Proposed Routing Algorithm Research Articles

Preventing Black Hole Attack in Wireless Sensor Network Using HMM

Secure routing is a difficult task because of the limited nature of wireless sensor network resources. This paper provides a Hidden Markov Model solution to identify malicious nodes in wireless sensor networks through prevention of black hole attack. Our proposed approach based on a new routing algorithm which analyses shortest path in order to avoid malicious node path. Our results demonstrate the success and the efficiency of our proposed routing algorithm.

Torus Pairwise Disjoint-Path Routing.

Modern supercomputers include hundreds of thousands of processors and they are thus massively parallel systems. The interconnection network of a system is in charge of mutually connecting these processors. Recently, the torus has become a very popular interconnection network topology. For example, the Fujitsu K, IBM Blue Gene/L, IBM Blue Gene/P, and Cray Titan supercomputers all rely on this topology. The pairwise disjoint-path routing problem in a torus network is addressed in this paper. This fundamental problem consists of the selection of mutually vertex disjoint paths between given vertex pairs. Proposing a solution to this problem has critical implications, such as increased system dependability and more efficient data transfers, and provides concrete implementation of green and sustainable computing as well as security, privacy, and trust, for instance, for the Internet of Things (IoT). Then, the correctness and complexities of the proposed routing algorithm are formally established. Precisely, in an n-dimensional k-ary torus (, ), the proposed algorithm connects c () vertex pairs with mutually vertex-disjoint paths of lengths at most , and the worst-case time complexity of the algorithm is . Finally, empirical evaluation of the proposed algorithm is conducted in order to inspect its practical behavior.

Routing algorithm for supporting data-differentiated service in hybrid wireless mesh networks in underground mines

Hybrid wireless mesh networks are more suitable to tunnels in underground mines because mesh clients can perform the routing functionality and provide end-user applications. Types of data transmitted among hybrid wireless mesh networks in underground mines are diverse and power supply of clients is limited. Using virtual potential field, a routing algorithm is proposed to support data-differentiated service in hybrid wireless mesh networks. Based on network parameters, the hybrid wireless mesh networks are abstracted as potential fields. The hop count is used to build the depth potential field. The residual energy of nodes is converted into penalty factor to symbolize the used buffer spaces of nodes, which is used to construct resource potential field to support different types of data. According to the different demands of data transmission, the proportion of these two potential fields is adjusted by a regulatory factor to form a hybrid potential field. The data packets are driven to move toward the gateway in the hybrid potential field at the same time to achieve high quality of service. Simulation results demonstrate that the proposed routing algorithm has shown better performance on end-to-end delay, delivery ratio, and network lifetime, compared with similar algorithms.

Practical Routing Algorithm Using a Congestion Monitoring System in Semiconductor Manufacturing

Automated material handling systems (AMHSs) used in semiconductor fabrication are sophisticated and integrated control systems that transfer materials from one machine to another within a manufacturing facility via hundreds of vehicles. Vehicular congestion in AMHSs causes significant production inefficiency. In this paper, we demonstrate the reasons for and classification of congestion, and suggest congestion metrics for material handling systems. We develop and implement a congestion monitoring system (CMS) based on the proposed metrics at an actual manufacturing site. We provide practical case studies on three applications to address the effectiveness of CMS: 1) congestion monitoring; 2) redistribution of routing; and 3) enhancing a routing algorithm. Simulation results demonstrated that the proposed routing algorithm decreased the average delivery time by 12.41% in heavy congestion. The shorter average delivery time reduces the loss in production by at least 1%.

Self-adaptive multi-purpose Grey Wolf optimization for efficient routing of wireless ad hoc networks

One of the well-known issues in the field of network routing is the Shortest Path Routing (SPR). The objective is to find the least-cost path with minimum delay and link breaks. Even though there are many algorithms to solve SPR, the cost, as well as link breaks, are indeed more thought-provoking in the real-time application. This paper intends to develop a routing approach that solves the challenges like route establishment and route recovery. The selection of the optimal route is done by adopting a generalized multi-purpose optimization algorithm named Grey Wolf Optimizer. Along with this, this paper adopts Neural Network (NN) to predict the node movements in the ad hoc network. The proposed routing algorithm is compared to the conventional approaches, and the significance of the approach is described clearly.

