Ambient intelligence pursues the integration of intelligent approaches on an IoT infrastructure, mainly using everyday objects of the environment. The main hypothesis of the work is that the way in which a user interacts with a door handle is suitable to be used in the identification task. Our proposal contributes with a new method to identify persons in a seamless and unobstrusive way, suitable to

Real-time Collaborative Editors (RCEs) are popular distributed applications that permit large and dynamic groups of collaborators to share and update large multimedia documents across many sites in real time. However, the state-of-the-art models are either locally centralized or globally distributed using a central cloud server to keep shared data synchronized. Multi-access Edge Computing (MEC) is

Pedestrian road-safety applications require geolocation data at high refresh rate. This is achieved through high rate beaconing of the data from the safety devices owned by road users to servers. The servers perform threat analysis and send alerts to road users in case of risk of collisions. However, when low-battery devices like smartphones are used for such applications, they drain their energy rapidly

Recent advancements in wireless technologies enable pervasive and device free gesture recognition that enable assisted living utilizing off the shelf commercial Wi-Fi devices. This paper proposes a Device-Free Wi-Fi-based Sign Language Recognition (DF-WiSLR) for recognizing 30 static and 19 dynamic sign gestures. The raw Channel State Information (CSI) acquired from the Wi-Fi device for 49 sign gestures

Blockchain in IoT applications obviates the need for dependence on a single trusted authority, enhancing the potential for scalability and reliability. Existing consensus methods used in blockchain require high energy consumption, massive computational power with trusted authorities, or proof for mining a block. Resource constrained IoT devices need a lightweight and low latency consensus mechanism

This paper focuses on building a spatio-temporal AI inference engine for estimating hard disk reliability. Most electronic systems such as hard disks routinely collect such reliability parameters in the field to monitor the health of the system. Changes in parameters as a function of time are monitored and any observed changes are compared with the known failure signatures. If the trajectory of the

In this study, a priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in internet of things is proposed. In this model, patients’ data is labeled according to the proposed prioritizing mechanism. This provides a prioritized and delay-less service in the server side for the patients with critical conditions. In the proposed model, the sensed data

As smartphones are increasingly an integral part of daily life, recent literature suggests a deeper relationship between personality traits and smartphone usage. However, this relationship depends on many complex factors such as geographic location, demographics, or cultural influence, just to name a few. These factors provide crucial knowledge for e.g. usage support, recommendations, marketing, general

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse task demands (e.g. heterogeneous tasks with different priority values) under interference constraints in the presence of limited communication and computational resources

Multi-access Edge Computing (MEC) recognized as an emerging technology that provides cloud computing services in the proximity of Radio Access Network (RAN). Mobile subscribers could offload their computation-intensive or memory-intensive tasks to the nearest Base Stations (BS) and deliver the cloud services from MEC servers. This technology can meet the requirements of low latency and location awareness

Achieving advanced Mobile Edge Computing (MEC) services such as dynamic resource assignment and slicing, maintaining Quality of Service (QoS), and enabling heterogeneous virtual functions are some of the technical challenges associated with edge-cloud enhanced 5G architectures now under consideration. This paper proposes a named-object based virtual network (NOVN) architecture to support low-latency

Automatic human activity detection, mainly transport-wise, is very relevant for architects and urban planners (among many others) when designing cities, roads, public transportation systems, etc. Such detection allows to better plan our cities and has been made possible with the widespread use of smartphones carrying several different sensors. We developed edgeTrans, a system based on a smartphone

The concept of present population is gaining increasing attention in official statistics. One possible approach to measure present population exploits data collected by Mobile Network Operators (MNO), from simple Call Detail Records (CDR) to more informative and complex signalling records. Such data, collected primarily for network operation processes, can be repurposed to infer patterns of human mobility

Maintaining a food journal can allow an individual to monitor eating habits, including unhealthy eating sessions, food items causing severe reactions, or portion size related information. However, manually maintaining a food journal can be burdensome. In this paper, we explore the vision of a pervasive, automated, completely unobtrusive, food journaling system using a commodity smartwatch. We present

