VSMURF:A Novel Sliding Window Cleaning Algorithm for RFID Networks

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things (IoT) and is used in many areas, such as mobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, which makes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small. This paper proposes a novel sliding window cleaning algorithm called VSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag’s speed is low or high. In particular, if the velocity parameter is set to 2 m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks

Review of the Strategic Importance of RFID data Concept for Examination Management Process

The main goal of the researcher in this study is to re-examine the RFID data concept from a new point of view The preponderance studies on RFID data concept have focused on substantial adoption in different sector In this study the investigator has tried to shift the focus to not only adoption but to the relevance of adoption in the management of examination and this has led to the understanding and conversation on the topic of an Automatic and Data Capture Technology AIDCT like RFID dat

A Survey on Multihop Ad Hoc Networks for Disaster Response Scenarios

Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure.The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to differentad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs.This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios.It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios.In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm

Indoor Localization System Based on Bluetooth Low Energy for Museum Applications

In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m

Missing tags detection algorithm for radio frequency identification (RFID) data stream

RFID technology is a radio frequency identification services that provide a reader reading the information of items from the tags. Nowadays, RFID system is rapidly become more common in our live because it cheaper and smaller to be track, trace and identify the items. However, missing tag detection in RFID can occur due to RFID operating environment such as signal collisions and interferences. Missing tags also called as false negative reads is a tag that is present but it cannot be read by the nearby reader. The consequences of this problem can be enormous to business, as it will cause the system to report incorrect data due to an incorrect number of tags being detected. In fact, the performance of RFID missing tag detection is largely affected by uncertainty, which should be considered in the detecting process phase to minimize its negative impact. Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). AC complement algorithm was used to compare the different in each set of data. Meanwhile, DFR algorithm was used to identify the false negative read that present in the set of data. There are many approaches has been proposed to include Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. This algorithm development has been guided by methodology in four stages. There stages including data preparation, simulation design, detecting false negative read strategy and performance measurement. MTDA can perform well in detecting false negative read with 100% detected in 3.25 second. This performance shows that the algorithm performs well in execution time in detecting false negative reads. In conclusion, it will give insight on the current challenges and open up to new solution to solve the problem of missing tag detection

Cost and Lightweight Modeling Analysis of RFID Authentication Protocols in Resource Constraint Internet of Things

Internet of Things (IoT) is a pervasive environment to interconnect the things like: smart objects, devices etc. in a structure like internet. Things can be interconnected in IoT if these are uniquely addressable and identifiable. Radio Frequency Identification (RFID) is one the important radio frequency based addressing scheme in IoT. Major security challenge in resource constraint RFID networks is how to achieve traditional CIA security i.e. Confidentiality, Integrity and Authentication. Computational and communication costs for Lightweight Mutual Authentication Protocol (LMAP), RFID mutual Authentication Protocol with Permutation (RAPP) and kazahaya authentication protocols are analyzed. These authentication protocols are modeled to analyze the delays using lightweight modeling language. Delay analysis is performed using alloy model over LMAP, RAPP and kazahaya authentication protocols where one datacenter (DC) is connected to different number of readers (1,5 or 10) with connectivity to 1, 5 or 25 tags associated with reader and its results show that for LMAP delay varies from 30-156 msec, for RAPP from 31-188 while for kazahaya from 61-374 msec. Further, performance of RFID authentication protocols is analyzed for group construction through more than one DC (1,5 or 10) with different number of readers (10, 50 or 100) and tags associated with these readers (50, 500, 1000) and results show that DC based binary tree topology with LMAP authentication protocol is having a minimum delay for 50 or 100 readers. Other authentication protocols fail to give authentication results because of large delays in the network. Thus, RAPP and Kazahaya are not suitable for scenarios where there is large amount of increase in number of tags or readers

RFID data reliability optimizer based on two dimensions bloom filter

Radio Frequency Identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. It provides several features such as to monitor, to identify and to track specific item hidden in a large group of objects in a short range of time. RFID system uses radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removed to ensure reliability of information produced from the data streams. A small occurrence of false positive can change the whole information, while duplicate readings unnecessarily occupied storage and processing resources. Many approaches have been proposed to remove false positive and duplicate readings, but they are done separately. These readings exist in the same data stream and must be removed using a single mechanism only. In this thesis, an efficient approach based on Bloom filters was proposed to remove both noisy and duplicate data from the RFID data streams. The noise and duplicate filter algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. In order to test the algorithm, synthetic data was generated by using Poisson distribution. The simulation results show that our proposed approach outperformed other existing approaches in terms of data reliability

