Dynamic Consensus: Increasing Blockchain Adaptability to Enterprise Applications

Decentralization powered by blockchain is validated for its capability to build trust like no other computational system before. The evolution of blockchain models has opened new use-cases that are becoming operational in many industry fields such as: energy, healthcare, banking, cross-border trade, aerospace, supply chain, and others. The core component of a decentralized architecture is the consensus algorithm - the set of rules that ensures an automated and fair agreement between the actors in the same network. Classic consensus algorithms are tailored to solve specific problems, but in an open ecosystem, each business case is unique and needs a certain level of customization. This paper introduces a new meta-consensus model called Dynamic Consensus, an architecture extension that allows multiple, complementary, consensus algorithms to run on the same platform. While classic consensus mechanisms are more appropriate for public or private systems (narrow set of rules), a dynamic approach would fit better for federated business consortiums (more rules and higher need for adaptability). The model is illustrated and analyzed as an ongoing experimental feature that can be added to enterprise blockchains designed to operate in cross-domain environments

Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World

© 1998-2012 IEEE. The 'Smart World' envisioned by technology will be achieved by the penetration of intelligence into ubiquitous things, including physical objects, cyber-entities, social-elements or individuals, and human thinking. The development of Smart World is enabled by diverse applications of wireless sensor networks (WSNs) into those components identified as things. Such a smart-world will have features controlled significantly by the location information. Control and Policy information of Smart World services, often addressed as location-based services (LBSs), are governed by location data. Localization thus becomes the key enabling technology for Smart World facilities. It is generally classified as active and passive techniques in nature. Active localization is a widely adopted localization scheme where the target is detected and tracked carries a tag or attached device. The other category, Passive methods, defines targets to be localized as free of carrying a tag or device, hence also referred to as device-free localization (DFL) or sensor-less localization. The passive approach is a well suited for the development of diverse smart world applications with ubiquitous localization. DFL schemes fall into a wide range of application scenarios within the Smart World ecosystem. A few notable examples are occupancy detection, identity definition, positioning, gesture detection, activity monitoring, pedestrian and vehicle-traffic flow surveillance, security safeguarding, ambient intelligence-based systems, emergency rescue operations, smart work-spaces and patient or elderly monitoring. In this paper, the revolution of DFL technologies have been reviewed and classified comprehensively. Further, the emergence of the Smart World paradigm is analyzed in the context of DFL principles. Moreover, the inherent challenges within the application domains have been extensively discussed and improvement strategies for multi-target localization and counting approach are discussed. Finally, current trends and future research directions have been presented

Towards Internet Scale Quality-of-Experience Measurement with Twitter

Part 3: Evaluation and Experimental Study of Rich Network ServicesInternational audienceAt present, Quality of Experience (QoE) measurements are accomplished by interrogating users for the perceived quality of a service they just have used. Influenced by many factors and often limited by domain or geographical region, this technique has several drawbacks when a general state of QoE for the internet as a whole is prospected. To achieve such a general metric, we leverage user complaints that we observe in real-time in social media. Such approaches have been successfully applied for the monitoring of specific and single services. We aim to extend existing methods in order to create an overall metric, define an internet wide QoE baseline, monitor changes and hence, provide a context for assessing smaller scale findings against a ground truth. The contribution of this work is to demonstrate the feasibility of using social media analysis for generating a meaningful value for quantifying the actual QoE of the internet

Circadian clocks and insulin resistance

Insulin resistance is a main determinant in the development of type 2 diabetes mellitus and a major cause of morbidity and mortality. The circadian timing system consists of a central brain clock in the hypothalamic suprachiasmatic nucleus and various peripheral tissue clocks. The circadian timing system is responsible for the coordination of many daily processes, including the daily rhythm in human glucose metabolism. The central clock regulates food intake, energy expenditure and whole-body insulin sensitivity, and these actions are further fine-tuned by local peripheral clocks. For instance, the peripheral clock in the gut regulates glucose absorption, peripheral clocks in muscle, adipose tissue and liver regulate local insulin sensitivity, and the peripheral clock in the pancreas regulates insulin secretion. Misalignment between different components of the circadian timing system and daily rhythms of sleep–wake behaviour or food intake as a result of genetic, environmental or behavioural factors might be an important contributor to the development of insulin resistance. Specifically, clock gene mutations, exposure to artificial light–dark cycles, disturbed sleep, shift work and social jet lag are factors that might contribute to circadian disruption. Here, we review the physiological links between circadian clocks, glucose metabolism and insulin sensitivity, and present current evidence for a relationship between circadian disruption and insulin resistance. We conclude by proposing several strategies that aim to use chronobiological knowledge to improve human metabolic health