Lightweight VLSI Architectures for Image Encryption Applications

Lightweight cryptography offers significant security service in constrained environments such as wireless sensor networks and Internet of Things. The focus of this article is to construct lightweight SPN block cipher architectures with substitution box based on finite fields. The paper also details the FPGA implementation of the lightweight symmetric block cipher algorithm of SPN type with combinational S-box. Restructuring of traditional look-up-table Substitution Box (S-Box) sub-structure with a combinational logic S-box is attempted. Elementary architectures namely the basic round architecture and reduced datawidth architecture incorporating look-up-table and combinational S-Box substructure are compared in terms of area and throughput. Proposed restructure mechanism occupies less FPGA resources with no comprise in the latency and also demonstrates performance efficiency and low power consumption in Xilinx FPGAs. Robustness of the proposed method against various statistical attacks has been analyzed through comparison with other existing encryption mechanisms.

Perspectives of Lower Primary Teachers Implementing New Numeracy Strategies in Maritime Schools in Fiji

<p>In Fiji, concerns about the quality of education and low standards of achievement, particularly salient in numeracy, have led to reform initiatives requiring teacher pedagogical shifts to more evidence-based and learner-centered approaches. Despite previously unsuccessful reforms, the capacity of maritime teachers to successfully adapt curricula in their geographically constrained environments has never been considered, despite them forming a significant proportion of the primary teaching force. This interpretative qualitative study examines the implementation of a recent reform-based numeracy strategy in lower primary mathematics classrooms of maritime schools. Specifically, teachers’ perspectives on the implementation process, their experiences with the new strategies, associated challenges, and maritime context-specific barriers have been investigated. Data were collected through two in-context focus group interviews with 13 participants and 62 returned questionnaires. The findings of the study reveal that teachers’ perceptions and receptivity to the new numeracy strategies were strongly influenced by factors such as past experiences with reforms and increased expectations. While teachers understood key ideas underlying the reform to improve student’s mathematical knowledge and were inclined to alter pedagogical practices, most teachers felt a disconnect in terms of not being supported well enough to fully incorporate the new strategies. Teachers identified the need for a contextually-relevant supportive network and structures, both professional and personal, as essential to overcoming numerous challenges they encountered while living and working in maritime areas of Fiji.</p>

Perspectives of Lower Primary Teachers Implementing New Numeracy Strategies in Maritime Schools in Fiji

<p>In Fiji, concerns about the quality of education and low standards of achievement, particularly salient in numeracy, have led to reform initiatives requiring teacher pedagogical shifts to more evidence-based and learner-centered approaches. Despite previously unsuccessful reforms, the capacity of maritime teachers to successfully adapt curricula in their geographically constrained environments has never been considered, despite them forming a significant proportion of the primary teaching force. This interpretative qualitative study examines the implementation of a recent reform-based numeracy strategy in lower primary mathematics classrooms of maritime schools. Specifically, teachers’ perspectives on the implementation process, their experiences with the new strategies, associated challenges, and maritime context-specific barriers have been investigated. Data were collected through two in-context focus group interviews with 13 participants and 62 returned questionnaires. The findings of the study reveal that teachers’ perceptions and receptivity to the new numeracy strategies were strongly influenced by factors such as past experiences with reforms and increased expectations. While teachers understood key ideas underlying the reform to improve student’s mathematical knowledge and were inclined to alter pedagogical practices, most teachers felt a disconnect in terms of not being supported well enough to fully incorporate the new strategies. Teachers identified the need for a contextually-relevant supportive network and structures, both professional and personal, as essential to overcoming numerous challenges they encountered while living and working in maritime areas of Fiji.</p>

A Secure and Efficient Image Transmission Scheme Based on Two Chaotic Maps

The application of multimedia sensors is widespread, and people need to transmit images more securely and efficiently. In this paper, an image transmission scheme based on two chaotic maps is proposed. The proposed scheme consists of two parts, secure image transmission between sensor nodes and sink nodes (SIT-SS) and secure image transmission between sensor nodes and receivers (SIT-SR). For resource-constrained environments, SIT-SS utilizes Tent-Logistic Map (TLM) to generate chaotic sequences and adopts TLM-Driven permutation and transformation to confuse image pixels. Then the cipher image is obtained through TLM-Driven two-dimensional compressed sensing. Compared with existing schemes, the secret key design of SIT-SS is more reasonable and requires fewer hardware resources. When sampling ratio is greater than 0.6, its image reconstruction quality has obvious advantages. For environments with huge security threats, SIT-SR adopts dynamic permutation and confusion based on discrete logarithms to confuse the image and exploits dynamic diffusion based on discrete logarithms to generate final cipher image. Similarly, compared with some existing schemes, the design of SIT-SR is more practical, and the statistical characteristics of the cipher image are better. Finally, extensive simulation tests confirm the superiority of the proposed scheme.

Blockchain-Based Context-Aware Authorization Management as a Service in IoT

Internet of Things (IoT) applications bring evolved and intelligent services that can help improve users’ daily lives. These applications include home automation, health care, and smart agriculture. However, IoT development and adoption face various security and privacy challenges that need to be overcome. As a promising security paradigm, context-aware security enables one to enforce security and privacy mechanisms adaptively. Moreover, with the advancements in edge computing, context-aware security services can dynamically be placed close to a user’s location and enable the support of low latency communication and mobility. Therefore, the design of an adaptive and decentralized access control mechanism becomes a necessity. In this paper, we propose a decentralized context-aware authorization management as a service based on the blockchain. The proposed architecture extends the Authentication and Authorization for Constrained Environments (ACE) framework with blockchain technology and context-awareness capabilities. Instead of a classic Open Authorization 2.0 (OAuth) access token, it uses a new contextual access token. The evaluation results show our proposition’s effectiveness and advantages in terms of usability, security, low latency, and energy consumption.

Configurable Hardware Core for IoT Object Detection

Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes a configurable hardware core on a field-programmable gate array (FPGA) for object detection. The configurability of the core allows its deployment on target devices with diverse hardware resources. The object detection accelerator is based on YOLO, for its good accuracy at moderate computational complexity. The solution was applied to the design of a core to accelerate the Tiny-YOLOv3, based on a CNN developed for constrained environments. However, it can be applied to other YOLO versions. The core was integrated into a full system-on-chip solution and tested with the COCO dataset. It achieved a performance from 7 to 14 FPS in a low-cost ZYNQ7020 FPGA, depending on the quantization, with an accuracy reduction from 2.1 to 1.4 points of mAP50.

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction.