TechNews digests: Jan - Nov 2006

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Wireless Sensor Networks for Fire Detection and Control

Due to current technological progress, the manufacturing of tiny and low price sensors became technically and economically feasible. Sensors can measure physical surroundings related to the environment and convert them into an electric signal. A huge quantity of these disposable sensors is networked to detect and monitor fire. This paper provides an analysis of utilisation of wireless sensor networks for fire detection and control

Implementation and evaluation of a 2.4 GHz multi-hop WSN: LoS, NLoS, different floors, and outdoor-to-indoor communications

In this paper, the communication reliability of a 2.4 GHz multi-hop wireless sensor network (WSN) in various test scenarios is evaluated through experiments. First, we implement an autonomous communication procedure for a multi-hop WSN on Tmote sky sensor nodes; 2.4 GHz, an IEEE 802.15.4 standard. Here, all nodes including a transmitter node (Tx), forwarder nodes (Fw), and a base station node (BS) can automatically work for transmitting and receiving data. The experiments have been tested in different scenarios including: i) in a room, ii) line-of-sight (LoS) communications on the 2nd floor of a building, iii) LoS and non-line-of-sight (NLoS) communications on the 1st floor to the 2nd floor, iv) LoS and NLoS communications from outdoor to the 1st and the 2nd floors of the building. The experimental results demonstrate that the communication reliability indicated by the packet delivery ratio (PDR) can vary from 99.89% in the case of i) to 14.40% in the case of iv), respectively. Here, the experiments reveal that multi-hop wireless commutations for outdoor to indoor with different floors and NLoS largely affect the PDR results, where the PDR more decreases from the best case (i.e., the case of a)) by 85.49%. Our research methodology and findings can be useful for users and researchers to carefully consider and deploy an efficient 2.4 GHz multi-hop WSN in their works, since different WSN applications require different communication reliability level

IoT-inspired Framework for Real-time Prediction of Forest Fire

Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS

Μέθοδοι κατανεμημένης επεξεργασίας σήματος και σύντηξης δεδομένων για εφαρμογές ασυρμάτων δικτύων αισθητήρων ευρείας κλίμακας

Σε αυτή τη Διδακτορική Διατριβή μελετάμε το πρόβλημα της παρακολούθησης και πρόβλεψης της εξέλιξης συνεχών αντικειμένων (π.χ. καταστροφικά περιβαλλοντικά φαινόμενα που διαχέονται) με τη χρήση Ασυρμάτων Δικτύων Αισθητήρων (ΑΔΑ) ευρείας κλίμακας. Προτείνουμε μια ευέλικτη αλλά και πρακτική προσέγγιση με δύο κύρια συστατικά: α) Ασύγχρονο συνεργατικό αλγόριθμο ΑΔΑ που εκτιμά, χρησιμοποιώντας δυναμικά σχηματιζόμενες ομάδες από τρεις συνεργαζόμενους κόμβους, τα τοπικά χαρακτηριστικά της εξέλιξης (διεύθυνση, φορά και ταχύτητα) του μετώπου, καθώς και β) Αλγόριθμο που ανακατασκευάζει το συνολικό μέτωπο του συνεχούς αντικειμένου συνδυάζοντας την πληροφορία των τοπικών εκτιμήσεων. Επιπλέον, ο αλγόριθμος ανακατασκευής, εκμεταλλευόμενος την δυνατότητα εκτίμησης της αβεβαιότητα ως προς τα τοπικά χαρακτηριστικά εξέλιξης, μπορεί να προβλέπει και την πιθανότητα το κάθε σημείο της περιοχής να έχει καλυφθεί από το συνεχές αντικείμενο σε κάθε χρονική στιγμή. Μέσω πλήθους προσομοιώσεων επικυρώσαμε την ικανότητα του συνεργατικού αλγορίθμου να εκτιμά με ακρίβεια τα τοπικά χαρακτηριστικά εξέλιξης πολύπλοκων συνεχών αντικειμένων, καθώς και την ευρωστία του σε αστοχίες των αισθητηρίων κόμβων κατά την επικοινωνία τους αλλά και λόγω της πιθανής ολοσχερούς καταστροφής τους. Τέλος, παρουσιάζουμε τη δυνατότητα του αλγορίθμου ανακατασκευής να παρακολουθεί με ακρίβεια την εξέλιξη μετώπων συνεχών αντικειμένων με πολύπλοκα σχήματα, χρησιμοποιώντας σχετικά μικρό αριθμό τοπικών εκτιμήσεων στις οποίες μπορεί να έχει υπεισέλθει και σημαντικό σφάλμα. In this Dissertation we study the problem of tracking the boundary of a continuous object (e.g. a hazardous diffusive phenomenon) and predicting its local and global spatio-temporal evolution characteristics using large-scale Wireless Sensor Networks (WSNs). We introduce a practical WSN-based approach consisting of two main components: a) An asynchronous collaborative in-network processing algorithm that estimates, using dynamically formed node triplets (clusters), local front model evolution parameters (orientation, direction and speed) of the expanding continuous object, and b) an algorithm that reconstruct the overall hazard's boundary by combining the produced local front estimates as they are becoming available to a fusion center. Based on the estimated uncertainties of local front model parameters, the reconstruction can provide for each point of the considered area the probability to be reached by the hazard’s front. Extensive computer simulations demonstrate that the proposed algorithm can estimate accurately the evolution characteristics of complex diffusive continuous objects, while it remains robust to sensor node and communication link failures. Finally, we show that it can track with accuracy the evolution of continuous objects with complex shapes, using a relatively small number of potentially distorted local front estimates

