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In this study, a new reaching law for sliding mode control of discrete time systems is proposed and applied to solve the problem of congestion avoidance in multi-source, connection oriented data transmission networks. Since the proposed reaching law does not require switching of the sliding variable between positive and negative values in each successive control step, it leads to chattering free operation, does not cause overshoots and helps achieve 100% exploitation of the bottleneck link available bandwidth. Furthermore, the proposed controller always generates bounded data transmission rates. The rates are limited by design parameters and they do not depend on the network initial conditions. The properties of the proposed controller are stated as three theorems, formally proved and verified in a simulation example.

This paper presents a comprehensive study on autonomous vehicle parking challenges, focusing on kinematic and reverse parking models. The research develops models for various scenarios, including turning, reverse, vertical, and parallel parking while using the minimum turning radius solution. The integration of the A* algorithm enhances trajectory optimization and obstacle avoidance. Innovative concepts like NTBPT and B-spline theory improve computational optimization. This study provides a foundation for understanding the dynamics and constraints of autonomous parking. The proposed model enhances efficiency and safety, reducing algorithm complexity and improving trajectory optimization. This research offers valuable insights and methodologies for addressing autonomous vehicle parking challenges and advocates for advancements in automated parking systems.

Purpose The purpose of this paper is to focus on the reference model of a grid-like supply network that enables formulation of delivery routing and scheduling problems in the context of the periodic vehicle routing problem. Design/methodology/approach The conditions for seamless (collision-free) synchronization of periodically executed local transport processes presented in this paper guarantee cyclic execution of supply processes, thereby preventing traffic flow congestion. Findings Systems that satisfy this characteristic, cyclic deliveries executed along supply chains are given and what is sought is the number of vehicles needed to operate the local transport processes in order to ensure delivery from and to specific loading/unloading points on given dates. Determination of sufficient conditions guaranteeing the existence of feasible solutions that satisfy these constraints makes it possible to solve the considered class of problems online. Practical implications The computer experiments reported in this paper show the possibilities of practical application of the proposed approach in the construction of decision support systems for food supply chain management. Originality/value The aim of the present work is to develop a methodology for the synthesis of regularly structured supply networks that would ensure fixed cyclic execution of local transport processes. The proposed methodology, which implements sufficient conditions for the synchronization of local cyclic processes, allows one to develop a method for rapid prototyping of supply processes that satisfies the time windows constraints given.

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT , for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×.

Vehicular ad hoc network (VANET) is a novice technique which has drawn the attention of several industries and academics. Security parameters in VANET are now receiving popularity in the research community. A defensive mechanism provides a solution to control the attacks across the VANET security. However, a single defence mechanism is unable to provide solution to the attack models as more sophisticated method is required for VANETs. This paper proposed a method termed heuristic approach for ant colony optimization (HAAC) for improved security in addition to better transportation, reliability and management. The heuristic based ant colony optimization is used to reduce the problem in finding known and unknown opponents in providing security to VANET. The characteristic of real ant colonies is used in VANET security in order to solve attack problems with shortest path. The Reinforcing VANET security using vehicle mode analysis is evaluated in an efficient manner using NS2 simulator. The excellent outcomes are obtained by an HAAC approach combined with a dynamic heuristic.

The channel availability of cognitive radio is often estimated through a two-state ON/OFF model of primary users’ (PUs) behaviour. The ON/OFF model estimates channel availability with respect to the presence of PUs. Multiple secondary users (SUs) may be aware of channel availability, and try to opportunistically access the channel when it becomes available. Hence, the ON/OFF model may overestimate channel capacity from the perspective of SUs, since the channel could be occupied not only by a resuming PU, but also by contending SUs. Proposed is a joint channel availability estimation through a Markov chain model which takes into account, within a single model, PU behaviour and the SU's carrier sensing with multiple access/collision avoidance contention access, without leading to the state explosion problem. Simulations performed show that the joint channel availability equation is able to estimate channel capacity with mean errors of around 2%.

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: 1) the elapsing of a predefined time interval, or, 2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3x compared to ECMP while packet loss is decreased up to 2x.