In the fields of co-simulation and component-based modelling, designers import models as building blocks to create a composite model that provides more complex functionalities. Modelling tools perform instantaneous cycle detection (ICD) on the composite models having feedback loops to reject the models if the loops are mathematically unsound and to improve simulation performance. In this case, the

Runtime verification of software (RV) often relies on two categories of tools : dynamic heavy-weight tools, which significantly impact performance, and lighter and more efficient but static tools, which require recompiling the binary. In this paper we propose a new framework for building efficient and targeted dynamic RV tools, bridging the gap between those two categories. This framework is separated

Crown scheduling is a static scheduling approach for sets of parallelizable tasks with a common deadline, aiming to minimize energy consumption on parallel processors with frequency scaling. We demonstrate that crown schedules are robust, i. e. that the runtime prolongation of one task by a moderate percentage does not cause a deadline transgression by the same fraction. In addition, by speeding up

Web data are experiencing a proliferation of video content for mobile platforms. This is accompanied by new advances in heterogeneous general purpose processor (GPP) cores embedded in mobile devices which offer a great opportunity to enhance both performance and energy efficiency of software (SW) video decoding. On the other hand, hardware (HW) video accelerators are more energy-efficient but are not

As an integral part of Internet of Things (IoT), mobile crowdsensing (MCS) via smart embedded devices has recently gained significant attention for being effective in a wide range of applications and services, particularly in smart environments. In order to collect data, MCS systems seek ubiquitous sensing services from smart mobile devices such as smartphones, tablets and wearables, which are equipped

Over the past few years, Convolution Neural Networks (CNN) have been extensively adopted in broad AI applications and have achieved noticeable effect. Deploying the feedforward inference of CNN on edge devices has now been considered a research hotspot in Edge Computing. In terms of the mobile embedded devices that exhibit constrained resources and power budget, the considerable parameters and computational

The keyword-based searchable encryption is a very promising technology in the cloud storage, which can supply data users with accurate search services over encrypted data based on queried keywords. An important security objective of this kind of schemes is to resist keyword privacy leakage because it may cause content leakage of to data itself and search preferences of data users. To protect keyword

Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive and latency-sensitive applications on the eve of the large-scale commercial operation of 5G. However, the slow-start phenomenon incurred by existing request history-based caching strategies

Recent telecommunication paradigms, such as big data, Internet of Things (IoT), ubiquitous edge computing (UEC), and machine learning, are encountering with a tremendous number of complex applications that require different priorities and resource demands. These applications usually consist of a set of virtual machines (VMs) with some predefined traffic load between them. The efficiency of a cloud

To meet the challenges posed by the explosive growth of mobile traffic, data caching at the network edge has been considered a key technology in future mobile networks, while the potential of device-to-device (D2D) communications in areas such as traffic offloading is also of great interest. Existing work does not have a network switching mechanism, which would ensure load balancing and improve quality

Nowadays, as the memory capacity of servers become larger and larger, distributed in-memory file systems, which enable applications to interact with data at fast speed, have been widely used. However, the existing distributed in-memory file systems still face the problem of low data access performance in small data reading, which seriously reduce their usefulness in many important big data scenarios

Low-power small-scale embedded sensing systems employing batteries generally impose high maintenance costs. To enable maintenance-free operation, they are powered from energy harvested from the environment thus making them batteryless. However, due to high variance of ambient energy, these batteryless embedded devices are unable to harvest enough energy from the environment required for continuous

The use of hardware caches became essential in modern embedded systems to address the speed gap between processor and memory. In such systems, cache-related preemption delay (CRPD) may represent a significant proportion of task execution time. Addressing this delay in scheduling simulation of these systems stays an open and under-examined problem. Assumptions are often made to simplify the computation

In GPU-based embedded systems, the problem of computation and data mapping for multiple applications while minimizing the completion time is quite challenging due to large size of the policy space. To achieve fast competition time, a fine-grain mapping framework that explores a set of critical factors is needed for heterogeneous embedded systems. In this paper, we present a theoretical framework that

With the development of informatization, smart city has become a new concept for the next generation of urbanization globally. In the face of intensive data requests and constrained resources, it is particularly important to design a safe and energy-efficient computing system.In this paper, we propose a multi-domain access control scheme suitable for smart city construction. We divide the huge equipment

