Lamred : location-aware and privacy preserving multi-layer resource discovery for IoT

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited computation power, the centralized approach leads to some efficiency and security issues. In this paper we proposed a location aware and decentralized multi layer model of resource discovery (LaMRD) in IoT. It allows a resource to be registered publicly or privately, and to be discovered in a decentralized scheme in the IoT network. LaMRD is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. Our proposed model utilizes Distributed Hash Table (DHT) technology to create a p2p scheme of communication among fog nodes. The resources are registered in LaMRD based on their locations which results in a low added overhead in the registration and discovery processes. LaMRD generates a single overlay and it can be generated without specific organizing entity or location based devices. LaMRD guarantees some important security properties and it showed a lower latency comparing to the cloud based and decentralized resource discovery

A Blockchain Application Prototype for the Internet of Things

The emergence of the Internet of things (IoT), associated with the explosion in the number of connected objects, and the growth in user needs, makes the Internet network very complex. IoT objects are diverse and heterogeneous, which requires establishing interoperability and efficient identity management on the one hand. On the other hand, centralized architectures such as cloud-based ones can have overhead and high latency, with a potential risk of failure. Facing these challenges, Blockchain technology, with its decentralized architecture based on a distributed peer-to-peer network, offers a new infrastructure that allows IoT objects to interact reliably and securely. In this paper, a new approach is proposed with a three-layer architecture: layer of sensing and collection of data made up of the IoT network, layer of processing and saving of data exchanges at the Blockchain level, and access and visualization layer via a web interface. The prototype implemented in this study allows all transactions (data exchanges) generated by IoT devices to be recorded and stored on a dedicated Blockchain, assuring the security of IoT objects\u27 communications. This prototype also enables access to and visualization of all data and information, thus enhancing the IoT network\u27s transparency

Choreographies in the wild

We investigate the use of choreographies in distributed scenarios where, as in the real world, mutually distrusting (and possibly dishonest) participants may be unfaithful to their expected behaviour. In our model, each participant advertises its promised behaviour as a contract. Participants may interact through multiparty sessions, created when their contracts allow to synthesise a choreography. We show that systems of honest participants (which always adhere to their contracts) enjoy progress and session fidelity

Multilateral Transparency for Security Markets Through DLT

For decades, changing technology and policy choices have worked to fragment securities markets, rendering them so dark that neither ownership nor real-time price of securities are generally visible to all parties multilaterally. The policies in the U.S. National Market System and the EU Market in Financial Instruments Directive— together with universal adoption of the indirect holding system— have pushed Western securities markets into a corner from which escape to full transparency has seemed either impossible or prohibitively expensive. Although the reader has a right to skepticism given the exaggerated promises surrounding blockchain in recent years, we demonstrate in this paper that distributed ledger technology (DLT) contains the potential to convert fragmented securities markets back to multilateral transparency. Leading markets generally lack transparency in two ways that derive from their basic structure: (1) multiple platforms on which trades in the same security are matched have separate bid/ask queues and are not consolidated in real time (fragmented pricing), and (2) highspeed transfers of securities are enabled by placing ownership of the securities in financial institutions, thus preventing transparent ownership (depository or street name ownership). The distributed nature of DLT allows multiple copies of the same pricing queue to be held simultaneously by a large number of order-matching platforms, curing the problem of fragmented pricing. This same distributed nature of DLT would allow the issuers of securities to be nodes in a DLT network, returning control over securities ownership and transfer to those issuers and thus, restoring transparent ownership through direct holding with the issuer. A serious objection to DLT is that its latency is very high—with each Bitcoin blockchain transaction taking up to ten minutes. To remedy this, we first propose a private network without cumbersome proof-of-work cryptography. Second, we introduce into our model the quickly evolving technology of “lightning networks,” which are advanced two-layer off-chain networks conducting high-speed transacting with only periodic memorialization in the permanent DLT network. Against the background of existing securities trading and settlement, this Article demonstrates that a DLT network could bring multilateral transparency and thus represent the next step in evolution for markets in their current configuration

