Ανάλυση Κυρίων Συνιστωσών για την Αποτελεσματική Μετάδοση Πληροφορίας σε Ασύρματα Δίκτυα Αισθητήρων

Οι εφαρμογές που βασίζονται σε Ασύρματα Δίκτυα Αισθητήρων (Wireless Sensor Networks, WSN) επηρεάζονται από πολλούς παράγοντες, όπως σφάλματα μετάδοσης, τοπολογία του δικτύου και την κατανάλωση ενέργειας. Κατά συνέπεια, η ανάπτυξη τέτοιων εφαρμογών εισάγει διάφορες ερευνητικές προκλήσεις. Στη διπλωματική εργασία προτείνεται ένα νέο σχήμα συμπίεσης πληροφορίας πλαισίου με τη βοήθεια των μαθηματικών τεχνικών της Ανάλυσης Κύριων Συνιστωσών (Principal Component Analysis). Το σχήμα αυτό επιτυγχάνει υψηλή συμπίεση σε συσχετισμένα δεδομένα (μετρήσεις θερμοκρασίας και υγρασίας που έχουν ληφθεί σε ανοικτούς χώρους), χωρίς παράλληλα να παρατηρείται σημαντική αύξηση στο σφάλμα.Applications based on Wireless Sensor Networks (WSN) are influenced by many factors such as transmission errors, network topology and power consumption. Consequently, developing such applications introduces several research challenges. The thesis proposes a new compression format information framework with using of mathematical techniques of Principal Component Analysis (Principal Component Analysis). The scheme achieves high compression associated data (temperature and humidity measurements taken outdoors), while no significant increase in error

Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O( N 2 ) linear programming problems for N -node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions. </jats:p

Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and off-line, suffering from low scalability and large computational complexity; typically solving O(N2 ) linear programming problems for N-node WSNs. This paper presents the first optimal distributed solution to this problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MicaZ motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions

Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and off-line, suffering from low scalability and large computational complexity; typically solving O(N2 ) linear programming problems for N-node WSNs. This paper presents the first optimal distributed solution to this problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MicaZ motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment

Energy Efficiency

This book is one of the most comprehensive and up-to-date books written on Energy Efficiency. The readers will learn about different technologies for energy efficiency policies and programs to reduce the amount of energy. The book provides some studies and specific sets of policies and programs that are implemented in order to maximize the potential for energy efficiency improvement. It contains unique insights from scientists with academic and industrial expertise in the field of energy efficiency collected in this multi-disciplinary forum

Cryptographic key distribution in wireless sensor networks: a hardware perspective

In this work the suitability of different methods of symmetric key distribution for application in wireless sensor networks are discussed. Each method is considered in terms of its security implications for the network. It is concluded that an asymmetric scheme is the optimum choice for key distribution. In particular, Identity-Based Cryptography (IBC) is proposed as the most suitable of the various asymmetric approaches. A protocol for key distribution using identity based Non-Interactive Key Distribution Scheme (NIKDS) and Identity-Based Signature (IBS) scheme is presented. The protocol is analysed on the ARM920T processor and measurements were taken for the run time and energy of its components parts. It was found that the Tate pairing component of the NIKDS consumes significants amounts of energy, and so it should be ported to hardware. An accelerator was implemented in 65nm Complementary Metal Oxide Silicon (CMOS) technology and area, timing and energy figures have been obtained for the design. Initial results indicate that a hardware implementation of IBC would meet the strict energy constraint of a wireless sensor network node