Computers — Open Access Journal

The development of software-defined radio (SDR) systems using field-programmable gate arrays (FPGAs) compels designers to reuse pre-existing Intellectual Property (IP) cores in order to meet time-to-market and design efficiency requirements. However, the low-level development difficulties associated with FPGAs hinder productivity, even when the designer is experienced with hardware design. These low-level difficulties include non-standard interfacing methods, component communication and synchronization challenges, complicated timing constraints and processing blocks that need to be customized through time-consuming design tweaks. In this paper, we present a methodology for automated and behavioral integration of dedicated IP cores for rapid prototyping of SDR applications. To maintain high performance of the SDR designs, our methodology integrates IP cores using characteristics of the dataflow model of computation (MoC), namely the static dataflow with access patterns (SDF-AP). We show how the dataflow is mapped onto the low-level model of hardware by efficiently applying low-level based optimizations and using a formal analysis technique that guarantees the correctness of the generated solutions. Furthermore, we demonstrate the capability of our automated hardware design approach by developing eight SDR applications in VHDL. The results show that well-optimized designs are generated and that this can improve productivity while also conserving the hardware resources used. Full article

Systems for application domains like robotics, aerospace, defense, autonomous vehicles, etc. are usually developed on System-on-Programmable Chip (SoPC) platforms, capable of supporting several multi-modal computation-intensive tasks on their FPGAs. Since such systems are mostly autonomous and mobile, they have rechargeable power sources and therefore, varying power budgets. They may also develop hardware faults due to radiation, thermal cycling, aging, etc. Systems must be able to sustain the performance requirements of their multi-task multi-modal workload in the presence of variations in available power or occurrence of hardware faults. This paper presents an approach for mitigating power budget variations and hardware faults (transient and permanent) by run-time structural adaptation of the SoPC. The proposed method is based on dynamically allocating, relocating and re-integrating task-specific processing circuits inside the partially reconfigurable FPGA to accommodate the available power budget, satisfy tasks’ performances and hardware resource constraints, and/or to restore task functionality affected by hardware faults. The proposed method has been experimentally implemented on the ARM Cortex-A9 processor of Xilinx Zynq XC7Z020 FPGA. Results have shown that structural adaptation can be done in units of milliseconds since the worst-case decision-making process does not exceed the reconfiguration time of a partial bit-stream. Full article

Healthcare sectors have been at the forefront of the adoption and use of IoT technologies for efficient healthcare diagnosis and treatment. Because healthcare IoT sensor technology obtains health-related data from patients, it needs to be integrated with the electronic healthcare records (EHR) system. Most EHR systems have not been designed for integration with IoT technology; they have been designed to be more patient-centric management systems. The use of the IoT in EHR remains a long-term goal. Configuring IoT in EHR can enhance patient healthcare, enabling health providers to monitor their patients outside of the clinic. To assist physicians to access data resources efficiently, a data model that is semantic and flexible is needed to connect EHR data and IoT data that may help to provide true interoperability and integration. This research proposes a semantic middleware that exploits ontology to support the semantic integration and functional collaborations between IoT healthcare Information Systems and EHR systems. Full article

Device-to-device (D2D) communication is affirmed as one of the dynamic techniques in improving the network throughput and capacity and reducing traffic load to the evolved Node B (eNB). In this paper, we propose a resource allocation and power control technique in which two-pairs of D2D users can simultaneously share same uplink cellular resource. In this case, interference between D2D users and cellular users is no longer insignificant so it must be properly handled. The proposed scheme considers fractional frequency reuse (FFR) scheme as a promising method that can relatively reduce the intra-cell interference. The main objective of the proposed scheme is to maximize the D2D communication throughput and overall system throughput by minimizing outage probability. Hence, we formulate an outage probability problem and overall system throughput optimization problem while guaranteeing minimum allowable signal-to-interference-plus-noise ratio (SINR). For fair distribution of cellular resources to multiple D2D pairs, we used Jain's fairness index (JFI) method. Simulation is conducted in MATLAB and our simulation results demonstrate that the proposed scheme achieves remarkable system performance as compared with existing methods. Full article

Open source software (OSS) security has been the focus of the security community and practitioners over the past decades. However, the number of new vulnerabilities keeps increasing in today’s OSS systems. With today’s increasingly important and complex OSS, lacking software security knowledge to handle security vulnerabilities in OSS development will result in more breaches that are serious in the future. Learning software security is a difficult and challenging task since the domain is quite context specific and the real project situation is necessary to apply the security concepts within the specific system. Many OSS proponents believe that the OSS community offers significant learning opportunities from its best practices. However, studies that specifically explore security knowledge sharing and learning in OSS communities are scarce. This research is intended to fill this gap by empirically investigating factors that affect knowledge sharing and learning about software security and the relationship among them. A conceptual model is proposed that helps to conceptualize the linkage between socio-technical practices and software security learning processes in OSS communities. A questionnaire and statistical analytical techniques were employed to test hypothesized relationships in the model to gain a better understanding of this research topic. Full article

Hybrid Automatic Repeat reQuest (HARQ) techniques are widely employed in the most important wireless systems, e.g., the Long Term Evolution (LTE) cellular standard, to increase the reliability of the communication. Despite these schemes have been widely studied in literature in the past several years, the recent results obtained by Polyanskiy, Poor, and Verdú on the finite-blocklength regime disclosed new possibilities for the research on HARQ schemes. Indeed, new communications trends, which are usually part of the Internet of Things (IoT) paradigm, are characterized by very short packet sizes and a high reliability requirement and, therefore, they call for efficient HARQ techniques. In many scenarios, the energy efficiency of the communication plays a key role as well. In this paper, we aim at providing a comprehensive performance comparison of various kinds of HARQ schemes in the context of short-packet transmissions with energy constraints. We derive optimal power allocation strategies and we show that a minimum 50% energy saving can be achieved after very few transmission attempts if we enable packet combining at the receiver side. Full article

