Development of a sparse RFID reader deployment algorithm for effective RFID network planning

A well planned Radio Frequency Identification (RFID) network achieves the following twin objectives, (a) reduces the cost of acquiring the readers to be deployed (b) completes coverage of tags deployed in the network. A reader deployment approach that considered the physical topology of passive tags that were sparsely deployed in a RFID network was developed. In the implemented technique, readers could not be deployed outside the work area which implied that tags located outside the work area could not be detected. Bidirectional communication between the readers and tags could only be established if their receiver sensitivity was less than or equal to the incoming signal. The developed algorithm was optimized by logically integrating some sub-algorithms such as Useless Reader Elimination Algorithm (UREA) and Redundant Reader Elimination Algorithm (RREA). The effectiveness of the developed Sparse Reader Deployment Algorithm (SRDA) was demonstrated through simulation of various scenarios. The implemented approach was compared to the Constrained Reader Deployment Approach (CORDA) and the former outperformed the latter in terms of Optimal Power Dissipated (OPD) which was 255mW. The impact of increasing the number of tags and coverage area on the OPD of SRDA was investigated in a bid to ascertain the robustness of the developed algorithm.Keywords: RFID; CORDA; SRDA; OPD

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

Approximate filtering of redundant RFID data streams in mobile environment

U zadnje vrijeme RFID tehnologija (Radio Frequency Identification Technology) se naveliko rabi u mnogim aplikacijama kao što su nadgledanje i praćenje objekta, zahvaljujući jedinstvenim značajkama kao što su beskontaktna, brza i simultana identifikacija više ciljeva. Međutim, zbog interferencije faktora okoline i potrebe za detekcijom u realnom vremenu, podaci koje su RFID čitači prikupili često su puni redundancije, a to može smanjiti učinkovitost obrade RFID aplikacijskih servera, pa čak rezultirati i donošenjem krivih zaključaka. Stoga je neophodno potrebno filtrirati redundantne podatke u RFID sustavima prije nego se prenesu do naprednijih aplikacija. U svrhu podržavanja aproksimativnog filtriranja RFID nizova podataka u mobilnom okruženju, u radu se pokušava analizirati mehanizam za učinkovito redundantno filtriranje modelom kliznog prozora. Najprije se daje razvoj aplikacije RFID nizova podataka i arhitektura RFID sustava utemeljeni na međusoftveru. Zatim se predlaže vremensko-prostorni Bloom filtar utemeljen na kliznim prozorima koji proširuje niz podataka s jednom dimenzijom u standardnom Bloom filtru na filtar s dvije dimenzije, pohranjujući i čitača IDs-a i promatrane vremenske oznake originalnih promatranih stavki. U međuvremenu, kako bi se osigralo da se lažno pozitivna brzina ne poveća zbog toga što se popunio prostor filtra, predlažemo strategiju slučajnog nestajanja za brisanje zastarjelih elemenata. Relativno učestale pogreške predloženog filtra, uključujući lažno pozitivne i lažno negativne, teorijski se analiziraju. Eksperimentalni rezultati pokazuju da predloženi filtar može učinkovito filtrirati vremenski redundantne podatke te uspješno locirati RFID objekte.Recently, RFID technology has been widely used in many applications such as object monitoring and tracing due to the unique features such as non-contact, automatic, fast and multi-target identification simultaneously. However, because of the interference of environmental factors and the requirement of real-time detection, the data collected by the RFID readers are often full of redundancy, which may reduce the processing efficiency of RFID application servers, even lead to making false decisions. Therefore, it is of definite necessity to filter the redundant data in RFID systems before transmitting them to the upper applications. In order to support approximate filtering of RFID data streams in mobile environment, this paper intends to study effective redundant filtering mechanism in the sliding window model. Firstly, we introduce the application background of RFID data streams and the RFID system architecture based on middleware. Then, we propose a temporal-spatial Bloom filter based on sliding windows, which extends the one-dimension array in the standard bloom filter to a two-dimension array, storing both reader IDs and the observed timestamps of original observation items. Meanwhile, in order to guarantee the false positive rate does not increase due to the reason that the space of the filter becomes full, we suggest a random decay strategy for deleting the expired elements. The error rates of the suggested filter, including false positives and false negatives, are analysed in theory. Experimental results show that the suggested filter can filter time redundant data effectively and has a good performance to deal with location movement of RFID objects

