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Data in the IoT: from Sensing to Meaning
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Data in the IoT: from Sensing to Meaning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 April 2016) | Viewed by 150454

Special Issue Editors

Prof. Dr. Luciano Lavagno

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
Interests: design and applications of wireless sensor networks; design methods and tools for embedded systems; high-level synthesis of digital hardware; asynchronous circuits
Special Issues, Collections and Topics in MDPI journals
Dr. Mihai T. Lazarescu

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, 24, 10129 Torino TO, Italy
Interests: reusable WSN/IoT platforms; sensing, indoor localization, and data processing for IoT; design for low power; embedded machine learning and neural networks; high-level HW/SW co-design; high-level synthesis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coined as an enabler for computers to perceive the world without relying on humans for data input, the Internet of Things (IoT) paradigm evolved full circle to aim at enriching our perception of the reality, encompassing new technologies and application fields along the way.

Data is central to the IoT paradigm: from primary sensor data, directly related to the physical world, to processed data that adds new meanings to our view of the world. Many challenges arise as the IoT permeates our world, especially for low-power resource-constrained devices, such as data integrity, proper processing and security, energy requirements, energy cost and harvesting, device cost, lifetime and quality of service, deployment and maintenance cost.

The aim of this Special Issue is to bring together innovative developments in areas related to data-centric IoT, including but not limited to:

  • data sensing (transducers, reliability, cost, energy)
  • data processing (on nodes, distributed, aggregation, discovery, big data)
  • making sense of the data, self-learning (pattern discovery, prediction, self-configuration)
  • data integrity (sensing accuracy and noise resilience, storage, transfer, efficient checking, tampering detection)
  • applications (both new and legacy ones enjoying a new life)
  • high-level methods and tools for application design
  • maintenance (troubleshooting, recurrent costs)
  • cost (nodes, energy, design, deployment, maintenance)
  • deployment (effort, error prevention, localization)
  • energy (reliability, management)

We solicit both review and original research manuscripts, especially related to challenging aspects of the IoT. Of particular interest are the advances towards overcoming the IoT adoption barriers and large scale deployment.

Dr. Mihai Lazarescu
Dr. Luciano Lavagno
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • internet of things
  • IoT adoption
  • large-scale deployment
  • low-power devices
  • low-power sensors
  • reliability
  • security
  • self-learning
  • distributed processing
  • localization
  • energy harvesting
  • energy management
  • wireless sensor networks

Published Papers (16 papers)

