Browsing by Author "Mazayev, Andriy"
Now showing 1 - 10 of 15
Results Per Page
Sort Options
- A bounded heuristic for collection-based routing in wireless sensor networksPublication . Schütz, Gabriela; Correia, Noélia; Martins, Jaime; Mazayev, Andriy; Barradas, ÁlvaroWireless sensor networks are used to monitor and control physical phenomena and to provide interaction between clients and the physical environment. Clients have been typically users or user applications, but next generation wireless sensor networks will also work in machine-to-machine scenarios where some nodes can be interested in some other nodes' data. These scenarios may run the risk of becoming overloaded with messaging, a pernicious fact in particular for constrained networks where both bandwidth and power supply are limited. Resource collections can be used in wireless sensor networks to improve bandwidth usage and to reduce energy consumption, reducing the overall number of notification packets and wrapping overhead, required for the delivery of sensor data. This article proposes a heuristic algorithm for the planning of both routing and collections, in wireless sensor networks. Results show that collections are always worthwhile, and that the heuristic is able to find feasible and cost effective solutions, approaching its lower bound.
- Attention-based model and deep reinforcement learning for distribution of event processing tasksPublication . Mazayev, Andriy; Al-Tam, Faroq; Correia, NoéliaEvent processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actorcritic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.
- Cognitive load balancing approach for 6G MEC serving IoT mashupsPublication . Attanasio, Barbara; Mazayev, Andriy; du Plessis, Shani; Correia, NoéliaThe sixth generation (6G) of communication networks represents more of a revolution than an evolution of the previous generations, providing new directions and innovative approaches to face the network challenges of the future. A crucial aspect is to make the best use of available resources for the support of an entirely new generation of services. From this viewpoint, the Web of Things (WoT), which enables Things to become Web Things to chain, use and re-use in IoT mashups, allows interoperability among IoT platforms. At the same time, Multi-access Edge Computing (MEC) brings computing and data storage to the edge of the network, which creates the so-called distributed and collective edge intelligence. Such intelligence is created in order to deal with the huge amount of data to be collected, analyzed and processed, from real word contexts, such as smart cities, which are evolving into dynamic and networked systems of people and things. To better exploit this architecture, it is crucial to break monolithic applications into modular microservices, which can be executed independently. Here, we propose an approach based on complex network theory and two weighted and interdependent multiplex networks to address the Microservices-compliant Load Balancing (McLB) problem in MEC infrastructure. Our findings show that the multiplex network representation represents an extra dimension of analysis, allowing to capture the complexity in WoT mashup organization and its impact on the organizational aspect of MEC servers. The impact of this extracted knowledge on the cognitive organization of MEC is quantified, through the use of heuristics that are engineered to guarantee load balancing and, consequently, QoS.
- Data gathering in wireless sensor networks using unmanned aerial vehiclesPublication . Mazayev, Andriy; Correia, Noélia; Schutz, G.In an IoT world sensor-enabled systems are all around us and accessible for management at any time and place. Besides other technological components, small unmanned aerial vehicles are also expected to have an important role in IoT as they fly at low-altitude becoming suitable data acquisition vehicles in certain situations. In this article we focus on data gathering using unmanned aerial vehicles for applications having delivery limit constraints. The problem is to design an efficient set of paths to gather sensor data at specific places, and to deliver it at the sink node, while accomplishing the delivery limit associated with data. After formalizing the problem, a heuristic approach is developed that incorporates solution improvement mechanisms suitable for data gathering purposes. Results show that the proposed approach is suitable to solve the data gathering problem and clues on how to adjust parameters, according to the nature of the data set, are given.
- Deep PC-MAC: a deep reinforcement learning pointer-critic media access protocolPublication . Al-Tam, Faroq; Mazayev, Andriy; Correia, Noélia; Rodriguez, J.Developing artificial intelligence (AI) solutions for communication problems is one of the hottest topics nowadays. This article presents Deep PC-MAC, a novel deep reinforcement learning (DRL) solution to solve the fair coexistence problem (FCP) between heterogeneous nodes in the unlicensed bands. It is based on a hybrid architecture between pointer networks (Ptr-nets) and advantage actor-critic (A2C), i.e., pointer-critic architecture. The proposed model allows base stations to fairly share unlicensed bands with incumbent nodes. It jointly protects the incumbent nodes from spectrum starvation and improves key-performance indicators (KPIs). Deep PC-MAC is trained from scratch with zero-knowledge about FCP and experimental results demonstrate its efficiency when compared to a baseline method.
