Journal Information
The Journal of Systems Architecture: Embedded Software Design (JSA)
Impact Factor:

Call For Papers
The Journal of Systems Architecture: Embedded Software Design (JSA) is a journal covering all design and architectural aspects related to embedded systems and software. It ranges from the microarchitecture level via the system software level up to the application-specific architecture level. Aspects such as real-time systems, operating systems, FPGA programming, programming languages, communications (limited to analysis and the software stack), mobile systems, parallel and distributed architectures as well as additional subjects in the computer and system architecture area will fall within the scope of this journal. Technology will not be a main focus, but its use and relevance to particular designs will be.

Types of Paper
The journal welcomes the following types of contributions:
• Original research articles
• Review articles, providing a comprehensive review on a scientific topic
• Fast Communications: Short, self-contained articles on ongoing research, or reporting interesting, possibly tentative, ideas, or comments on previously published research
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on ubiquitous and intelligent embedded systems (VSI:UIES20)
Submission Date: 2020-09-30

Ubiquitous and intelligence embedded represents a new generation of smart cooperative objects that are equipped with AI software/hardware to enable a wide range of intelligent environments, e.g. smart cities, intelligent manufacturing, ambient assisted living, autonomous driving, just name of few. This results in the urgent and pressing needs of divergent intelligent embedded systems to fit into those specific working contexts with more scalability at multiple designing dimensions of models and systems. However, the system-level strong coupling of resource-hungry AI and resource-constrained embedded platform addresses new computing issues on how to make full usage of limited hardware to support energy efficient AI systems and how to efficiently migrate traditional intelligent algorithms to on-edge devices. Designing such ubiquitous and intelligent embedded systems also brings many challenging research issues of domain-specific optimization in software/hardware, such as intelligent sensor, 5G communication, IoT, industrial internet, system reliability, and so on. The goal of this special issue is to design smart embedded systems beyond the in-hand standards and technologies and deploy intelligence to the any places they need. Potential topics include, but are not limited to: Design of intelligent embedded systems Lightweight deep learning for IoTs Low-power design for intelligent embedded systems Credible embedded systems Ubiquitous computing and context-ware computing Heterogeneous multi-core system design Design of system-level software for embedded systems On-edge computing CPS and IoTs Embedded vision SW/HW co-design Real-time scheduling model and algorithm for embedded AI Applications for embedded systems (consumer electronics, 5G communication, manufacturing, aerospace engineering, health-care)
Last updated by Dou Sun in 2020-05-27
Special Issue on High-Performance-Computing-Communications for Cyber-Physical-Social Systems
Submission Date: 2020-12-30

Cyber-Physical-Social-Systems (CPSS) is an emerging cross-disciplinary research area that features the combination of Cyber-Physical-Systems (CPS) and Social Networks. It is also an extension of CPS, which emphasizes sensing and processing the combined information from cyberspace, physical assets, and human social knowledge. In practice, CPSS is a complex system integrating the objects in cyber, physical, and social space to enable proactive services and applications. Recently CPSS has raised in multiple sectors such as Internet-of-Things (IoT), smart cities, healthcare, intelligent transportation, to name a few. CPSS poses fundamental challenges and opens research issues in multiple aspects due to the deeply complex intertwining among different components. For example, CPSS is expected to deal with data directly from trans-domain applications, which can be in various forms such as GPS coordinates, temperature, vehicle speed, electricity consumption, etc. Moreover, the high velocity of data accumulation rate leads to the ever-increasing huge volume of large-scale CPSS data. Therefore, innovative technologies coordinating various applications of heterogeneous systems and facilitating an efficient collection, storage, and processing of massive amounts of CPSS data are highly desired. High-Performance-Computing-Communications (HPCC) describes the technology of parallelization and distribution algorithms to perform complex tasks faster with strong data processing power and computing abilities. As a result, HPCC is increasingly becoming a prevalent solution to a wide range of CPSS applications. Large and complex CPSS data management problems must cope with very various constraints such as tight timing schedules. This "High-Performance-Computing-Communications for Cyber-Physical-Social Systems" special issue aims to cover the most recent research and development on the enabling technologies for HPCC in CPSS and to stimulate more discussions in this field. Original and unpublished high-quality research results are solicited to explore various challenging topics which include, but are not limited to: Parallel and distributed system architectures for CPSS Information fusion and data mining for CPSS High-performance computing and many-task computing for CPSS Parallel resource and service management, scheduling, and migration for CPSS Enabling information, communication and control technologies for CPSS Exploring social cloud networks and social cloud computing for CPSS CPSS for novel applications such as smart cities, smart grid, smart transportation, etc. M2M and D2D communication technologies for CPSS Big data analytics for CPSS Mobile computing and wireless communications for CPSS
Last updated by Dou Sun in 2020-08-24
Special Issue on AI driven embedded system architectures and protocols for big data (VSI:ADES-BD)
Submission Date: 2020-12-30

