scholarly journals IoT Notary : Attestable Sensor Data Capture in IoT Environments

2022 ◽  
Vol 3 (1) ◽  
pp. 1-30
Author(s):  
Nisha Panwar ◽  
Shantanu Sharma ◽  
Guoxi Wang ◽  
Sharad Mehrotra ◽  
Nalini Venkatasubramanian ◽  
...  
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Third Party ◽  
Sensor Data ◽  
Published Data ◽  
Resource Limited ◽  
User Data ◽  
Data Capturing ◽  
Iot Devices ◽  
The University

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Approach in the development of lightweight microservice architecture for small data center monitoring system

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Milan Malić ◽  
Dalibor Dobrilović ◽  
Dušan Malić ◽  
Željko Stojanov
Keyword(s):  
Software Architecture ◽  
Real Time ◽  
Data Center ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Data ◽  
Test Results ◽  
Iot Devices ◽  
Sqlite Database ◽  
Microservice Architecture

In the past decade there is a significant trend of implementing IoT technologies and standards in different industries. This trend brings cost reductions to the companies and other benefits as well. One of the main benefits is real-time and uniform data collection. The data are transferred using diverse communication protocols, from the sensor nodes to the centralized application. So far, current approaches in developing applications are not proved itself to be efficient enough in scenarios when a significant amount of data needs to be stored and analyzed. The focus of this paper is on development of software architecture suitable for usage in Internet of Things (IoT) systems where the larger amount of data can be processed in real-time. The software architecture is developed in order to support the sensor network for monitoring the small data center and it is based on microservices. Besides the system and its architecture, this paper presents the method of analysis of system performances in real-time environment. The proposal for lightweight microservice architecture, presented in this paper, is developed with .NET Core and RabbitMQ, with the utilization of MongoDB and SQLite databases systems for storing data collected with IoT devices. In this paper, the system evaluation and research results in different stress scenarios are also presented. Because of its complexity, only the most significant segments of architecture will be presented in this paper. The proposed solution showed that proposed lightweight architecture based on microservices could deal with the larger amount of sensor data in the case of using MongoDB. On the other hand, the usage of SQLite database is not recommended due to the lower performances and test results.

scholarly journals Double Cache Approach with Wireless Technology for Preserving User Privacy

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Adnan A. Abi Sen ◽  
Fathy B. Eassa ◽  
Mohammad Yamin ◽  
Kamal Jambi
Keyword(s):  
Access Point ◽  
Location Based Services ◽  
Third Party ◽  
Wireless Technology ◽  
User Privacy ◽  
Improve Performance ◽  
Privacy And Security ◽  
User Data ◽  
Security Levels ◽  
Simulation Results

Several methods use cache for decreasing the number of connections to protect privacy of user data and improve performance in Location Based Services (LBS). Many of these methods require users to trust other users or third parties, which could be servers. An intruder could be disguised as a user or a third party. In this article, we propose a new method, known as “Double Cache Approach”, which uses a pair of caches to reduce the vulnerability of trust between users or third party and offers a vast improvement in privacy and security of user data in healthcare and other applications that use LBS. This approach divides the area into many cells and manages the cooperation among users within two caches at the access point with wireless communication. To demonstrate the superiority, we also provide simulation results of user queries, comparing the proposed method with those using only one cache. We believe that our approach would solve the trust problem optimally, achieve a comprehensive protection for users’ data, and enhance the privacy and security levels.

scholarly journals An Empirical Analysis of using Blockchain Tech-nology with Internet of Things and its Application

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1469-1475
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Third Party ◽  
Time Data ◽  
Privacy And Security ◽  
Third Party Intervention ◽  
Security Issues ◽  
Blockchain Technology ◽  
Real Time Data ◽  
Iot Devices

