scholarly journals 3-of-3 multisignature approach for enabling lightning network micro-payments on IoT devices

2021 ◽  
Vol 2 (5) ◽  
pp. 53-67
Author(s):  
Ahmet Kurt ◽  
Suat Mercan ◽  
Enes Erdin ◽  
Kemal Akkaya
Keyword(s):  
Daily Life ◽  
Raspberry Pi ◽  
Communication Delays ◽  
Payment Systems ◽  
Resource Constrained ◽  
Iot Applications ◽  
Secure Protocol ◽  
Transaction Fees ◽  
Iot Devices ◽  
Innovative Concept

Bitcoin's success as a cryptocurrency enabled it to penetrate into many daily life transactions. Its problems regarding the transaction fees and long validation times are addressed through an innovative concept called the Lightning Network (LN) which works on top of Bitcoin by leveraging off-chain transactions. This made Bitcoin an attractive micropayment solution that can also be used within certain IoT applications (e.g., toll payments) since it eliminates the need for traditional centralized payment systems. Nevertheless, it is not possible to run LN and Bitcoin on resource-constrained IoT devices due to their storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions through a gateway LN node even if it is not trusted. The idea is to involve the IoT device only in signing operations, which is possible by replacing LN's original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Once the gateway is delegated to open a channel for the IoT device in a secure manner, our protocol enforces the gateway to request the IoT device's cryptographic signature for all further operations on the channel such as sending payments or closing the channel. LN's Bitcoin transactions are revised to incorporate the 3-of-3 multisignature channels. In addition, we propose other changes to protect the IoT device's funds from getting stolen in possible revoked state broadcast attempts. We evaluated the proposed protocol using a Raspberry Pi considering a toll payment scenario. Our results show that timely payments can be sent and the computational and communication delays associated with the protocol are negligible.

scholarly journals An Optimal Multilevel Encryption Technique for Securing the Data Transmission in IoT

2020 ◽  
Vol 9 (3) ◽  
pp. 1239-1242
Keyword(s):  
Internet Of Things ◽  
Elliptic Curve ◽  
Data Transmission ◽  
Data Security ◽  
Elliptic Curve Cryptography ◽  
Daily Life ◽  
Secret Message ◽  
Critical Component ◽  
Iot Applications ◽  
Iot Devices

Internet of Things(IoT) is playing a pivotal role in our daily life as well as in various fields like Health, agriculture, industries etc. In the go, the data in the various IoT applications will be easily available to the physical dominion and thus the process of ensuringthe security of the data will be a major concern. For the extensive implementation of the numerous applications of IoT , the data security is a critical component. In our work, we have developed an encryption technique to secure the data of IoT. With the help of Merkle-Hellman encryption the data collected from the various IoT devices are first of all encrypted and then the secret message is generated with the help of Elliptic Curve Cryptography.

scholarly journals Privacy and Security for Resource-Constrained IoT Devices and Networks: Research Challenges and Opportunities

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1935 ◽  
Author(s):  
Shancang Li ◽  
Houbing Song ◽  
Muddesar Iqbal
Keyword(s):  
Cyber Physical Systems ◽  
Resource Constrained ◽  
Privacy And Security ◽  
Physical Systems ◽  
Iot Applications ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Iot Devices ◽  
Application Requirements ◽  
The Internet Of Things

With the exponential growth of the Internet of Things (IoT) and cyber-physical systems (CPS), a wide range of IoT applications have been developed and deployed in recent years. To match the heterogeneous application requirements in IoT and CPS systems, many resource-constrained IoT devices are deployed, in which privacy and security have emerged as difficult challenges because the devices have not been designed to have effective security features.

scholarly journals A Secure Architecture For Resource Constrained Iot Environment

2021 ◽  
Vol 23 (09) ◽  
pp. 248-264
Author(s):  
Omar Farooq ◽  
◽  
Parminder Singh ◽  
Keyword(s):  
Significant Rise ◽  
Learning Approach ◽  
Data Traffic ◽  
Resource Constrained ◽  
Iot Applications ◽  
Digital Currency ◽  
Machine Learning Approach ◽  
Cloud Environments ◽  
Iot Devices ◽  
The Internet Of Things

