Resource Constraints Of Devices Research Articles

Enhancement process of AES: a lightweight cryptography algorithm-AES for constrained devices

The restricted devices have a small memory, simple processor, and limited power. To secure them, we need lightweight cryptography algorithms, taking into account the limited specifications. Lightweight cryptography (LWC) algorithms provide confidentiality and maintain information integrity for devices with limited resources. This paper improves and enhances advanced encryption standard (AES) algorithm by reducing algorithm computation power and improving cryptography performance from the point of resource constraint devices. The proposed algorithm is fast and lightweight, which is essential for securing all kinds of data. Besides, the use of mix column overhead is dispensing with, and the ciphertext is processed by the mathematical function (continued fraction) to compress the ciphertext and make it more confusing and also to increasing the data transfer speed. The proposed lightweight cryptography-AES (LWC-AES) algorithm highly suitable for the timely execution of encryption and decryption (such as when encrypt text has (45.1 KB) encryption execution time for AES was (294 ms), while in LWC-AES was (280 ms), as well as suitable for the memory size of the resource-constrained devices for all types of data, than the AES algorithm. The proposed algorithm tested for security analysis using the Avalanche Effect parameter, and this test showed acceptable and within required security results

Learning Sparse Neural Networks Using Non-Convex Regularization

Deep Neural Networks (DNNs) is the computing paradigm that has achieved remarkable success in various fields of engineering in recent years, primarily visual recognition. DNNs owe its success to the presence of large number of weight parameters (and increased depth), which led to huge computation and memory costs for implementation. These limiting factors hinder the scalability of such algorithms on resource constraint devices (like IoT devices). In general, DNNs are believed to be over parametrized i.e., the parameters are highly redundant, thus can be structurally removed without significant loss of performance. To solve these issues, we propose to use non-convex T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> regularizer along with the additional effect of sparse group lasso to completely remove the redundant neurons/filters that is, to introduce structured sparsity. The network has been trained using the proximal gradient method, which is useful in optimizing functions with the combination of smooth and non-smooth terms. We show that proposed regularizer manages to achieve competitive performances as well as extremely compact networks. Detailed experiments are performed on several benchmark datasets that illustrate the efficiency of the approach. On the ImageNet dataset, our approach removes more than 50% of parameters of convolutional layers and 85% parameters of fully connected layers of Alexnet with no drop in accuracy.

DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning

On-the-fly learning is unavoidable for applications that demand instantaneous deep neural network (DNN) training or where transferring data to the central system for training is costly. Hyperparameter optimization plays a significant role in the performance and reliability in deep learning. Many hyperparameter optimization algorithms have been developed for obtaining better validation accuracy in DNNs. Most state-of-the-art hyperparameter optimization techniques are computationally expensive due to the focus on validation accuracy. Therefore, they are unsuitable for on-the-fly learning applications that require faster computation on resource constraint devices (e.g., edge devices). In this paper, we develop a novel greedy approach-based hyperparameter optimization (GHO) algorithm enabling faster computing on edge devices for on-the-fly learning applications. In GHO, we obtain the validation accuracy locally for each of the hyperparameter configurations. The GHO algorithm optimizes each hyperparameter while keeping the others constant in order to converge to the locally optimal solution in the expectation that this choice will lead to a globally optimal solution. We perform an empirical study to compute the performance such as computation time and energy consumption of the GHO and compare it with two state-of-the-art hyperparameter optimization algorithms. We also deploy the GHO algorithm in an edge device to validate the performance of our algorithm. We perform post-training quantization on the GHO algorithm to reduce the inference time. Our GHO algorithm is more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> energy efficient and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> faster than two state-of-the-art hyperparameter optimization techniques on both DNNs and datasets studied in this paper.