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Research

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5315 KiB  
Article
A Tagless Indoor Localization System Based on Capacitive Sensing Technology
by Alireza Ramezani Akhmareh, Mihai Teodor Lazarescu, Osama Bin Tariq and Luciano Lavagno
Sensors 2016, 16(9), 1448; https://doi.org/10.3390/s16091448 - 07 Sep 2016
Cited by 27 | Viewed by 8901
Abstract
Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 [...] Read more.
Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. We also compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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3567 KiB  
Article
A Web Service Protocol Realizing Interoperable Internet of Things Tasking Capability
by Chih-Yuan Huang and Cheng-Hung Wu
Sensors 2016, 16(9), 1395; https://doi.org/10.3390/s16091395 - 31 Aug 2016
Cited by 19 | Viewed by 5327
Abstract
The Internet of Things (IoT) is an infrastructure that interconnects uniquely-identifiable devices using the Internet. By interconnecting everyday appliances, various monitoring, and physical mashup applications can be constructed to improve human’s daily life. In general, IoT devices provide two main capabilities: sensing and [...] Read more.
The Internet of Things (IoT) is an infrastructure that interconnects uniquely-identifiable devices using the Internet. By interconnecting everyday appliances, various monitoring, and physical mashup applications can be constructed to improve human’s daily life. In general, IoT devices provide two main capabilities: sensing and tasking capabilities. While the sensing capability is similar to the World-Wide Sensor Web, this research focuses on the tasking capability. However, currently, IoT devices created by different manufacturers follow different proprietary protocols and are locked in many closed ecosystems. This heterogeneity issue impedes the interconnection between IoT devices and damages the potential of the IoT. To address this issue, this research aims at proposing an interoperable solution called tasking capability description that allows users to control different IoT devices using a uniform web service interface. This paper demonstrates the contribution of the proposed solution by interconnecting different IoT devices for different applications. In addition, the proposed solution is integrated with the OGC SensorThings API standard, which is a Web service standard defined for the IoT sensing capability. Consequently, the Extended SensorThings API can realize both IoT sensing and tasking capabilities in an integrated and interoperable manner. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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10876 KiB  
Article
A Network Coverage Information-Based Sensor Registry System for IoT Environments
by Hyunjun Jung, Dongwon Jeong, Sukhoon Lee, Byung-Won On and Doo-Kwon Baik
Sensors 2016, 16(8), 1154; https://doi.org/10.3390/s16081154 - 25 Jul 2016
Cited by 4 | Viewed by 5541
Abstract
The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which [...] Read more.
The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which constitute the core infrastructure of the IoT. To address this problem, a Sensor Registry System (SRS) is used. By using a SRS, the information of the heterogeneous sensed data remains pure. If users move along a road, their mobile devices predict their next positions and obtain the sensed data for that position from the SRS. If the WSNs in the location in which the users move are unstable, the sensed data will be lost. Consider a situation where the user passes through dangerous areas. If the user’s mobile device cannot receive information, they cannot be warned about the dangerous situation. To avoid this, two novel SRSs that use network coverage information have been proposed: one uses OpenSignal and the other uses the probabilistic distribution of the users accessing SRS. The empirical study showed that the proposed method can seamlessly provide services related to sensing data under any abnormal circumstance. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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4281 KiB  
Article
Telecommunication Platforms for Transmitting Sensor Data over Communication Networks—State of the Art and Challenges
by Kamil Staniec and Marcin Habrych
Sensors 2016, 16(7), 1113; https://doi.org/10.3390/s16071113 - 19 Jul 2016
Cited by 5 | Viewed by 8228
Abstract
The importance of constructing wide-area sensor networks for holistic environmental state evaluation has been demonstrated. A general structure of such a network has been presented with distinction of three segments: local (based on ZigBee, Ethernet and ModBus techniques), core (base on cellular technologies) [...] Read more.
The importance of constructing wide-area sensor networks for holistic environmental state evaluation has been demonstrated. A general structure of such a network has been presented with distinction of three segments: local (based on ZigBee, Ethernet and ModBus techniques), core (base on cellular technologies) and the storage/application. The implementation of these techniques requires knowledge of their technical limitations and electromagnetic compatibility issues. The former refer to ZigBee performance degradation in multi-hop transmission, whereas the latter are associated with the common electromagnetic spectrum sharing with other existing technologies or with undesired radiated emissions generated by the radio modules of the sensor network. In many cases, it is also necessary to provide a measurement station with autonomous energy source, such as solar. As stems from measurements of the energetic efficiency of these sources, one should apply them with care and perform detailed power budget since their real performance may turn out to be far from expected. This, in turn, may negatively affect—in particular—the operation of chemical sensors implemented in the network as they often require additional heating. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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5137 KiB  
Article
A Proof-of-Concept for Semantically Interoperable Federation of IoT Experimentation Facilities
by Jorge Lanza, Luis Sanchez, David Gomez, Tarek Elsaleh, Ronald Steinke and Flavio Cirillo
Sensors 2016, 16(7), 1006; https://doi.org/10.3390/s16071006 - 29 Jun 2016
Cited by 26 | Viewed by 9058
Abstract
The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. The potential of IoT technologies and deployments has been already demonstrated in a number of different application areas, including transport, energy, safety and healthcare. However, despite the [...] Read more.
