ACCESS CONTROL METHODS FOR INTERNET OF MEDICAL THINGS NETWORKS BASEDON ATTRIBUTE MODELS

The term «Internet of Medical Things» (IoMT ) refers to a set of devices and technologies for remote monitoring of patients’ health using wearable devices. One primary problem with pa-tient’s data is ensuring privacy and resource intensive protection when it is transmitted over open communication channels and stored in cloud systems. However, when it comes to millions of IoT devices, technologies that have already become classic for Internet resources are not suit-able in many aspects at once: low computing power, out of memory, limited battery capacity and etc. The work considered Attribute-based encryption for ensuring security of personified data in IoMT networks. Also, the research studied the issues of patient’s data confidentiality in cloud systems, management of cryptographic keys and data sharing control. The algorithms for effective and secure solution were proposed. We have proposed a framework for processing patient data from portable diagnostic devices using ABE methods. The results of load testing of the prototype are presented too

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Performance evaluation of Attribute-Based Encryption on constrained IoT devices

Computer Communications ◽

10.1016/j.comcom.2021.02.012 ◽

2021 ◽

Vol 170 ◽

pp. 151-163

Author(s):

Pericle Perazzo ◽

Francesca Righetti ◽

Michele La Manna ◽

Carlo Vallati

Keyword(s):

Performance Evaluation ◽

Attribute Based Encryption ◽

Iot Devices

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Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01875-2 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Fei Meng ◽

Leixiao Cheng ◽

Mingqiang Wang

Keyword(s):

Smart City ◽

Keyword Search ◽

Sensitive Information ◽

Security Model ◽

Computational Overhead ◽

Diffie Hellman ◽

Attribute Based Encryption ◽

Iot Devices ◽

Ciphertext Policy ◽

Access Policies

AbstractCountless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

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Analysis of State-of-the-Art Spin-Transfer-Torque Nonvolatile Flip-Flops Considering Restore Yield in the Near/Sub-Threshold Voltage Region

Electronics ◽

10.3390/electronics9122118 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2118

Author(s):

Gwang Hui Choi ◽

Taehui Na

Keyword(s):

Threshold Voltage ◽

State Of The Art ◽

Spin Transfer Torque ◽

Spin Transfer ◽

Model Parameters ◽

Offset Cancellation ◽

Battery Lifetime ◽

Battery Capacity ◽

Iot Devices ◽

Voltage Region

Recently, the leakage power consumption of Internet of Things (IoT) devices has become a main issue to be tackled, due to the fact that the scaling of process technology increases the leakage current in the IoT devices having limited battery capacity, resulting in the reduction of battery lifetime. The most effective method to extend the battery lifetime is to shut-off the device during standby mode. For this reason, spin-transfer-torque magnetic-tunnel-junction (STT-MTJ) based nonvolatile flip-flop (NVFF) is being considered as a strong candidate to store the computing data. Since there is a risk that the MTJ resistance may change during the read operation (i.e., the read disturbance problem), NVFF should consider the read disturbance problem to satisfy reliable data restoration. To date, several NVFFs have been proposed. Even though they satisfy the target restore yield of 4σ, most of them do not take the read disturbance into account. Furthermore, several recently proposed NVFFs which focus on the offset-cancellation technique to improve the restore yield have obvious limitation with decreasing the supply voltage (VDD), because the offset-cancellation technique uses switch operation in the critical path that can exacerbate the restore yield in the near/sub-threshold region. In this regard, this paper analyzes state-of-the-art STT-MTJ based NVFFs with respect to the voltage region and provides insight that a simple circuit having no offset-cancellation technique could achieve a better restore yield in the near/sub-threshold voltage region. Monte–Carlo HSPICE simulation results, using industry-compatible 28 nm model parameters, show that in case of VDD of 0.6 V, complex NVFF circuits having offset tolerance characteristic have a better restore yield, whereas in case of VDD of 0.4 V with sizing up strategy, a simple NVFF circuit having no offset tolerance characteristic has a better restore yield.

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Cryptographic Keys Generating and Renewing System for IoT Network Nodes—A Concept

Sensors ◽

10.3390/s20175012 ◽

2020 ◽

Vol 20 (17) ◽

pp. 5012

Author(s):

Janusz Furtak

Keyword(s):

Data Exchange ◽

Security And Privacy ◽

Critical Element ◽

Certification Authority ◽

Trusted Platform Module ◽

Network Nodes ◽

Cryptographic Keys ◽

Iot Devices ◽

System Nodes ◽

Trusted Platform

Designers and users of the Internet of Things (IoT) are devoting more and more attention to the issues of security and privacy as well as the integration of data coming from various areas. A critical element of cooperation is building mutual trust and secure data exchange. Because IoT devices usually have small memory resources, limited computing power, and limited energy resources, it is often impossible to effectively use a well-known solution based on the Certification Authority. This article describes the concept of the system for a cryptographic Key Generating and Renewing system (KGR). The concept of the solution is based on the use of the hardware Trusted Platform Module (TPM) v2.0 to support the procedures of creating trust structures, generating keys, protecting stored data, and securing data exchange between system nodes. The main tasks of the system are the secure distribution of a new symmetric key and renewal of an expired key for data exchange parties. The KGR system is especially designed for clusters of the IoT nodes but can also be used by other systems. A service based on the Message Queuing Telemetry Transport (MQTT) protocol will be used to exchange data between nodes of the KGR system.