Recent advancements in intelligent embedded systems are paving the way for modern large-scale data systems through a wide variety of protocols, architectures, services and configurations. Technologies such as smart sensing, RFID tagging, embedded internet, edge computing, and predictive data mining all work to permeate intelligence and decision making into the physical world with the ultimate aim of continually enhancing human experience in real-time. “Big data” is the recent buzzword in which analytics provides real-time insights, which need to be actioned upon quickly to support decisions, gain better value, and mitigate risk. Concurrently, artificial intelligence (AI), and its dominant form - machine learning, has been intensively applied to deal with large-scale heterogeneous data to help innovate and transform businesses. The convergence of these two technology paths is highly promising. Data is considered the “blood” of artificial intelligence, whereas AI systems learn from the data in order to accomplish their function. The aim of this special issue is to discuss how large-scale data systems and AI can be leveraged to enhance the learning, reasoning, and decision-making in embedded systems, in real-time. Data governance, data integration, data storage, data quality and data security are some criticalities associated with this problem, while conventional embedded system architectures and protocols are used to prepare data are inadequate. Unlike traditional data sets that are commonly associated with embedded systems, big data tends to be unstructured, multi-modal, and in the case of human-centric text, perhaps multi-lingual. The incompleteness, fuzziness and uncertainty make it even more intricate to tap and analyse information using contemporary tools. We invite researchers to discuss intelligent embedded system protocols and architectures. Novel approaches to information discovery and decision making which use multiple intelligent technologies such as machine learning, deep learning, artificial intelligence, natural language processing and image recognition among others are required to understand data & then generate insights. We also welcome implementation papers on analyzing and processing of big data and practical data-driven decision making by discovering and understanding knowledge from the data. The topics of interest include, but are not limited to: Smart embedded system designs for cloud-based big data. AI-empowered modelling of embedded systems for big data. Intelligent embedded system protocols for high performance/parallel computing platforms. Embedded-aware protocols for data quality and integrity. Privacy preservation, trust and security in intelligent embedded systems. Intelligent embedded computer vision and natural language processing systems using big data. Intelligent sensing for smart cities. Embedded systems for large-scale smart healthcare, avionics, transportation, and automotive. Experimental test-beds of intelligent embedded system frameworks for big data.
Last updated by Dou Sun in 2020-08-24
Special Issue on Security and Privacy in Fog Computing-based Critical Infrastructures
Submission Date: 2021-01-31