- Event processing in web of thingsPublication . Mazayev, Andriy; Correia, N.The incoming digital revolution has the potential to drastically improve our productivity, reduce operational costs and improve the quality of the products. However, the realization of these promises requires the convergence of technologies — from edge computing to cloud, artificial intelligence, and the Internet of Things — blurring the lines between the physical and digital worlds. Although these technologies evolved independently over time, they are increasingly becoming intertwined. Their convergence will create an unprecedented level of automation, achieved via massive machine-to-machine interactions whose cornerstone are event processing tasks. This thesis explores the intersection of these technologies by making an in-depth analysis of their role in the life-cycle of event processing tasks, including their creation, placement and execution. First, it surveys currently existing Web standards, Internet drafts, and design patterns that are used in the creation of cloud-based event processing. Then, it investigates the reasons for event processing to start shifting towards the edge, alongside with the standards that are necessary for a smooth transition to occur. Finally, this work proposes the use of deep reinforcement learning methods for the placement and distribution of event processing tasks at the edge. Obtained results show that the proposed neural-based event placement method is capable of obtaining (near) optimal solutions in several scenarios and provide hints about future research directions.
- GACN: Self-clustering genetic algorithm for constrained networksPublication . A. Martins, J.; Mazayev, Andriy; Correia, Noélia; Schutz, G.; Barradas, A.Extending the lifespan of a wireless sensor network is a complex problem that involves several factors, ranging from device hardware capacity (batteries, processing capabilities, and radio efficiency) to the chosen software stack, which is often unaccounted for by the previous approaches. This letter proposes a genetic algorithm-based clustering optimization method for constrained networks that significantly improves the previous state-of-the-art results, while accounting for the specificities of the Internet engineering task force, Constrained RESTful Environment (CoRE), standards for data transmission and specifically relying on CoRE interfaces, which fit this purpose very well.
- Hypermedia APIs for the Web of ThingsPublication . Martins, Jaime; Mazayev, Andriy; Correia, NoéliaThe Web of Things is a new and emerging concept that defines how the Internet of Things can be connected using common Web technologies, by standardizing device interactions on upper-layer protocols. Even for devices that can only communicate using proprietary vendor technologies, upper-layer protocols can generally provide the necessary contact points for a high degree of interoperability. One of the major development issues for this new concept is creating efficient hypermedia-enriched application programming interfaces (APIs) that can map physical Things into virtual ones, exposing their properties and functionality to others. This paper does an in-depth comparison of the following six hypermedia APIs: 1) the JSON Hypertext Application Language from IETF; 2) the Media Types for Hypertext Sensor Markup from IETF; 3) the Constrained RESTful Application Language from IETF'; 4) the Web Thing Model from Evrythng; 5) the Web of Things Specification from W3C; and 6) the Web Thing API from Mozilla.
- Integration of a real-time stochastic routing optimization software with an enterprise resource plannerPublication . Cardoso, Pedro J. S.; Schuetz, Gabriela; Semiao, Jorge; Monteiro, Janio; Rodrigues, Joao; Mazayev, Andriy; Ey, Emanuel; Viegas, MicaelIn order to manage their activities in a centralized manner, an Enterprise Resource Planning (ERP) software is a fundamental tool to many companies. As a generic software, many times it's necessary to add new functionalities to the ERP in order to improve and to adapt/suite it to the companies' processes. The Intelligent Fresh Food Fleet Router (i3FR) project aims to meet the needs expressed by several companies, namely the usefulness of a tool that makes "intelligent" management of the food distribution logistics. This "intelligence" presupposes interconnection capacity of various platforms (e.g., fleet management, GPS, and logistics), and active communication between them in order to optimize and enable integrated decisions.This paper presents a multi-layered architecture to integrate existing ERPs with a route optimization and a temperature data acquisition module. The optimization module is prepared to deal with dynamic scenarios, as new demands may appear during the optimization process and the routes will admit several states (e.g., open, locked and closed), according with the ERP manager instructions. The data aquisition module implements the retrieve of some vehicles parameters (e.g., chambers' temperatures and vehicle's global positioning system data), used to validate the routes and provide information to the company's manager.A distribution company was selected as case-study, having up to 5000 daily deliveries and a fleet of 120 vehicles. The integration of the developed modules with the company's ERP allowed the maintainance of most of the existing procedures, avoiding routines disruption.
- Interoperability in IoT through the semantic profiling of objectsPublication . Mazayev, Andriy; Martins, Jaime; Correia, NoéliaThe emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.