From subjective reputation to verifiable experiences - augmenting peer-control mechanisms for open service ecosystems

Peer reviewe

Patient Acuity as a Predictor of Length of Hospital Stay and Discharge Disposition After Open Colorectal Surgery

Major areas of concern within the US healthcare system today include the quality and cost of healthcare. Open colorectal surgery patients have a higher prevalence of prolonged length of hospital stay (LOS) than most other types of surgery patients and are likely to be discharged to home care or other healthcare settings (DHCS), both of which contribute to increased costs. The ability to predict which patients are at risk for these outcomes early after open colorectal surgery could prompt nursing interventions aimed at improving quality of care and reducing healthcare costs. Radwin and Fawcett’s Refined Quality Health Outcomes Model served as the conceptual framework for this study. In this retrospective cross sectional study of adult open colorectal surgery patients (N=789), nursing documentation in the electronic health record (EHR) was reused to examine the relationships among patient acuity, LOS, and discharge disposition (DD). At the large Midwest healthcare system where this study took place, a patient acuity software system generated real-time patient acuity scores from discrete nursing assessment data fields in the EHR. This information was being used by unit nurse managers to guide nurse staffing decisions. Patient data were stratified by three discharge diagnostic-related groups (DRG) for colorectal surgeries, DRG 329, 330, and 331, to provide some control for comorbidities and post-operative complications. Multiple regression analysis for each DRG examined how patient acuity and select patient characteristics predicted prolonged LOS. Findings included that having a high patient acuity score on Day 2 or 3 after open colorectal surgery was a significant predictor of prolonged LOS for subjects in each DRG (DRG 329: B=1.985, p\u3c0.05; DRG 330: B=1.956, p\u3c0.01; DRG 331: B=0.967, p\u3c0.01). Logistic regression analysis results also indicated that high patient acuity scores on Day 2 or 3 after surgery significantly predicted DHCS for each DRG (DRG 329: OR=3.65, 95% CI [1.39, 9.59], p\u3c0.05; DRG 330: OR=2.86, 95% CI [1.58, 5.16], p\u3c0.01; DRG 331: OR=8.62, 95% CI [2.04, 39.48], p\u3c0.05). Implications for nursing include the need for further research to examine the use of patient acuity information to support evidence-based clinical decision making to improve healthcare quality and contain costs

Los Angeles Labor Negotiations Study

[Excerpt] Sjoberg Evashenk Consulting and Cornell University have completed a study of the City of Los Angeles’ labor negotiation policies, processes and practices, under contract with the City Controller’s Office. The objectives of the study are to: • Review negotiations executed within the last three years for lessons learned, as well as review negotiations currently underway. • Evaluate and map the City’s current collective bargaining process. • Conduct a nationwide search for promising practices the City could incorporate into the collective bargaining process. • Evaluate the fiscal impacts of labor negotiations. • Evaluate the role of and incentives for each party in the process. • Evaluate the labor-management relationships outside of the bargaining process. • Identify opportunities for improving labor-management relations. Cornell University addressed the City’s current labor relations process and identified areas for improvement or consideration (Sections I and III), while Sjoberg Evashenk Consulting focused on the financial implications of the City’s collective bargaining practices (Section II). Cornell ILR faculty who contributed their time to this study include: Associate Dean Suzanne Bruyere, Marcia Calicchia (Project Lead), Lou Jean Fleron, Professor Emeritus and former Associate Dean Lois S. Gray, Dean Harry Katz, Sally Klingel, Peter Lazes, Tom Quimby, Jane Savage, Rocco Scanza, Scott Sears, and Associate Dean and Vice Provost for Land Grant Affairs Ronald Seeber. Pam Strausser in Cornell’s Office of Human Resources and Mildred Warner in Cornell’s Department of City and Regional Planning also provided invaluable assistance