Optimal Path Selection Research Articles

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security.

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

AbstractAn enhanced Elman spike neural network (EESNN) optimized with hybrid wild horse optimization and chameleon swarm algorithm is proposed in this manuscript for multi‐objective cluster head selection and energy aware routing in wireless sensor network (CH‐EESNN‐Hyb‐WH‐CSOA‐WSN). Initially, EESNN is used to intelligent selection of cluster head. Then, the optimal cluster head utilized the selected data transferring process with the consideration of multi‐objective fitness function based EESNN. Here, some of the multi‐objective fitness function factors are considered, like energy, delay, throughput, distance between the nodes, traffic rate and cluster density. The hybrid wild horse optimization and chameleon swarm algorithm (Hyb‐WH‐CSOA) is taken into account for the optimal route path selection with minimal delay. The proposed CH‐EESNN‐Hyb‐WH‐CSOA‐WSN method is activated in network simulator 3 (NS‐3) tool. The performance of the proposed method is examined under certain performance metrics, like count of alive nodes, drop, network lifetime, delay, throughput, energy consumption, and packet delivery ratio. Finally, the proposed method attains 98.78%, 97.21%, 99.61% lower delay, 98.78%, 99.21%, 96.78% higher delivery ratio, and 99.57%, 98.67%, 98.88% lower packet drop compared with the existing methods, like optimal secure cluster head placement through source coding techniques in wireless sensor networks (CHP‐HMC‐WSN), optimal placement of single cluster head in wireless sensor networks via clustering (CHP‐K‐Means C‐PSO‐WSN) and hybrid firefly approach along particle swarm optimization for energy efficient optimum cluster head selection in wireless sensor networks (CHP‐HFAPSO‐WSN) respectively.

Low-Latency Autonomous Service Orchestration for the Power Communication Network

Power communication network based on optical technology can support the operation, production and scheduling of power systems. However, due to the dynamic and information uncertainty factors in the network environment, the independent service arrangement of the power communication network is faced with many challenges. To address these challenges, a software-defined network (SDN)-empowered the power communication network architecture. In this paper, we proposed a routing algorithm based on ε-greed to minimize total transmission delay of service data flow, which can support the autonomous, intelligent, and flexible orchestration for the power communication network. Simulation verify the superiority of the proposed ε-greedy algorithm in the aspect of delay and optimal path selection.

Multi-Faceted Clustering with Enhanced Multi-channel Allocation for Optimal Path Selection in Wireless Sensor Networks

Multi-Faceted Clustering with Enhanced Multi-channel Allocation for Optimal Path Selection in Wireless Sensor Networks

CVFP: Energy and trust aware data routing protocol based on Competitive Verse Flower Pollination algorithm in IoT

Internet of Things (IoT) is a network of things that are interconnected to provide a variety of services. These things can include smartphones, actuators, sensors, computers, and wearables. The IoT enables the collection and exchange of data between these objects. The expansion of smart networks is what has given the Internet of Things its global influence. Routing is an important strategy that involves the creation of routes and the transmission of data packets from the source to the intended recipient in the network. Energy and trust are considered to be the two most important parts that provide secure data transmission in a network. However, secure routing in the highly distributed and dynamic environment of IoT still results in a great challenge due to the heterogeneity of devices. Hence, an efficient secure data routing mechanism is designed with the proposed Competitive Verse Flower Pollination (CVFP) algorithm that considers trust, energy, delay, distance, and link lifetime (LLT) parameters to find an optimal routing path. Moreover, the selection of optimal path is made by involving CH nodes such that nodes have higher energy are chosen in the routing strategy. The quality of the link is measured using the LLT constraint in order to decide whether data can be routed or to perform a route maintenance mechanism to ensure reliability in data routing. The performance of the proposed algorithm is analyzed using the simulated data. The proposed algorithm attains a lower delay of 0.204 s, higher residual energy of 0.093 J, a maximum throughput of 94.59%, and a higher trust of 0.713.

Research on travel path choice considering complex environment: theory, model and case

In this paper, considering the heterogeneity of travelers’ decision-making behavior caused by travel environment factors, thus affecting the choice of travel path, the theories and methods of travel path choice based on improved cumulative prospect theory (ICPT) in complex environment were proposed. On the basis of cumulative prospect theory (CPT), the value function was improved, and the parameter value range was enlarged. The nonlinear curve of value function and weight function of cumulative prospect theory was fitted through thousands of data tests and experiments. Then according to the decision preference, the decision makers were divided into different categories and the reference point value relationship of heterogeneous decision makers was found. In this paper, fuzzy travel time reference point and periodic dynamic risk degree reference point were set up, and a dynamic path selection model based on heterogeneous double reference point was established to improve the cumulative prospect value. Taking the highway network in Sichuan-Tibet region for example, the optimal path selection scheme of heterogeneous travel groups under the complex environmental factors such as debris flow and landslide in each time stage was studied, and the influence of preference parameter of travel time and risk degree on path choice was analyzed, and then the parameter sensitivity in the cumulative prospect theory (ICPT) was analyzed. The research results verified the rationality of the improved theory and method proposed in this study, which not only provide a new way of thinking for the study of travel path choice in complex environment but also provide theoretical guidance value for supporting regional traffic planning and construction in complex environment.

