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Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.
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This paper provides a remote access control experiment for students who can't go to the campus because of the COVID-19 pandemic. This paper utilizes the SCADA (supervisory control and data acquisition) using LabView with the Internet of things technology to control the laboratory remotely in real-time. Remote access experiments of a Linear actuator, PID algorithm, Dynamics and Control of Second-order system response, and survey questionnaires were applied and used as an example to show how effective the research study is. The safety of the SCADA system was also considered by using the Virtual Private Network as the primary connection between the student and the server. The remote access laboratory will give a solution to the current problem of the academe for not providing a real-time laboratory equipment experiment. © 2022 IEEE.
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PurposeThe purpose of this paper is to identify the radio frequency identification (RFID) strategic value attributes (RFIDSVAs) mechanism selections preferences and also integration of RFID tags with technology coordination tools (IRTWTCTs) alternatives ranking performance decisions in supply chain management (SCM). RFID-enabled techno-economic feasibility decisions are enhancing the SC visibility in apparel supply chains (ASCs). The RFIDSVAs mechanism selections have performed significant agility to strategic competitive advantages, namely, inventory visibility, multi-tags ownership transfer within trusted third party, etc.Design/methodology/approachFuzzy analytical hierarchy process (FAHP) and FAHP-fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) approaches have been used to evaluate the quantitative assessment of RFIDSVA mechanisms selection decision based on weight priority orders and IRTWTCTs alternatives selection in ASC networks. The comparison of FAHP and FAHP-FTOPSIS approaches to evaluate the integrated framework develop in RFIDSVAs mechanisms and IRTWTCTs alternatives selection decisions in Indian multi-tier ASC networks.FindingsThe result found that the FAHP-FTOPSIS approaches have used to prioritizing the RFIDSVA mechanism selection weights and also identify the IRTWTCTs alternatives ranking preferences order in apparel SCM. The comparison between the FAHP and FAHP-FTOPSIS approach to quantitative assessments from RFIDSVA mechanisms and IRTWTCTs alternatives selection decisions, which enable them SC agility potential across multi-tier visibility in ASC networks. ASC stakeholders can be benefited by techno-economic feasibility decisions, RFID-enabled shop floor activities, multi-tags ownerships transfer in SCs and knowledge-based cryptography tags/items separation in SCs.Research limitations/implicationsThe research work has considered only five RFIDSVA mechanisms and also three integration of RFIDTWTCTs alternatives in multi-tier ASC. The strategic competitive advantages are achieved by RFID-enabled break-even tags price decisions and also techno-economic feasibility decision by contractual design multi-tier SC stakeholder's involvements.Practical implicationsThe pilot project study explores that the quantitative assessment decision has based on RFID-enable techno-economic feasibility in ASCs. Stakeholders can be benefited by inventory control of the financial losses, reducing the inventory inaccuracies and multi-tags ownership transfer within trusted third-party traceability in ASC networks.Originality/valueThis study explores the RFID-enabled apparel SC process and activities visibility (natural fibre's fibre producer, fibre dyeing producer, yarn spinning producer, knitting and finishing producer).
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With the continuous developing of the COVID-2019, more and more countries and regions have strictly supervised the personal information and location data of confirmed patients and their close contacts. At the same time, how to share the necessary information of patients while ensuring that the personal privacy of patients and their close contacts is not leaked, the access process is transparent, traceable, and data is not tampered with, has become a key issue that needs to be solved urgently. Based on this, we propose an accountable medical attribute pass (AMAP) access control scheme in this paper. The scheme first combines the blockchain with an attribute-based access control model. While introducing the blockchain to trace the source of the access process, the access control strategy and key steps in the access system are deployed on the blockchain in the form of smart contracts, so that the entire system can not only ensure the safe access of users to data, but also trace the source of the entire access process. In particular, the solution introduces the medical attribute pass module. Users apply for access in the form of a pass, which avoids multiple matches between subject attributes and access control strategies in the traditional access control model. While achieving fine-grained access control to medical data, a certain degree Improved access efficiency. Finally, the security analysis shows that this scheme can resist denial of service attacks, malicious tampering attacks, single point of failure attacks, main body masquerading attacks, replay attacks, etc. Experiments and performance analysis show that this solution is compared with other solutions. Under the same access control strategy, the more access times, the more obvious the advantages of this solution;the more access control strategies have the same access control strategy, the more effective the solution is. The more obvious the advantages. © 2023 Journal of Propulsion Technology. All rights reserved.
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In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.
