scholarly journals Cooperative Demand Response Framework for a Smart Community Targeting Renewables: Testbed Implementation and Performance Evaluation

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2910
Author(s):  
Carlos Cruz ◽  
Esther Palomar ◽  
Ignacio Bravo ◽  
Alfredo Gardel
Keyword(s):  
Performance Evaluation ◽  
Demand Response ◽  
Key Management ◽  
Operation Time ◽  
Time Frame ◽  
Data Encryption ◽  
Security And Privacy ◽  
Raspberry Pi ◽  
Green Consumption ◽  
Energy Chain

Demand response (DR) is emerging as the workhorse of achieving energy efficiency and reducing our carbon footprint, which persists as a major challenge amongst all the different energy-chain players, i.e., the utility providers, policy makers, consumers, and the technology sector. For instance, the Internet-of-Things (IoT) paradigm and network-enabled appliances/devices have escalated the expectations of what technology could do for the acceptance of DR programs. In this work, we design, deploy on a scalable pilot testbed, and evaluate a collaboration-based approach to the demand-side management of a community of electricity consumers that jointly targets green consumption. The design of the framework architecture is centralized via the so-called aggregator, which optimizes the demand scheduled by consumers along with their time frame preferences towards the maximization of the consumption of renewables. On the pilot, we opt for lightweight, yet efficient platforms such as Raspberry Pi boards, and evaluate them over a series of network protocols, i.e., MQTT-TLS and CoAP-DTLS, paying special attention to the security and privacy of the communications over Z-Wave, ZigBee, and WiFi. The experiments conducted are configured using two active Living Labs datasets from which we extract three community scenarios that vary according to the flexibility or rigidity of the appliances’ operation time frame demand. During the performance evaluation, processing and communication overheads lie within feasible ranges, i.e., the aggregator requires less than 2 s to schedule a small consumer community with four appliances, whereas the latency of its link to households’ controllers adds less than 100 ms. In addition, we demonstrate that our implementations running over WiFi links and UDP sockets on Raspberry Pi 4 boards are fast, though insecure. By contrast, secure CoAP (with DTLS) offers data encryption, automatic key management, and integrity protection, as well as authentication with acceptable overheads.

A Clinical Observational Study on the Application of Enhanced Recovery After Laparoscopic Pancreaticoduodenectomy

2021 ◽  
Author(s):  
Rui Liao ◽  
Ping Che ◽  
Jun-Cai Li ◽  
Jie Chen ◽  
Xiong Yan
Keyword(s):  
Enhanced Recovery ◽  
Operation Time ◽  
Perioperative Period ◽  
Medical Costs ◽  
Time Frame ◽  
Laparoscopic Pancreaticoduodenectomy ◽  
Readmission Rates ◽  
Retrospective Clinical Study ◽  
Previously Treated ◽  
The Impact

Abstract Background: The safety and feasibility of enhanced recovery after surgery (ERAS) for laparoscopic pancreaticoduodenectomy (LPD) are unclear. The aim of this retrospective clinical study was to evaluate the impact of ERAS protocols for LPD.Methods: Between March 2016 and December 2018, a total of 34 consecutive patients with ERAS for LPD were prospectively enrolled and compared with 68 consecutive patients previously treated for non-ERAS after LPD during an equal time frame. The intraoperative and postoperative data were collected and comparatively analyzed. Results: The mean length of postoperative hospital stay (15.8±3.4 and 23.1±5.1 days, P<0.001) and total medical costs (¥14.3±4.8 x104 and ¥15.8±4.9 x104, P=0.017) were reduced significantly in ER group than those in non-ER group. The operation time (462.7±117.0 vs 450.9±109.8 min, P=0.627) and intraoperative blood loss (523.5±270.0 vs 537.5±241.8 mL, P=0.800) were similar in the two groups. The complications of patients with ERAS protocols were not increased (P>0.05). No difference in mortality and readmission rates was found.Conclusions: The ERAS is safe and effective in the perioperative period of LPD. It could effectively reduce the length of postoperative stay and medical costs, and does not increase the incidence of postoperative complications.