Optimised Elliptical Curve Cryptographic Point Multiplier with Reversible Vedic Logic (Ecpm-Rvm) for Resource Constraint Device Security

Optimised Elliptical Curve Cryptographic Point Multiplier with Reversible Vedic Logic (Ecpm-Rvm) for Resource Constraint Device Security

Lightweight and Secure Elliptical Curve Cryptography (ECC) Key Exchange for Mobile Phones

Open networks enable data communication between different types of mobile devices that showcase the need to enforce elevated security measures. Securing sensitive or confidential data in mobile phones is accomplished by implementing a diverse range of cryptographic techniques. While encryption algorithms, such as Rivest–Shamir–Adleman (RSA) may offer secure solutions that are often difficult to compromise, these in turn prerequisite high speed computational resources for effective operation. Elliptical curve cryptography (ECC) is well thought-out standard that offers a workable and feasible methods of encryption/decryption, whilst being applicable to resource constraint devices. This paper implements a novel key exchange mechanism that helps to secure exchange of data between the communicating mobile devices. The study aims to address the limitation of Elliptic Curve Deffie Hellman, which is susceptible to Man-in-the-Middle attack and proposes an enhanced Elliptic Curve Deffie Hellman (ECDH) technique for secure data communication in open networks. The study results reveal, how the implementation of ECDH allows exchange of keys between the two communicating devices with limited resources.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based searchable encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage. However, it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations. In a healthcare cloud-based cyber-physical system (CCPS), the data is often collected by resource-constraint devices; therefore, here also, we cannot directly apply ABSE schemes. In the proposed work, the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network. Thus, it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS. With the assistance of blockchain technology, the proposed scheme offers two main benefits. First, it is free from a trusted authority, which makes it genuinely decentralized and free from a single point of failure. Second, it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network. Specifically, the task of initializing the system, which is considered the most computationally intensive, and the task of partial search token generation, which is considered as the most frequent operation, is now the responsibility of the consensus nodes. This eliminates the need of the trusted authority and reduces the burden of data users, respectively. Further, in comparison to existing decentralized fine-grained searchable encryption schemes, the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users. It has been verified both theoretically and practically in the performance analysis section.

Lightweight Multi-party Authentication and Key Agreement Protocol in IoT-based E-Healthcare Service

Internet of Things (IoT) is playing a promising role in e-healthcare applications in the recent decades; nevertheless, security is one of the crucial challenges in the current field of study. Many healthcare devices (for instance, a sensor-augmented insulin pump and heart-rate sensor) collect a user’s real-time data (such as glucose level and heart rate) and send them to the cloud for proper analysis and diagnosis of the user. However, the real-time user’s data are vulnerable to various authentication attacks while sending through an insecure channel. Besides that, the attacks may further open scope for many other subsequent attacks. Existing security mechanisms concentrate on two-party mutual authentication. However, an IoT-enabled healthcare application involves multiple parties such as a patient, e-healthcare test-equipment, doctors, and cloud servers that requires multi-party authentication for secure communication. Moreover, the design and implementation of a lightweight security mechanism that fits into the resource constraint IoT-enabled healthcare devices are challenging. Therefore, this article proposes a lightweight, multi-party authentication and key-establishment protocol in IoT-based e-healthcare service access network to counter the attacks in resource constraint devices. The proposed multi-party protocol has used a lattice-based cryptographic construct such as Identity-Based Encryption (IBE) to acquire security, privacy, and efficiency. The study provided all-round analysis of the scheme, such as security, power consumption, and practical usage, in the following ways. The proposed scheme is tested by a formal security tool, Scyther, to testify the security properties of the protocol. In addition, security analysis for various attacks and comparison with other existing works are provided to show the robust security characteristics. Further, an experimental evaluation of the proposed scheme using IBE cryptographic construct is provided to validate the practical usage. The power consumption of the scheme is also computed and compared with existing works to evaluate its efficiency.

Trust-Based Blockchain Authorization for IoT

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user’s attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.

