Swarm Agent-Based Architecture Suitable for Internet of Things and Smartcities

Smart cities are proposed as a medium-term option for all cities. This article aims to propose an architecture that allows cities to provide solutions to interconnect all their elements. The study case focuses in locating and optimized regulation of traffic in cities. However, thanks to the proposed structure and the applied algorithms, the architecture is scalable in size of the sensor network, in functionality or even in the use of resources. A simulation environment which is able to show the operation of the architecture in the same way that a real city would, is presented

Towards a Reference Architecture for Swarm Intelligence-based Internet of Things

International audienceThe Internet of Things (IoT) represents the global network which interconnects digital and physical entities. It aims at providing objects with intelligence that allows them to perceive, decide and cooperate with other objects, machines, systems and even humans to enable a whole new class of applications and services. Agent-Based Computing paradigm has been exploited to deal with the IoT system development. Many research works focus on making objects able to think by themselves thus imitating human brain. Swarm Intelligence studies the collective behavior of systems composed of many individuals who interact locally with each other and with their environment using decentralized and self-organized control to achieve complex tasks. Swarm intelligence-based systems provide decentralized, self-organized and robust systems with consideration of coordination frameworks. We explore in this paper the exploitation of swarm intelligence-based features in IoT-based systems. Therefore, we present a reference swarm-based architectural model that enables cooperation among devices in IoT systems

Robotic Services for New Paradigm Smart Cities Based on Decentralized Technologies

This article describes different methods of organizing robotic services for smart cities using secure encrypted decentralized technologies and market mechanisms—as opposed to models based on centralized solutions based (or not) on using cloud services and stripping citizens of the control of their own data. The basis of the proposed methods is the Ethereum decentralized computer with the mechanism of smart contracts. In this work, special attention is paid to the integration of technical and economic information into one network of transactions, which allows creating a unified way of interaction between robots—the robot economy. Three possible scenarios of robotic services for smart cities based on the economy of robots are presented: unmanned aerial vehicles (UAVs), environmental monitoring, and smart factories. In order to demonstrate the feasibility of the proposed scenarios, three experiments are presented and discussed. Our work shows that the Ethereum network can provide, through smart contracts and their ability to activate programs to interact with the physical world, an effective and practical way to manage robot services for smart cities

Artificial Intelligence techniques for big data analysis

During my stay in Salamanca (Spain), I was fortunate enough to participate in the BISITE Research Group of the University of Salamanca. The University of Salamanca is the oldest university in Spain and in 2018 it celebrates its 8th centenary. As a computer science researcher, I participated in one of the many international projects that the research group has active, especially in big data analysis using Artificial Intelligence (AI) techniques. AI is one of BISITE's main lines of research, along with bioinformatics and robotics. In addition, they combine all these fields working with Internet of Things (IoT) in all its parts: sensors, communications, data analysis using Big Data techniques and visualization software with the latest technologies

Automatic UAVs path planning

My work at the University of Salamanca took place between 14th September 2017 and 1st December 2017. During these months, I have had the opportunity to work with the BISITE Research Group, attend different congresses held in Spain and learn new computer techniques related to artificial intelligence. The work has been focused on the development of software that implements algorithms for the control of UAVs (Unmanned Aerial Vehicles) autonomously. The algorithms are capable of guiding each UAV in such a way that they make an optimal route when travelling the area covered by a perimeter introduced by the user. As an important part of the algorithms, it is emphasized that when calculating changes of direction in the route, it is necessary to take into account the type of camera and its opening. This ensures that the captured images do not overlap or overlap with the minimum required to avoid spaces in 3D reconstruction software. As part of the work, the bibliography indicated in the References section has been used

Swarm intelligence via the internet of things and the phenomenological turn

Considering the current advancements in biometric sensors and other related technologies, as well as the use of bio-inspired models for AI improvements, we can infer that the swarm intelligence paradigm can be implemented in human daily spheres through the connectivity between user gadgets connected to the Internet of Things. This is a first step towards a real Ambient Intelligence, but also of a Global Intelligence. This unconscious (by the user) connectivity may alter the way by which we feel the world. Besides, with the arrival of new augmented ways of capturing and providing information or radical new ways of expanding our bodies (through synthetic biology or artificial prosthesis like brain-computer connections), we can be very close to a change which may radically affect our experience of ourselves and of the feeling of collectivity. We call it the techno-phenomenological turn. We show social implications, present challenges, and and open questions for the new kind of swarm intelligence-enhanced society, and provide the taxonomy of the field of study. We will also explore the possible roadmaps of this next possible situation

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Smart Cities Simulation Environment for Intelligent Algorithms Evaluation

This article presents an adaptive platform that can simulate the centralized control of different smart city areas. For example, public lighting and intelligent management, public zones of buildings, energy distribution, etc. It can operate the hardware infrastructure and perform optimization both in energy consumption and economic control from a modular architecture which is fully adaptable to most cities. Machine-to-machine (M2M) permits connecting all the sensors of the city so that they provide the platform with a perfect perspective of the global city status. To carry out this optimization, the platform offers the developers a software that operates on the hardware infrastructure and merges various techniques of artificial intelligence (AI) and statistics, such as artificial neural networks (ANN), multi-agent systems (MAS) or a Service Oriented Approach (SOA), forming an Internet of Services (IoS). Different case studies were tested by using the presented platform, and further development is still underway with additional case studies

Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives