Optimal Path Identification Research Articles

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

An Efficient IoT-based Ubiquitous Networking Service for Smart Cities Using Machine Learning Based Regression Algorithm

In recent days, the smart city project has been emerging concept all over the world. In this process, the proper communication between the sensors and the smart devices, and identification of optimal path between sensors and mutation sensors in large geographical area is very difficult. The main objective has been considered to overcome the drawbacks as mentioned above. The proposed algorithm is efficient to provide integrated communication of IoT-based ubiquitous networking (UBN) devices to improve in large geographically distributed area. The data storage capacity and accuracy of sensors and smart devices are enhanced using the proposed algorithm. The communication latency and data pre-processing of IoT-based UBN nodes deployed in smart cities are reduced. The proposed algorithm also analyses the performance of IoT-based UBN nodes by considering geographical testbeds that represent a smart city scenario. The analysis and comparison are carried out by considering the heuristic parameters. The proposed algorithm will also optimize the communication latency and data pre-processing time by analyzing various sensitivity levels by considering the heuristic parameters in different probability of nodes in smart cities. The proposed IoT-based UBN computing devices improve the objective function due to proper integrated communication between the sensors using a machine learning based regression algorithm. The proposed algorithm also identifies the probability sensitivity of optimal path between smart devices in a smart city thereby enhancing the connectivity of mutated sensor nodes. The proposed algorithm also enhances the probability of smart device connectivity to improve the accuracy, flexibility and large geographical coverage using machine learning based regression algorithm.

Dispersal Foraging Strategy With Cuckoo Search Optimization Based Path Planning in Unmanned Aerial Vehicle Networks

Unmanned aerial vehicles (UAVs) are assumed to be a promising model of automatic emergency tasks in dynamic marine ecosystems. But, the real-time communication efficacy betwixt UAVs and base platforms is developing a serious challenge. The compact-sized powerful flying robots can be wirelessly controlled and accomplish end tasks with and without human involvement. UAVs still face severe challenges that limit the dream of completely autonomous unmanned flying machines. The main difficulties contain path planning and hindrance avoidance of such unmanned flying robots, which are mandatory but carry out the application-specific functionality in either indoor or outdoor environments. This study introduces a new Dispersal Foraging Strategy with Cuckoo Search Optimization based Path Planning (DFSCSO-PP) technique for UAV networks. In the presented DFSCSO-PP technique, the identification of optimal paths for data transmission is performed in the UAV network. In addition, the presented DFSCSO-PP technique involves the optimal allocation of resources while finding the optimal paths in the network. Moreover, the DFSCSO technique can be designed by integrating the DFS concept into the CSO method to avoid local optima problems. A widespread simulation analysis is performed to exhibit the enhanced outcome of the DFSCSO-PP approach. A detailed set of comparative studies assured the improved performance of the DFSCSO-PP technique over other approaches.

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Internet of Vehicles (IoV) is one of the developing models in the Vehicular adhoc networks (VANETs) with the vast improvement of communication technologies. In order to improve data transmission among the multiple communities without link breakage, a novel Trilateral Location Identified Maximum Weighted Directive Spanning Tree (TLIMWDST) technique is introduced. The proposed TLIMWDST technique consists of two major phases, namely location identification and optimal path identification to improve the reliability of data transmission from source vehicle to destination vehicle. In the first phase, the location of the neighboring vehicles is identified by applying a trilateration technique. After the location identification, an optimal route path between the source and destination is identified using Maximum Weighted Directive Spanning Tree (MWDST) through the intermediate nodes. The performance of the TLIMWDST technique is assessed through simulation as compared to the previous path selection techniques in terms of different routing metrics such as packet delivery ratio, packet loss rate, end-to-end delay and throughput with respect to the number of data packets.

An integrated incentive and trust-based optimal path identification in ad hoc on-demand multipath distance vector routing for MANET

A Mobile Ad hoc Network (MANET) can work well only when the mobile nodes behave cooperatively in packet routing. To reduce the hazards from malicious nodes and enhance the security of the network, this paper extends an Ad hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol, named as an Integrated Incentive and Trust-based optimal path identification in AOMDV (IIT-AOMDV) for MANET. The proposed IIT-AOMDV routing protocol integrates an Intrusion Detection System (IDS) with the Bayesian Network (BN) based trust and payment model. The IDS utilises the empirical first-and second-hand trust information of BN, and it underpins the cuckoo search algorithm to map the QoS and trust value into a single fitness metric, tuned according to the presence of malicious nodes. The simulation results show that the IIT-AOMDV improves the detection accuracy and throughput by 20% and 16.6%, respectively, more than that of existing AOMDV integrated with the IDS (AID).

An integrated incentive and trust-based optimal path identification in ad hoc on-demand multipath distance vector routing for MANET

A Mobile Ad hoc Network (MANET) can work well only when the mobile nodes behave cooperatively in packet routing. To reduce the hazards from malicious nodes and enhance the security of the network, this paper extends an Ad hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol, named as an Integrated Incentive and Trust-based optimal path identification in AOMDV (IIT-AOMDV) for MANET. The proposed IIT-AOMDV routing protocol integrates an Intrusion Detection System (IDS) with the Bayesian Network (BN) based trust and payment model. The IDS utilises the empirical first-and second-hand trust information of BN, and it underpins the cuckoo search algorithm to map the QoS and trust value into a single fitness metric, tuned according to the presence of malicious nodes. The simulation results show that the IIT-AOMDV improves the detection accuracy and throughput by 20% and 16.6%, respectively, more than that of existing AOMDV integrated with the IDS (AID).