Perceptual service-level QoE and network-level QoS control model for mobile video transmission

The cumulative effects of network transmission link imperfections and real-time limitations of video data result in multiple challenges for mobile video transmission. The challenges, together with the increasing expectations of users for well-displayed videos, further extend the complexity particularly in dense areas. To address the issues and provide efficient mobile video transmission, this work proposes a model with two phases: network-related settings (NRS) and video-related settings (VRS). The NRS is related to the mobile transmission link limits and develops four separate mobile networks, including long term evolution, 802.11 ax dual-band, and 802.11 ac. The VRS, on the other hand, is related to the video real-time constraints and covers five well-known compression algorithms used in the reference video preparation process. With comprehensibility in mind, the model comprises different factors that have a profound impact on the mobile video transmission process. The model is implemented and the results determine the video delivery efficiency in terms of the network-level quality of service and service-level quality of experience. To further validate the model, a testbed is set up and the measured experimental results are compared with those of the simulation. The results establish a baseline for efficient mobile video transmission on resource constraint devices.

Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed. We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.

Novel Modelling of the Hash-based Authentication of Data in Dynamic Cloud Environment

A datacenter in a cloud environment houses a massive quantity of data in a distributed manner. However, with the increasing number of threats like data deduplication attack over the cloud environment, it is quite challenging to ascertain data's full-fledged security. In this regard, data integrity and security are highly questionable. A review of existing literature shows that the existing solutions are not much suitable to meet the requirements and support the existing distributed storage system's security demands concerning data integrity due to the usage of the inferior authentication mechanism. Also, the most frequently used public-key encryption is found not to be purely suitable resource constraint devices. Therefore, this manuscript presents a unique model of authentication of data where a simplified hashing proposition has been designed towards scheduling a distributed chain of data. The idea is to perform dynamic authentication that is present of any form of the adversary. The design of proposed scheme is lightweight which offers cross-verifiable hash-based challenges matching scheme with the provision of the non-repudiation of the tractions using the inclusion of a cloud auditor units. The experiment was carried on numerical computing tool considering, data volume, verification count and verification delay as prime performance metrics. The simulation outcomes shows that the proposed system excels in better security performance as well it is flexible compared to the existing system.

Modelling protocol gateways for cyber-physical systems using Architecture Analysis & Design Language

Developing, configuring, and deploying legacy protocol gateways for existing cyber-physical systems to improve interoperability remains challenging. A possible solution is using model-driven engineering languages such as the Architecture Analysis & Design Language (AADL) that enables combined modelling of software, hardware, and communication for distributed systems. Experiences made while creating an OPC Unified Architecture (OPC UA) / Data Distribution Service (DDS) gateway indicate the suitability of AADL to model complex software artefacts. Moreover, a proposed process reduces the development and configuration effort for platform-specific gateway instances. A challenge to be addressed is the generation of executable code for resource-constraint devices.

ECC-CoAP: Elliptic Curve Cryptography Based Constraint Application Protocol for Internet of Things

Constraint Application Protocol (CoAP), an application layer based protocol, is a compressed version of HTTP protocol that is used for communication between lightweight resource constraint devices in Internet of Things (IoT) network. The CoAP protocol is generally associated with connectionless User Datagram Protocol (UDP) and works based on Representational State Transfer architecture. The CoAP is associated with Datagram Transport Layer Security (DTLS) protocol for establishing a secure session using the existing algorithms like Lightweight Establishment of Secure Session for communication between various IoT devices and remote server. However, several limitations regarding the key management, session establishment and multi-cast message communication within the DTLS layer are present in CoAP. Hence, development of an efficient protocol for secure session establishment of CoAP is required for IoT communication. Thus, to overcome the existing limitations related to key management and multicast security in CoAP, we have proposed an efficient and secure communication scheme to establish secure session key between IoT devices and remote server using lightweight elliptic curve cryptography (ECC). The proposed ECC-based CoAP is referred to as ECC-CoAP that provides a CoAP implementation for authentication in IoT network. A number of well-known cryptographic attacks are analyzed for validating the security strength of the ECC-CoAP and found that all these attacks are well defended. The performance analysis of the ECC-CoAP shows that our scheme is lightweight and secure.

