With expected widespread implementation of 5G networks and Internet Things (IoT), indoor localization is to become even further importance. Although Global Positioning System (GPS) ensures efficient outdoor localization, generally speaking, systems fail provide the same level efficiency. In this regard, there has been recent attention Angle Arrival (AoA) with application on Switch Antenna Array (SAA), as an method due its potential in determining location low estimation error. The AoA,...

Bluetooth Low Energy (BLE) is one of the key technologies empowering Internet Things (IoT) for indoor positioning. In this regard, Angle Arrival (AoA) localization most reliable techniques because its low estimation error. BLE-based AoA localization, however, in infancy as only recently direction-finding feature introduced to BLE specification. Furthermore, AoA-based approaches are prone noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network...

Bluetooth Low Energy (BLE) is one of the key enabling technologies emerging Internet Things (IoT) concept. When it comes to BLE-based dynamic indoor tracking, however, due drastic fluctuations Received Signal Strength Indicator (RSSI), highly acceptable accuracies are not yet achieved. Although very recent introduction BLE v 5.1 promises prosperous future for following two issues in path: (i) Despite being age big data with huge emphasis on reproducibility research, there no unified dataset...

Internet of Things (IoT) has penetrated different aspects our modern life where smart sensors enabled with Bluetooth Low Energy (BLE) are deployed increasingly within surrounding indoor environments. BLE-based localization is, typically, performed based on Received Signal Strength Indicator (RSSI), which suffers from drawbacks due to its significant fluctuations. In this paper, we focus a multiplemodel estimation framework for analyzing and addressing effects orientation BLE-enabled device...

Background: There has been an increasing surge of interest on development advanced Reinforcement Learning (RL) systems as intelligent approaches to learn optimal control policies directly from smart agents' interactions with the environment. Objectives: In a model-free RL method continuous state-space, typically, value function states needs be approximated. this regard, Deep Neural Networks (DNNs) provide attractive modeling mechanism approximate using sample transitions. DNN-based...

Angle of Arrival (AoA) approach with applications to Bluetooth Low Energy (BLE) has been recognized as an effective indoor localization method because its ability for position determination low estimation error. However, there are several issues including Carrier Frequency Offset (CFO), multipath effect, Inter-Symbol Interference (ISI), noise, and phase shifting faced by the AoA. To tackle these issues, we first highlight wireless signal model in BLE standard formulate transmitted signal,...

The paper is motivated by the importance of Smart Cities (SC) concept for future management global urbanization. Among all Internet Things (IoT)-based communication technologies, Blue-tooth Low Energy (BLE) plays a vital role in city-wide decision making and services. Extreme fluctuations Received Signal Strength Indicator (RSSI), however, prevent this technology from being reliable solution with acceptable accuracy dynamic indoor tracking/localization approaches ever-changing SC...

Development of distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted an increasing surge interest lately. Generally speaking, conventional Model-Based (MB) or Model-Free (MF) RL are not directly applicable to the MARL problems due utilization a fixed reward model for learning underlying value function. While Deep Neural Network (DNN)-based solutions perform well, they still prone overfitting, high sensitivity parameter selection, and sample inefficiency. In this...

Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent spread this highly contagious virus and be prepared for potential future ones. Since spreading probability novel in indoor environments is much higher than that outdoors, there an urgent unmet quest develop/design efficient, autonomous, trustworthy, secure CT solutions. Despite such urgency, field still its infancy. This article addresses...

Of particular interest to this paper is indoor positioning via integration of information fusion, localization, and tracking technologies with Internet Things (IoT) devices equipped sensing, processing, Bluetooth Low Energy (BLE) communication capabilities. In particular, the objective development advanced signal processing machine learning solutions micro-locate track a person within delimited physical space (e.g. building) using BLE locating infrastructure installed space. regard as first...