Electronic visual display enabled by touchscreen technologies evolves as one of the universal multimedia output methods and a popular input intermediate with touch–interaction. As a result, we can always gain access of an intelligent machine by obtaining control of its display contents. Since remote screen sharing systems are also increasingly prevalent, we propose a cross-platform middleware infrastructure which supports remote monitoring and control functionalities based on remote streaming for networked intelligent devices such as smart phone, computer and smart watch, etc. and home appliances such as smart refrigerator, smart air-conditioner and smart TV, etc. We aim to connect all these devices with display screens, so as to make possible remote monitoring and controlling a certain device by whichever one (usually the nearest one) of display screens among the network. The system is a distributed network consisting of multiple modular nodes of server and client, and is compatible to prevalent operating systems such as Windows, macOS, Unix-like/Linux and Android, etc. Full article

This work proposes the first scaler designed specifically for the three-moduli set $M_1=\{2^{n+1}-1,2^n,2^n-1\}$. Hence, there is no other functionally similar scaler to compare the proposed scaler with. However, when compared with the latest published scalers for a different moduli set, $M_2=\{2^n+1,2^n,2^n-1\}$, the proposed scaler has a better area and power performance, while it requires a longer time delay. As demonstrated in earlier publications, replacing the $(2^n+1)$ channel in the $M_2$ moduli set by the $(2^{n+1}-1)$ channel, to form the $M_1$ moduli set, considerably improves the overall time performance of residue-based multiply–accumulate arithmetic units. Full article

Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study. Full article

The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes. Full article

The scale of modern neural networks is growing rapidly, with direct hardware implementations providing significant speed and energy improvements over their software counterparts. However, these hardware implementations frequently assume global connectivity between neurons and thus suffer from communication bottlenecks. Such issues are not found in biological neural networks. It should therefore be possible to develop new architectures to reduce the dependence on global communications by considering the connectivity of biological networks. This paper introduces two reconfigurable locally-connected architectures for implementing biologically inspired neural networks in real time. Both proposed architectures are validated using the segmented locomotive model of the C. elegans, performing a demonstration of forwards, backwards serpentine motion and coiling behaviours. Local connectivity is discovered to offer up to a 17.5× speed improvement over hybrid systems that use combinations of local and global infrastructure. Furthermore, the concept of locality of connections is considered in more detail, highlighting the importance of dimensionality when designing neuromorphic architectures. Convolutional Neural Networks are shown to map poorly to locally connected architectures despite their apparent local structure, and both the locality and dimensionality of new neural processing systems is demonstrated as a critical component for matching the function and efficiency seen in biological networks. Full article

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner. Full article

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed. Full article

A Ring Oscillator Physical Unclonable Function (RO PUF) is an application-constrained hardware security primitive that can be used for authentication and key generation. PUFs depend on variability during the fabrication process to produce random outputs that are nevertheless stable across multiple measurements. Though industry has a growing need for PUF implementations on Field Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASIC), the bit errors in PUF responses become a bottleneck and limit the usage. In this work, we comprehensively evaluate the RO PUF’s stability on FPGAs, and we propose a phase calibration process to improve the stability of RO PUFs. We also make full use of the instability of PUFs to provide a novel solution for authentication. The results show that the bit errors in our PUFs are reduced to less than 1%. Full article

While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems. Full article

The end of Dennard scaling led to the use of heterogeneous multi-processor systems-on-chip (MPSoCs). Heterogeneous MPSoCs provide a high efficiency in terms of energy and performance due to the fact that each processing element can be optimized for an application task. However, the evolution of MPSoCs shows a growing number of processing elements (PEs), which leads to tremendous communication costs, tending to become the performance bottleneck. Networks-on-chip (NoCs) are a promising and scalable intra-chip communication technology for MPSoCs. However, these technological advances require novel and effective programming methodologies to efficiently exploit them. This work presents a novel router architecture called application-specific instruction-set router (ASIR) for field-programmable-gate-arrays (FPGA)-based MPSoCs. It combines data transfers with application-specific processing by adding high-level synthesized processing units to routers of the NoC. The execution of application-specific operations during data exchange between PEs exploits efficiently the transmission time. Furthermore, the processing units can be programmed in C/C++ using high-level synthesis, and accordingly, they can be specifically optimized for an application. This approach enables transferred data to be processed by a processing element, such as a MicroBlaze processor, before the transmission or by a router during the transmission. Moreover, a static mapping algorithm for applications modeled by a Kahn process network-based graph is introduced that maps tasks to the MicroBlaze processors and processing units. The mapping algorithm optimizes the communication cost by allocating tasks to nearest neighboring PEs. This complete methodology significantly simplifies the design and programming of ASIR-based MPSoCs. Furthermore, it efficiently exploits the heterogeneity of processing capabilities inside the routers and MicroBlaze processors. Full article

Biometrics play an avid role in today’s mobile security realm. Security experts have attempted to implement different forms of biometrics, from finger, hand, and signature to voice, retina, and iris. Recently, facial biometrics has been added to this list and has introduced another method for a more secure form of authentication. Various organizations believe that by using something you are, like biometrics, their system would be strongly secured. As this may be true, applications used for facial biometrics have lost their credibility when easily defeated. This may be through printed photographs, electronic images, and even look alike. This paper explores the scientific background as to why facial biometrics have become a trusted form of authentication, a user-friendly method, and explores the security of mobile device applications available for Android and iOS systems. We test several applications with our developed methods and discuss the results. Full article

Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving. Full article