Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

UAV-CLOUD: A PLATFORM FOR UAV RESOURCES AND SERVICES ON THE CLOUD

UAVs - Unmanned Aerial Vehicles – have gained significant attention recently, due to the increasingly growing range of applications. However, developing collaborative UAV applications using traditional technologies in a tightly coupled design requires a great deal of development effort, time, and budget especially for heterogeneous UAVs. Moreover, monitoring and accessing UAV resources using traditional communication media suffer from several restrictions and limitations. This research aims to simplify the efforts, reduce the time, and lower the costs of developing collaborative applications for distributed heterogeneous UAVs. In addition, the research aims to provide ubiquitous UAV resources access. A platform is proposed for developing distributed UAVs. This platform provides services to simplify application development. In this approach, UAVs are integrated with the Cloud Computing paradigm to provide ubiquitous access to their resources and services. Due to the limited capabilities of UAVs, a lightweight architecture is adopted. UAV resources and services are modeled in a Resource Oriented Architecture which is a new flexible web service design pattern with loosely coupled interaction between services. Hence, they are accessed as Representational State Transfer RESTful services using HTTP. Moreover, the research proposes using a broker architecture to increase efficiency by separating responsibilities. Therefore, it separates the requester’s logic and functionalities from the provider’s. It also takes the responsibility for allocating the issued request to the available and suitable UAV(s). To test the proposed platform, I first developed the UAV resources as a payload subsystem then provided them with Internet connectivity. Then, resource identifiers and uniform interfaces were developed using the RESTful Application Programming Interfaces (APIs). I also developed the broker service along with a database containing the information of the registered UAVs and their resources. The platform system components were tested using a requester interface in a browser by issuing a request for a resource to the broker to find and request the service from a suitable UAV. The test was done for retrieving data from UAVs as well as requesting actions from them. The main contributions of this research are proposing the UAV-Cloud platform for simplifying the development of ubiquitous UAV applications and its vii perspectives, as well as a lightweight loosely coupled design for UAV resources. Another contribution is developing the broker architecture for separating responsibilities in this platform

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application