There has been a recent surge of interest on development news-oriented Deep Neural Network (DNN) architectures to predict stock trend movements. Limited focus is, however, devoted reliability fusing different available information resources. In this regard, paper proposes Noisy Stock Movement Prediction Fusion framework (ND-SMPF) for price movement prediction. The proposed ND-SMPF predictive uses fusion combine twitter data with extended horizon market historical prices boost the accuracy...

The paper is motivated by the importance of Smart Cities (SC) concept for future management global urbanization. Among all Internet Things (IoT)-based communication technologies, Bluetooth Low Energy (BLE) plays a vital role in city-wide decision making and services. Extreme fluctuations Received Signal Strength Indicator (RSSI), however, prevent this technology from being reliable solution with acceptable accuracy dynamic indoor tracking/localization approaches ever-changing SC...

The paper is motivated by the importance of Smart Cities (SC) concept for future management global urbanization and energy consumption. Multi-agent Reinforcement Learning (RL) an efficient solution to utilize large amount sensory data provided Internet Things (IoT) infrastructure SCs city-wide decision making managing demand response. Conventional ModelFree (MF) Model-Based (MB) RL algorithms, however, use a fixed reward model learn value function rendering their application challenging ever...

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread this highly contagious virus and be prepared for potential future ones. Since spreading probability novel coronavirus in indoor environments is much higher than that outdoors, there an urgent unmet quest develop/design efficient, autonomous, trustworthy, secure CT solutions. Despite such urgency, field still its infancy. The paper addresses gap proposes...

A probabilistic Gaussian mixture model (GMM) of the Received Signal Strength Indicator (RSSI) is proposed to perform indoor localization via Bluetooth Low Energy (BLE) sensors. More specifically, deal with fact that RSSI-based solutions are prone drastic fluctuations, GMMs trained more accurately represent underlying distribution RSSI values. For assigning real-time observed vectors different zones, first a Kalman Filter applied smooth vector and form its model, which then compared in...

The paper focuses on development of model-free and distributed Reinforcement Learning (RL) algorithms for multi-agent networks. goal is to learn optimal control policies directly from smart agents' cooperative interactions among themselves with the environment. In RL methods continuous state-space, typically, value function needs be approximated. this regard, Deep Neural Networks (DNNs) provide an attractive modeling mechanism approximate using sample transitions. Direct utilization...

Communication of high-frequency and equidistantly sampled Inertial Measurement Unit (IMU) measurements is impractical as it drains power the handhold device even may result in crashing phone's operating system. In this regard, paper proposes a novel Event-Triggered (ET) IMU monitoring/communication strategy for indoor localization IoT applications. This is, to best our knowledge, first work develop ET methodologies within context where communication only occurs when specific events are...

Fast-paced and ever-growing advances in Signal Processing Machine Learning (ML) models have initiated works on autonomous medical monitoring/screening tasks to assess patients' cognitive state. In conventional assessment systems, a physician evaluates the mental abilities of brain by rating patient's numerical, verbal, logical responses. Development an system that replaces is critically challenging task. As first step towards achieving this objective, paper Automated Virtual Cognitive...

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at start 3rd decade 21st century. Particularly, COVID-19 pandemic negatively affected financial econometrics stock markets across globe. Artificial Intelligence (AI) Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have potential to act a key enabling factor reduce adverse effects future possible ones on markets. In this regard, first, unique related...

There has been an increasing surge of interest on development advanced Reinforcement Learning (RL) systems as intelligent approaches to learn optimal control policies directly from smart agents' interactions with the environment. Objectives: In a model-free RL method continuous state-space, typically, value function states needs be approximated. this regard, Deep Neural Networks (DNNs) provide attractive modeling mechanism approximate using sample transitions. DNN-based solutions, however,...

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased. There is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, secure indoor CT solutions. Despite such urgency, this field still in its infancy. In context, paper proposes Trustworthy Blockchain-enabled system for Indoor (TB-ICT) framework. The proposed TB-ICT, blockchain-enabled solution, trustworthy secure. For localization module we...