The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. The potential of IoT technologies and deployments has been already demonstrated in a number of different application areas, including transport, energy, safety and healthcare. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming application silos. A lightweight data centric integration and combination of these silos presents several challenges that still need to be addressed. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potentiality of smart applications in terms of size, scope and targeted business context. In this article, a proof-of-concept implementation that federates two different IoT experimentation facilities by means of semantic-based technologies will be described. The specification and design of the implemented system and information models will be described together with the practical details of the developments carried out and its integration with the existing IoT platforms supporting the aforementioned testbeds. Overall, the system described in this paper demonstrates that it is possible to open new horizons in the development of IoT applications and experiments at a global scale, that transcend the (silo) boundaries of individual deployments, based on the semantic interconnection and interoperability of diverse IoT platforms and testbeds. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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7278 KiB  
Article
A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
by Chao Sha, Jian-mei Qiu, Shu-yan Li, Meng-ye Qiang and Ru-chuan Wang
Sensors 2016, 16(6), 923; https://doi.org/10.3390/s16060923 - 21 Jun 2016
Cited by 12 | Viewed by 6341
Abstract
To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three [...] Read more.
To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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11663 KiB  
Article
Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project
by Christos Antonopoulos, Katerina Asimogloy, Sarah Chiti, Luca D’Onofrio, Simone Gianfranceschi, Danping He, Antonio Iodice, Stavros Koubias, Christos Koulamas, Luciano Lavagno, Mihai T. Lazarescu, Gabriel Mujica, George Papadopoulos, Jorge Portilla, Luis Redondo, Daniele Riccio, Teresa Riesgo, Daniel Rodriguez, Giuseppe Ruello, Vasilis Samoladas, Tsenka Stoyanova, Gerasimos Touliatos, Angela Valvo and Georgia Vlahoyadd Show full author list remove Hide full author list
Sensors 2016, 16(6), 804; https://doi.org/10.3390/s16060804 - 02 Jun 2016
Cited by 3 | Viewed by 8052
Abstract
In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and [...] Read more.
In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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11024 KiB  
Article
Travel Mode Detection with Varying Smartphone Data Collection Frequencies
by Muhammad Awais Shafique and Eiji Hato
Sensors 2016, 16(5), 716; https://doi.org/10.3390/s16050716 - 18 May 2016
Cited by 57 | Viewed by 7730
Abstract
Smartphones are becoming increasingly popular day-by-day. Modern smartphones are more than just calling devices. They incorporate a number of high-end sensors that provide many new dimensions to smartphone experience. The use of smartphones, however, can be extended from the usual telecommunication field to [...] Read more.
Smartphones are becoming increasingly popular day-by-day. Modern smartphones are more than just calling devices. They incorporate a number of high-end sensors that provide many new dimensions to smartphone experience. The use of smartphones, however, can be extended from the usual telecommunication field to applications in other specialized fields including transportation. Sensors embedded in the smartphones like GPS, accelerometer and gyroscope can collect data passively, which in turn can be processed to infer the travel mode of the smartphone user. This will solve most of the shortcomings associated with conventional travel survey methods including biased response, no response, erroneous time recording, etc. The current study uses the sensors’ data collected by smartphones to extract nine features for classification. Variables including data frequency, moving window size and proportion of data to be used for training, are dealt with to achieve better results. Random forest is used to classify the smartphone data among six modes. An overall accuracy of 99.96% is achieved, with no mode less than 99.8% for data collected at 10 Hz frequency. The accuracy is observed to decrease with decrease in data frequency, but at the same time the computation time also decreases. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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8105 KiB  
Article
An Energy Saving System for a Beam Pumping Unit
by Hongqiang Lv, Jun Liu, Jiuqiang Han and An Jiang
Sensors 2016, 16(5), 685; https://doi.org/10.3390/s16050685 - 13 May 2016
Cited by 27 | Viewed by 7086
Abstract
Beam pumping units are widely used in the oil production industry, but the energy efficiency of this artificial lift machinery is generally low, especially for the low-production well and high-production well in the later stage. There are a number of ways for energy [...] Read more.
Beam pumping units are widely used in the oil production industry, but the energy efficiency of this artificial lift machinery is generally low, especially for the low-production well and high-production well in the later stage. There are a number of ways for energy savings in pumping units, with the periodic adjustment of stroke speed and rectification of balance deviation being two important methods. In the paper, an energy saving system for a beam pumping unit (ESS-BPU) based on the Internet of Things (IoT) was proposed. A total of four types of sensors, including load sensor, angle sensor, voltage sensor, and current sensor, were used to detect the operating conditions of the pumping unit. Data from these sensors was fed into a controller installed in an oilfield to adjust the stroke speed automatically and estimate the degree of balance in real-time. Additionally, remote supervision could be fulfilled using a browser on a computer or smartphone. Furthermore, the data from a practical application was recorded and analyzed, and it can be seen that ESS-BPU is helpful in reducing energy loss caused by unnecessarily high stroke speed and a poor degree of balance. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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7729 KiB  
Article
Traffic Congestion Detection System through Connected Vehicles and Big Data
by Néstor Cárdenas-Benítez, Raúl Aquino-Santos, Pedro Magaña-Espinoza, José Aguilar-Velazco, Arthur Edwards-Block and Aldo Medina Cass
Sensors 2016, 16(5), 599; https://doi.org/10.3390/s16050599 - 28 Apr 2016
Cited by 67 | Viewed by 15050
Abstract
This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, [...] Read more.
This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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8726 KiB  
Article
Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
by Yuan Zhuang, Jun Yang, You Li, Longning Qi and Naser El-Sheimy
Sensors 2016, 16(5), 596; https://doi.org/10.3390/s16050596 - 26 Apr 2016
Cited by 370 | Viewed by 18852
Abstract
Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and [...] Read more.
Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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3166 KiB  
Article
An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network Coding
by Jun Yin, Yuwang Yang and Lei Wang
Sensors 2016, 16(4), 462; https://doi.org/10.3390/s16040462 - 01 Apr 2016
Cited by 13 | Viewed by 5492
Abstract
Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on [...] Read more.
Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering—CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes—MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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7254 KiB  
Article
XpertTrack: Precision Autonomous Measuring Device Developed for Real Time Shipments Tracker
by Liviu Viman, Mihai Daraban, Raul Fizesan and Mircea Iuonas
Sensors 2016, 16(3), 355; https://doi.org/10.3390/s16030355 - 10 Mar 2016
Viewed by 5357
Abstract
This paper proposes a software and hardware solution for real time condition monitoring applications. The proposed device, called XpertTrack, exchanges data through the GPRS protocol over a GSM network and monitories temperature and vibrations of critical merchandise during commercial shipments anywhere on the [...] Read more.
This paper proposes a software and hardware solution for real time condition monitoring applications. The proposed device, called XpertTrack, exchanges data through the GPRS protocol over a GSM network and monitories temperature and vibrations of critical merchandise during commercial shipments anywhere on the globe. Another feature of this real time tracker is to provide GPS and GSM positioning with a precision of 10 m or less. In order to interpret the condition of the merchandise, the data acquisition, analysis and visualization are done with 0.1 °C accuracy for the temperature sensor, and 10 levels of shock sensitivity for the acceleration sensor. In addition to this, the architecture allows increasing the number and the types of sensors, so that companies can use this flexible solution to monitor a large percentage of their fleet. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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2961 KiB  
Article
An Open Source “Smart Lamp” for the Optimization of Plant Systems and Thermal Comfort of Offices
by Francesco Salamone, Lorenzo Belussi, Ludovico Danza, Matteo Ghellere and Italo Meroni
Sensors 2016, 16(3), 338; https://doi.org/10.3390/s16030338 - 07 Mar 2016
Cited by 34 | Viewed by 11231
Abstract
The article describes the design phase, development and practical application of a smart object integrated in a desk lamp and called “Smart Lamp”, useful to optimize the indoor thermal comfort and energy savings that are two important workplace issues where the comfort of [...] Read more.
The article describes the design phase, development and practical application of a smart object integrated in a desk lamp and called “Smart Lamp”, useful to optimize the indoor thermal comfort and energy savings that are two important workplace issues where the comfort of the workers and the consumption of the building strongly affect the economic balance of a company. The Smart Lamp was built using a microcontroller, an integrated temperature and relative humidity sensor, some other modules and a 3D printer. This smart device is similar to the desk lamps that are usually found in offices but it allows one to adjust the indoor thermal comfort, by interacting directly with the air conditioner. After the construction phase, the Smart Lamp was installed in an office normally occupied by four workers to evaluate the indoor thermal comfort and the cooling consumption in summer. The results showed how the application of the Smart Lamp effectively reduced the energy consumption, optimizing the thermal comfort. The use of DIY approach combined with read-write functionality of websites, blog and social platforms, also allowed to customize, improve, share, reproduce and interconnect technologies so that anybody could use them in any occupied environment. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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7884 KiB  
Article
Parametric Cognitive Modeling of Information and Computer Technology Usage by People with Aging- and Disability-Derived Functional Impairments
by Rebeca I. García-Betances, María Fernanda Cabrera-Umpiérrez, Manuel Ottaviano, Matteo Pastorino and María T. Arredondo
Sensors 2016, 16(2), 266; https://doi.org/10.3390/s16020266 - 22 Feb 2016
Cited by 16 | Viewed by 6687
Abstract
Despite the speedy evolution of Information and Computer Technology (ICT), and the growing recognition of the importance of the concept of universal design in all domains of daily living, mainstream ICT-based product designers and developers still work without any truly structured tools, guidance [...] Read more.
Despite the speedy evolution of Information and Computer Technology (ICT), and the growing recognition of the importance of the concept of universal design in all domains of daily living, mainstream ICT-based product designers and developers still work without any truly structured tools, guidance or support to effectively adapt their products and services to users’ real needs. This paper presents the approach used to define and evaluate parametric cognitive models that describe interaction and usage of ICT by people with aging- and disability-derived functional impairments. A multisensorial training platform was used to train, based on real user measurements in real conditions, the virtual parameterized user models that act as subjects of the test-bed during all stages of simulated disabilities-friendly ICT-based products design. An analytical study was carried out to identify the relevant cognitive functions involved, together with their corresponding parameters as related to aging- and disability-derived functional impairments. Evaluation of the final cognitive virtual user models in a real application has confirmed that the use of these models produce concrete valuable benefits to the design and testing process of accessible ICT-based applications and services. Parameterization of cognitive virtual user models allows incorporating cognitive and perceptual aspects during the design process. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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Review

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1568 KiB  
Review
Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services
by David Gil, Antonio Ferrández, Higinio Mora-Mora and Jesús Peral
Sensors 2016, 16(7), 1069; https://doi.org/10.3390/s16071069 - 11 Jul 2016
Cited by 160 | Viewed by 19147
Abstract
The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the [...] Read more.
The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term. Full article
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
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