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PurposeThis study examines the impact of the COVID-19 pandemic on the well-being and mental health of the seafarers who had to overstay on ships after their contracts expired, identifies topics that affect their mental distress and recommends measures to overcome these.Design/methodology/approachFour research questions about the impacts on the seafarers before and during the COVID-19 pandemic were raised. A literature review and a questionnaire survey were conducted to find answers. Ship officers were asked to assess and fill in the questionnaires for the stranded seafarers onboard in order to collect sufficient samples rapidly for this study.FindingsDespite the guidelines provided by the shipping companies being adequate to protect the seafarers from COVID-19, their mental distress levels have been worsened under the pandemic. The crew change crisis causes anxiety and negatively impacts on their working performance;however, the repatriation expectation of the stranded seafarers is of the highest concern. Three topics were identified as having impacts on the mental health of the stranded seafarers: crew change crisis, low vaccination rate and the lack of key worker recognition. While international stakeholders are advocating for support in these issues, the shipping companies and the seafarers need to do their parts to exacerbate the mental distress, and to survive and thrive beyond the pandemic.Originality/valueThe findings of this study will help the shipping companies to navigate the challenges and the seafarers to overcome issues caused by the COVID-19 pandemic.
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Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).
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The prevalence cloud security has privacy preserving problems that major challenges due to humanity's need protect the sensitive and non-sensitive data to decision-making and resolve data leakage problems. One of the most difficult aspects is the reuse and sharing of accurate and detailed clinical data about PHR collected via Personal Health Records (PHRs) cloud transition is difficult. PHRs are often privacy preserving patient-centric models for exchanging medical information outsourced to third parties, such as Cloud Service Providers (CSP). A unique PHR patient information to ensure security with encryption before storage in the cloud. But still, issues such as security issues, flexible access and a valid user privacy risk management, efficiency and remain an important challenge to achieve better data access sensitive and non-sensitive imposition of control in cloud storage. To achieve high efficiency of PHR and modular data access control, Rail Fence Data Encryption (RFDE) algorithm provided to encrypt the PHR file to make high privacy standards. RFDE is also a form of transposition cipher called zigzag encryption, and the unauthorized user can't access the information. The proposed algorithm encrypts the PHR information it generates the secret key. The receiver decrypts the PHR information using the private key. The proposed algorithm provide efficient performance compared with previous algorithm. © 2023 IEEE.
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COVID-19 has exposed major weaknesses in the healthcare settings. The surge in COVID-19 cases increases the demands of health care, endangers vulnerable patients, and threats occupational safety. In contrast to a hospital outbreak of SARS leading to a whole hospital quarantined, at least 54 hospital outbreaks following a COVID-19 surge in the community were controlled by strengthened infection prevention and control measures for preventing transmission from community to hospitals as well as within hospitals. Access control measures include establishing triage, epidemic clinics, and outdoor quarantine stations. Visitor access restriction is applied to inpatients to limit the number of visitors. Health monitoring and surveillance is applied to healthcare personnel, including self-reporting travel declaration, temperature, predefined symptoms, and test results. Isolation of the confirmed cases during the contagious period and quarantine of the close contacts during the incubation period are critical for containment. The target populations and frequency of SARS-CoV-2 PCR and rapid antigen testing depend on the level of transmission. Case investigation and contact tracing should be comprehensive to identify the close contacts to prevent further transmission. These facility-based infection prevention and control strategies help reduce hospital transmission of SARS-CoV-2 to a minimum in Taiwan.
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In the contemporary time of technology, security is the utmost concern for every building automation system. Access Control Systems are the backbone of any security system being employed in any intelligent building, and can be operated in a biometric or non-biometric manner. There are various types of recognition systems available, depending upon the required level of safety and security. The ongoing pandemic has challenged and tested Access Control System in many aspects.This paper aims to review the various forms of access control systems and their viability in the context of COVID-19. It is found that some access control solutions fail to provide the required security during this global epidemic due to their contact-based operations. So, in the midst of the worldwide pandemic, a realistic integrated electronic access control system can be designed to meet the requirements of users. © 2022 IEEE.
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COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.
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The recent covid-19 pandemic created a barrier to every activity that needed physical interaction and involvement, especially in the judiciary. Careful research of some courts in Nigeria shown that case records are still been manually processed and stored and some courts operate a semi-digital and semi-manual processing pattern, which also has its own shortcoming of preprocessing manual records and converting them into digital records and physical presence is required to access court records. This research develops a secure electronic Cybercrime Cases Database System (eCCDBS), for prosecuted cybercrime in the judicial service in Nigeria. The system will provide an efficient method for collecting, retrieving, preserving, and management of court case records. The Rapid Application Development (RAD) methodology is used for the system development, because of its speed and time friendliness and can be easily restructured to meet the client's requirement at any point in time during the development life span. RAD can also present a prototype of the final system software to the client. Access control mechanism and secure password hashing were used to ensure the security of the system. The system was implemented and evaluated through deployment and found to have functioned according to the specification. The application subunits of records' creation, submission, modification, deletion, retrieval, and storage functioned effectively. Hence this system provides a secure online repository specifically for cybercrime case records that have elements of confidentiality, integrity and availability.