Système de gestion de flux pour l'Internet des objets intelligents

The Internet of Things (IoT) is currently characterized by an ever-growing number of networked Things, i.e., devices which have their own identity together with advanced computation and networking capabilities: smartphones, smart watches, smart home appliances, etc. In addition, these Things are being equipped with more and more sensors and actuators that enable them to sense and act on their environment, enabling the physical world to be linked with the virtual world. Specifically, the IoT raises many challenges related to its very large scale and high dynamicity, as well as the great heterogeneity of the data and systems involved (e.g., powerful versus resource-constrained devices, mobile versus fixed devices, continuously-powered versus battery-powered devices, etc.). These challenges require new systems and techniques for developing applications that are able to (i) collect data from the numerous data sources of the IoT and (ii) interact both with the environment using the actuators, and with the users using dedicated GUIs. To this end, we defend the following thesis: given the huge volume of data continuously being produced by sensors (measurements and events), we must consider (i) data streams as the reference data model for the IoT and (ii) continuous processing as the reference computation model for processing these data streams. Moreover, knowing that privacy preservation and energy consumption are increasingly critical concerns, we claim that all the Things should be autonomous and work together in restricted areas as close as possible to the users rather than systematically shifting the computation logic into powerful servers or into the cloud. For this purpose, our main contribution can be summarized as designing and developing a distributed data stream management system for the IoT. In this context, we revisit two fundamental aspects of software engineering and distributed systems: service-oriented architecture and task deployment. We address the problems of (i) accessing data streams through services and (ii) deploying continuous processing tasks automatically, according to the characteristics of both tasks and devices. This research work lead to the development of a middleware layer called Dioptase, designed to run on the Things and abstract them as generic devices that can be dynamically assigned communication, storage and computation tasks according to their available resources. In order to validate the feasability and the relevance of our work, we implemented a prototype of Dioptase and evaluated its performance. In addition, we show that Dioptase is a realistic solution which can work in cooperation with legacy sensor and actuator networks currently deployed in the environment.L'Internet des objets (ou IdO) se traduit à l'heure actuelle par l'accroissement du nombre d'objets connectés, c'est-à-dire d'appareils possédant une identité propre et des capacités de calcul et de communication de plus en plus sophistiquées : téléphones, montres, appareils ménagers, etc. Ces objets embarquent un nombre grandissant de capteurs et d'actionneurs leur permettant de mesurer l'environnement et d'agir sur celui-ci, faisant ainsi le lien entre le monde physique et le monde virtuel. Spécifiquement, l'Internet des objets pose plusieurs problèmes, notamment du fait de sa très grande échelle, de sa nature dynamique et de l'hétérogénéité des données et des systèmes qui le composent (appareils puissants/peu puissants, fixes/mobiles, batteries/alimentations continues, etc.). Ces caractéristiques nécessitent des outils et des méthodes idoines pour la réalisation d'applications capables (i) d'extraire des informations utiles depuis les nombreuses sources de données disponibles et (ii) d'interagir aussi bien avec l'environnement, au moyen des actionneurs, qu'avec les utilisateurs, au moyen d'interfaces dédiées. Dans cette optique, nous défendons la thèse suivante : en raison de la nature continue des données (mesures physiques, évènements, etc.) et leur volume, il est important de considérer (i) les flux comme modèle de données de référence de l'Internet des objets et (ii) le traitement continu comme modèle de calcul privilégié pour transformer ces flux. En outre, étant donné les préoccupations croissantes relatives à la consommation énergétique et au respect de la vie privée, il est préférable de laisser les objets agir au plus près des utilisateurs, si possible de manière autonome, au lieu de déléguer systématiquement l'ensemble des tâches à de grandes entités extérieures telles que le cloud. À cette fin, notre principale contribution porte sur la réalisation d'un système distribué de gestion de flux de données pour l'Internet des objets. Nous réexaminons notamment deux aspects clés du génie logiciel et des systèmes distribués : les architectures de services et le déploiement. Ainsi, nous apportons des solutions (i) pour l'accès aux flux de données sous la forme de services et (ii) pour le déploiement automatique des traitements continus en fonction des caractéristiques des appareils. Ces travaux sont concrétisés sous la forme d'un intergiciel, Dioptase, spécifiquement conçu pour être exécuté directement sur les objets et les transformer en fournisseurs génériques de services de calcul et de stockage.Pour valider nos travaux et montrer la faisabilité de notre approche, nous introduisons un prototype de Dioptase dont nous évaluons les performances en pratique. De plus, nous montrons que Dioptase est une solution viable, capable de s'interfacer avec les systèmes antérieurs de capteurs et d'actionneurs déjà déployés dans l'environnement

Chapter 11 A SURVEY OF RFID DATA PROCESSING

Radio Frequency Identification (RFID) is a new technology which allows a sensor (reader) to read, from a distance, and without line of sight, a unique product identification code (EPC) associated with a tag. Such tags are very useful in inventory management and logistics, because they can be used in order to track the movement and locations of large volumes of items in a cost effective way. This leads to massive streams of noisy data, which can be used in the context of a variety of data management and event processing algorithms. The use of RFID also has a number of privacy challenges associated with it, because a tag on an item being carried by a person, also becomes a unique location tag for that person. Therefore, methods need to to be designed to increase the privacy and security of RFID technology. This chapter will provide a broad overview and survey of a variety of RFID data management, mining and processing techniques. We will also discuss the privacy and security issues associated with the use of RFID technology