Integrating Internet of Things, Provenance, and Blockchain to Enhance Trust in Last Mile Food Deliveries

Marković, Milan; Jacobs, Naomi; Dryja, Konrad; Edwards, Peter; Strachan, Norval James Colin

doi:10.3389/fsufs.2020.563424

Cited by 16 publications

(18 citation statements)

References 25 publications

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“…In addition, blockchain networks are useful to store graphs in a secured way. To assist in analyzing raw sensor data for food safety compliance, machine-processable guidelines need to be included in future food safety management systems (Markovic et al , 2020).…”

Section: Practical Implications and Recommendationsmentioning

confidence: 99%

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

Elkhwesky

Elkhwesky²

2022

IJCHM

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Purpose Internet of Things (IoT) is a key enabler of big data and it is critical toward a new age of firm's architecture and proper decision-making for the industry. IoT adoption by the hospitality and tourism (H&T) industries is still rare and with contrasting evidence. The purpose of this study is to highlight the concept of IoT, its benefits, its challenges and its applications in the hospitality industry. Design/methodology/approach This study presents an in-depth and critical review of the 78 articles published on IoT in hospitality, spanning more than seven years (from 2015 to March 2022). The eligible 78 articles were collected from the “Web of Science database” and H&T Social Sciences Citation Index-listed journals. Findings This systematic review exhibits that IoT in hospitality has advanced in the past seven years. IoT has brought many advantages and challenges in hospitality. However, hospitality IoT research is scarce, necessitating a profound and more thorough investigation of many significant matters. Practical implications This systematic review provides significant implications and recommendations for hospitality sectors, IoT developers and manufacturers, governments and policymakers. This review ends with an in-depth research agenda with several research questions and a framework to achieve theoretical and empirical advances in future IoT research. Originality/value To the best of the authors’ knowledge, the current research is one of the first to systematically and critically review IoT with its concept, benefits, challenges and applications in a hospitality context. This major review provides a comprehensive idea of the focus of the past studies and what must be concentrated in future research.

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Section: Practical Implications and Recommendationsmentioning

confidence: 99%

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

Elkhwesky

Elkhwesky²

2022

IJCHM

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“…Otherwise, the information is discarded. Finally, the transaction is released to the network [33][34][35].…”

Section: Design Of a Trusted Traceability Survey On The Blockchainmentioning

confidence: 99%

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Jiang

Lei

2022

Computational Intelligence and Neuroscience

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To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), T e t r i s ∗ , Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

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“…Such technology could then support the traceability scenario in both directions: as a centralised repository where all data is stored within a single knowledge graph; and also as a collection of distributed repositories, each under the control of individual data owners and publishing only a portion of a knowledge graph. Many semantic resources such as vocabularies, ontologies, and taxonomies already exist for different parts of the agri-food sector [26,25,51,50]. However, these are currently mostly used by the research community while the industry actors are influenced by, for example, GS1 family of standards 9 .…”

Section: Leveraging Semantic Technologies and Blockchain To Facilitat...mentioning

confidence: 99%

“…Immutability of blockchain records may support trust in data sharing mechanisms while semantic web technologies are used to describe the data in machineunderstandable form. In order to reduce the amount of data stored on blockchain networks to address scaleability challenges, semantically annotated raw data may be automatically abstracted to form more concise reports [51,49], or the data may include only digital signatures required to validate less critical data stored as external resources (e.g., large RDF datasets accessed from distributed repositories) [20]. For example, a traceability system may store the most critical information regarding the product on a blockchain to enable basic traceability functionality and also provide information to discover and validate more elaborate description of a production context stored in a third party semantic repository.…”

Section: Leveraging Semantic Technologies and Blockchain To Facilitat...mentioning

confidence: 99%

How might technology rise to the challenge of data sharing in agri-food?

Durrant

Marković

May

et al. 2021

Global Food Security

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Integrating Internet of Things, Provenance, and Blockchain to Enhance Trust in Last Mile Food Deliveries

Cited by 16 publications

References 25 publications

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

How might technology rise to the challenge of data sharing in agri-food?

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