Forthcoming and Online First Articles

International Journal of Systems, Control and Communications

International Journal of Systems, Control and Communications (IJSCC)

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International Journal of Systems, Control and Communications (2 papers in press)

Regular Issues

  • Weighted L2-gain analysis and fault-tolerant control of nonlinear discrete-time saturated switched systems with time-varying delays   Order a copy of this article
    by Hu Guo, Lijun Lin, Xinquan Zhang 
    Abstract: In this paper, the problem of the weighted L2-gain analysis and fault-tolerant control of a class of nonlinear discrete-time switched systems with time-varying delay and actuator saturation is studied by using the minimum dwell time strategy. Firstly, the fault-tolerant state feedback controllers and the switching law are designed such that the closed-loop system with actuator failures satisfies the disturbance attenuation performance indicator. The problem of estimating the capacity of admissible disturbance is transformed into a constrained optimisation problem to ensure that the state trajectory of the closed-loop system is bounded under the action of external disturbances. Also the upper bound of weighted L2-gain is estimated by solving constrained optimisation problems. Then, when the fault-tolerant controller can be regard as the design variable, the optimisation problems above are adjusted for solving control synthesis problems. Finally, the reliability of the design method is verified by examples.
    Keywords: nonlinear switched systems; minimum dwell time; actuator saturation; fault-tolerant control; weighted L2-gain.

  • Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks   Order a copy of this article
    by Smritikona Barai, Anindita Kundu, Parama Bhaumik 
    Abstract: Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behavior features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios.
    Keywords: challenged networks; opportunistic networks; trust; black-hole attack; trust-based protocols; machine-learning; security; wireless communications.

 

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