ON–OFF: a reactive routing algorithm for dynamic thermal management in 3D NoCs

Dynamic thermal management (DTM) techniques of three-dimensional (3D) Network-on-Chips (NoCs) are employed to rescue the chip from thermal difficulties. Reactive routing algorithms, which utilise router throttling technique as a popular DTM, disregard distribution of heat generation of routers resulting in more throttled routers as well as long packet delays in throttled processing elements. This study proposes a reactive routing algorithm for 3D NoCs to (i) dynamically detour packets from hot zones containing throttled routers and (ii) minimise the number of required router throttling in the network. The proposed routing algorithm defines two virtual networks to enhance the path diversity for packets in each layer of 3D NoCs. The selection of diverse paths distributes heat generation to alleviate the thermal variance. The proposed routing algorithm is analysed by turn model to achieve deadlock freedom. Access Noxim simulator is also used to evaluate the performance and the thermal behaviour of the proposed routing algorithm in the variety of conditions. Results show that the proposed routing algorithm improves temperature variance by 9-39% and reduces number of throttled routers by 16-86%, which is achieved at the cost of one extra virtual channel per each physical channel in the XY -plane.

Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

Algorithms for distributed coordination and control are increasingly being used in smart grid applications including peer-to-peer energy trading and sharing to improve reliability and efficiency of the power system. However, for realistic deployment of these algorithms, their designs should take into account the suboptimal conditions of the communication network, in particular the communication links that connect the energy trading entities in the energy network. This study proposes a distributed adaptive primal (DAP) routing algorithm to facilitate communication and coordination among proactive prosumers in an energy network over imperfect communication links. The proposed technique employs a multi-commodity flow optimization scheme in its formulation with the objective to minimize both the communication delay and loss of energy transactional messages due to suboptimal network conditions. Taking into account realistic constraints relating to network delay and communication link capacity between the peers, the DAP routing algorithm is used to evaluate network performance using various figures of merit such as probability of signal loss, message delay, congestion and different network topologies. Further, we address the link communication delay problem by redirecting traffic from congested links to less utilized ones. The results show that the proposed routing algorithm is robust to packet loss on the communication links with a 20% reduction in delay compared with hop-by-hop adaptive link state routing algorithm.

Routing algorithms for IEEE802.1Qbv networks

The recently published IEEE 802.1Qbv standard specifies enhancements for providing real-time communication guarantees for time-triggered flows while also handling best-effort traffic in a converged Ethernet network. The enhancements include a programmable time-based gating mechanism for stipulating which of the queues of an egress port are available for transmission at any given point of time. By appropriately programming (opening and closing) these gates, the traversal of packets through the network can be controlled to precisely follow a precomputed schedule that satisfies the timing constraints of the time-triggered flows. Computing such transmission schedules requires routing of the flows in the first step, followed by the computation of gate schedules for the flows along their respective routes. So far off-the-shelf algorithms like shortest path routing, which optimize the number of hops over which flows are routed, have been used for computing routes for the time-triggered traffic. In this paper, we explore how the routing of time-triggered flows affects their schedulability. Moreover, we identify additional parameters that must be considered while routing time-triggered traffic and propose ILP-based algorithms for the purpose. Our evaluations show that the proposed routing algorithms could improve the slack in the computed schedules by upto 60 % and 30 % compared to shortest path routing and equal cost multi-pathing (ECMP), respectively, and, thus, increase the capacity of the network to accommodate more time-triggered traffic.

Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks

This paper aims at solving the end-to-end delay-constrained routing problem in a local way for flying ad hoc networks (FANETs). Due to the high mobility, it is difficult for each node in FANETs to obtain the global information. To solve this issue, we propose an adaptive delay-constrained routing with the aid of a stochastic model, which allows the senders to deliver the packets with only local information. We represent the problem in a mathematical form, where the effective transmission rate is viewed as the optimization objective and the link quality and end-to-end delay as the constraints. And, some mathematical tools are used to obtain the approximate solutions for the optimization problem. Before designing the routing scheme, the senders calculate the transition probability for its relay node by jointly considering local delay estimation and expected one-hop delay. Then, the sender transmits the packets to their relay node with transition probability. Finally, we prove the convergence of the proposed routing algorithm and analyse its performances. The simulation results show that the proposed routing policy can improve the network performance effectively in terms of throughput, loss rate, and end-to-end delay.

Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks

This paper focuses on the routing algorithm for the communications between vehicles and places in urban vehicular ad hoc networks. As one of the basic transportation facilities in an urban setting, buses periodically run along their fixed routes and cover many city streets. The trajectory of bus lines can be seen as a submap of a city. Based on the characters of bus networks, we propose a bus-trajectory-based street-centric (BTSC) routing algorithm, which uses buses as the main relay to deliver messages. In BTSC, we build a routing graph based on the trajectories of bus lines by analyzing the probability of bus appearing on every street. We propose two novel concepts, i.e., the probability of street consistency and the probability of path consistency, which are used as metrics to determine routing paths for message delivery. This aims to choose the best path with higher density of busses and lower probability of transmission direction deviating from the routing path. In order to improve the bus forwarding opportunity, we design a bus-based forwarding strategy with ant colony optimization to find a reliable and a steady multihop link between two relay buses in order to decrease the end-to-end delay. The BTSC makes improvements in the selection of routing paths and a strategy of message forwarding. Simulation results show that our proposed routing algorithm has a better performance in terms of the transmission ratio, transmission delay, and adaptability to different networks.

T-Whale

One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.

Lifetime enhancement in wireless sensor networks using binary search tree based data aggregation

Energy efficient data aggregation is a key solution to enhance the lifetime of wireless sensor networks since sensor nodes are battery-powered and deployed in remote environments. This article explore a two-hop data aggregation tree construction algorithm using binary search tree to reduce the total energy consumption of sensor nodes in wireless sensor networks. An adaptive and hybrid routing algorithm for simultaneous data aggregation and exploit the data correlation between nodes using the two-hop data aggregation tree framework is proposed. Routes are chosen based on the shortest response time for the broadcasting request to minimize the total energy expended by the network. This paper also proposes a high secure asymmetric key cryptography algorithm to provide the secure data communication among the network. The data aggregation function that is used in the proposed routing algorithm enhances the lifetime of sensor network by resolving the delay, collision and security issues. Simulations results show that the binary tree based data aggregation can appreciably reduces the total energy consumption and resolves the maximum data aggregation issues in wireless sensor network.

Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks

This paper studies the problem of robust spectrum-aware routing in a multi-hop, multi-channel Cognitive Radio Network (CRN) with the presence of malicious nodes in the secondary network. The proposed routing scheme models the interaction among the Secondary Users (SUs) as a stochastic game. By allowing the backward propagation of the path utility information from the next-hop nodes, the stochastic routing game is decomposed into a series of stage games. The best-response policies are learned through the process of smooth fictitious play, which is guaranteed to converge without flooding of the information about the local utilities and behaviors. To address the problem of mixed insider attacks with both routing-toward-primary and sink-hole attacks, the trustworthiness of the neighbor nodes is evaluated through a multi-arm bandit process for each SU. The simulation results show that the proposed routing algorithm is able to enforce the cooperation of the malicious SUs and reduce the negative impact of the attacks on the routing selection process.

Optimal Routing for Multihop Social-Based D2D Communications in the Internet of Things

With the development of wireless communications and the intellectualization of machines, the Internet of Things (IoT) has been of interest to both industry and academia. Multihop routing and relaying are key technologies that will underpin IoT mesh networks in the future. This paper investigates optimal routing based on the trusted connectivity probability (T-CP) for multihop, underlay, device-to-device (D2D) communications with decode-and-forward relaying. Both random and fixed locations for base stations (BSs) are considered, where the former case assumes that the locations of the BSs are modeled as a Poisson point process (PPP). First, we derive two expressions for the connectivity probability (CP): 1) a tight lower bound and 2) an exact closed-form. Analysis is carried out for the cases where the channel state information (CSI) between BSs and the D2D transmitter is known (CSI-aware) and unknown (no-CSI). Interference from active cellular user equipments (CUEs) is characterized by modeling CUE locations as a PPP. Moreover, motivated by results that have shown that social behavior leads to D2D devices communicating with nearby neighbors, we derive the trust probability for D2D connections by using a rank-based model. Finally, we propose a novel routing algorithm that can achieve the highest T-CP for any pair of D2D devices in a distributed manner. The derived analytical results are verified by Monte Carlo simulations. We show that the proposed routing algorithm achieves almost the same performance as that attained through an exhaustive search. When BSs are located randomly, the optimal path based on the CP is the shortest path between the D2D transmitter and receiver. However, for fixed BSs, the optimal path selection depends on the locations of the BSs, which provides a very useful insight in designing the multihop D2D system for 5G IoT.