This paper presents a participatory framework to improve the reliability of sensor emulation by using non-dedicated and crowdsourced sensory data to cover several dedicated sensors in smart environments. To this end, GPS-less vehicle localization in a public transportation network by vehicular crowd-sensing and machine intelligence is considered as a potential use case. Our proposed architecture aims

This paper presents an opportunistic sensing based solution to detect crowdedness in public transportation buses. The solution uses data of accelerometer and Global Positioning System (GPS) sensors available in smartphones carried by the commuters. These data are used to accurately identify bus boarding event and whether a commuter got a seat during his/her trip. The solution is energy efficient as

Music can produce a positive effect in runners’ motivation and performance. Nevertheless, these effects vary depending on the user’s location, the emotions that she/he feels at each moment or the type of training session. In this paper, a context and emotion-aware system for the recommendation and playing of Spotify songs is presented. It consists in a location-based mobile application that interacts

Critical urban events take places at a random way and they need to be dealt with by public authorities quickly to maintain the proper operation of cities. The main challenges for an efficient handling fall in the random nature of the event, and in the speed and accuracy of its notification to the authority. The pervasiveness of vehicles in the cities, and their communication and monitoring capabilities

The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study

Mobile crowdsensing is a people-centric sensing system based on users’ contributions and incentive mechanisms aim at stimulating them. In our work, we have rethought the design of incentive mechanisms through a game-theoretic methodology. Thus, we have introduced a multi-layer social sensing framework, where humans as social sensors interact on multiple social layers and various services. We have proposed

To achieve trust and continuity in the smart contracts-based business process execution, the verification of such smart contracts is mandatory. A blockchain-based smart contract should work as intended before using it. Due to the immutable nature of blockchain, any bugs or errors will become permanent once published and could lead to huge economic losses. To avoid such problems, verification is required

People involved in mass emergencies increasingly publish information-rich contents in Online Social Networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing

Indoor localization has attracted increasing attention due to demands for location awareness, where GPS (global positioning system) does not work. Fingerprint-based indoor localization is one of standard approaches, but it does not gain recognition as a practical technique due to labor-intensive database maintenance. To protect the degradation of localization accuracy caused by the environmental variations

In the last few years, classical social networking is turning into the more complex social internetworking and is extending from human users to objects. Indeed, objects are becoming increasingly complex, smart and social so that several authors have recently started to investigate the Social Internet of Things (SIoT) and the Multiple IoT (MIoT) paradigms. SIoT is more oriented to the technological

Smart cities represent an archetypal example of infrastructures where the fog computing paradigm can express its potential: we have a large set of sensors deployed over a large geographic area where data should be pre-processed (e.g., to extract relevant information or to filter and aggregate data) before sending the result to a collector that may be a cloud data center, where relevant data are further

The evolution of Location Based Services (LBS) is expected to play a significant role in the future smart city. The ever-increasing amount of data produced, along with the emergence of next-generation computationally intensive applications, requires new service delivery models. Such models should capitalize on the Edge Computing (EC) paradigm for supporting the data offloading process, by considering

In this paper we study and implement real-time adaptation schemes for video encoding and channel selection that work in tandem to facilitate HD and 360° video streaming for secondary users in a dynamic spectrum access network. Out-of-band feedbacks on instantaneous pathloss of the signal between the transmitter and the receiver, the received signal strength indicator (RSSI) at the receiver, and the

The wearable devices market has been growing exponentially in the last few years and it is expected to count up to 930 million devices by the end of 2021. A common application of wearable devices is Human Activity Recognition (HAR), i.e., the ability of using the sensing capabilities of these devices to monitor and infer human activities. However, Animal Activity Recognition (AAR) has received significantly

To reconstruct the target-induced attenuation image, the existing radio tomographic imaging techniques often search the solution in the attenuation signal space and regularize the solution to be consistent with explicitly pre-defined prior knowledge of the attenuation signal. However, the performance will inevitably deteriorate when the prior knowledge fails to be consistent with the signal, especially