The increasing integration of information and communication technologies has undoubtedly boosted the efficiency of Critical Infrastructures (CI). However, the first wave of IoT devices, together with the management of enormous amount of data generated by modern CIs, has created serious architectural issues. Fog computing has emerged as a viable solution for many large-scale latency-sensitive CI-based applications. Usually, the concept of fog computing is useful for various mission critical applications that require real-time data processing. Despite the concept of fog computing offering several notable features (e.g., low latency, dynamic per user optimization, etc.), many issues remain for it to be an efficient computing paradigm. Especially, some issues related to performance and security urgently need to be considered. Given the importance of security considerations for IoT environments and the growing interest in fog computing and its associated technologies, this special issue is focused on security and privacy issues in fog computing-based critical Infrastructures (like smart-grid, IoT-based e-healthcare system, Vehicle-to-grid network, etc) Here we welcome high quality research from both academia and industry, with particular emphasis on novel ideas and techniques. Only technical papers describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or a journal will be considered. We will recommend submission of multimedia with each paper as this significantly increases the visibility, downloads, and citations of articles. Selection and Evaluation Criteria Relevance to the topics of this special issue Research novelty (e.g., new techniques) and potential impact Readability Potential topics include, but are not limited to: Authentication for fog computing- based critical Infrastructures Privacy for fog computing-based critical Infrastructures Accountability for fog computing-based critical infrastructures Block-chain security for fog-enabled critical infrastructures Hardware security of devices in fog-enabled critical infrastructures Safety issues in fog-enabled critical infrastructures Access control and key-management for fog-enabled critical infrastructures Theories, methods and applications in machine learning addressing security issues and solutions for fog-enabled critical infrastructures Trust management for fog computing Test-bed, prototype implementation and fog-based security applications New paradigms facilitating fog-enabled critical infrastructures Data security in fog-enabled critical infrastructures Deception Technologies for securing critical infrastructures
Last updated by Dou Sun in 2020-07-04
Special Issue on Safe and Intelligent Embedded Software and Systems
Submission Date: 2021-02-01

With the constraint of limited resources, embedded systems demand novel security techniques to protect their critical operations. For robustness, various resource-efficient fault tolerance techniques for reliable operations are also needed. On the other hand, Artificial Intelligence/Machine Learning (AI/ML) has become the key enabling technology for many applications, from recommendation systems to facial recogintion. However, the Deep Learning and Deep Reinforcement Learning, as the most effective AI/ML techniques, are generally quite complex where the model training are very computation-intensive and are typically performed offline and in the cloud. Even the runtime model inference may demand significant computing power from the computing platform, which may be a heterogeneous mixture of multicore CPUs, GPUs, DSPs, FPGAs and ASICs. Such complex requirements on safety, security, reliability and intelligence call for innovative security, fault tolerance, and AI/ML techniques for resource-constained embedded systems, such as Internet of Things (IoTs), where many challenging research issues of performance, efficiency, power-consumption, reliability, dependability and security need to be addressed. This special section aims to present a collection of papers on the following topics in the context of safe and intelligent embedded software and systems: Novel secure designs for embedded software and systems Innovative fault tolerance techniques for embedded software and systems Power efficient fault tolerance approaches embedded software and systems Intelligent algorithms and architectures for resource-constrained platforms AI/ML techniques for specialized heteorgenos platforms with GPUs/DSPs/FPGAs Performance optimization for safe and intelligent embedded systems
Last updated by Dou Sun in 2020-07-04
Special Issue on Ubiquitous Edge Computing for Next Generation IoT and 6G: Architecture, Modelling and Systems (VSI:UECNx-IoT)
Submission Date: 2021-03-15