Crosstalk Free Routing in Optical Multistage Interconnection Networks using Fast Window Method

A major problem in parallel computing systems which are based on optical interconnection networks is optical crosstalk that degrades the performance of network, and limits the network size. This paper discusses the implementation of fast scheduling methods (fast window methods) to the traditional zero routing algorithm and in term of running time and the ability of enlarging the network. The proposed routing algorithm reduces the running time and gives the ability to increase the network size.

MINIMUM POWER CONSUMING ALGORITHM TO SELECT RELAY NODE OF MIMO INTEGRATED MOBILE AD HOC NETWORK

Objectives: To design an MIMO integrated Mobile Ad Hoc Network (MANET) in an energy efficient way. As MANETs are battery operated, so energy constrained is an important factor to design this integrated network. It is very essential to develop a routing algorithm using energy awareness consideration. Methods/Statistical Analysis: A Minimum Power Consumed Routing (MPCR) algorithm is developed which is an integration of cooperative transmission process. Most of existing routing algorithm of Mobile Ad Hoc Network is based on distance and metric value, but this proposed algorithm is based on effective received power by the mobile nodes. Minimum effective power consumes minimum energy for transmission. The route selection of this algorithm is simulated in MATLAB environment and comparative study is done of this algorithm with some other existing algorithm. Findings: This algorithm selects relay node and supports short distance communication which can improve the Signal to Noise Interference Ratio (SNIR). The performance analysis of proposed routing algorithm improves Signal to Noise Interference Ratio (SNIR) resulting in better cooperative transmission. Novelty/ Applications: This algorithm suitably works on any hazardous environment where structured network implementation is not possible. Keywords: Algorithm, MANET, MIMO, Minimum Power, Relay Node

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Simulation Process for Mobile Nodes Information Using Location Aided Routing in MANET

Location-aided routing protocol (LAR) utilizes the position information for routing. This system is simulated position information in specific areas by using mobile nodes. Movement of hosts results in routes, requiring some mechanism for determining new routes. This system finds position information using Location aided routing algorithm between each nodes. In real application, mobile nodes can apply without time consuming and saving cost to get location information using wireless sensor network. Each of the nodes is involved with calculating its position. If the position information is searched in the routing protocol, then position-based routing protocol is used. After creating the position table, all mobile nodes can transmit 64B data within active mobile nodes via Base station. The algorithm is determined locations of nodes based on the connectivity for every node in specific area. This system is demonstrated location information by using Location-Aided Routing (LAR) protocol for route discovery. This system can assist to detect location of the specific areas using mobiles nodes. Simulation results are used to draw conclusions regarding to the proposed routing algorithm. The simulation of system is implemented mobile nodes and node movement function by using C# programming language.

Energy–efficient grid–based routing algorithm using intelligent fuzzy rules for wireless sensor networks

In Wireless Sensor Networks, the sensor nodes are energy constrained and hence the design of energy- efficient routing protocol is an important challenge to be addressed. Among the different clustering procedures used for energy-efficient routing, grid-based clustering method is more efficient in energy optimization. Therefore, we propose an Energy-Efficient Grid-based Routing algorithm for sensor network in this work for preserving the energy in sensor nodes and to enhance the network lifetime. Moreover, routing is performed through a Grid Coordinator that applies fuzzy rules to find the optimal route in order to reduce the number of hops in the routing process. From the simulations carried out in this work, it is observed that the proposed routing algorithm provides better performance in terms of residual energy and network lifetime when it is compared with other existing grid and cluster based routing protocols.

Energy-efficient chain-cluster based intelligent routing technique for Wireless Sensor Networks

With the increasing acclaim of Wireless Sensor Networks and its diverse applications, research has been directed into optimising and prolonging the network lifetime. Energy efficiency has been a critical factor due to the energy resource impediment of batteries in sensor nodes. The proposed routing algorithm therefore aims at extending lifetime of sensors by enhancing load distribution in the network. The scheme is based on the chain-based routing technique of the PEGASIS (Power Energy GAthering in Sensor Information Systems) protocol and uses Ant Colony Optimisation to obtain the optimal chain. The contribution of the proposed work is the integration of the clustering method to PEGASIS with Ant Colony Optimisation to reduce redundancy of data, neighbour nodes distance and transmission delay associated with long links, and the employment an appropriate cluster head selection method. Simulation results indicates proposed method’s superiority in terms of residual energy along with considerable improvement regarding network lifetime, and significant reduction in delay when compared with existing PEGASIS protocol and optimised PEG-ACO chain respectively.