Deep Neural Networks (DNNs) are increasingly adopted across various industries, driving the demand for deploying their capabilities on mobile devices. However, current mobile inference frameworks often rely on a single processor to execute each model inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNN accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, performing parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Existing studies have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose the Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an offline subgraph partitioning strategy that considers both hardware support for operations and scheduling granularity. It also employs a processor-state-aware scheduling algorithm to dynamically balance workloads based on real-time system conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results demonstrate that, compared to vanilla inference frameworks, ADMS achieves a 4.04× reduction in multi-DNN inference latency.

In Wireless Sensor Networks (WSNs), relay sensor nodes can aggregate data from edge sensor node into a summary information before sending to the sink. Due to the vast number of sensor nodes in a distributed edge computing (DEC) network, these relay sensor nodes may receive a high number of aggregation requests. This increases the chance of conflicting transmissions, which further leads to unwanted latency. Designing a conflict-free and minimal latency data aggregation schedule remains an open question. Moreover, existing related works have been conducted in traditional WSNs. By leveraging multiple antennas, the Multiple Input Multiple Output (MIMO) and cooperative MIMO called virtual MIMO (V-MIMO) enable broadband wireless communication, thereby improving the performance of WSNs. However, compared with traditional WSNs, MIMO and V-MIMO introduce distinct interference models requiring careful consideration. The work proposes a solution to an NP-hard problem, addressing three challenges: (i) interference; (ii) latency; and (iii) dynamic changes in network topology. Firstly, to counter interference, we propose a model where multiple nodes can simultaneously send data to the same parent by connecting different antennas. Secondly, to minimize latency, we propose a novel distributed heuristic data aggregation scheduling method, which intertwines the construction of an optimal data aggregation tree and conflict-free scheduling. Finally, to handle dynamic network topology changes, we propose lightweight adaptive strategies that do not increase data aggregation latency. Simulation results and theoretical analysis demonstrate superior performance in reducing data aggregation latency. When compared with state-of-the-art solutions, our proposed method decreases data aggregation latency by at least 2.6× on average.

Data aggregation is a critical operation in wireless sensor networks (WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multichannel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multichannel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of β is adaptive. The Markov approximation method-based adaptive-β is more flexible and efficient than the single β approximation. The experiments show that DEDAS-MC outperforms the existing competing schemes.

More and more organizations and individuals start to pay attention to real-time threat intelligence to protect themselves from the complicated, organized, persistent and weaponized cyber attacks. However, most users worry about the trustworthiness of threat intelligence provided by TISPs (Threat Intelligence Sharing Platforms). The trust evaluation mechanism has become a hot topic in applications of TISPs. However, most current TISPs do not present any practical solution for trust evaluation of threat intelligence itself. In this paper, we propose a graph mining-based trust evaluation mechanism with multidimensional features for large-scale heterogeneous threat intelligence. This mechanism provides a feasible scheme and achieves the task of trust evaluation for TISP, through the integration of a trust-aware intelligence architecture model, a graph mining-based intelligence feature extraction method, and an automatic and interpretable trust evaluation algorithm. We implement this trust evaluation mechanism in a practical TISP (called GTTI), and evaluate the performance of our system on a real-world dataset from three popular cyber threat intelligence sharing platforms. Experimental results show that our mechanism can achieve 92.83% precision and 93.84% recall in trust evaluation. To the best of our knowledge, this work is the first to evaluate the trust level of heterogeneous threat intelligence automatically from the perspective of graph mining with multidimensional features including source, content, time, and feedback. Our work is beneficial to provide assistance on intelligence quality for the decision-making of human analysts, build a trust-aware threat intelligence sharing platform, and enhance the availability of heterogeneous threat intelligence to protect organizations against cyberspace attacks effectively.

Data aggregation is widely used in wireless sensor networks (WSNs) due to the resource constraints of computational capability, energy and bandwidth. Because WSNs are often deployed in an unattended hostile environment, WSNs are prone to various attacks. The traditional security technologies such as privacy protection and encryption technology can not address the attacks from the internal nodes of network. Therefore, the trust management mechanism for data aggregation has become a hot research topic, and an efficient trust management mechanism plays an important role in data aggregation.

Mobile crowd sensing (MCS) arises as an appealing paradigm, which utilizes participants to contribute sensing data generated from sensors embedded in smart devices in the internet of things (IoT) for the people-centric service delivery and crowd intelligence extraction. Due to the inherent selfishness of human and network's openness, the quality of the data submitted by the participants is not always satisfying. To cope with this problem, a dynamic-trust-based recruitment framework (DTRF) for MCS system is proposed to recruit suitable participants who are trustworthy and always submit high-quality sensing data on time. In this model, we first give the definition of trust, and evaluate the overall trust degree of the participant from multi-dimensional trust evaluation factors: direct trust, feedback trust and incentive function. Then we develop an adaptive weight allocation approach based on information entropy theory, and the algorithm realization is given. Extensive simulations verifies that DTRF can achieve good performance in terms of trustworthy participants selection and task completion rate, compared with trust without feedback model.

Mobile Crowd Sensing (MCS) is a promising paradigm in which mobile users collect and share sensor data from their local environment using wireless mobile devices. The inherent openness of this platform and the selfishness of individuals make it easy to contribute low-quality sensor data, so the recruitment of suitable participants who are trustable and contribute high-quality sensor data, becomes a fundamental requirement for MCS system. In this paper, we propose a dynamic-trust-based recruitment framework (DTRF) for MCS system. Real-time direct trust and lightweight feedback aggregation trust are combined to select the well-suited participants. In addition, we adopt an adaptive weight allocation approach to calculate the overall trust degree of the participants. Theoretical analysis and extensive simulation confirm that DTRF can efficiently select the trustworthy participants and effectively stimulate the participants to contribute high-quality sensor data and thus get high task completion rate and data quality.