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

State-of-the-Art Sensors Technology in Spain 2015: Volume 1

This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

Key management for wireless sensor network security

Wireless Sensor Networks (WSNs) have attracted great attention not only in industry but also in academia due to their enormous application potential and unique security challenges. A typical sensor network can be seen as a combination of a number of low-cost sensor nodes which have very limited computation and communication capability, memory space, and energy supply. The nodes are self-organized into a network to sense or monitor surrounding information in an unattended environment, while the self-organization property makes the networks vulnerable to various attacks.Many cryptographic mechanisms that solve network security problems rely directly on secure and efficient key management making key management a fundamental research topic in the field of WSNs security. Although key management for WSNs has been studied over the last years, the majority of the literature has focused on some assumed vulnerabilities along with corresponding countermeasures. Specific application, which is an important factor in determining the feasibility of the scheme, has been overlooked to a large extent in the existing literature.This thesis is an effort to develop a key management framework and specific schemes for WSNs by which different types of keys can be established and also can be distributed in a self-healing manner; explicit/ implicit authentication can be integrated according to the security requirements of expected applications. The proposed solutions would provide reliable and robust security infrastructure for facilitating secure communications in WSNs.There are five main parts in the thesis. In Part I, we begin with an introduction to the research background, problems definition and overview of existing solutions. From Part II to Part IV, we propose specific solutions, including purely Symmetric Key Cryptography based solutions, purely Public Key Cryptography based solutions, and a hybrid solution. While there is always a trade-off between security and performance, analysis and experimental results prove that each proposed solution can achieve the expected security aims with acceptable overheads for some specific applications. Finally, we recapitulate the main contribution of our work and identify future research directions in Part V

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

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Distributed computing in space-based wireless sensor networks

This thesis investigates the application of distributed computing in general and wireless sensor networks in particular to space applications. Particularly, the thesis addresses issues related to the design of "space-based wireless sensor networks" that consist of ultra-small satellite nodes flying together in close formations. The design space of space-based wireless sensor networks is explored. Consequently, a methodology for designing space-based wireless sensor networks is proposed that is based on a modular architecture. The hardware modules take the form of 3-D Multi-Chip Modules (MCM). The design of hardware modules is demonstrated by designing a representative on-board computer module. The onboard computer module contains an FPGA which includes a system-on-chip architecture that is based on soft components and provides a degree of flexibility at the later stages of the design of the mission.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Redes inalámbricas de sensores: Una nueva arquitectura eficiente y robusta basada en jerarquía dinámica de grupos

Las Redes Inalámbricas de Sensores (RIS) gozan de un gran auge hoy en día, habiendo sido identificadas como una de las tecnologías más prometedoras por diversos analistas tecnológicos y revistas especializadas, debido a que dan respuesta a las exigencias actuales referentes al establecimiento de redes que cubran necesidades de comunicación de forma flexible -en tiempo y espacio- y autónoma -autoconfiguración e independencia de una estructura fija-. La posibilidad de implementar dispositivos de bajo coste y elevada duración capaces de obtener información del entorno y reenviarla de forma inalámbrica a un centro de coordinación ofrece posibilidades inimaginables en multitud de aplicaciones. En la mayoría de las aplicaciones se pretende que los nodos no requieran mantenimiento, explotando el concepto de nodos de "usar y tirar", puesto que una vez desplegados no son recuperables. En este entorno, los mayores desafíos se encuentran en minimizar dos factores fundamentales: coste y consumo, maximizando el tiempo de servicio. En los últimos años se han propuesto numerosos protocolos para RIS. Sin embargo, la mayoría de estas propuestas, o bien realizan asunciones poco realistas, o bien resultan poco escalables, lo cual se traduce en aproximaciones poco prácticas. Por otro lado, hasta la fecha se han obviado características deseables como tolerancia a fallos, seguridad y acotación de tiempos en las comunicaciones, generalmente no consideradas en la inmensa mayoría de los protocolos existentes, y cuando lo son, se obtienen sacrificando la eficiencia energética. En la presente tesis se ha propuesto una novedosa solución integral orientada principalmente a reducir el consumo de energía. La nueva arquitectura denominada EDETA (Energy-efficient aDaptative hiErarchical and robusT Architecture) es además escalable, apropiada tanto para RIS homogéneas como heterogéneas -cada vez con mayor auge-, auto-configurable, soporta de forma transparente múltiples sumideros, y proporciona características como tolerancia a fallos y tiempos acotados, sin degradar las prestaciones de la red. La arquitectura propuesta está basada en una jerarquía de dos niveles, el nivel inferior basado en clústeres, gobernado por un protocolo interno del clúster denominado Intra- Cluster-Communication y el nivel superior formado por un árbol dinámico de nodos líderes de clúster que ejecutan un protocolo entre clústeres denominado Inter-Cluster-Routing.Capella Hernández, JV. (2010). Redes inalámbricas de sensores: Una nueva arquitectura eficiente y robusta basada en jerarquía dinámica de grupos [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8417Palanci

Provision of adaptive and context-aware service discovery for the Internet of Things

The IoT concept has revolutionised the vision of the future Internet with the advent of standards such as 6LoWPAN making it feasible to extend the Internet into previously isolated environments, e.g., WSNs. The abstraction of resources as services, has opened these environments to a new plethora of potential applications. Moreover, the web service paradigm can be used to provide interoperability by offering a standard interface to interact with these services to enable WoT paradigm. However, these networks pose many challenges, in terms of limited resources, that make the adaptability of existing IP-based solutions infeasible. As traditional service discovery and selection solutions demand heavy communication and use bulky formats, which are unsuitable for these resource-constrained devices incorporating sleep cycles to save energy. Even a registry based approach exhibits burdensome traffic in maintaining the availability status of the devices. The feasible solution for service discovery and selection is instrumental to enable the wide application coverage of these networks in the future. This research project proposes, TRENDY, a new compact and adaptive registry-based SDP with context awareness for the IoT, with more emphasis given to constrained networks, e.g., 6LoWPAN It uses CoAP-based light-weight and RESTful web services to provide standard interoperable interfaces, which can be easily translated from HTTP. TRENDY's service selection mechanism collects and intelligently uses the context information to select appropriate services for user applications based on the available context information of users and services. In addition, TRENDY introduces an adaptive timer algorithm to minimise control overhead for status maintenance, which also reduces energy consumption. Its context-aware grouping technique divides the network at the application layer, by creating location-based groups. This grouping of nodes localises the control overhead and provides the base for service composition, localised aggregation and processing of data. Different grouping roles enable the resource-awareness by offering profiles with varied responsibilities, where high capability devices can implement powerful profiles to share the load of other low capability devices. Thus, it allows the productive usage of network resources. Furthermore, this research project proposes APPUB, an adaptive caching technique, that has the following benefits: it allows service hosts to share their load with the resource directory and also decreases the service invocation delay. The performance of TRENDY and its mechanisms is evaluated using an extensive number of experiments performed using emulated Tmote sky nodes in the COOJA environment. The analysis of the results validates the benefit of performance gain for all techniques. The service selection and APPUB mechanisms improve the service invocation delay considerably that, consequently, reduces the traffic in the network. The timer technique consistently achieved the lowest control overhead, which eventually decreased the energy consumption of the nodes to prolong the network lifetime. Moreover, the low traffic in dense networks decreases the service invocations delay, and makes the solution more scalable. The grouping mechanism localises the traffic, which increases the energy efficiency while improving the scalability. In summary, the experiments demonstrate the benefit of using TRENDY and its techniques in terms of increased energy efficiency and network lifetime, reduced control overhead, better scalability and optimised service invocation time