A Review of Techniques and Methods for IoT Applications in Collaborative Cloud-Fog Environment

Cloud computing is widely used for its powerful and accessible computing and storage capacity. However, with the development trend of Internet of Things (IoTs), the distance between cloud and terminal devices can no longer meet the new requirements of low latency and real-time interaction of IoTs. Fog has been proposed as a complement to the cloud which moves servers to the edge of the network, making it possible to process service requests of terminal devices locally. Despite the fact that fog computing solves many obstacles for the development of IoT, there are still many problems to be solved for its immature technology. In this paper, the concepts and characteristics of cloud and fog computing are introduced, followed by the comparison and collaboration between them. We summarize main challenges IoT faces in new application requirements (e.g., low latency, network bandwidth constraints, resource constraints of devices, stability of service, and security) and analyze fog-based solutions. The remaining challenges and research directions of fog after integrating into IoT system are discussed. In addition, the key role that fog computing based on 5G may play in the field of intelligent driving and tactile robots is prospected.

RISQ-V: Tightly Coupled RISC-V Accelerators for Post-Quantum Cryptography

Empowering electronic devices to support Post-Quantum Cryptography (PQC) is a challenging task. PQC introduces new mathematical elements and operations which are usually not easy to implement on standard processors. Especially for low cost and resource constraint devices, hardware acceleration is usually required. In addition, as the standardization process of PQC is still ongoing, a focus on maintaining flexibility is mandatory. To cope with such requirements, hardware/software co-design techniques have been recently used for developing complex and highly customized PQC solutions. However, while most of the previous works have developed loosely coupled PQC accelerators, the design of tightly coupled accelerators and Instruction Set Architecture (ISA) extensions for PQC have been barely explored. To this end, we present RISQ-V, an enhanced RISC-V architecture that integrates a set of powerful tightly coupled accelerators to speed up lattice-based PQC. RISQ-V efficiently reuses processor resources and reduces the amount of memory accesses. This significantly increases the performance while keeping the silicon area overhead low. We present three contributions. First, we propose a set of powerful hardware accelerators deeply integrated into the RISC-V pipeline. Second, we extended the RISC-V ISA with 29 new instructions to efficiently perform operations for lattice-based cryptography. Third, we implemented our RISQ-V in ASIC technology and on FPGA. We evaluated the performance of NewHope, Kyber, and Saber on RISQ-V. Compared to the pure software implementation on RISC-V, our co-design implementations show a speedup factor of up to 11.4 for NewHope, 9.6 for Kyber, and 2.7 for Saber. For the ASIC implementation, the energy consumption was reduced by factors of up to 9.5 for NewHope, 7.7 for Kyber, and 2.1 for Saber. The cell count of the CPU was increased by a factor of 1.6 compared to the original RISC-V design, which can be considered as a moderate increase for the achieved performance gain.

Model compression via pruning and knowledge distillation for person re-identification

Person re-identification (ReID) is an important problem in intelligent monitoring. Recently, with the development of deep learning, convolutional neural networks have achieved state-of-the-art performance on person ReID problems. However, the deep neural network models used by these methods tend to have large number of parameters and high computational cost, thereby hindering their deployment on resource-constraint devices or real-time applications. In this study, we propose a method that distills the knowledge to a pruned model to reduce the parameters, which can be divided into two stages: one is to apply unstructured pruning method on over-parameterized models, whereas the other is to carry out representation and metric learning-based knowledge distillation on the model after pruning to improve performance. Finally, the proposed method can effectively reduce the total number of parameters by 8.4 with only 0.1% drop of rank-1 accuracy on the Market1501 dataset and no drop of rank-1 accuracy on the DukeMTMC-reID dataset.