Analysis of current and potential sensor network technologies and their incorporation as embedded structural system

This document provides a brief overview of the actual wireless ad hoc sensor networks technologies and standards available, especially in view of their possible implementation for shipping container protection and monitoring within the framework of the STEC Action aiming at analyzing possible technical solutions to improve the security of the millions of containers moving in and out of Europe. Examples of applications and research projects are reported from the literature to give insights on the possibility of implementation of wireless sensor networks in real world scenarios.JRC.G.5-European laboratory for structural assessmen

Distributed Signal Processing and Data Fusion Methods for Large Scale Wireless Sensor Network Applications

Σε αυτή τη Διδακτορική Διατριβή μελετάμε το πρόβλημα της παρακολούθησης και πρόβλεψης της εξέλιξης συνεχών αντικειμένων (π.χ. καταστροφικά περιβαλλοντικά φαινόμενα που διαχέονται) με τη χρήση Ασυρμάτων Δικτύων Αισθητήρων (ΑΔΑ) ευρείας κλίμακας. Προτείνουμε μια ευέλικτη αλλά και πρακτική προσέγγιση με δύο κύρια συστατικά: α) Ασύγχρονο συνεργατικό αλγόριθμο ΑΔΑ που εκτιμά, χρησιμοποιώντας δυναμικά σχηματιζόμενες ομάδες από τρεις συνεργαζόμενους κόμβους, τα τοπικά χαρακτηριστικά της εξέλιξης (διεύθυνση, φορά και ταχύτητα) του μετώπου, καθώς και β) Αλγόριθμο που ανακατασκευάζει το συνολικό μέτωπο του συνεχούς αντικειμένου συνδυάζοντας την πληροφορία των τοπικών εκτιμήσεων. Επιπλέον, ο αλγόριθμος ανακατασκευής, εκμεταλλευόμενος την δυνατότητα εκτίμησης της αβεβαιότητα ως προς τα τοπικά χαρακτηριστικά εξέλιξης, μπορεί να προβλέπει και την πιθανότητα το κάθε σημείο της περιοχής να έχει καλυφθεί από το συνεχές αντικείμενο σε κάθε χρονική στιγμή. Μέσω πλήθους προσομοιώσεων επικυρώσαμε την ικανότητα του συνεργατικού αλγορίθμου να εκτιμά με ακρίβεια τα τοπικά χαρακτηριστικά εξέλιξης πολύπλοκων συνεχών αντικειμένων, καθώς και την ευρωστία του σε αστοχίες των αισθητηρίων κόμβων κατά την επικοινωνία τους αλλά και λόγω της πιθανής ολοσχερούς καταστροφής τους. Τέλος, παρουσιάζουμε τη δυνατότητα του αλγορίθμου ανακατασκευής να παρακολουθεί με ακρίβεια την εξέλιξη μετώπων συνεχών αντικειμένων με πολύπλοκα σχήματα, χρησιμοποιώντας σχετικά μικρό αριθμό τοπικών εκτιμήσεων στις οποίες μπορεί να έχει υπεισέλθει και σημαντικό σφάλμα.In this Dissertation we study the problem of tracking the boundary of a continuous object (e.g. a hazardous diffusive phenomenon) and predicting its local and global spatio-temporal evolution characteristics using large-scale Wireless Sensor Networks (WSNs). We introduce a practical WSN-based approach consisting of two main components: a) An asynchronous collaborative in-network processing algorithm that estimates, using dynamically formed node triplets (clusters), local front model evolution parameters (orientation, direction and speed) of the expanding continuous object, and b) an algorithm that reconstruct the overall hazard's boundary by combining the produced local front estimates as they are becoming available to a fusion center. Based on the estimated uncertainties of local front model parameters, the reconstruction can provide for each point of the considered area the probability to be reached by the hazard’s front. Extensive computer simulations demonstrate that the proposed algorithm can estimate accurately the evolution characteristics of complex diffusive continuous objects, while it remains robust to sensor node and communication link failures. Finally, we show that it can track with accuracy the evolution of continuous objects with complex shapes, using a relatively small number of potentially distorted local front estimates

Five years of designing wireless sensor networks in the Doñana Biological Reserve (Spain): an applications approach

Wireless Sensor Networks (WSNs) are a technology that is becoming very popular for many applications, and environmental monitoring is one of its most important application areas. This technology solves the lack of flexibility of wired sensor installations and, at the same time, reduces the deployment costs. To demonstrate the advantages of WSN technology, for the last five years we have been deploying some prototypes in the Doñana Biological Reserve, which is an important protected area in Southern Spain. These prototypes not only evaluate the technology, but also solve some of the monitoring problems that have been raised by biologists working in Doñana. This paper presents a review of the work that has been developed during these five years. Here, we demonstrate the enormous potential of using machine learning in wireless sensor networks for environmental and animal monitoring because this approach increases the amount of useful information and reduces the effort that is required by biologists in an environmental monitoring task

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society