In this paper, we investigate machine learning approaches addressing the problem of geolocation. First, we review some classical learning methods to build a radio map. These methods are split in two categories, which we refer to as likelihood-based methods and fingerprinting methods. Then, we provide a novel geolocation approach in each of these two categories. The first proposed technique relies on

Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information

Accurate and efficient estimation of caloric expenditure during daily activities is desirable in tracking personal activity and health. Kinetic energy harvesting (KEH) has created an opportunity for wearable devices with limited battery power to realize long-term human health monitoring. We postulate to utilize KEH device for calorie estimation instead of accelerometer considering that the kinetic

Air pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although

For task allocation of mobile crowd sensing, aiming at the problem that the task cannot​ be completed normally due to the change of sensing state and the data quality is reduced because the sensor willingness is not satisfied, a task allocation method with active learning ability based on the normal cloud model is proposed. Firstly, the data quality, sensing environment and network state of sensors

Despite continuous efforts, designing both a resource-efficient and secure authentication protocol for Internet of Things (IoT) edge devices is still a great challenge for the industry. To address these concerns, in this paper, we present a new and more efficient method of providing secure communication between IoT edge devices and cloud servers, using a secure Elliptic Curve Cryptography (ECC)-based

The voluminous amount of data generated by individuals’ mobile sensors and wearable devices is considered of a great value for the benefits of patients and clinical research. Recent advances incorporating data mining and cloud computing have leveraged the great potential of these data. However, the introduction of such technologies in the process of mobile crowd sensed data mining and analytics could

Home Voice Assistants (HVAs) like the Amazon Echo or the Google Home are increasingly being used to control smart devices in a smarthome. A HVA device takes in a spoken command (“Turn off the living-room lights and television”), and converts it into commands for respective devices. This paper presents CANVAS system, an Context AwareNess for Voice ASsistants system designed for multi-occupant smarthomes

The combination of Mobile Crowdsensing (MCS) with Opportunistic Networking (OppNet) allows mobile users to share sensed data easily and conveniently without the use of fixed infrastructure. OppNet is based on intermittent connectivity among wireless mobile devices, in which mobile nodes may store, carry and forward messages (sensing information) by taking advantage of wireless ad hoc communication

In a disaster or battlefield zone, rescue workers, soldiers, and other survivors (referred to as nodes) may need to survey damages and send images to the command and control center (the server) in a hop-by-hop fashion in the absence of any communication infrastructure. The server considers some area/landmark as the point of interest (POI) and distributes a request to the nodes to collect more information

The ubiquity of mobile technology has opened the door to the new era of mobile sensing. Through this new paradigm, physical phenomena can be observed in a distributed way, crowd-sourcing the data measurement tasks to smartphones and/or other popular smart wearables. Mobile sensing and wireless communications can hence be employed to gather data and generate new information and services, benefiting

The Internet of Things technology allows devices automatically connect with others or a server for the purposes of exchanging data. People can conveniently integrate data from those devices for a smart home, vehicular ad-hoc network, e-Health, etc. In 2017, Wang et al. proposed a simple authentication scheme for the Internet of Things. Although they formally proved that their scheme is secure, they

Recent years have seen a widespread use of information and communication technology (ICT) in the implementation of smart city applications. A key enabler in the smart city paradigm is the Internet-of-Things (IoT), which facilitates automated real-time sensing, communication, and actuation, assisting in unmanned monitoring of physical phenomenon and supports intelligent decision making. Nevertheless

An increased modal share of bicycle traffic is a key mechanism to reduce emissions and solve traffic-related problems. However, a lack of (perceived) safety keeps people from using their bikes more frequently. To improve safety in bicycle traffic, city planners need an overview of accidents, near miss incidents, and bike routes. Such information, however, is currently not available. In this paper,

