SHRP - Secure Hybrid Routing Protocol over Hierarchical Wireless Sensor Networks

A data collection via secure routing in wireless sensor networks (WSNs) has given attention to one of security issues. WSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. Thus, how to gather data securely and efficiently based on routing protocol is an important issue of WSNs. In this paper, we propose a secure hybrid routing protocol, denoted by SHRP, which combines the geographic based scheme and hierarchical scheme. First of all, SHRP differentiates sensor nodes into two categories, nodes with GPS (NG) and nodes with antennas (NA), to put different roles. After proposing a new clustering scheme, which uses a new weight factor to select cluster head efficiently by using energy level, center weight and mobility after forming cluster, we propose routing scheme based on greedy forwarding. The packets in SHRP are protected based on symmetric and asymmetric cryptosystem, which provides confidentiality, integrity and authenticity. The performance analyses are done by using NS2 and show that SHRP could get better results of packet loss rate, delivery ratio, end to end delay and network lifetime compared to the well known previous schemes

On-Node Correlation Based Data Reduction in WSN for Smart Agriculture

International audienceNowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimizing the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network. In this paper, for data reduction, a data correlation and prediction technique is proposed both at the sensor node level and at the sink level. The aim of this approach is to reduce the amount of transmitted data to the sink, depending on the degree of correlation between different parameters. In this work we propose the Pearson Data Correlation and Prediction (PDCP) algorithm to detect this correlation. This data reduction maintains the accuracy of the information while reducing the amount of data sent from the nodes to the sink. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach by reducing the amount of data by a percentage up to 69% while maintaining the accuracy of the information. The humidity values prediction based on the temperature parameter is accurate and the deviation from the real value does not surpass 7% of humidity

K-Predictions Based Data Reduction Approach in WSN for Smart Agriculture

International audienceNowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimising the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network and increases the number of costly interference. Data reduction is one of the solutions for this kind of challenges. In this paper, data correlation is investigated and combined with a data prediction technique in order to avoid sending data that could be retrieved mathematically in the objective to reduce the energy consumed by sensor nodes and the bandwidth occupation. This data reduction technique relies on the observation of the variation of every monitored parameter as well as the degree of correlation between different parameters. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach which reduces the amount of data by a percentage up to 88% while maintaining the accuracy of the information having a standard deviation of 2 degrees for the temperature and 7% for the humidity

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Characterization Of Somatosensation In The Brainstem And The Development Of A Sensory Neuroprosthesis

Innovations in neuroprosthetics have restored sensorimotor function to paralysis patients and amputees. However, to date there is a lack of solutions available to adequately address the needs of spinal cord injury patients (SCI). In this dissertation we develop a novel sensor-brain interface (SBI) that delivers electric microstimulation to the cuneate nucleus (CN) to restore somatosensory feedback in patients with intact limbs. In Chapter II, we develop a fully passive liquid metal antenna using gallium-indium (GaIn) alloy injected in polydimethylsiloxane (PDM) channels to measure forces within the physiological sensitivity of a human fingertip. In Chapter III, we present the first chronic neural interface with the CN in primates to provide access to long-term unit recordings and stimulation. In Chapter IV, we demonstrate that microstimulation to the CN is detectable in a Three Alternative Force Choice Oddity task in awake behaving primates. In Chapter V, we explore the downstream effects of CN stimulation on primary somatosensory cortex, in the context of spontaneous and evoked spindles under sedation. In summary, these findings constitute a proof-of-concept for the sensory half of a bidirectional sensorimotor prosthesis in the CN

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration

Mantenimiento Predictivo: Historia, una guía de implementación y enfoques actuales

Debido al aumento del número de sensores utilizados en las plantas de producción, la posibilidad de obtener datos de estas ha incrementado considerablemente. Esto conlleva la posibilidad de detectar fallos antes de que estos ocurran y futuras paradas que afecten a las plantas de producción. Las tecnologías de mantenimiento predictivo permiten predecir eventos futuros, convirtiéndolas en herramientas para afrontar los retos que surjan en los mercados competitivos. Esta tesis está dividida en cinco partes. La primera, describe el mantenimiento a lo largo de la historia, mientras que la segunda está enfocada en el mantenimiento predictivo. El tercer punto es una guía de implementación de un programa de mantenimiento predictivo para cualquier organización interesada en el tema. Finalmente, las dos últimas partes hacen referencia a los enfoques más comunes en inteligencia artificial donde se explican técnicas importantes como “Artificial Neural Networks” y “Machine Learning”, describiendo algunos ejemplos donde fueron usadas para realizar mantenimiento predictivo.Departamento de Organización de Empresas y Comercialización e Investigación de MercadosHochschule Albstadt-SigmaringenGrado en Ingeniería en Organización Industria