A Road Health Monitoring System Using Sensors in Optimal Deep Neural Network

Road health monitoring is a prominent area of research in the transportation system to ensure a safer and smoother flow of traffic. It helps to determine the type of road, including smooth, rough, zigzag, etc. Though there exists a significant amount of work towards road health monitoring, that requires high-end machine or cloud support to perform road classification. In this paper, we propose a road health monitoring system using sensors. The system learns deep learning based classifiers, which runs on resource constraint devices such as smartphone, to identify the type of the road. The system optimizes the deep neural network model based on the available resources on the resource constraint device. The optimization is solved for selecting the appropriate model version that matches the current resource availability of the device. We also evaluate the proposed system on the collected sensory dataset to study the impact of background services and residual energy of the device on accuracy and inference time.

Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT

Remote integrity verification plays a paramount role in resource-constraint devices owing to emerging applications such as Internet-of-Things (IoT), smart homes, e-health, and so on. The concept of Virtual Proof of Reality (VPoR) proposed by Rührmair et al. in 2015 has come up with a Sense-Prove-Validate framework for integrity checking of abundant data generated from billions of connected sensors. It leverages the unreliability factor of Physically Unclonable Functions (PUFs) with respect to ambient parameter variations such as temperature, supply voltages, and so on, and claims to prove the authenticity of the sensor data without using any explicit keys. The state-of-the-art authenticated sensing protocols majorly lack in limited authentications and huge storage overhead. These protocols also assume that the behaviour of the PUF instances varies unpredictably for different levels of ambient factors, which in turn makes them hard to go beyond the theoretical concept. We address these issues in this work 1 and propose a Machine Learning (ML) assisted PUF calibration scheme to predict the Challenge-Response Pair (CRP) behaviour of a PUF instance in a specific environment, given the CRP behaviour in a pivot environment. Here, we present a new class of authenticated sensing protocols where we leverage the beneficence of ML techniques to validate the authenticity and integrity of sensor data over ambient factor variations. The scheme also reduces the storage complexity of the verifier from O ( p * K * l * ( c + r )) to O ( p * l *( c + r )), where p is the number of PUF instances deployed in the framework, l is the number of challenge-response pairs used for authentication, c is the bit lengths of the challenge, r is the response bits of the PUF, and K is the number of levels of ambient factor variations. The scheme alleviates the issue of limited authentication as well, whereby every CRP is used only once for authentication and then deleted from the database. To validate the proposed protocol through actual experiments on FPGA, we propose 5-4 Double Arbiter PUF, which is an extension of Double Arbiter PUFs (DAPUFs) as this design is more suited for FPGA, and implement it on Xilinx Artix-7 FPGAs. We characterise the proposed PUF instance from −20° C to 80° C and use Random Forest --based ML technique to generate a soft model of the PUF instance. This model is further used by the verifier to authenticate the actual PUF circuit. According to the FPGA-based validation, the proposed protocol with DAPUF can be effectively used to authenticate sensor devices across wide variations of temperature values.

Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement

Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles to support diverse vertical applications by connecting heterogenous devices, machines and industry processes. Conventional authentication and authorization schemes are insufficient in dealing the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security level. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection lead to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI) enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the gateway to identify the access time slots or frequencies of resource-constraint devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for analyzing the complex dynamic environment and achieving adaptive access control. These new AI enabled approaches establish the connections between transceivers quickly and enhance security progressively, so that communication latency can be reduced and security risks are well-controlled in large-scale IoT. Finally, we outline several areas for AI-enabled security provisioning for future researches.

Towards Light Weight Cryptography Schemes for Resource Constraint Devices in IoT

The Internet of Things (IoT) is becoming the most relevant next Internet-related revolution in the world of Technology. It permits millions of devices to be connected and communicate with each other. Beside ensuring reliable connectivity their security is also a great challenge. Abounding IoT devices have a minimum of storage and processing capacity and they usually need to be able to operate on limited power consumption. Security paths that depend maximum on encryption are not good for these resource constrained devices, because they are not suited for performing complicated encryption and decryption tasks quickly to be able to transmit data securely in real-time. This paper contains an overview of some of the cryptographic-based schemes related to communication and computational costs for resource constrained devices and considers some approaches towards the development of highly secure and lightweight security mechanisms for IoT devices.