473 research outputs found
Using Hybrid Query Tree to Cope with Capture Effect in RFID Tag Identification
Tag collision is one of the important issues in RFID systems. Many algorithms were proposed to address this issue. One of these algorithms is Query Tree (QT) which is an effective method. In addition, RFID suffers from Capture Effect (CE). CE occurs when a reader identifies one tag in the presence of a collision. We consider this as a bad phenomenon for QT, because under CE reader will not identify all of collided tags. Besides, CE is good phenomenon for some algorithms like Dynamic Framed Slotted Aloha (DFSA), because it can identify one tag even in collision slots. So we combine QT and DFSA to improve the QT performance, then we evaluate our proposed algorithm, called Hybrid QT, to show that it outperforms other similar algorithm
Intelligent Sensor Networks
In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts
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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
Routing in heterogeneous wireless ad hoc networks
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2008.Includes bibliographical references (p. 135-146).Wireless ad hoc networks are used in several applications ranging from infrastructure monitoring to providing Internet connectivity to remote locations. A common assumption about these networks is that the devices that form the network are homogeneous in their capabilities. However in reality, the networks can be heterogeneous in the capabilities of the devices. The main contribution of this thesis is the identification of issues for efficient communication in heterogeneous networks and the proposed solutions to these issues. The first part of the thesis deals with the issues of unambiguous classification of devices and device identification in ad hoc networks. A taxonomical approach is developed, which allows devices with wide range of capabilities to be classified on the basis of their functionality. Once classified, devices are characterized on the basis of different attributes. An IPv6 identification scheme and two routing services based on this scheme that allow object-object communication are developed. The identification scheme is extended to a multi-addressing scheme for wireless ad hoc networks. These two issues and the developed solutions are applicable to a broad range of heterogeneous networks. The second part of the thesis deals with heterogeneous networks consisting of omnidirectional and directional antennas. A new MAC protocol for directional antennas, request-to-pause-directional-MAC (RTP-DMAC) protocol is developed that solves the deafness issue, which is common in networks with directional antennas. Three new routing metrics, which are extensions to the expected number of transmissions (ETX) metric are developed. The first metric, ETX1, reduces the route length by increasing the transmission power. The routing and MAC layers assume the presence of bidirectional links for their proper operation. However networks with omnidirectional and directional antennas have unidirectional links. The other two metrics, unidirectional-ETX (U-ETX) and unidirectional-ETX1 (U-ETX1), increase the transmission power of the directional nodes so that the unidirectional links appear as bidirectional links at the MAC and the routing layers. The performance of these metrics in different scenarios is evaluated.by Sivaram M.S.L. Cheekiralla.Ph.D
Towards end-to-end security in internet of things based healthcare
Healthcare IoT systems are distinguished in that they are designed to serve human beings, which primarily raises the requirements of security, privacy, and reliability. Such systems have to provide real-time notifications and responses concerning the status of patients. Physicians, patients, and other caregivers demand a reliable system in which the results are accurate and timely, and the service is reliable and secure. To guarantee these requirements, the smart components in the system require a secure and efficient end-to-end communication method between the end-points (e.g., patients, caregivers, and medical sensors) of a healthcare IoT system.
The main challenge faced by the existing security solutions is a lack of secure end-to-end communication. This thesis addresses this challenge by presenting a novel end-to-end security solution enabling end-points to securely and efficiently communicate with each other. The proposed solution meets the security requirements of a wide range of healthcare IoT systems while minimizing the overall hardware overhead of end-to-end communication. End-to-end communication is enabled by the holistic integration of the following contributions.
The first contribution is the implementation of two architectures for remote monitoring of bio-signals. The first architecture is based on a low power IEEE 802.15.4 protocol known as ZigBee. It consists of a set of sensor nodes to read data from various medical sensors, process the data, and send them wirelessly over ZigBee to a server node. The second architecture implements on an IP-based wireless sensor network, using IEEE 802.11 Wireless Local Area Network (WLAN). The system consists of a IEEE 802.11 based sensor module to access bio-signals from patients and send them over to a remote server. In both architectures, the server node collects the health data from several client nodes and updates a remote database. The remote webserver accesses the database and updates the webpage in real-time, which can be accessed remotely.
The second contribution is a novel secure mutual authentication scheme for Radio Frequency Identification (RFID) implant systems. The proposed scheme relies on the elliptic curve cryptography and the D-Quark lightweight hash design. The scheme consists of three main phases: (1) reader authentication and verification, (2) tag identification, and (3) tag verification. We show that among the existing public-key crypto-systems, elliptic curve is the optimal choice due to its small key size as well as its efficiency in computations. The D-Quark lightweight hash design has been tailored for resource-constrained devices.
The third contribution is proposing a low-latency and secure cryptographic keys generation approach based on Electrocardiogram (ECG) features. This is performed by taking advantage of the uniqueness and randomness properties of ECG's main features comprising of PR, RR, PP, QT, and ST intervals. This approach achieves low latency due to its reliance on reference-free ECG's main features that can be acquired in a short time. The approach is called Several ECG Features (SEF)-based cryptographic key generation.
