This article investigates how Denial-of-Service (DoS) attacks impact the time and energy costs (ECs) associated with the emergence of flocking dynamics in the Cucker-Smale system. We propose resilient finite-time and fixed-time control protocols against DoS attacks and establish conditions under which the Cucker-Smale (C-S) system can achieve flocking within a bounded time. The attack patterns are modeled stochastically and are constrained by the effective duration of the attack. Explicit upper bounds for both time and ECs are derived, demonstrating their dependence not only on the group size and control parameters, but also on the duration of DoS attacks. Theoretical analysis and numerical simulations consistently demonstrate that shorter attack durations facilitate faster convergence and lower energy consumption. Additionally, our analysis uncovers a tradeoff between time cost and EC under DoS attacks, suggesting that optimal communication intensity should be carefully adjusted to meet specific performance requirements of practical applications.

Federated Learning (FL) offers a privacy-preserving and cost-efficient paradigm to collaboratively train a global model without exposing local data of clients in IoT. However, since client data remain decentralized, it makes the learning vulnerable to malicious behaviors, such as data poisoning attacks, through which, updates can be manipulated to corrupt the global model and damage the overall IoT system. To address such a security threat, gradient similarity-based methods have been proposed to remedy the impact of data poisoning in securing FL, mainly under independent and identically distributed (IID) scenarios. Since heterogeneous Non-IID data are widely distributed in IoT systems, and direct aggregation on local models is more native for FL to update the global model, it becomes challenging for current solutions to distinguish whether models are learned from clients with poisoned or imbalanced data samples. Therefore, this paper proposes FedCon, a model consistency-based mechanism to secure the FL process. It leverages the consistency of local models to identify benign models for global model aggregation. Experimental results demonstrate that FedCon can outperform state-of-the-art methods in defending against data poisoning attacks under both IID and Non-IID scenarios to not only boost model accuracy but also reduce attack success rate.

This article investigates the global predefined-time consensus (PTC) of multiagent system (MAS) via constructing a duplex communication network. Unlike the traditional finite-/fixed-time convergence, our method allows the upper-bound of settling-time to be an explicit constant, which is tunable and can be set beforehand without relating with the network information, controlling parameters, and initial conditions. In particular, our approach uses a smooth, nonchattering consensus scheme that avoids conventional discontinuous functions like signum and absolute value functions. By the Lyapunov stability analysis, the sufficient criterion is deduced for ensuring the PTC of MAS. Finally, simulations confirm the effectiveness of our proposed nonchattering scheme.

The time cost (TC) of fixed-time consensus (FXTC) is investigated in this article for nonlinear multiagent systems (MASs), with or without noise. By integrating the interaction between agents with the advantages of fixed-time control techniques, economical adaptive switching protocols are devised, facilitating the achievement of FXTC while mitigating energy costs (ECs). Theoretical analyses of the TC and EC to achieve FXTC are provided. Sufficient conditions for stochastic FXTC are derived by employing the stability theory and algebraic graph theory. Finally, in numerical simulations, the tradeoff between TC and EC is explored and the effectiveness of the designed protocols is substantiated.

Abstract This paper considers the real-time scheduling of a parallel task with reclaiming computing resources, which can be utilized for soft real-time tasks or switching to low-energy mode to save energy. Existing works allocate a rectangular piece of computing resources based on the worst-case characterizations of the task to guarantee the deadline, which inherently incurs severe resource wasting due to coarse-grained resource allocation. To address this resource-wasting problem, this paper proposes the ladder-like resource allocation (i.e., a series of rectangular pieces of computing resources). To characterize the ladder-like resource allocation, we present two concepts called resource distribution and allocation vector, which serve as the interfaces between hard and soft real-time tasks. For the former, we derive schedulability tests under the given two interfaces; for the latter, we discuss the methods of determining the two interfaces to reclaim computing resources. This paper is the first work to fully explore the concept of ladder-like resource allocation and its potential consequences on computing resources, soft real-time tasks, and energy. Experiments demonstrate that the proposed approach can effectively reclaim more computing resources than existing approaches while maintaining hard real-time guarantees.

Heterogeneous multi-core platforms have been used in many fields to meet the increasing requirement of computation. In this paper, we study the response time bound of typed DAG (directed acyclic graph) tasks on heterogeneous multi-core platforms. The existing bound has exponential time complexity. In this paper, we propose a new bound that can be computed with complexity$O(\vert V\vert +\vert E\vert)$and is only slightly larger than the state-of-the-art. Experiments demonstrate that the computation of our bound is significantly more efficient than the existing bound and our bound has almost the same tightness as the existing bound.

In this article, the estimation of time cost for stochastic consensus of second-order nonlinear multiagent systems (MASs) is investigated. Compared with previous studies, the effect of noise and nonlinear inherent dynamics of agents are considered. In order to achieve the finite/fixed-time stochastic consensus while minimizing energy consumption, the pinning protocols are designed, which only need to control a small fraction of agents. Sufficient conditions for the stochastic consensus are established by employing the stability theory of stochastic differential equations and the algebra graph theory. Finally, the proposed protocols are verified through numerical simulation on the consensus of unmanned aerial vehicles (UAVs) system.

In this article, we study the synchronization of multiplex networks with random topologies and any number of layers. We propose a general framework for analyzing the synchronization of multiplex networks with or without layer similarity. Using random matrix theory, we obtain sufficient conditions of synchronization for multiplex networks under strong and weak interlayer coupling, respectively. We show, both analytically and numerically, that for weak interlayer coupling, increasing layer similarity can first promote synchronization and then hinder synchronization, while for strong interlayer coupling, synchronization can be suppressed. However, increasing the number of layers can always facilitate synchronization only if the interlayer coupling is strong. Finally, the theoretical results are verified by numerical simulations.