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On the Feasibility of Attribute-Based Encryption on Constrained IoT Devices for Smart Systems

2019 IEEE International Conference on Smart Computing (SMARTCOMP) ◽

10.1109/smartcomp.2019.00057 ◽

2019 ◽

Cited By ~ 4

Author(s):

Benedetto Girgenti ◽

Pericle Perazzo ◽

Carlo Vallati ◽

Francesca Righetti ◽

Gianluca Dini ◽

...

Keyword(s):

Smart Systems ◽

Attribute Based Encryption ◽

Iot Devices

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Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

Future Internet ◽

10.3390/fi11040100 ◽

2019 ◽

Vol 11 (4) ◽

pp. 100 ◽

Cited By ~ 20

Author(s):

Maurizio Capra ◽

Riccardo Peloso ◽

Guido Masera ◽

Massimo Ruo Roch ◽

Maurizio Martina

Keyword(s):

Internet Of Things ◽

Mobile Devices ◽

Information Exchange ◽

Large Scale ◽

Operating Time ◽

Edge Computing ◽

The Internet ◽

Power Load ◽

Battery Capacity ◽

Iot Devices

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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Proposal for a Localization System for an IoT Ecosystem

Electronics ◽

10.3390/electronics10233016 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3016

Author(s):

Juraj Machaj ◽

Peter Brida ◽

Slavomir Matuska

Keyword(s):

Service Providers ◽

Inertial Sensors ◽

Multipath Propagation ◽

Dead Reckoning ◽

Global Navigation Satellite Systems ◽

High Signal ◽

Indoor Environments ◽

Localization System ◽

Battery Capacity ◽

Iot Devices

In the last decade, positioning using wireless signals has gained a lot of attention since it could open new opportunities for service providers. Localization is important, especially in indoor environments, where the widely used global navigation satellite systems (GNSS) signals suffer from high signal attenuation and multipath propagation, resulting in poor accuracy or a loss of positioning service. Moreover, in an Internet of things (IoT) environment, the implementation of GNSS receivers into devices may result in higher demands on battery capacity, as well as increased cost of the hardware itself. Therefore, alternative localization systems that are based on wireless signals for the communication of IoT devices are gaining a lot of attention. In this paper, we provide a design of an IoT localization system, which consists of multiple localization modules that can be utilized for the positioning of IoT devices that are connected thru various wireless technologies. The proposed system can currently perform localization based on received signals from LoRaWAN, ZigBee, Wi-Fi, UWB and cellular technologies. The implemented pedestrian dead reckoning algorithm can process the data measured by a mobile device that is equipped with inertial sensors to construct a radio map and thus help with the deployment of the positioning services based on a fingerprinting approach.

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An improved LogNNet classifier for IoT applications

Journal of Physics Conference Series ◽

10.1088/1742-6596/2094/3/032015 ◽

2021 ◽

Vol 2094 (3) ◽

pp. 032015

Author(s):

H Heidari ◽

A A Velichko

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Chaotic Map ◽

Network Architectures ◽

Direct Relation ◽

Swarm Optimization ◽

Computing Power ◽

Iot Applications ◽

Special Matrix ◽

Iot Devices

Abstract In the age of neural networks and Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. Designing suitable algorithms for IoT applications is an important task. The paper proposes a feed forward LogNNet neural network, which uses a semi-linear Henon type discrete chaotic map to classify MNIST-10 dataset. The model is composed of reservoir part and trainable classifier. The aim of the reservoir part is transforming the inputs to maximize the classification accuracy using a special matrix filing method and a time series generated by the chaotic map. The parameters of the chaotic map are optimized using particle swarm optimization with random immigrants. As a result, the proposed LogNNet/Henon classifier has higher accuracy and the same RAM usage, compared to the original version of LogNNet, and offers promising opportunities for implementation in IoT devices. In addition, a direct relation between the value of entropy and accuracy of the classification is demonstrated.

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Toward a Fuzzy-based Approach for Computational Load Offloading of IoT Devices

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.2020.077 ◽

2020 ◽

Vol 26 (11) ◽

pp. 1455-1474

Author(s):

Lelio Campanile ◽

Mauro Iacono ◽

Fiammetta Marulli ◽

Michele Mastroianni ◽

Nicola Mazzocca

Keyword(s):

Power Management ◽

Management Strategy ◽

Technological Development ◽

Computing Power ◽

External Resources ◽

Market Expansion ◽

Power Management Strategy ◽

Computing Paradigm ◽

Iot Devices ◽

Towards Time-Sensitive and Verifiable Data Aggregation for Mobile Crowdsensing

Security and Communication Networks ◽

10.1155/2021/6679157 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Tao Zhang ◽

Xiongfei Song ◽

Lele Zheng ◽

Yani Han ◽

Kai Zhang ◽

...

Keyword(s):

Access Control ◽

Data Aggregation ◽

Large Scale ◽

Response Speed ◽

Aggregated Data ◽

Mobile Crowdsensing ◽

Time Sensitivity ◽

Attribute Based Encryption ◽

Personalized Services ◽

Iot Devices

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.

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