Statistical Analysis on IoT Research Trends: A Survey

Internet of Things (IoT) is a novel and emerging paradigm to connect real/physical and virtual/logical world together. So, it will be necessary to apply other related scientific concepts in order to achieve this goal. The main focus of this paper is to identify the research topics in IoT. For this purpose, a comprehensive study has been conducted on the vast range of research articles. IoT concepts and issues are classified into some research domains and sub-domains based on the analysis of reviewed papers that have been published in 2015 & 2016. Then, these domains and sub-domains have been discussed as well as it is reported their statistical results. The obtained results of analysis show the most of the IoT research works are concentrated on technology and software services domains similarly at first rank, communication at second rank and trust management at third rank with 19%, 14% and 13% respectively. Also, a more accurate analysis indicates the most important and challenging sub-domains of mentioned domains which are: WSN, cloud computing, smart applications, M2M communication and security. Accordingly, this study will offer a useful and applicable broad viewpoint for researchers. In fact, our study indicates the current trends of IoT area

Magneto-inductive wireless underground sensor networks: novel longevity model, communication concepts and workarounds to key theoretical issues using analogical thinking

This research has attempted to devise novel workarounds to key theoretical issues in magneto-inductive wireless underground sensor networks (WUSNs), founded on analogical thinking (Gassmann & Zeschky 2008). The problem statement for this research can be summarized as follows. There has been a substantial output of research publications in the past 5 years, devoted to theoretically analysing and resolving the issues pertaining to deployment of MI based WUSNs. However, no alternate solution approaches to such theoretical analyses have been considered. The goal of this research was to explore such alternate solution approaches. This research has used the principle of analogical thinking in devising such alternate solution approaches. This research has made several key contributions to the existing body of work. First, this research is the first of its kind to demonstrate by means of review of state-of-the-art research on MI based WUSNs, the largely theoretical genus of the research to the exclusion of alternate solution approaches to circumvent key theoretical issues. Second, this research is the first of its kind to introduce the notion of analogical thinking as a solution approach in finding viable workarounds to theoretical impediments in MI based WUSNs, and validate such solution approach by means of simulations. Third, this research is the first of its kind to explore novel communication concepts in the realm of MI based WUSNs, based on analogical thinking. Fourth, this research is the first of its kind to explore a novel longevity model in the realm of MI based WUSNs, based on analogical thinking. Fifth, this research is also the first to extend the notion of analogical thinking to futuristic directions in MI based WUSNs research, by means of providing possible indicators drawn from various other areas of contemporary research. In essence, the author believes that the findings of this research mark a paradigm shift in the research on MI based WUSNs