The fourth contribution is devising a novel secure and efficient end-to-end security scheme for mobility enabled healthcare IoT. The proposed scheme consists of: (1) a secure and efficient end-user authentication and authorization architecture based on the certificate based Datagram Transport Layer Security (DTLS) handshake protocol, (2) a secure end-to-end communication method based on DTLS session resumption, and (3) support for robust mobility based on interconnected smart gateways in the fog layer.
Finally, the fifth and the last contribution is the analysis of the performance of the state-of-the-art end-to-end security solutions in healthcare IoT systems including our end-to-end security solution. In this regard, we first identify and present the essential requirements of robust security solutions for healthcare IoT systems. We then analyze the performance of the state-of-the-art end-to-end security solutions (including our scheme) by developing a prototype healthcare IoT system
Identifying Early-Life Behavior to Predict Mothering Ability in Swine Utilizing NUtrack System
Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits include angle rotated (degree), average speed (m/s), distance travelled (m), time spent eating (s), lying lateral (s), lying sternal (s), standing (s), and sitting (s). Final daily activity values were averaged across the period under cameras. Parity one data was collected for all gilts considered. Data were analyzed using linear regression models and odds ratios (R version 4.0.2). GL was significantly impacted by angle rotated (p = 0.03), average speed (p = 0.07), distance travelled (p = 0.05), time spent lying lateral (p = 0.003), and lying sternal (0.02). NBA was significantly impacted by time spent lying lateral (p = 0.01), lying sternal (p = 0.07), and time spent sitting (p = 0.08). NW was significantly impacted by time spent eating (p = 0.09), time spent lying lateral (p = 0.04), and time spent sitting (p = 0.007). Estimated odds ratios showed gilts traveling below average speeds and spending below average time lying sternal were positively associated with below average GL. Gilts spending below average time lying lateral are associated with below average NW. Gilts spending below average time sitting were negatively associated with below average NW. Gilts spending below average time lying sternal were negatively associated with below average HL. This analysis suggests early-life gilt behavior is associated with sow productivity traits of importance. Further examination of the link between behavior and reproductive traits is necessitated. Utilization of the NUtrack video monitoring system to isolate behavioral differences offers potential to aide in selection decisions.
Advisor: Benny Mot
Security of Ubiquitous Computing Systems
The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license
Mining a Small Medical Data Set by Integrating the Decision Tree and t-test
[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI
Exploring the relationship between spatial cognitive ability and movement ecology
Spatial cognitive ability is hypothesised to be a key determinant of animal movement patterns. However, empirical demonstrations linking intra-individual variations in spatial cognitive ability with movement ecology are rare. I reared ~200 simultaneously hatched pheasant chicks per year over three years in standardised conditions without parents, controlling for the confounding effects of experience, maternal influences and age. I tested the chicks on spatial cognitive tasks from three weeks old to obtain measures of inherent, early-life spatial cognitive ability. Each year, I released birds when 10 weeks old into an open-topped enclosure in woodland. Birds dispersed from this enclosure after about one-month. Importantly, all birds were released into the same, novel area simultaneously, thus their experiences and opportunities were standardised. I remotely tracked pheasant movement through either RFID antenna placed under 43 supplementary feeders situated throughout our field site (2016) or by using a novel reverse-GPS tracking system (2017-2018). Spatial cognitive ability, determined through binary spatial discrimination (2016) or a Barnes maze (2017), was related to the diversity of foraging sites an individual used (Chapter 2: 2016). Those with better spatial cognitive ability used a more diverse range of artificial feeders than poor performing counterparts, perhaps to retain a buffer of alternative foraging sites where resource profitability was known. I found no relationship between the timing of daily foraging onset between birds of differing cognitive ability (Chapter 3; 2016), which I had hypothesised to be a consequence of birds developing efficient routes between refuges and feeders. After establishing a reverse GPS system on our field site (Chapter 4: 2017), I collected more detailed information about pheasant movement and found that birds with higher accuracy scores on the cognition tasks initially moved between foraging and resting sites more slowly than inaccurate birds in novel environments, perhaps to gather more detailed information. Accurate birds increased their speed over one month to match the same speed as inaccurate birds. All birds increased the straightness of their routes at a similar rate. Lastly, I found intraspecific differences in the orientation strategy that birds used to solve a dual strategy maze task (Chapter 5: 2018). These differences predicted habitat use after release: birds that utilised landmarks (allocentric strategies) showed less aversion to urban habitats (farm buildings/yards) than egocentric/mixed strategy birds, which is potentially due to the presence of large, stable landmarks within these habitats. In this thesis, I provide several empirical links between spatial cognitive ability and movement ecology across a range of ecological contexts. I suggest that very specific cognitive processes may govern particular movement behaviours and that there is not one overarching general spatial ability.European Commissio
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