Bellman-Ford Routing Algorithm Research Articles

STREAMING NETWORK ROUTING BASED ON BELLMAN-FORD ALGORITHM MODIFICATION

The operational task of automating the construction and routing of the network model with the known coordinates of the conditional goals set for a group of vessels to achieve them in the minimum time is solved; it makes it possible to obtain the reserves of running time necessary for saving fuel and energy, taking into account the load, the cost of cargo, transportation costs, logistics characteristics, etc. It is emphasized that in stormy weather conditions and vessel management in situations related to schedule correction, flexible operational decisions of dispatching services, made on the basis of numerical optimization methods using modern computing environments, are necessary. In this regard, the method of dynamic programming, implemented using the Bellman-Ford routing algorithm, which is supplemented by a recursive step-by-step optimization procedure that removes the limitation of the algorithm in the presence of inversely oriented edges with negative weights in the graph, is discussed in the paper. In the presence of negative weights, there are conditions for the appearance of a negative cycle in the graph, in which the practical implementation of the Bellman-Ford algorithm will become impossible due to an endless cycle of relaxations (attenuation) of the vertices weights included in this cycle. Hence, at a limited period of time for weighing all vertices (passes on all edges), the algorithm can give a knowingly false result. The proposed procedure for modifying the well-known Bellman-Ford algorithm eliminates this limitation and allows it to be used not only for estimating the shortest paths in a network containing arcs with negative weights, but also to detect negative cycles in it. The modified Bellman-Ford algorithm is implemented as a program compiled in MATLAB codes, and it is demonstrated by the example of automated construction and calculation of a network model containing both positive and negative edges (flows), using a recursive procedure of step-by-step optimization. It is shown that the proposed model, unlike the known models, eliminates the limitations caused by the presence of negative cycles in the network model, which makes it possible to automate the search for the shortest paths to conditional goals by the functional means of the MATLAB environment. The constructed computer model is simple and compact. The proposed algorithm and the recursive procedure are recommended for finding energy-efficient solutions for managing mobile objects in water transport.

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85%

Data Flow in Wireless Sensor Network Protocol Stack by using Bellman-Ford Routing Algorithm

Wireless sensor network consists various sensor nodes that are used to monitor any target area like forest fire detection by our army person and monitoring any industrial activity by industry manager. Wireless sensor networks have been deployed in several cities to monitor the concentration of dangerous gases for citizens. In wireless sensor network when sensor nodes communicate from each other then routing protocol are used for communication between protocol layers. Wireless sensor network protocol stack consist five layers such as Application layer, Transport layer, Network layer, MAC Layer, Physical layer. In this paper we study and analysis Bellman-Ford routing algorithm and check the flow of data between these protocol layers. For simulation purpose we are using Qualnet 5.0.2 simulator tool.

Routing algorithms applied to an advanced academic network know as CUDI

Dijkstra and Bellman-Ford routing algorithms were applied to the CUDI (Consorcio Universitario para el Desarrollo de Internet 2 - University Consortium for the Development of Internet 2) advanced network backbone infrastructure. Some calculations and considerations are discussed in a very didactic way to understand how to apply both algorithms to a real advanced network backbone infrastructure such as the CUDI network, but these algorithms could also be applied to other advanced networks. A step by step simulation and emulation were developed in order to get an approach to actual function for CUDI, where OSPF V2 protocol is used, which uses the Dijkstra algorithm for routing.

Accelerating IP routing algorithm using graphics processing unit for high speed multimedia communication

This paper presents a Graphics Processing Unit (GPU)-based implementation of a Bellman-Ford (BF) routing algorithm using NVIDIA's Compute Unified Device Architecture (CUDA). In the proposed GPU-based approach, multiple threads run concurrently over numerous streaming processors in the GPU to dynamically update routing information. Instead of computing the individual vertex distances one-by-one, a number of threads concurrently update a larger number of vertex distances, and an individual vertex distance is represented in a single thread. This paper compares the performance of the GPU-based approach to an equivalent CPU implementation while varying the number of vertices. Experimental results show that the proposed GPU-based approach outperforms the equivalent sequential CPU implementation in terms of execution time by exploiting the massive parallelism inherent in the BF routing algorithm. In addition, the reduction in energy consumption (about 99 %) achieved by using the GPU is reflective of the overall merits of deploying GPUs across the entire landscape of IP routing for emerging multimedia communications.

Cross-Layer Design for Multihop Wireless Relaying Networks

Employing channel state information at the network layer, efficient routing protocols for equal-power and optimal-power allocation in a multihop network in fading are proposed. The end-to-end outage probability from source to destination is used as the optimization criterion. The problem of finding the optimal route is investigated under either known mean channel state information (CSI) or known instantaneous CSI. The analysis shows that the proposed routing strategy achieves full diversity order, equal to the total number of nodes in the network excluding the destination, only when instantaneous CSI is known and used. The optimal routing algorithm requires a centralized exhaustive search which leads to an exponential complexity, which is infeasible for large networks. An algorithm of polynomial complexity for a centralized environment is developed by reducing the search space. A distributed approach based on the Bellman-Ford routing algorithm is proposed which achieves a good implementation complexity-performance trade-off.

Cross-Layer Design for Multihop Wireless Relaying Networks

Employing channel state information at the network layer, efficient routing protocols for equal-power and optimal-power allocation in a multihop network in fading are proposed. The end-to-end outage probability from source to destination is used as the optimization criterion. The problem of finding the optimal route is investigated under either known mean channel state information (CSI) or known instantaneous CSI. The analysis shows that the proposed routing strategy achieves full diversity order, equal to the total number of nodes in the network excluding the destination, only when instantaneous CSI is known and used. The optimal routing algorithm requires a centralized exhaustive search which leads to an exponential complexity, which is infeasible for large networks. An algorithm of polynomial complexity for a centralized environment is developed by reducing the search space. A distributed approach based on the Bellman-Ford routing algorithm is proposed which achieves a good implementation complexity-performance trade-off.

On the Connections Between Stabilization, Network Routing and Receding Horizon Optimal Control Problems

The paper is devoted to a general nonlinear regulator design problem. In this context some connections between discrete version of the receding horizon control problem and the generalized network routing problem are considered. Except “standard” definition of the Lyapunov function with the help of the Bellman optimal cost-to-go function, it is shown, that the optimizing mapping corresponds to the Bellman-Ford routing algorithm. As such, it can be implemented asynchronously, e.g. in a Hopfield type neural network. The obtained routing tables will be control rules of an optimal receding horizon regulator. At the end some results of the application of this approach to an inverted pendulum control problem are presented.