One of the most exciting emerging concepts nowadays is the Internet of Things. However, digital currency has run into issues with how quickly it has been adopted. The number of IoT devices is increasing exponentially, and presently we have more than 20000 million objects connected to the network. The amount of data and complexity circulating across networks is also growing exponentially. IoT plays a measure role in this growth rate of IoT data traffic, resulting in a significant rise in data traffic reaching the cloud or data centers. The response time of IoT systems is affected by the growth of data traffic as this may not be appropriate for sensitive environments. This paper presents a framework and a machine learning approach for the data management of IoT edge-cloud environments with resource-constrained IoT applications. In this paper, the security aspect has also been discussed for the resource-constrained IoT framework.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

2021 ◽  
Author(s):  
Srivatsan Krishnan ◽  
Behzad Boroujerdian ◽  
William Fu ◽  
Aleksandra Faust ◽  
Vijay Janapa Reddi
Keyword(s):  
Reinforcement Learning ◽  
Embedded System ◽  
Broad Class ◽  
Visual Navigation ◽  
Raspberry Pi ◽  
Latency Distribution ◽  
Hardware In The Loop ◽  
Resource Constrained ◽  
Aerial Robot ◽  
Policy Optimization

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

An Efficient Container Management Scheme for Resource Constrained Intelligent IoT Devices

2020 ◽  
pp. 1-1
Author(s):  
Prateek Chhikara ◽  
Rajkumar Tekchandani ◽  
Neeraj Kumar ◽  
Mohammad S. Obaidat
Keyword(s):  
Resource Constrained ◽  
Management Scheme ◽  
Container Management ◽  
Iot Devices

scholarly journals State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1598
Author(s):  
Sigurd Frej Joel Jørgensen Ankergård ◽  
Edlira Dushku ◽  
Nicola Dragoni
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Resource Constrained ◽  
Remote Attestation ◽  
Comprehensive Overview ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Iot Devices ◽  
Specialized Hardware

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.

scholarly journals Enhanced Algorithm Implementation for Low Powered IoT Devices Using Authenticator to Improve Data Integrity

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 733-751
Author(s):  
D.M. Sheeba
Keyword(s):  
Data Security ◽  
Low Cost ◽  
Easy Access ◽  
Raspberry Pi ◽  
Network Availability ◽  
Services Delivery ◽  
Encryption And Decryption ◽  
Arduino Uno ◽  
Iot Devices ◽  
Algorithm Implementation

Internet of Things enables many industries to connect to end customers and provide seamless products and services delivery. Due to easy access to network, availability of devices, penetration of IoT services exponentially Growing. Meanwhile, Ensuring the Data Security and Integrity of devices connected to network is paramount. In this work, we bring the efficient way of implementing Secure Algorithm for low powered devices and enhancing the encryption and decryption process. In addition to the data security, to enhance node integrity with less power, Authenticator and intermediate network manager introduced which will acts as a firewall and manager of data flow. To demonstrate the approach, same is implemented using low cost Arduino Uno, Raspberry Pi boards. Arduino Uno used to demonstrate low powered encryption process using EDIA Algorithm and raspberry pi used as nodal manager to manage the integrity of nodes in a low-powered environment. Data Security and Integrity is ensured by the way of enhanced Algorithm and Integrity through BlockChain and results are provided and discussed. Finally result and future enhancement are explained.

scholarly journals Visual Assistant for Blind People using Raspberry Pi

2021 ◽  
pp. 671-675
Author(s):  
Tejal Adep ◽  
Rutuja Nikam ◽  
Sayali Wanewe ◽  
Dr. Ketaki B. Naik
Keyword(s):  
Visually Impaired ◽  
Daily Life ◽  
Artificial Vision ◽  
Raspberry Pi ◽  
Blind People ◽  
Python Language ◽  
Visually Impaired People ◽  
Impaired People ◽  
Visual Aid ◽  
Impaired Person

Blind people face the problem in daily life. They can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision-based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects and signboards. This will help the visually impaired person to manage day-to-day activities and navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

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