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Interest in leveraging blockchain technology to boost healthcare and e-health solutions has lately increased. Blockchain has proven to have enormous promise in a range of e-health industries because of its decentralized and reliable nature, including the secure exchange of electronic health records (EHRs) and database access management among numerous medical entities. A unique paradigm known as the "patient-centric approach” places the patient at the center of the healthcare system and gives them complete control over who has access to and can share their personal health information. Strong confidentiality and safety requirements are necessary for health information. Additionally, other concerns must be resolved, such as secrecy, interoperability, scalability, cost-effectiveness, and timeliness. This paper offers a patient-centric privacy-preserving framework for an efficient and safe medical record to address these problems. Based on three parameters transaction cost, execution time, and gas cost. Three blockchain platforms are compared by using the smart contract to find out the suitable platform for the implementation of this framework. Blockchain platforms served as a benchmark for the performance assessment of a designed framework. Although blockchain will not fix every issue in healthcare organizations, it will undoubtedly assist in dramatically reducing some of the most critical ones.
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IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE
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In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations. Author
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The three key advantages of cloud computing are adaptability, scale, and availability. These features were produced by combining virtualization techniques with internet services. The conventional management techniques and tools, unfortunately, appear inadequate in comparison to the scalability and flexibility of cloud services since they often require local software installation with ongoing upgrades and modifications. System administrators still do a major portion of the manual work involved in deploying and managing cloud configurations. Working with cloud services from multiple providers is also challenging since the solutions are sometimes private and only adhere to the cloud service capabilities of certain service providers. Incorporating autonomy into cloud management would mean giving the cloud manager the capacity to autonomously upgrade or decrease the variety of deployed images and virtual machines to fulfill Customer service contracts for efficiency, etc. In this paper, we introduce Multi-objective Cat Swarm - Aurora (MCS-Aurora), a highly scalable infrastructure as a service (IaaS) cloud manager that enables access to cloud services even in the scenario that the manager itself fails. By providing network automation, MCS-Aurora and the role-based access control mechanism provide flexible and effective resource management. Enhancing user authentication, data access mechanisms, and data security are the main goals of the suggested manager. The manager is in charge of achieving the cloud's service level agreement for handling and storing data. To demonstrate the effectiveness of the system, the suggested technique is contrasted with current methods.
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Privacy in Electronic Health Records (EHR) has become a significant concern in today's rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR', we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients' medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain.
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This research develops a contactless and secure access control system based on face recognition and body temperature measurement. This research aims to establish a security system that also fulfills health protocols for COVID-19 spreading, in this case, the limitation of physical contact. The PRESENT algorithm, a lightweight block cipher encryption-decryption algorithm, is implemented to keep the transmitted data safe. The face recognition method consists of the Viola-Jones face detection algorithm and LBPH face recognition algorithm. The body temperature is measured using a contactless sensor. The performance tests show the accuracies of recognizing faces are 68% under 198 Lux lighting and 52% under 105 Lux lighting. The precision of measuring body temperature using the sensor reaches 98,85%. Based on the sniffing attack test of the system, the encrypted data transmitted from the system to the web-based database is safe from attackers. Besides the face spoofing attack tests, the system will not authenticate attackers with face photos or face videos. © 2022 IEEE.
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Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system. In this study we propose drowsiness detection system utilizing current novel classification model such as Deep Neural Network (DNN), combined with Haar Cascade. The DNN is utilized to detect face, while Haar Cascade is utilized for detecting the eyes and its state on the detected face. In this study, due to Covid19 pandemic, we focused on developing the classifiers for detecting the face with mask. Apart from that, our proposed classifiers are also capable of identifying non-masked faces. The experimental result showed that by utilizing DNN and Haar Cascade, our proposed system could reach accuracy, precision, recall, and f1 measure as much as 81%, 88%, 80%, and 84%, respectively. © 2022 IEEE.
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Driven by recent calls for more research that examines forms of crowdsourcing used to address social challenges, in this article, we contribute to the broader literature on open innovation and crowdsourcing by investigating how crowdsourcing platforms enable the transformation of crowd-based resources. We have focused on initiatives with broader social purposes, rather than those that are for-profit and single firm-driven, where the resulting resources are usually solely controlled by a specific organization. By analyzing 19 crowd-based initiatives with a similar context—responding to the coronavirus disease pandemic—we studied a variety of initiatives and identified three distinct types of crowdsourcing platforms that enable resource transformation: resource pooling;resource cocreation;and resource enabling beyond the platform boundaries. We depict how access to and control of resources vary across initiatives. We have framed our contribution as crowd-resourcing, providing a reference model for the design of platforms based on the type of involvement and expected degree of resource transformation. IEEE