The technological enhancement in the field of smart communication from daily life to industrial applications leading towards the development of more efficient and persuasive system for the emerging phenomenon of next generation IoT. The 5G and beyond technology is already making big differences for the edge computing system and services paradigm. But in the future, the sheer volume of things which will be connected to the internet for various smart embedded products or areas such as traffic safety, automated vehicles and industrial can’t be handled by these beyond technologies as it requires low latency and high speed that leads to distributed computing to save time and bandwidth. The future technology with the evolution towards 6G demands ubiquitous edge computing (UEC) and fog computing which includes the big data architectures, protocols and management along with data security and distributive ubiquitous edge applications systems. The International Telecommunication Union (ITU) has designed the protocols and architectures in 3GPP ecosystem for Industrial IoT based UEC models. The 6G technology offers advanced computational services for ambient intelligent embedded systems with the optimization and resource management models using machine learning (AI). This UEC based networking extends the system design, deployment and performance management for next generation IoT applications along with secure and green networking. The special issue will serve a platform for the academicians and industrial researchers to present their state-of-art ideas and contributions toward UEC based smart embedded protocols, architecture, modelling, simulations and systems for next generation IoT and 6G applications. The submitted work should be unpublished technical articles with substantial novel contribution towards the scope. The list of topics in scope for this issue includes, but is not limited to: Smart embedded systems for 6G Ubiquitous networking for secure next generation IoT Resource allocation and optimization in UEC AI/ML based protocols and architecture for next generation IoT Cloud virtualization and management for 6G applications Machine learning techniques for Fog computing and UEC Ambient intelligent embedded systems and architecture for 6G Intelligent architectures for distributed computing in next generation IoT Modelling, system architecture and deployment for 6G Heterogeneous resource management UEC systems Content and information centric architecture for UEC systems Modelling and simulations for 6G architecture for IIoT application Software defined networking for next generation IoT applications Load balancing architecture, systems and security in 6G applications AI based persuasive system for mobile edge computing based IIoT applications Terahertz and cell-less protocols and hardware systems for 6G applications Infrastructure management architecture and simulations for UEC systems
Last updated by Dou Sun in 2020-08-24
Special Issue on Trustworthiness and Privacy in Sensor-Cloud Systems (VSI: TPSCS)
Submission Date: 2021-03-31

Cloud Computing and Wireless Sensor Networks have received tremendous attention from both academia and industry, due to the numerous exciting applications in the Internet of Things and Cyber-Physical Systems, e.g., industrial process control, video surveillance, structural health monitoring, mobile commerce, mobile learning, and mobile gaming. Sensor-Cloud is the product of combining WSNs and Cloud Computing, allowing truly pervasive computation between the physical world and the cyber world. However, new trustworthiness and privacy challenges need to be addressed to accelerate the development of these integrated applications. First, Sensor Network Provider (SNP), Cloud Service Provider (CSP), and Cloud Service User (CSU) are included in the Sensor-Cloud system. Without trust and reputation management as well as management of CSPs and SNPs, other parties with low trust and reputation are highly likely chosen. Second, in the Sensor-Cloud system, the data is generally related to privacy while the storage space is shallow in WSNs. If massive data is stored in the cloud, it is easy to be disclosed. Third, due to sensors' weak abilities in processing and communication, it is difficult for underlying networks to afford enough computation ability for solving these new arriving trustworthiness and privacy problems. Furthermore, lots of new computing models and technologies are arising, such as fog computing, edge computing, artificial intelligence, big data, and the Internet of Things. This calls for the urgent need to consider and develop new mechanisms to fill the gap between WSNs and cloud computing. Future sensors and cloud-integrated systems need to close the trustworthiness and privacy gap in the face of challenges in different circumstances. This Special Issue is proposed to bring together researchers to exchange the state-of-art research results to tackle the trustworthiness and privacy issues of sensor-cloud systems. The topics of interest include, but are not limited to: Concepts, theory, standardization and modeling, and methodologies for sensor-cloud systems Trustworthiness model and architecture in sensor-cloud Data protection in sensor-cloud systems Security in cloud computing and pervasive/ubiquitous computing Data trustworthiness in sensor-cloud systems Trust and reputation evaluation in sensor-cloud Data protection and data integrity in sensor-cloud systems Privacy issues in sensor-cloud systems Privacy metrics and policies in sensor-cloud systems Reliability issues in sensor-cloud systems Security computing clouds for cyber-physical systems Security and applications in sensor-cloud systems Mobile sensing applications, detection, transmission and tracking in sensor-cloud systems Trustworthiness levels and relations, metrics, and measures for sensor-cloud systems Edge computing in sensor-cloud systems Intelligent algorithm and applications for sensor-cloud systems
Last updated by Dou Sun in 2020-09-07
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