COSIBAS Platform-Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange.

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT.

At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques [...].

Effect of Vagus Nerve Stimulation on the GASH/Sal Audiogenic-Seizure-Prone Hamster.

Vagus nerve stimulation (VNS) is an adjuvant neuromodulation therapy for the treatment of refractory epilepsy. However, the mechanisms behind its effectiveness are not fully understood. Our aim was to develop a VNS protocol for the Genetic Audiogenic Seizure Hamster from Salamanca (GASH/Sal) in order to evaluate the mechanisms of action of the therapy. The rodents were subject to VNS for 14 days using clinical stimulation parameters by implanting a clinically available neurostimulation device or our own prototype for laboratory animals. The neuroethological assessment of seizures and general behavior were performed before surgery, and after 7, 10, and 14 days of VNS. Moreover, potential side effects were examined. Finally, the expression of 23 inflammatory markers in plasma and the left-brain hemisphere was evaluated. VNS significantly reduced seizure severity in GASH/Sal without side effects. No differences were observed between the neurostimulation devices. GASH/Sal treated with VNS showed statistically significant reduced levels of interleukin IL-1ß, monocyte chemoattractant protein MCP-1, matrix metalloproteinases (MMP-2, MMP-3), and tumor necrosis factor TNF-α in the brain. The described experimental design allows for the study of VNS effects and mechanisms of action using an implantable device. This was achieved in a model of convulsive seizures in which VNS is effective and shows an anti-inflammatory effect.

Developing Smartness in Emerging Environments and Applications with a Focus on the Internet of Things.

The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [...].

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired.

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

Discovering the Value Creation System in IoT Ecosystems.

Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home.

Intelligent Platform Based on Smart PPE for Safety in Workplaces.

It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, workers are as safe and protected as possible. Thanks to Smart Personal Protective Equipment (PPE) and wearable technologies, information about the workers and their environment can be extracted to reduce the rate of accidents and occupational illness, leading to a significant improvement. This article proposes an architecture that employs three pieces of PPE: a helmet, a bracelet and a belt, which process the collected information using artificial intelligence (AI) techniques through edge computing. The proposed system guarantees the workers' safety and integrity through the early prediction and notification of anomalies detected in their environment. Models such as convolutional neural networks, long short-term memory, Gaussian Models were joined by interpreting the information with a graph, where different heuristics were used to weight the outputs as a whole, where finally a support vector machine weighted the votes of the models with an area under the curve of 0.81.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris-Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence.

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers' environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.

SmartFire: Intelligent Platform for Monitoring Fire Extinguishers and Their Building Environment.

Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always close to the points that are most likely to be affected by a fire and where they are visible and accessible for use. Fire extinguishers are pressure devices, which means that they require maintenance operations that ensure they will function properly in the case of a fire. The purpose of manual and periodic fire extinguisher checks is to verify that their labeling, installation and condition comply with the standards. Security seals, inscriptions, hose and other seals are thoroughly checked. The state of charge (weight and pressure) of the extinguisher, the bottle of propellant gas (if available), and the state of all mechanical parts (nozzle, valves, hose, etc.) are also checked. To ensure greater safety and reduce the economic costs associated with maintaining fire extinguishers, it is necessary to develop a system that allows monitoring of their status. One of the advantages of monitoring fire extinguishers is that it will be possible to understand what external factors affect them (for example, temperature or humidity) and how they do so. For this reason, this article presents a system of soft agents that monitors the state of the extinguishers, collects a history of the state of the extinguisher and environmental factors and sends notifications if any parameter is not within the range of normal values.The results rendered by the SmartFire prototype indicate that its accuracy in calculating pressure changes is equivalent to that of a specific data acquisition system (DAS). The comparative study of the two curves (SmartFire and DAS) shows that the average error between the two curves is negligible: 8% in low pressure measurements (up to 3 bar) and 0.3% in high pressure (above 3 bar).

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants.

The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user's energy bill is greatly reduced with the implemented system.

Agreement Technologies for Energy Optimization at Home.

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

Increasing the Intensity over Time of an Electric-Assist Bike Based on the User and Route: The Bike Becomes the Gym.

Nowadays, many citizens have busy days that make finding time for physical activity difficult. Thus, it is important to provide citizens with tools that allow them to introduce physical activity into their lives as part of the day's routine. This article proposes an app for an electric pedal-assist-system (PAS) bicycle that increases the pedaling intensity so the bicyclist can achieve higher and higher levels of physical activity. The app includes personalized assist levels that have been adapted to the user's strength/ability and a profile of the route, segmented according to its slopes. Additionally, a social component motivates interaction and competition between users based on a scoring system that shows the level of their performances. To test the training module, a case study in three different European countries lasted four months and included nine people who traveled 551 routes. The electric PAS bicycle with the app that increases intensity of physical activity shows promise for increasing levels of physical activity as a regular part of the day.

Energy Optimization Using a Case-Based Reasoning Strategy.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring.

Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system's real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking.

We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management.

Energy Efficiency in Public Buildings through Context-Aware Social Computing.

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.