A Physarum Centrality Measure of the Human Brain Network

The most important goals of brain network analyses are to (a) detect pivotal regions and connections that contribute to disproportionate communication flow, (b) integrate global information, and (c) increase the brain network efficiency. Most centrality measures assume that information propagates in networks with the shortest connection paths, but this assumption is not true for most real networks given that information in the brain propagates through all possible paths. This study presents a methodological pipeline for identifying influential nodes and edges in human brain networks based on the self-regulating biological concept adopted from the Physarum model, thereby allowing the identification of optimal paths that are independent of the stated assumption. Network hubs and bridges were investigated in structural brain networks using the Physarum model. The optimal paths and fluid flow were used to formulate the Physarum centrality measure. Most network hubs and bridges are overlapped to some extent, but those based on Physarum centrality contain local and global information in the superior frontal, anterior cingulate, middle temporal gyrus, and precuneus regions. This approach also reduced individual variation. Our results suggest that the Physarum centrality presents a trade-off between the degree and betweenness centrality measures.

PSOBLAP: Particle Swarm Optimization-Based Bandwidth and Link Availability Prediction Algorithm for Multipath Routing in Mobile Ad Hoc Networks

In mobile ad hoc network (MANET), optimal path identification is the main problem for implementing the Multipath routing technique. MANET desires an efficient algorithm for improving the performance of the network by improving the connectivity of network organization. MANET routing protocol will consider so many parameters like extended power, the superiority of wireless associations, path failures, desertion, obstruction, and topological adjusts are generated for the discovery of optimal path for increasing the original routing algorithms. Further advancement in multipath routing algorithm proposal will be based on local rerouting called particle swarm optimization-based bandwidth and link availability prediction algorithm for multipath routing and to ensure forwarding continuity with compound link failures. In the route discovery phase, each node establishes a link between their neighboring nodes. If there is any route failure resulting in data loss and overhead will occur. Hence routing in MANET is developed by the movement of a node (mobility). In this paper, the particle swarm optimization based on available bandwidth and link quality based on mobility prediction algorithm is used to provide the multipath routing in MANET. In this prediction phase, the available bandwidth, link quality, and mobility parameters are used to select the node based on their fuzzy logic. The selected node will broadcast information among all the nodes and details are verified before transmission. In the case of link failure, the nodes are stored into a blacklisted link. Furthermore, the routes are diverted and backward to find a good link as a forwarder or intermediate node. The proposed scheme is able to attain a significant progress in the packet delivery ratio, path optimality, and end-to-end delay.

An Optimal Path Identification in the Intermodal Freight Transportation Network

Intermodal transportation is a research topic of great interest at present. This paper presents a route choice analysis on an intermodal freight transportation network. The aim of this study was to determine the optimum route and mode of transportation based on least distance and least time criteria for the movement of containers from origin to the destination. Geographic Information System (GIS) was adopted to build the hypothetical freight transportation network and MATLAB software was used to model the travel distance and travel time. The results showed that the model can be used effectively to identify the shortest path and modes of transportation according to objective functions.

Pre-Processing Influence on Identification of Optimal Path Length for NIR Analysis of Tea Polyphenols

We investigated the optical path length to tea polyphenols (TP) determination in Puer tea by near infrared (NIR) spectroscopy. The NIR spectra samples include three path lengths (1mm, 2mm and 5mm). Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. To study the influence of pre-processing on identification of optimal path for NIR analysis of tea polyphenols, we applied five techniques to pre-process spectra, including normalization, standardization, centralization, derivative and discrete wavelet transform. Comparison of the mean absolute percentage error (MAPE) of the models with different path lengths show that the models constructed with spectra collected in 2mm path length gave the best results. 1mm path length gained the uncorrected determination results. Normalization, centralization and derivative are better than standardization or discrete wavelet transform for pre-processing.

Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.

Optimal Electrical Distribution Systems Reinforcement Planning Using Gas Micro Turbines by Dynamic Ant Colony Search Algorithm

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new energy trading strategies and new technologies. In this paper, an optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. In the new deregulated energy market and considering the incentives coming from the political and economical fields, it is reasonable to consider distributed generation (DG) as a viable option for systems reinforcement. In the paper, the DG technology is considered as a possible solution for distribution systems capacity problems, along several years. Therefore, compound solutions comprising the installation of both feeders and substations reinforcement and DG integration at different times are considered in the formulation of a minimum cost distribution systems reinforcement strategy problem. An application on a medium size network, hypothesizing a scenario of reinforcement also using as DG gas micro-turbines, is carried out using a novel optimization technique allowing the identification of optimal paths in trees or graphs. The proposed technique is the Dynamic Ant Colony Search algorithm

Adaptive and Dynamic Ant Colony Search Algorithm for Optimal Distribution Systems Reinforcement Strategy

The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic search optimization problems having a large search space and mixed integer variables. The problem to which it has been applied is an electrical distribution systems management problem. This kind of issues is indeed getting increasingly complicated due to the introduction of new energy trading strategies, new environmental constraints and new technologies. In particular, in this paper, the problem of finding the optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. Utilities indeed need efficient software tools to take decisions in this new complex scenario. In past times, utilities project the load growth for several years and then estimate when the capacity limit will be exceeded. Designers then consider some feasible alternatives and select the optimal one in terms of performance and costs. In this paper, the Distributed Generation, DG, technology considered in compound solutions with the installation of feeder and substations is viewed as a new option for solving distribution systems capacity problems, along several years. The objective to be minimized is therefore the overall cost of distribution systems reinforcement strategy in a given timeframe. An application on a medium size network is carried out using the proposed technique that allows the identification of optimal paths in extremely large or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push exploration or exploitation as the search procedure stops in a local minimum. The algorithm allows the easy investigation of these kinds of complex problems, and allows to make useful comparisons as the intervention strategy and type of DG sources vary.