With the advances in automotive industry and telecommunication technologies, more and more vehicles are connected which paves the pathway to future intelligent transportation system (ITS). The safety of both the passengers and pedestrians has been a major concern in ITS. Vehicular ad hoc network (VANET) is a promising technology that can provide a wide range of services, such as safety, convenience

Locking protocol is a crucial component in scheduling of real-time systems. The digraph real-time task model (DRT) is the state-of-the-art graph-based task model, which is a generalization of most previous real-time task models. To our best knowledge, the only work addressing resource sharing problem in DRT task model proposes a resource sharing protocol, called ACP, as well as a scheduling strategy

The explosive generation of Internet of Things (IoT) data calls for cloud service providers (CSP) to further provide more secure and reliable transmission, storage, and management services. This requirement, however, goes against the honest and curious nature of CSP, to the extent that existing methods introduce the third-party audit (TPA) to check data security in the cloud. TPA solves the problem

Mobile Device Cloud (MDC) has become a promising and lucrative cloud environment that exploit nearby mobile devices’ idle resources to improve compute-intensive applications. Computing code at nearby mobile devices rather than a distant master cloud helps improve real-time applications’ performance. However, it is non-trivial to motivate the worker devices to participate voluntarily in sharing their

Efficient task scheduling improves offloading performance in mobile edge computing (MEC) environment. The jobs offloaded by different users would have different dependent tasks with diverse resource demands at different times. Meanwhile, due to the heterogeneity of edge servers configurations in MEC, offloaded jobs may frequently have placement constraints, restricting them to run on a particular class

Network-on-Chips (NoC) are widely used in industrial applications since they provide communication parallelism and reduce energy consumption. The use of NoC has been recently extended to real-time systems, whose execution has to meet temporal constraints. Communication delays introduced by the network make the scheduling analysis challenging. In this article, we propose a new NoC communication model

Many parallel applications do not scale with the number of threads. Several online and offline strategies have been proposed in order to optimize this number. While the former strategy can capture some behaviors that can only be known at runtime, the latter do not impose any execution overhead and can use more complex and efficient algorithms. However, the learning algorithm in these offline strategies

With the advancement in sensor technology and the proliferation of low-cost electronic circuits, Internet of Things (IoT) is emerging as a promising technology for realization of smart cities. However, challenges such as security, privacy, trust, scalability, verifiability, and centralization prevent faster adaptations of IoT-driven smart cities. Thus, in this paper, a Trustworthy Privacy-Preserving

In last few decades, smart-grid architecture has evolved as a new power service provider that can accomplish regulations of power generation with significant capabilities to monitor power consumption behaviour of the consumers and help in stabilization of the power system. With the huge progress of the delay sensitive smart grid network, it has to encounter various challenges. One of them is cyber-threats

Over the years, the nature of processing platforms is witnessing a significant shift in most of the battery supported real-time systems, which now support a combination of specialized multicores to meet the demands of modern applications. Devising energy-efficient schedulers has become a critical issue for such kinds of devices. Hence, this research presents a low-overhead heuristic strategy named

The osmotic computing paradigm sets out the principles and algorithms for simplifying the deployment of Internet of Things (IoT) applications in integrated edge-cloud environments. Various existing simulation frameworks can be used to support integration of cloud and edge computing environments. However, none of these can directly support an osmotic computing environment due to the complexity of IoT

Recently, deep learning is considered an important concept that is used in a lot of important applications, which require accurate models, such as image classification, identification of audio, intrusion detection, and face recognition. However, building a good deep learning model needs a huge amount of data that is not easily provided, especially in the applications that need sensitive information

As one of the internet of things (IoT) use cases, wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. Videos captured by surveillance cameras are required to be uploaded for further storage and analysis, while the large amount of its raw data brings great challenges to the transmission through resource-constraint wireless networks

Computation offloading is a promising solution for resource-limited IoT devices to accomplish computation-intensive tasks. In order to promote the service trading between edge computing service providers and IoT devices, a series of works have explored incentive mechanisms for IoT-edge computing. However, most traditional incentive mechanisms (such as stackelberg game-based approaches) expose privacy

