Pervasive service discovery in low-power and lossy networks

Pervasive Service Discovery (SD) in Low-power and Lossy Networks (LLNs) is expected to play a major role in realising the Internet of Things (IoT) vision. Such a vision aims to expand the current Internet to interconnect billions of miniature smart objects that sense and act on our surroundings in a way that will revolutionise the future. The pervasiveness and heterogeneity of such low-power devices requires robust, automatic, interoperable and scalable deployment and operability solutions. At the same time, the limitations of such constrained devices impose strict challenges regarding complexity, energy consumption, time-efficiency and mobility. This research contributes new lightweight solutions to facilitate automatic deployment and operability of LLNs. It mainly tackles the aforementioned challenges through the proposition of novel component-based, automatic and efficient SD solutions that ensure extensibility and adaptability to various LLN environments. Building upon such architecture, a first fully-distributed, hybrid pushpull SD solution dubbed EADP (Extensible Adaptable Discovery Protocol) is proposed based on the well-known Trickle algorithm. Motivated by EADPs’ achievements, new methods to optimise Trickle are introduced. Such methods allow Trickle to encompass a wide range of algorithms and extend its usage to new application domains. One of the new applications is concretized in the TrickleSD protocol aiming to build automatic, reliable, scalable, and time-efficient SD. To optimise the energy efficiency of TrickleSD, two mechanisms improving broadcast communication in LLNs are proposed. Finally, interoperable standards-based SD in the IoT is demonstrated, and methods combining zero-configuration operations with infrastructure-based solutions are proposed. Experimental evaluations of the above contributions reveal that it is possible to achieve automatic, cost-effective, time-efficient, lightweight, and interoperable SD in LLNs. These achievements open novel perspectives for zero-configuration capabilities in the IoT and promise to bring the ‘things’ to all people everywhere

Hybrid CoAP-based resource discovery for the Internet of Things

Enabling automatic, efficient and scalable discovery of the resources provided by constrained low-power sensor and actuator networks is an important element to empower the transformation towards the Internet of Things (IoT). To this end, many centralized and distributed resource discovery approaches have been investigated. Clearly, each approach has its own motivations, advantages and drawbacks. In this article, we present a hybrid centralized/distributed resource discovery solution aiming to get the most out of both approaches. The proposed architecture employs the well-known Constrained Application Protocol (CoAP) and features a number of interesting discovery characteristics including scalability, time and cost efficiency, and adaptability. Using such a solution, network nodes can automatically and rapidly detect the presence of Resource Directories (RDs), via a proactive RD discovery mechanism, and perform discovery tasks through them. Nodes may, alternatively, fall back automatically to efficient fully-distributed discovery operations achieved through Trickle-enabled, CoAP-based technics. The effectiveness of the proposed architecture has been demonstrated by formal analysis and experimental evaluations on dedicated IoT platforms

Dynamic Clustering for IoT Key Management in Hostile Application Area

© 2019, Springer Nature Switzerland AG. The IoT development area has drawn the attention of nowadays researchers, some of them made assumptions regarding the use of clustering in their key management schemes. For example, in CL-EKM (Certificateless Effective Key Management) protocol, cluster-heads are assumed to be with high-processing capabilities and deployed within a grid topology. In fact, this is only possible in a controlled environment. In a hostile environment, such as battlefields, this assumption cannot be satisfied. In this work, an enhancement of the CL-EKM scheme has been proposed by introducing a distributed clustering algorithm. The performance of the implemented and enhanced system proved our assumptions

CPU-Based Data Acquisition in Assessing the Impact of Inclination on Solar Panels

A data logging system has been deployed to monitor two solar panels positioned at distinct inclination angles. This system records crucial parameters such as current, voltage, solar radiation incident on the panels, and panel temperatures. Comprising an Arduino microcontroller, a current sensor, a current and voltage sensor, and a Memory Card, the data logger captures and stores data in .txt files at 20-minute intervals. Employing a real-time acquisition system, the obtained results indicate that the data logger effectively archives and presents a wealth of information about solar panel characteristics. Notably, the data reveals superior performance of the solar panels at a 35-degree tilt angle compared to 32 degrees during April in the Ouargla region of Algeria

Genetic background modulates phenotypic expressivity in OPA1 mutated mice, relevance to DOA pathogenesis

Dominant optic atrophy (DOA) is mainly caused by OPA1 mutations and is characterized by the degeneration of retinal ganglion cells (RGCs), whose axons form the optic nerve. The penetrance of DOA is incomplete and the disease is marked by highly variable expressivity, ranging from asymptomatic patients to some who are totally blind or who suffer from multisystemic effects. No clear genotype–phenotype correlation has been established to date. Taken together, these observations point toward the existence of modifying genetic and/or environmental factors that modulate disease severity. Here, we investigated the influence of genetic background on DOA expressivity by switching the previously described DOA mouse model bearing the c.1065 + 5G → A Opa1 mutation from mixed C3H; C57BL/6 J to a pure C57BL/6 J background. We no longer observed retinal and optic nerve abnormalities; the findings indicated no degeneration, but rather a sex-dependent negative effect on RGC connectivity. This highlights the fact that RGC synaptic alteration might precede neuronal death, as has been proposed in other neurodegenerative diseases, providing new clinical considerations for early diagnosis as well as a new therapeutic window for DOA. Furthermore, our results demonstrate the importance of secondary genetic factors in the variability of DOA expressivity and offer a model for screening for aggravating environmental and genetic factors

On the application of contextual IoT service discovery in Information Centric Networks

The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several efforts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among different devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios