Directed Acyclic Graphs (DAGs) are widely deployed as task models in autonomous systems, including vehicles and drones, to capture functional dependency. DAG scheduling has been extensively investigated by various communities to shorten makespan, under the common assumption that the model itself is given a priori. This work studies a rarely touched problem - construction of DAG models - and considers time-triggered blended task chains predominant in autonomous systems. We report representation semantics and a topology optimization method. Experiments show that the average end-to-end response time reduction is 4.8 times of the conventional Floyd algorithm. Our time complexity is $\mathcal{O}\left(n^{2}\right)$, making it suitable for handling dynamic tasks as well.

Incentive mechanisms play a crucial role in mobile crowdsensing between mobile users, third-party platforms, and clients. However, existing mechanisms often do not sufficiently consider users’ future potential and past contributions to data sharing when recruiting or retaining them. Moreover, few people consider that the client in this mechanism requires high-quality data collection for training artificial intelligence models. To solve these problems, this paper proposes an innovative mobile crowdsensing incentive mechanism based on reverse auctions. This mechanism not only attracts users who provide high-quality data but also helps the client collect an appropriate amount of cost-effective data for model training. This is achieved by setting prerequisites for the release of tasks by the platform, that is, only after the client’s model training effect reaches a certain level of improvement, the platform can release new tasks for data collection. Additionally, when recruiting users to participate in auctions, the platform will selectively recruit based on their potential value to ensure high-quality data collection. In cases where users might consider leaving the platform, measures for retention will be implemented. This will involve offering rewards adjusted proportionally according to their potential and contribution value to encourage them to stay. The comprehensive simulation results demonstrate the effectiveness of this new mechanism, highlighting its superior performance over existing mechanisms.

Cyber-physical systems (CPSs) realize the automatic control of entities through computing systems and networks. Stability is an important factor in CPS for system upgrading and troubleshooting. Traditional analysis methods focus on simulation and formal analysis, which have two major limitations: first, the current state information of CPS is difficult to obtain; second, most CPS face the state space explosion problem. These problems can be avoided and a good analysis can be provided based on empirical data. The main work of this article is summarized as follows: first, a phase space reconstruction method is designed to divide the dataset into several subsequences with the same shape; second, we propose a stability analysis method based on correlation dimensions. Results indicate that the proposed approach can obtain a stable correlation dimension. CPS perform better if the correlation dimension is maintained within a certain range; otherwise, a destabilizing factor exists. The proposed stability analysis has less complexity and running time.

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

As an essential part of Internet of Things, monocular depth estimation (MDE) predicts dense depth maps from a single red-green-blue (RGB) image captured by monocular cameras. Past MDE methods almost focus on improving accuracy at the cost of increased latency, power consumption, and computational complexity, failing to balance accuracy and efficiency. Additionally, when speeding up depth estimation algorithms, researchers commonly ignore their adaptation to different hardware architectures on edge devices. This article aims to solve these challenges. First, we design an efficient MDE model for precise depth sensing on edge devices. Second, We employ a reinforcement learning algorithm and automatically prune redundant channels of MDE by finding a relatively optimal pruning policy. The pruning approach lowers model runtime and power consumption with little loss of accuracy through achieving a target pruning ratio. Finally, we accelerate the pruned MDE while adapting it to different hardware architectures with a compilation optimization method. The compilation optimization further reduces model runtime by an order of magnitude on hardware architectures. Extensive experiments confirm that our methods are effective for images of different sizes on two public datasets. The pruned and optimized MDE achieves promising depth sensing with a better tradeoff among model runtime, accuracy, computational complexity, and power consumption than the state of the arts on different hardware architectures.

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement. Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.

Multiprocessor systems are increasingly deployed in real-time applications, where reliability, energy consumption, and makespan are often the main scheduling objectives. In this work, we investigate the dynamic scheduling of tasks modeled by directed acyclic graphs (DAGs), which is an NP-hard problem with all existing methods being heuristics. Our contributions have two steps: 1) assuming that the allocation of DAG nodes to processors is given, we propose optimal energy allocation (OEA) and search-based OEA (SOEA)-the first optimal methods that minimize the energy consumption while satisfying the reliability requirement-for homogeneous and heterogeneous systems, respectively and 2) we present a novel scheduling algorithm out-degree scheduling (ODS) that allocates the DAG nodes according to their out-degrees, and considering energy consumption, reliability, as well as dynamic finish time. ODS dominates the widely applied heterogeneous earliest finish time (HEFT) in makespan. Combining SOEA with ODS makes a complete solution to the problem of dynamic DAG scheduling on multiprocessor systems, and achieves generally better results compared to the existing approaches. Specifically, in most cases, we are better on all the three objectives, i.e., reliability, energy, as well as makespan, and in other cases, we are better on some of the objectives.

In this paper, the joint optimization problem with energy efficiency and effective resource utilization is investigated for heterogeneous and distributed multi-core embedded systems. The system model is considered to be fully a heterogeneous model, that is, all nodes have different maximum speeds and power consumption levels from the perspective of hardware while they can employ different scheduling strategies from the perspective of applications. Since the concerned problem by nature is a multi-constrained and multi-variable optimization problem in which a closed-form solution cannot be obtained, our aim is to propose a power allocation and load balancing strategy based on Lagrange theory. Furthermore, when the problem cannot be fully solved by Lagrange approach, a data fitting method is employed to obtain core speed first, and then load balancing schedule is solved by Lagrange method. Several numerical examples are given to show the effectiveness of the proposed method and to demonstrate the impact of each factor to the present optimization system. Finally, simulation and practical evaluations show that the theoretical results are consistent with the practical results. To the best of our knowledge, this is the first work that combines load balancing, energy efficiency, hardware heterogeneity and application heterogeneity in heterogeneous and distributed embedded systems.