Multi-agent decision making is a fundamental problem in edge intelligence. In this paper, we study this problem for IoT networks under the distributed Multi-Armed Bandits (MAB) model. Most of existing works for distributed MAB demand long-time stable networks connected by powerful devices and hence may not be suitable for mobile IoT networks with harsh IoT constraints. To meet the challenge of resource

In space missions, boot software is in charge of the initialisation sequence of flight computers. The processor module in which it runs has a high tolerance to radiation, although not all devices have the same tolerance level. A boot software design capable of recovering from errors in the most vulnerable devices shall provide greater system reliability. This work has been carried out in the context

The UML Activity Diagram (UAD) is mostly used for modeling behavioral aspects of objects and systems. OCL (Object Constraint Language) is used together with UAD to specify guard conditions and action constraints. Due to the ambiguous semantics of UAD, it is relevant to formalize such diagrams using formal semantics and formal methods. In this paper, we opt for a formal transformation of UML activity

In the big data era, in order to relieve computational pressure on overloaded CPU caused by ever increasing amount of data, many researches focus on hardware acceleration using FPGA for data-intensive applications. In this paper, a novel FPGA-based storage engine is proposed for DBMS in the cloud with focus on data filtering operation. A hardware data filter is designed which can significantly speedup

Scheduling is a decision-making mechanism that allows resource sharing among several activities by determining their order of execution on the available resources. In the heterogeneous distributed systems, it is a great challenge to schedule concurrent workflows submitted by different users at different times. Scheduling with deadline and budget constraints are becoming an even more challenging issue

Efficient prototyping is an invaluable resource for modern enterprises and research centers. An efficient prototyping tool exhibits high throughput while maintaining flexibility, and reduces design and validation efforts, resulting in low time-to-market and high competitiveness. This paper presents a modular implementation of high-performance software (SW) libraries running on a Heterogeneous Computing

The goal of Industry 4.0 is to be faster, more efficient and more customer-centric, by enhancing the automation and digitalisation of production systems. Frequently, the production in Industry 4.0 is categorized as safety-critical, for example, due to the interactions between autonomous machines and hazardous substances that can result in human injury or death, damage to machines, property or the environment

At present, various types of code vulnerabilities lead to a huge workload of detection. Although multi-party cooperation detection methods can be applied to improve efficiency, common crowdsourcing models are difficult to guarantee the privacy of code and identity. We design a code and identity privacy vulnerability crowdsourcing detection system ConGradetect. The system is built on a blockchain and

Integrating idle embedded devices into cloud computing is a promising approach to support Distributed Machine Learning (DML). In this paper, we approach to address the data hiding problem in such DML systems. For the purpose of the data encryption in DML systems, we introduce the tripartite asymmetric encryption theorem to provide theoretical support. Based on the theorem, we design a general image

The legacy storage data path is largely structured in black-box layers and has four major limitations: (1) functional redundancies across layers, (2) poor cross-layer coordination and data tracking, (3) presupposition of high-latency storage devices, and (4) poor support for new storage data models. While addressing all these limitations is a daunting challenge, we introduce ADAPT, an auxiliary storage

With the explosive growth of battery-free and energy-harvesting devices, the energy harvesting powered system has gained more attentions and been widely used in different fields. However, unstable harvested energy is a challenge of energy-harvesting devices since the program execution would be interrupted frequently. Non-volatile processor (NVP) is proposed to back up volatile logics before energy

Efficient energy management and fault tolerance are two key issues that demand judicious handling while scheduling real-time task-sets. Standby-sparing technique is available in literature for fault tolerance, while dynamic power management (DPM) and dynamic voltage scaling (DVS) schemes are exploited for energy management. However, usage of auxiliary processor for running backup jobs leads to increased

Wireless Body Area Networks (WBAN) is often envisioned as a paradigm shift from the traditional healthcare system to the modern E-Healthcare system. The patient's vitals sensed by the sensors are highly sensitive, confidential, and susceptible to various attacks from adversaries. For the WBAN being a concrete application of the healthcare system, it is paramount to ensure that the data sensed by the

Developing efficient embedded vision applications requires exploring various algorithmic optimization trade-offs and a broad spectrum of hardware architecture choices. This makes navigating the solution space and finding the design points with optimal performance trade-offs a challenge for developers. To help provide a fair baseline comparison, we conducted comprehensive benchmarks of accuracy, run-time

Action recognition in video involves interpreting complex semantic information, which remains a challenge in computer vision. Research on the use of deep neural networks for video action recognition has made significant progress in recent years, but most available recognition frameworks cannot satisfactorily perform the video action recognition task in privacy scene. In light of this issue, this paper

In today’s ChipMultiprocessors (CMPs), multiple cores share the common Last Level Cache (LLC), divided into multiple banks. As the data requirement is increasing the demand for larger LLC sizes is also increasing. The traditional SRAM technology is not area efficient to design such larger LLCs as demanded by the modern CMPs. From the last few years, DRAM technologies have been used to propose LLC.