We propose a novel approach for textual input which is based on air-writing recognition using smart-bands. The proposed approach enables the user to hand-write in the air in an intuitive and natural way, where text is recognized by analyzing the motion signals captured by an off-the-shelf smart-band worn by the user. Unlike existing studies that proposed the use of motion signals to recognize written

Knowing in real-time the position of objects and people, both in indoor and outdoor spaces, allows companies and organizations to improve their processes and offer new kind of services. Nowadays Location-based Services (LBS) generate a significant amount of data thank to the widespread of the Internet of Things; since they have been quickly perceived as a potential source of profit, several companies

While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more

The increased maturity level of technological achievements towards the realization of the Internet of Things (IoT) vision allowed sophisticated solutions to emerge, offering reliable monitoring in highly dynamic environments that lack well-defined and well-designed infrastructures. In this paper, we use a bio-inspired IoT architecture, which allows flexible creation and discovery of sensor-based services

Small, low-cost, wireless cameras are becoming increasingly commonplace making surreptitious observation of people more difficult to detect. Previous work in detecting hidden cameras has only addressed limited environments in small spaces where the user has significant control of the environment. To address this problem in a less constrained scope of environments, we introduce the concept of similarity

Vehicular Cloud (VC) improves the utilization of idle resources in vehicles by providing data services for other vehicles or entities such as data computing and storage. However, the vehicles in the VC still face privacy and security issues, especially when querying data from the road side unit. To this end, in this paper, we propose an efficient privacy-preserving data query and dissemination scheme

This paper addresses the problem of joint multicast routing, channel selection, scheduling, and call admission control in multi-cell multi-channel multi-radio cognitive radio wireless mesh networks. The applied model relies on the QoS sessions, where each one is expressed with a bandwidth requirement. We propose a cross-layer algorithm named “Multicast Routing, Channel selection, Scheduling, and Call

Text input is a very challenging task in the constrained screen real-estate of Augmented Reality headsets. Typical keyboards spread over multiple lines and occupy a significant portion of the screen. In this article, we explore the feasibility of single-line text entry systems for smartglasses. We first design FITE, a dynamic keyboard where the characters are positioned depending on their probability

Sensor-based human activity recognition is to recognise human daily activities through a collection of ambient and wearable sensors. It is the key enabler for many healthcare applications, especially in ambient assisted living. The advance of sensing and communication technologies has driven the deployment of sensors in many residential and care home settings. However, the challenge still resides in

The high performance of mobile devices has enabled deep learning to be extended to also exploit its strengths on such devices. However, because their computing power is not yet sufficient to perform on-device training, a pre-trained model is usually downloaded to mobile devices, and only inference is performed on them. This situation leads to the problem that accuracy may be degraded if the characteristics

Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users’ devices. These transformations aim at eliminating patterns that can be used for user re-identification

The capacity and coverage needs of 5G NR (New Radio) networks are satisfied by deploying a high number of small cells in the coverage area of a macro cell. In 5G Dual Connectivity (DC), these small cells are represented as eNBs with 4G characteristics different from the macro cells which are the 5G NR gNBs. The mobile node (MN) can establish a connection with these cells in different technologies at

One key challenge for sensor networks is to provide the real-time high reliable sensor measurements with the minimum resource consumption. Outlier clearance in sensor networks can ensure the quality of sensor measurements and dependable monitoring. In this paper, we propose two online distributed outlier clearance approaches with low computational complexity and memory usage that can identify and remove

Online Social Networks (OSNs) have become one of the most popular application of the daily life of users in the worldwide. Today the number of Social Media users is about 3 billion, and this trend increases year per year with high impact on the privacy issue. During the last years, decentralization of social services has been considered a big opportunity to overcome the main issues in OSNs. Blockchain

One important promise of the blockchain technology is the concept of smart contracts. They offer means for the secure execution of procedures that no entity can manipulate. This enables applications like the automation of business processes, i.e. entire business relationships in peer-to-peer collaborations can be automated securely. While the blockchain guarantees proper execution it assures the correctness

People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information