Blockchain technology uses the cryptographic technique to create expanding list of data records called blocks. Along with transaction and timestamp data, each block holds a hash value obtained using cryptographic technique. Blockchain gains importance for its decentralized data transaction and authorization without the need for third-party intervention. Although, it is mostly used in Finance sector these days, due to its inherent ability to protect data it can be applied to every field of computation especially in fields where data transaction is voluminous. Internet of Things (IoT) is one such area where it involves collection, transfer and processing of real time data from objects, humans and sensors to automate various tasks. Hence, this paper reviews the blockchain technology, and how it can be coupled with IoT to overcome the privacy and security issues. This paper first systematically introduces the concept of blockchain technology, its applications along with the need for IoT devices and its implementation. Finally, it discusses the blockchain based IoT (BIoT) its architecture, advantages, challenges in implementation

scholarly journals Combining Blockchain and IoT: Food-Chain Traceability and Beyond

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3820
Author(s):  
Jacopo Grecuccio ◽  
Edoardo Giusto ◽  
Fabio Fiori ◽  
Maurizio Rebaudengo
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Food Chain ◽  
Industry 4.0 ◽  
Third Party ◽  
Software Framework ◽  
Use Case ◽  
Fish Products ◽  
Main Application ◽  
Iot Devices

Recently, the interest around the Blockchain concept has grown faster and, as a consequence, several studies about the possibility of exploiting such technology in different application domains have been conducted. Most of these studies highlighted the benefits that the use of the blockchain could bring in those contexts where integrity and authenticity of the data are important, e.g., for reasons linked to regulations about consumers’ healthcare. In such cases, it would be important to collect data, coming in real-time through sensors, and then store them in the blockchain, so that they can become immutable and tamper-proof. In this paper, the design and development of a software framework that allows Internet-of-Things (IoT) devices to interact directly with an Ethereum-based blockchain are reported. The proposed solution represents an alternative way for integrating a wide category of IoT devices without relying on a centralized intermediary and third-party services. The main application scenario for which the project has been conceived regards food-chain traceability in the Industry 4.0 domain. Indeed, the designed system has been integrated into the depiction of a use case for monitoring the temperature of fish products within a warehouse and during the delivery process.

Sex (Similarities and) Differences in Friendship Jealousy

2020 ◽  
Author(s):  
Jaimie Krems ◽  
Keelah Williams ◽  
Laureon Allison Watson ◽  
Douglas Kenrick ◽  
Athena Aktipis
Keyword(s):  
Sex Differences ◽  
Cognitive Architecture ◽  
Third Party ◽  
Published Data ◽  
Best Friends ◽  
Adaptive Challenges ◽  
The Face ◽  
Initial Support ◽  
Fitness Benefits ◽  
Material Benefits

Friendships provide material benefits, bolster health, and may help solve adaptive challenges. However, a recurrent obstacle to sustaining those friendships—and thus enjoying many friendship-mediated fitness benefits—is interference from other people. Friendship jealousy may be well-designed for helping both men and women meet the recurrent, adaptive challenge of retaining friends in the face of such third-party interference. Although we thus expect several sex similarities in the general cognitive architecture of friendship jealousy (e.g., it is attuned to friend value), there are also sex differences in friendship structures and historical functions, which might influence the inputs of friendship jealousy (e.g., the value of any one friendship). If so, we should also expect some sex differences in friendship jealousy. Findings from a reanalysis of previously-published data and a new experiment, including both U.S. student and adult community participants (N = 993), provide initial support for three predicted sex differences: women (versus men) report greater friendship jealousy at the prospective loss of best friends to others, men (versus women) report greater friendship jealousy at the prospective loss of acquaintances to others, and men’s (but not women’s) friendship jealousy is enhanced in the context of intergroup contests.

A New User-controlled and Efficient Encrypted Data Sharing Model in Cloud Storage

2019 ◽  
Vol 13 (4) ◽  
pp. 356-363
Author(s):  
Yuezhong Wu ◽  
Wei Chen ◽  
Shuhong Chen ◽  
Guojun Wang ◽  
Changyun Li
Keyword(s):  
Data Sharing ◽  
Data Security ◽  
Cloud Storage ◽  
Screening Program ◽  
Original Data ◽  
Third Party ◽  
Active System ◽  
Encrypted Data ◽  
Active User ◽  
User Data

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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