Multicast routing is one of the most important services in Multi Radio Multi Channel (MRMC) Wireless Mesh Networks (WMN). Multicast routing performance in WMNs could be improved by choosing the best routes and the routes that have minimum interference to reach multicast receivers. In this paper we want to address the multicast routing problem for a given channel assignment in WMNs. The channels that are assigned to the network graph are given to the algorithm as an input. To reduce the problem complexity and decrease the problem size, we partition the network to balanced clusters. Fuzzy logic is used as a tool for clustering in our method. After clustering and electing most suitable nodes as cluster head, we take a mathematical method to solve the multicast tree construction problem. We conducted several simulations to verify the performance of our method and the simulation results demonstrated that our proposed method outperforms CAMF algorithm in terms of throughput and end to end delay. تفاصيل المقالة

Many appealing multicast services such as on-demand TV, teleconference, online games and etc. can benefit from high available bandwidth in multi-radio multi-channel wireless mesh networks. When multiple simultaneous transmissions use a similar channel to transmit data packets, network performance degrades to a large extant. Designing a good multicast tree to route data packets could enhance the performance of the multicast services in such networks. In this paper we want to address the problem of multicast routing in multi-radio multi-channel wireless mesh networks aiming at minimizing intermediate nodes. It is assumed that channel assignment is known at prior and channels are assigned to the links in advance. Aiming at constructing multicast tree with minimum number of intermediate nodes and minimum number of interfered nodes we propose a heuristic algorithm called Maximum Multicast Group Nodes (MMGN). Simulation results demonstrated that our proposed method outperforms LC-MRMC algorithm in terms of throughput and packet delivery ratio. تفاصيل المقالة

In Software Defined Network (SDN), controller plane is separated from the data plane simplifying management. In these networks, data forwarding cannot be conducted just one controller. Therefore, it is needed to use multiple controllers in control plane. Since, switch-controller propagation delays and inter-controller latencies affect the performance, the problem of determining appropriate number of controllers as well as their suitable locations are two main challenges, which are known as NP-Hard. In this paper, a new clustering method based on K-means, K-Harmonics means and firefly algorithm named CPP-KKF is proposed for controller placement in SDN. Result obtained by CPP- KKF algorithm is benefitted by the advantages of all techniques. The proposed algorithm is evaluated on four topologies of TopologyZoo with different scales, that include Aarnet, Colt, Cognet, and DFN and the conducted simulations demonstrate that the proposed solution outperforms K-means, K-means++, Firefly and GSO algorithms in terms of aforementioned performance issues. تفاصيل المقالة

This paper proposes an efficient Tracking method for velocity control of an electro-hydraulic servo system (EHSS) in the presence of flow nonlinearities and internal friction. The tracking method controller is a kind of feedback error learning structure. In the proposed method, the Feedback Error Learning (FEL) algorithm is used to control the velocity. There is no need to compute the system jacobian in FEL method which in turn makes its using more suitable for practical scenarios. This procedure illustrates that EHSS control can be successfully. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. تفاصيل المقالة

This study proposes a combination of a fuzzy sliding mode controller (FSMC) with integral-proportion-Derivative switching surface based superconducting magnetic energy storage (SMES) and PID tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. The goal of design is to improve the dynamic response of power systems after load demand changes. In the proposed method, an adaptive fuzzy controller is utilized to mimic a feedback linearization control law. To compensate the compensation error between the feedback linearization and adaptive fuzzy controller, a hitting controller is developed. The Lyapunov stability theory is used to obtain an adaption law so that the closed-loop system stability can be guaranteed. The optimal PID controller problem is formulated into a multi-objective optimization problem. A Pareto set of global optimal solutions to the given multi-objective optimization problem is generated by a genetic algorithm (GA)-based solution technique. The best compromise solution from the generated Pareto solution set is selected by using a fuzzy-based membership value assignment method. Simulations are presented and compared with conventional PID controller and another new controller. These results demonstrate that the proposed controller confirms better disturbance rejection, keeps the control quality in the wider operating range, reduces the frequency’s transient response avoiding the overshoot and is more robust to uncertainties in the system. تفاصيل المقالة

The Networked Control System in modern control widely uses to decrease the implementation cost and increasing the performance. NCS in addition to its advantages is inevitable. Nevertheless they suffer of some limitations and deficiencies. Packet loss is one of the main limitations which affect the control system in different conditions and finally may lead to system instability. For this reason, it is important to model the system properly. In this paper, a new model has been proposed that is very simple and independent from the main system. This model based on Robust Theory structure. Robust theory is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. Robust control methods are designed to function properly so long as uncertain parameters or disturbances are within some (typically compact) set. تفاصيل المقالة

An efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. The implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. According to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses Fuzzy Inference Systems to select cluster head. The algorithm not only balances the energy load of all nodes, but also provides a reliable selection of a new cluster head and optimality routing for the whole networks. Simulation results demonstrate that the proposed algorithm effectively increases the accuracy to select a cluster head and prolongs the network lifetime تفاصيل المقالة

In this paper, a novel multi-objective bus stop location model is proposed, which considers not only the coverage of demand and minimization of access time but also the necessities of suitable stops for transit network design phase. Three objective functions are considered including minimizing (I) sum of the total access distance (time), (II) the weighted combination of stops, and (III) the number of stops. A sum-weighted method is used to solve the proposed multi-objective model considering the different scenarios of weights. A detailed analysis is carried out Tehran CBD to generate sensible stops results. تفاصيل المقالة

Traffic prediction systems can play an essential role in intelligent transportation systems (ITS). Prediction and patterns comprehensibility of traffic characteristic parameters such as average speed, flow, and travel time could be beneficiary both in advanced traveler information systems (ATIS) and in ITS traffic control systems. However, due to their complex nonlinear patterns, these systems are burdensome. In this paper, we have applied some supervised data mining techniques (i.e. Classification Tree, Random Forest, Naïve Bayesian and CN2) to predict the next state of Traffic by a categorical traffic variable (level of service (LOS)) in different short-time intervals and also produce simple and easy handling if-then rules to reveal road facility characteristic. The analytical results show prediction accuracy of 80% on average by using methods تفاصيل المقالة

A Wireless Sensor Network (WSN) is a wireless decentralized structure network consists of many nodes. Nodes can be fixed or mobile. WSN applications typically observe some physical phenomenon through sampling of the environment so determine the location of events is an important issue in WSN. Wireless Localization used to determine the position of nodes. The precise localization in WSNs is a complex issue that requires consideration of many prominent aspects such as energy consumption at the nodes as well as the algorithm execution time. In this article, we optimize a system called Spotlight. The spotlight is a localization system that delivers high-location estimation accuracy at low cost. We propose several methods to reduce execution time compared with previous methods in Spotlight. We proposed ILS, LAS and PAS methods that improve execution time about 25%, 50% and 75%. Execution time of the proposed scheme is restricted by the size of deployment area. Furthermore, in these methods, there is no need to equip the nodes with any special hardware. تفاصيل المقالة