Smart Bike Sharing System to make the City even Smarter

These last years with the growing population in the smart city demands an efficient transportation sharing (bike sharing) system for developing the smart city. The Bike sharing as we know is affordable, easily accessible and reliable mode of transportation. But an efficient bike sharing capable of not only sharing bike also provides information regarding the availability of bike per station, route business, time/day-wise bike schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. We have been based on our study analysis data to predict regarding the bike's available at stations, bike schedule, a location of the nearest hub where a bike is available etc., reduce the user time and effort

Micro-Evidence on Rent Sharing from Different Perspectives

This article provides evidence of rent sharing from orthogonal directions by exploiting different dimensions in the same data. Taking advantage of a rich matched employer-employee dataset for France over the period 1984-2001, we consistently compare across-industry heterogeneity in rent-sharing parameters derived from three different approaches. The accounting approach and the standard labor economics approach are compatible with distinct labor bargaining settings (right-to-manage, efficient bargaining, labor hoarding) whereas the productivity approach hinges on the assumption of efficient bargaining. Across the different approaches, we evidently find differences in dispersion of the rent-sharing parameter estimates which could be attributable to differences in modeling assumptions and/or data requirements but these estimates lie within a comparable range. We interpret the latter finding as lending empirical support to efficient bargaining as the nature of the bargaining process in France over the considered period.rent sharing, wage equation, production function, matched employer-employee data

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

Focus on sharing individual patient data distracts from other ways of improving trial transparency

The International Committee of Medical Journal Editors (ICMJE) recently reiterated its commitment to improving trial transparency by sharing individual patient data from randomised trials.1 2 But, although sharing individual patient data contributes to transparency, it is not sufficient by itself. Trial transparency requires a data sharing package, which begins with trial registration and contains other elements such as protocols, summary results, and other trial materials. Valuable as sharing individual patient data can be,3 discussion about it has hijacked the broader conversation about data sharing and trial transparency.4-6 For example, we identified 76 articles published in the six leading general medical journals that had “data” and “sharing” in their title and were about clinical trials. In 64 (84%) articles, the content was focused on individual patient data and did not mention any of the other components of trial transparency (see appendix on bmj.com). Much of the discussion has focused on the complexities and practical problems associated with sharing individual patient data and on the processes and systems needed for responsible data sharing.6-9 However, many of the data sharing activities that are needed for trial transparency are not complex. We believe that trying to solve the complex issues around availability of individual patient data should not eclipse or distract from a more pressing problem: the unavailability of even summary data and protocols from all controlled trials. Current estimates are that around 85% of research is avoidably “wasted” because of design flaws, poor conduct, non-publication, and poor reporting.10 Focusing efforts and attention on making individual patient data accessible might paradoxically exacerbate this waste in research. We argue that simpler and more cost efficient activities should be prioritised.</p

Making open data work for plant scientists

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers’ needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity

Sharing in the Rain: Secure and Efficient Data Sharing for the Cloud

Cloud storage has rapidly become a cornerstone of many businesses and has moved from an early adopters stage to an early majority, where we typically see explosive deployments. As companies rush to join the cloud revolution, it has become vital to create the necessary tools that will effectively protect users' data from unauthorized access. Nevertheless, sharing data between multiple users' under the same domain in a secure and efficient way is not trivial. In this paper, we propose Sharing in the Rain – a protocol that allows cloud users' to securely share their data based on predefined policies. The proposed protocol is based on Attribute-Based Encryption (ABE) and allows users' to encrypt data based on certain policies and attributes. Moreover, we use a Key-Policy Attribute-Based technique through which access revocation is optimized. More precisely, we show how to securely and efficiently remove access to a file, for a certain user that is misbehaving or is no longer part of a user group, without having to decrypt and re-encrypt the original data with a new key or a new policy

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.Comment: 38 pages, 10 figure