A Study on Cloud Storage Security Method Using Data Classification

2021 ◽  
Vol 23 (09) ◽  
pp. 1105-1121
Author(s):  
Dr. Ashish Kumar Tamrakar ◽  
◽  
Dr. Abhishek Verma ◽  
Dr. Vishnu Kumar Mishra ◽  
Dr. Megha Mishra ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Storage Systems ◽  
Data Classification ◽  
Data Encryption ◽  
Security Requirements ◽  
Security And Privacy ◽  
Security Level ◽  
Distributed Resources ◽  
Storage Security ◽  
Public Data

Cloud computing is a new model for providing diverse services of software and hardware. This paradigm refers to a model for enabling on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal service provider interaction .It helps the organizations and individuals deploy IT resources at a reduced total cost. However, the new approaches introduced by the clouds, related to computation outsourcing, distributed resources and multi-tenancy concept, increase the security and privacy concerns and challenges. It allows users to store their data remotely and then access to them at any time from any place .Cloud storage services are used to store data in ways that are considered cost saving and easy to use. In cloud storage, data are stored on remote servers that are not physically known by the consumer. Thus, users fear from uploading their private and confidential files to cloud storage due to security concerns. The usual solution to secure data is data encryption, which makes cloud users more satisfied when using cloud storage to store their data. Motivated by the above facts; we have proposed a solution to undertake the problem of cloud storage security. In cloud storage, there are public data that do not need any security measures, and there are sensitive data that need applying security mechanisms to keep them safe. In that context, data classification appears as the solution to this problem. The classification of data into classes, with different security requirements for each class is the best way to avoid under security and over security situation. The existing cloud storage systems use the same Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 9, September – 2021 Page-1105 key size to encrypt all data without taking into consideration its confidentiality level. Treating the low and high confidential data with the same way and at the same security level will add unnecessary overhead and increase the processing time. In our proposal, we have combined the K-NN (K Nearest Neighbors) machine learning method and the goal programming decision-making method, to provide an efficient method for data classification. This method allows data classification according to the data owner security needs. Then, we introduce the user data to the suitable security mechanisms for each class. The use of our solution in cloud storage systems makes the data security process more flexible, besides; it increases the cloud storage system performance and decreases the needed resources, which are used to store the data.

Healthcare SaaS Based on a Data Model With Built-In Security and Privacy

2019 ◽  
pp. 744-759 ◽  
Author(s):  
Ruchika Asija ◽  
Rajarathnam Nallusamy
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Model ◽  
Health Data ◽  
Software As A Service ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Access Controls ◽  
Software And Hardware ◽  
Security Levels

Cloud computing is a major technology enabler for providing efficient services at affordable costs by reducing the costs of traditional software and hardware licensing models. As it continues to evolve, it is widely being adopted by healthcare organisations. But hosting healthcare solutions on cloud is challenging in terms of security and privacy of health data. To address these challenges and to provide security and privacy to health data on the cloud, the authors present a Software-as-a-Service (SaaS) application with a data model with built-in security and privacy. This data model enhances security and privacy of the data by attaching security levels in the data itself expressed in the form of XML instead of relying entirely on application level access controls. They also present the performance evaluation of their application using this data model with different scaling indicators. To further investigate the adoption of IT and cloud computing in Indian healthcare industry they have done a survey of some major hospitals in India.

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

scholarly journals Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1207 ◽  
Author(s):  
Lei Hang ◽  
Do-Hyeun Kim
Keyword(s):  
Access Control ◽  
Comprehensive Evaluation ◽  
Runtime Verification ◽  
Security And Privacy ◽  
Raspberry Pi ◽  
Task Management ◽  
Business Logic ◽  
Blockchain Technology ◽  
Smart Contract ◽  
Networking Technologies

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.

Performance Evaluation of IEEE 1588 Protocol Using Raspberry Pi over WLAN

2018 ◽  
Author(s):  
Irfan Allahi ◽  
Bilal Khan ◽  
Aamir Sohail Nagra ◽  
Rabbia Idrees ◽  
Shahid Masud
Keyword(s):  
Performance Evaluation ◽  
Raspberry Pi ◽  
Ieee 1588
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