the minimum cost service bundle selection problem for workflows in the presence of singleton subscription costs and service bundle offerings and compatibility requirements. Given a workflow specifying a set of tasks and a set of candidate services for each task, with a set of compatibility constraints between services, the selection problem has the objective of selecting the most suitable service offering(s) for each task. In this article, we analyze the selection problem in the presence of service bundle offerings. We present a novel multi-partite hyper-graph visualization of the selection problem and analyze its hardness. Additionally we present a novel combination of ILP and abstraction refinement as a potential solution, that is shown to expedite a naïve ILP based solution. We present experiments to substantiate this claim.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Smart Futures Based Resource Trading and Coalition Formation for Real-Time
           Mobile Data Processing

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      Authors: Ruitao Chen;Xianbin Wang;Xue Liu;
      Pages: 3047 - 3060
      Abstract: Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • SPESC-Translator: Towards Automatically Smart Legal Contract Conversion
           for Blockchain-Based Auction Services

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      Authors: E Chen;Bohan Qin;Yan Zhu;Weijing Song;Shengdian Wang;Cheng-Chung William Chu;Stephen S. Yau;
      Pages: 3061 - 3076
      Abstract: In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Temporal Knowledge Graph Embedding for Effective Service Recommendation

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      Authors: Haithem Mezni;
      Pages: 3077 - 3088
      Abstract: Over the last decade, service selection and recommendation had been two strongly related service filtering steps. While service selection aims to filter the best available services according to QoS and contextual criteria, service recommendation refines the selection results by taking into account additional criteria, such as users feedbacks and ratings, similarities between users tastes, etc. However, the ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining. Experimental studies have proven the effectiveness and accuracy of our approach, compared to traditional TASR methods and time-unaware KG-based recommendation.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
  • Virtdev: Towards Providing Edge Services

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      Authors: Yi-Wei Ci;Xiao-Ke Zhao;Yan-Peng Li;Zibin Zheng;
      Pages: 3089 - 3100
      Abstract: With the increase in the number of Internet of Things (IoT) devices, more resources can locate at the edge of the Internet. These devices not only collect data about the environment but also affect the environment after certain computations. Conventionally, an IoT application that processes the data provided by devices located in different places, often requires a hierarchical architecture that allows for the data processing and the data collection to occur in different layers. This can be complex for applications requiring direct access to the data provided by remote devices, especially when the data must be exchanged among devices utilizing various machine-to-machine protocols. In this article, an edge service-based architecture is proposed to facilitate the construction of IoT applications. By abstracting the heterogeneous components as virtual devices, it is also possible to construct an IoT application only according to the combination of the virtual devices. The evaluation of the tasks formed by the virtual device shows that it can be efficient to adopt this abstraction method for the utilization of the heterogeneous resources for edge computing.
      PubDate: Sept.-Oct. 1 2022
      Issue No: Vol. 15, No. 5 (2022)
       
 
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