The rapid development of the Internet of Vehicles (IoVs) leads to a significant increase in the number of automobiles connected to the Internet. Therefore, to make all vehicles under a shared Internet, we need distributed systems and efficient communication design known as the vehicles cloud. When IoV transmits the monitored data to the server, it can delete, modify, or eavesdrop. Thus, this can cause

In this article, we propose Palisade, a distributed framework for streaming anomaly detection. Palisade is motivated by the need to apply multiple detection algorithms for distinct anomalies in the same scenario. Our solution blends low latency detection with deployment flexibility and ease-of-modification. This work includes a thorough description of the choices made in designing Palisade and the

Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous

Convolutional neural networks (CNNs) have proven to be a disruptive technology in most vision, speech and image processing tasks. Given their ubiquitous acceptance, the research community is investing a lot of time and resources on deep neural networks. Custom hardware such as ASICs are proving to be extremely worthy platforms for running such programs. However, the ever-increasing complexity of these

The vehicles in Internet of Vehicles (IoV) can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities in an IoV-based smart city deployment. To address this issue, we aim to design a novel blockchain-enabled batch authentication scheme in Artificial

To alleviate the performance degradation introduced by cryptographic operations in web-based services, dedicated hardware accelerators have emerged for calculation offloading. However, accelerators from different vendors can be endowed with variable run-time modes and optimized for different workload types. It is necessary but challenging to fully and fairly quantify the associated acceleration benefits

Heterogeneous multi-cores utilize the strength of different architectures for executing particular types of workload, and usually offer higher performance and energy efficiency. In this paper, we study a federated scheduling. Federated scheduling is a promising approach to schedule non-typed parallel real-time tasks, where each heavy task exclusively executes on a number of dedicated processors, while

In last decade, a proliferation growth in the development of computer malware has been done. Nowadays, cybercriminals (attacker) use malware as a weapon to carry out the attacks on the computer systems. Internet is the main media to execute the malware attack on the computer systems through emails, malicious websites and by drive and download software. Malicious software can be a virus, trojan horse

Intermittent computing (ImC) refers to the scenario where periods of program execution are separated by reboots. ImC systems are generally powered by energy-harvesting (EH) devices: they start executing a program when the accumulated energy reaches a threshold and stop when the energy buffer is exhausted. Since ImC does not depend on a fixed supply of power, it can be used in a wide range of scenarios/devices

Ciphertext-policy attribute-based encryption(CP-ABE) has been widely studied and used in access control schemes for secure data sharing. Since in most of the existing attribute-based encryption methods, all user attributes are managed by a single central authority, it is easy to cause a single point of failure. Therefore, several multi-authority CP-ABE schemes propose to manage user attributes by multiple

Offset-based response time analysis techniques obtain tight worst-case response time (WCRT) bounds by accounting release time dependencies between tasks. The maximum response time variation of a task or a message (i.e., the difference between the worst-case and best-case response time) is used to compute the end-to-end delays of distributed systems (Palencia and Harbour, 1998), (Tindell and Clark,

Users’ trip location information is of great value in the study of social computing and big data. Based on the personalized characteristics of user trajectory data, this paper proposes a Trajectory-based-based Identity Authentication Method (TIAM). TIAM consists of the registration phase and the authentication phase. Users initialize the trip routes and the coordinates of home and office in the registration

Transform methods, such as the Laplace and the Fourier transforms, are widely used for analyzing the continuous dynamics of the physical components of Cyber-physical Systems (CPS). Traditionally, the transform methods based analysis of CPS is conducted using paper-and-pencil proof methods, computer-based simulations or computer algebra systems. However, all these methods cannot capture the continuous