Ageneric predictive information system for resource planning and optimisation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe purpose of this research work is to demonstrate the feasibility of creating a quick response decision platform for middle management in industry. It utilises the strengths of current, but more importantly creates a leap forward in the theory and practice of Supervisory and Data Acquisition (SCADA) systems and Discrete Event Simulation and Modelling (DESM). The proposed research platform uses real-time data and creates an automatic platform for real-time and predictive system analysis, giving current and ahead of time information on the performance of the system in an efficient manner. Data acquisition as the backend connection of data integration system to the shop floor faces both hardware and software challenges for coping with large scale real-time data collection. Limited scope of SCADA systems does not make them suitable candidates for this. Cost effectiveness, complexity, and efficiency-orientation of proprietary solutions leave space for more challenge. A Flexible Data Input Layer Architecture (FDILA) is proposed to address generic data integration platform so a multitude of data sources can be connected to the data processing unit. The efficiency of the proposed integration architecture lies in decentralising and distributing services between different layers. A novel Sensitivity Analysis (SA) method called EvenTracker is proposed as an effective tool to measure the importance and priority of inputs to the system. The EvenTracker method is introduced to deal with the complexity systems in real-time. The approach takes advantage of event-based definition of data involved in process flow. The underpinning logic behind EvenTracker SA method is capturing the cause-effect relationships between triggers (input variables) and events (output variables) at a specified period of time determined by an expert. The approach does not require estimating data distribution of any kind. Neither the performance model requires execution beyond the real-time. The proposed EvenTracker sensitivity analysis method has the lowest computational complexity compared with other popular sensitivity analysis methods. For proof of concept, a three tier data integration system was designed and developed by using National Instruments’ LabVIEW programming language, Rockwell Automation’s Arena simulation and modelling software, and OPC data communication software. A laboratory-based conveyor system with 29 sensors was installed to simulate a typical shop floor production line. In addition, EvenTracker SA method has been implemented on the data extracted from 28 sensors of one manufacturing line in a real factory. The experiment has resulted 14% of the input variables to be unimportant for evaluation of model outputs. The method proved a time efficiency gain of 52% on the analysis of filtered system when unimportant input variables were not sampled anymore. The EvenTracker SA method compared to Entropy-based SA technique, as the only other method that can be used for real-time purposes, is quicker, more accurate and less computationally burdensome. Additionally, theoretic estimation of computational complexity of SA methods based on both structural complexity and energy-time analysis resulted in favour of the efficiency of the proposed EvenTracker SA method. Both laboratory and factory-based experiments demonstrated flexibility and efficiency of the proposed solution.The Engineering and Physical Sciences Research Council

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Indoor Localization Using Channel State Information with Regression Artificial Neural Network

RÉSUMÉ Dans cette recherche, les informations sur l'état du canal (CSI) sont utilisées pour localiser les stations mobiles dans un environnement intérieur. À cette fin, deux ordinateurs portables équipés de la carte Intel Wireless Wi-Fi Wireless Link 5300 disponible dans le commerce sont utilisés. Les informations CSI sont collectées en établissant une connexion sans fil entre deux machines de plus de 200, 70 et 52 points de référence (RP) aux sixième, cinquième et troisième étages respectivement, dans l’immeuble Lassonde de Polytechnique Montréal servant de banc d’essai expérimental. Différentes approches de localisation sont étudiées et comparées les unes aux autres en termes de précision de localisation. Dans la première approche, les CSI collectés alimentent directement le réseau de neurones artificiels (RNA) en tant que caractéristiques d’entrée et le RNA appris est utilisé en tant qu’algorithme de correspondance du modèle afin de prédire la position de l’utilisateur. La deuxième approche consiste à appliquer à l’entrée de RNA les paramètres pertinents du canal extrait représentant le nombre réduit d’entités à l’entrée de RNA. Enfin, une exploration est effectuée pour trouver la meilleure configuration de couches cachées et de facteurs d'étalement pour les réseaux Perceptron multicouche (MLP) et Réseaux de neurones à régression générale (GRNN), respectivement.----------ABSTRACT In this research, the Channel State Information (CSI) is leveraged to locate mobile stations in an indoor environment. For this purpose, two laptops equipped with the off-the-shelf Intel Wi-Fi Wireless Link 5300 (NIC card) are used. CSI information is collected by establishing a wireless connection between two machines over 200, 70 and 52 reference points (RP) on sixth, fifth, and third floors respectively, in Lassonde building of Polytechnique Montreal as the experimental testbed. Different geolocation approaches are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are directly fed to the artificial neural network (ANN) as input features and the learned ANN is used as the patternmatching algorithm in order to predict the user’s location. The second approach consists in applying at the input of the ANN the extracted channel relevant parameters representing the reduced number of features at the input of ANN. Finally, exploration is performed to find the best configuration of hidden layers and spread factors for Multilayer Perceptron (MLPs) and General Regression Neural Networks (GRNNs), respectively