cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.27204003
title: Supplementary information for Reputation-based coalition formation for secure self-organized and scalable sharding in IoT blockchains with mobile-edge computing

description: Article abstractWe propose a fully distributed system architecture and a scalable self-organized sharding scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security without reducing its throughput. In the system, the IoT devices are supported by the set of blockchain peers that gather, process, verify, and store the blocks of IoT transaction records. To support communications among peers, the system is realized in the mobile-edge computing (MEC) network. We design a new consensus mechanism in which each peer votes on the outputs of each block task in its shard. The peer's voting power is computed from its reputation, i.e., trustworthiness in the system. By adopting a reputation-based coalitional game model, we formulate a novel self-organized shard formation algorithm in which each peer acts as a rational player aiming to maximize both its payoff and the coalitional reputation. We prove that the algorithm converges to the reputation-based stable shard structure, i.e., a structure that maximizes the payoff and coalitional reputation of each peer without negatively affecting other peers. The algorithm shows a superior performance in terms of system security and throughput when compared to state-of-the-art sharding schemes and reputation-based blockchains.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204003.v1
title: Supplementary information for Reputation-based coalition formation for secure self-organized and scalable sharding in IoT blockchains with mobile-edge computing

description: Article abstractWe propose a fully distributed system architecture and a scalable self-organized sharding scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security without reducing its throughput. In the system, the IoT devices are supported by the set of blockchain peers that gather, process, verify, and store the blocks of IoT transaction records. To support communications among peers, the system is realized in the mobile-edge computing (MEC) network. We design a new consensus mechanism in which each peer votes on the outputs of each block task in its shard. The peer's voting power is computed from its reputation, i.e., trustworthiness in the system. By adopting a reputation-based coalitional game model, we formulate a novel self-organized shard formation algorithm in which each peer acts as a rational player aiming to maximize both its payoff and the coalitional reputation. We prove that the algorithm converges to the reputation-based stable shard structure, i.e., a structure that maximizes the payoff and coalitional reputation of each peer without negatively affecting other peers. The algorithm shows a superior performance in terms of system security and throughput when compared to state-of-the-art sharding schemes and reputation-based blockchains.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.15468/dl.gy2etk
title: Occurrence Download

description: A dataset containing 61324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)",
 "Year 2017-2024"
 ]
}

 The dataset includes 61324 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007788-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pbg4uj
title: Occurrence Download

description: A dataset containing 6695246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Haliaeetus leucocephalus (Linnaeus, 1766)"
 ]
}

 The dataset includes 6695246 records from 139 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007772-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.27204006
title: Supplementary information for Fast and secure computational offloading with Lagrange coded mobile edge computing

description: Article abstractThis paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as 'masters' which offload their computations to edge devices acting as 'workers'. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204006.v1
title: Supplementary information for Fast and secure computational offloading with Lagrange coded mobile edge computing

description: Article abstractThis paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as 'masters' which offload their computations to edge devices acting as 'workers'. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.57745/po1vdg
title: RéseauPRO_GuideMethodo_juillet2015.pdf

description: :unav
DOI: 10.17028/rd.lboro.27204009
title: Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks

description: Article abstractWe study the problem of distributed computing in the multi-operator multi-access edge computing (MEC) network for dependent tasks. Every task comprises several sub-tasks which are executed based on logical precedence modelled as a directed acyclic graph. In the graph, each vertex is a sub-task, each edge – precedence constraint, such that a sub-task can only be started after all its preceding sub-tasks are completed. Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as a distributed “primary-secondary node” system where each MEC server acts as a primary node (PN) deciding on sub-tasks assigned to its secondary nodes (SNs), i.e., nearby edge devices. The PN's decision problem is complex, as its SNs can be associated with other neighboring PNs. In this case, the available processing resources of SNs depend on the sub-task assignment decisions of all neighboring PNs. Since PNs are controlled by different operators, they do not coordinate their decisions, and each PN is uncertain about the sub-task assignments of its neighbors (and, thus, the available resources of its SNs). To address this problem, we propose a novel framework based on a graphical Bayesian game, where PNs play under uncertainty about their neighbors' decisions. We prove that the game has a perfect Bayesian equilibrium (PBE) yielding unique optimal values, and formulate new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).© IEEEThis accepted manuscript is made available under the Creative Commons Attribution licence (CC BY) under the JISC UK green open access agreement.
DOI: 10.17028/rd.lboro.27204009.v1
title: Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks

description: Article abstractWe study the problem of distributed computing in the multi-operator multi-access edge computing (MEC) network for dependent tasks. Every task comprises several sub-tasks which are executed based on logical precedence modelled as a directed acyclic graph. In the graph, each vertex is a sub-task, each edge – precedence constraint, such that a sub-task can only be started after all its preceding sub-tasks are completed. Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as a distributed “primary-secondary node” system where each MEC server acts as a primary node (PN) deciding on sub-tasks assigned to its secondary nodes (SNs), i.e., nearby edge devices. The PN's decision problem is complex, as its SNs can be associated with other neighboring PNs. In this case, the available processing resources of SNs depend on the sub-task assignment decisions of all neighboring PNs. Since PNs are controlled by different operators, they do not coordinate their decisions, and each PN is uncertain about the sub-task assignments of its neighbors (and, thus, the available resources of its SNs). To address this problem, we propose a novel framework based on a graphical Bayesian game, where PNs play under uncertainty about their neighbors' decisions. We prove that the game has a perfect Bayesian equilibrium (PBE) yielding unique optimal values, and formulate new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).© IEEEThis accepted manuscript is made available under the Creative Commons Attribution licence (CC BY) under the JISC UK green open access agreement.
DOI: 10.15468/dl.mjtvqw
title: Occurrence Download

description: A dataset containing 61324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)",
 "Year 2017-2024"
 ]
}

 The dataset includes 61324 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007791-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a3jcg4
title: Occurrence Download

description: A dataset containing 93475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)",
 "Year 2017-2024"
 ]
}

 The dataset includes 93475 records from 170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007790-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.27178344
title: Supplementary information for Efficient dynamic distributed resource slicing in 6G multi-access edge computing networks with online ADMM and message passing graph neural networks

description: Article abstractWe consider the problem of resource slicing in the 6th generation multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable - when the node generates/propagates no data errors; ii) unreliable - when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes' states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM) - extension of the well-known classical 'static' ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27178344.v1
title: Supplementary information for Efficient dynamic distributed resource slicing in 6G multi-access edge computing networks with online ADMM and message passing graph neural networks

description: Article abstractWe consider the problem of resource slicing in the 6th generation multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable - when the node generates/propagates no data errors; ii) unreliable - when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes' states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM) - extension of the well-known classical 'static' ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.15468/dl.766fcz
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippophae neurocarpa S.W.Liu & T.N.He"
 ]
}

 The dataset includes 123 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007793-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.27204012
title: Supplementary information for Combining contract theory and Lyapunov optimization for content sharing with edge caching and device-to-device communications

description: Article abstractThe paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users' locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204012.v1
title: Supplementary information for Combining contract theory and Lyapunov optimization for content sharing with edge caching and device-to-device communications

description: Article abstractThe paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users' locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.15468/dl.yava9m
title: Occurrence Download

description: A dataset containing 7982 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bison C.H.Smith, 1827",
 "Year 2017-2024"
 ]
}

 The dataset includes 7982 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007795-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.