3,329 research outputs found
Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading
Personal IoT data is a new economic asset that individuals can trade to
generate revenue on the emerging data marketplaces. Typically, marketplaces are
centralized systems that raise concerns of privacy, single point of failure,
little transparency and involve trusted intermediaries to be fair. Furthermore,
the battery-operated IoT devices limit the amount of IoT data to be traded in
real-time that affects buyer/seller satisfaction and hence, impacting the
sustainability and usability of such a marketplace. This work proposes to
utilize blockchain technology to realize a trusted and transparent
decentralized marketplace for contract compliance for trading IoT data streams
generated by battery-operated IoT devices in real-time. The contribution of
this paper is two-fold: (1) we propose an autonomous blockchain-based
marketplace equipped with essential functionalities such as agreement
framework, pricing model and rating mechanism to create an effective
marketplace framework without involving a mediator, (2) we propose a mechanism
for selection and allocation of buyers' demands on seller's devices under
quality and battery constraints. We present a proof-of-concept implementation
in Ethereum to demonstrate the feasibility of the framework. We investigated
the impact of buyer's demand on the battery drainage of the IoT devices under
different scenarios through extensive simulations. Our results show that this
approach is viable and benefits the seller and buyer for creating a sustainable
marketplace model for trading IoT data in real-time from battery-powered IoT
devices.Comment: Accepted in SmartComp 202
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
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Improving shared access to Cloud of Things resources.
Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed.
Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants.
The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability
Intelligence artificielle à la périphérie du réseau mobile avec efficacité de communication
L'intelligence artificielle (AI) et l'informatique à la périphérie du réseau (EC) ont permis de mettre en place diverses applications intelligentes incluant les maisons intelligentes, la fabrication intelligente, et les villes intelligentes. Ces progrès ont été alimentés principalement par la disponibilité d'un plus grand nombre de données, l'abondance de la puissance de calcul et les progrès de plusieurs techniques de compression. Toutefois, les principales avancées concernent le déploiement de modèles dans les dispositifs connectés. Ces modèles sont préalablement entraînés de manière centralisée. Cette prémisse exige que toutes les données générées par les dispositifs soient envoyées à un serveur centralisé, ce qui pose plusieurs problèmes de confidentialité et crée une surcharge de communication importante. Par conséquent, pour les derniers pas vers l'AI dans EC, il faut également propulser l'apprentissage des modèles ML à la périphérie du réseau.
L'apprentissage fédéré (FL) est apparu comme une technique prometteuse pour l'apprentissage collaboratif de modèles ML sur des dispositifs connectés. Les dispositifs entraînent un modèle partagé sur leurs données stockées localement et ne partagent que les paramètres résultants avec une entité centralisée. Cependant, pour permettre l' utilisation de FL dans les réseaux périphériques sans fil, plusieurs défis hérités de l'AI et de EC doivent être relevés. En particulier, les défis liés à l'hétérogénéité statistique des données à travers les dispositifs ainsi que la rareté et l'hétérogénéité des ressources nécessitent une attention particulière. L'objectif de cette thèse est de proposer des moyens de relever ces défis et d'évaluer le potentiel de la FL dans de futures applications de villes intelligentes.
Dans la première partie de cette thèse, l'accent est mis sur l'incorporation des propriétés des données dans la gestion de la participation des dispositifs dans FL et de l'allocation des ressources. Nous commençons par identifier les mesures de diversité des données qui peuvent être utilisées dans différentes applications. Ensuite, nous concevons un indicateur de diversité permettant de donner plus de priorité aux clients ayant des données plus informatives. Un algorithme itératif est ensuite proposé pour sélectionner conjointement les clients et allouer les ressources de communication. Cet algorithme accélère l'apprentissage et réduit le temps et l'énergie nécessaires. De plus, l'indicateur de diversité proposé est renforcé par un système de réputation pour éviter les clients malveillants, ce qui améliore sa robustesse contre les attaques par empoisonnement des données.
Dans une deuxième partie de cette thèse, nous explorons les moyens de relever d'autres défis liés à la mobilité des clients et au changement de concept dans les distributions de données. De tels défis nécessitent de nouvelles mesures pour être traités. En conséquence, nous concevons un processus basé sur les clusters pour le FL dans les réseaux véhiculaires. Le processus proposé est basé sur la formation minutieuse de clusters pour contourner la congestion de la communication et est capable de traiter différents modèles en parallèle.
Dans la dernière partie de cette thèse, nous démontrons le potentiel de FL dans un cas d'utilisation réel impliquant la prévision à court terme de la puissance électrique dans un réseau intelligent. Nous proposons une architecture permettant l'utilisation de FL pour encourager la collaboration entre les membres de la communauté et nous montrons son importance pour l'entraînement des modèles et la réduction du coût de communication à travers des résultats numériques.Abstract : Artificial intelligence (AI) and Edge computing (EC) have enabled various applications
ranging from smart home, to intelligent manufacturing, and smart cities. This progress
was fueled mainly by the availability of more data, abundance of computing power, and
the progress of several compression techniques. However, the main advances are in relation
to deploying cloud-trained machine learning (ML) models on edge devices. This premise
requires that all data generated by end devices be sent to a centralized server, thus raising
several privacy concerns and creating significant communication overhead. Accordingly,
paving the last mile of AI on EC requires pushing the training of ML models to the
edge of the network. Federated learning (FL) has emerged as a promising technique for
the collaborative training of ML models on edge devices. The devices train a globally
shared model on their locally stored data and only share the resulting parameters with
a centralized entity. However, to enable FL in wireless edge networks, several challenges
inherited from both AI and EC need to be addressed. In particular, challenges related
to the statistical heterogeneity of the data across the devices alongside the scarcity and
the heterogeneity of the resources require particular attention. The goal of this thesis is
to propose ways to address these challenges and to evaluate the potential of FL in future
applications. In the first part of this thesis, the focus is on incorporating the data properties of FL in
handling the participation and resource allocation of devices in FL. We start by identifying
data diversity measures allowing us to evaluate the richness of local datasets in different
applications. Then, we design a diversity indicator allowing us to give more priority to
clients with more informative data. An iterative algorithm is then proposed to jointly select
clients and allocate communication resources. This algorithm accelerates the training
and reduces the overall needed time and energy. Furthermore, the proposed diversity
indicator is reinforced with a reputation system to avoid malicious clients, thus enhancing
its robustness against poisoning attacks. In the second part of this thesis, we explore ways to tackle other challenges related to
the mobility of the clients and concept-shift in data distributions. Such challenges require
new measures to be handled. Accordingly, we design a cluster-based process for FL for the
particular case of vehicular networks. The proposed process is based on careful clusterformation
to bypass the communication bottleneck and is able to handle different models
in parallel. In the last part of this thesis, we demonstrate the potential of FL in a real use-case involving
short-term forecasting of electrical power in smart grid. We propose an architecture
empowered with FL to encourage the collaboration among community members and show
its importance for both training and judicious use of communication resources through
numerical results
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