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

Wireless sensor network (WSN) combines and transmit the data in internet of things (IoT) applications. The nodes in pervasive Wireless sensor network are battery-operated and it needed to develop an energy-efficient approach, which lessens the energy consumption and increases the network lifetime. The existing methods not provide effective cluster head (CH) selection and node computation.In this manuscript, Multi-Objective Cluster Head Based Energy Aware Routing using Self-attention based progressive generative adversarial network optimized with African vulture optimization is proposed for Secured Data aggregation in WSN (MOCH-SAPGAN-AVO). Initially, the proposed model performs the routing process through Cluster heads (CH). So, the data aggregation using Self-attention based progressive generative adversarial network selects CH based on the multi-objective fitness function. These multi-objective fitness functions consider factors, like energy, delay, throughput, distance between the nodes and cluster density. After the cluster head selection, the optimal path is used to transfer the data to the base station. The optimal path is based on three parameters, such as degree of satisfication, connectivity and Rate of service (RoS). Hence, these three parameters are optimized with the help of African vulture optimization approach for optimal path selection. Finally, the optimal path is used for transferring the aggregated data to the base station (BS) and vice versa. The proposed method is implemented in network simulator (NS2) tool and the performance of proposed method is estimated. The proposed method attains 30.39%, 34.64%, 45.36%, and 33.32% lower delay, 39.056%, 44.12% and 28.97% improved packet delivery ratio and 45.96%, 48.06%, and 38.16% lower packet drop compared with existing methods, like hybrid and energy-efficient secure data aggregation algorithm and fuzzy logic based secure data aggregation in wireless sensor network (FL-CHESDA-WSN), multi objective fostered male lion optimization algorithm (DA-WSN-MLOA) and an Adaptive Source Location Privacy Preservation Technique and Principle Component Analysis (IDAF-ASLPP-RR).

QoS correlation-based service composition algorithm for multi-constraint optimal path selection

QoS correlation-based service composition algorithm for multi-constraint optimal path selection

Fuzzy based hybrid BAT and firefly algorithm for optimal path selection and security in wireless sensor network

Nodes are deployed randomly in the network area of the WSN. data transmission from source to destination via intermediate nodes should be done in a secure fashion. Due to the large size of packet loss and energy consumption of sensor nodes, a secure and energy-efficient path must be required. The main objective of this research is to provide secure data transmission among node-to-node for efficient delivery of data packets to the destination. The system uses a novel hybrid firefly and BAT algorithm for path selection, an innovative trust value generation, and optimal neighborhood selection using fuzzy logic. The research employs Elliptic Curve Cryptography (ECC) combined with Diffie-Hellman exchange for key generation and key exchange. Path selection is done by fuzzy logic and optimization of selection has been carried out by hybrid BAT and Firefly algorithms. Key generation includes a time-based randomness factor that increases the complexity of cryptanalysis, thereby providing the most security. The performance of the simulation is analyzed and depicted in terms of delay, throughput, energy, and processing time. The research has been carried out using a network simulator with nodes deployed randomly in the network area with mobility as the primary concern that requires dynamic path selection.

Increase of the Lifetime of Wireless Sensor Network using Clustering Algorithm and Optimal Path Selection Method

Increase of the Lifetime of Wireless Sensor Network using Clustering Algorithm and Optimal Path Selection Method

Dynamic and Optimized Routing Scheme based on Fuzzy with Trustable Transmission in Wireless Sensor Networks

In this research work, an effective proposed scheme is used named, Dynamic and Optimized Routing Scheme with Secure Transmission in WSN to overcome above many in networks. It provides load balanced and optimized path selection using Secure Fuzzy Logic Optimization and finds a stable path between the source and destination meeting the delay requirement. To conquer security challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids attacks through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. Our proposed scheme which maximizes end-to-end connectivity in the network and minimizes faults at link or/and node level. Secure Fuzzy Logic Optimization Routing (SFLOR) where the energy efficiency is taken as a major criterion for performing routing and deriving optimized path for data forwarding and processing to base node. The SFLOR generates a whole new path of routing by taking energy as fitness value to judge different path and choose best optimized path whose energy consumption is less as compared to other routing paths.

Refined Network Topology for Improved Reliability and Enhanced Dijkstra Algorithm for Optimal Path Selection during Link Failures in Cluster Microgrids

Cluster microgrids are a group of interoperable smart microgrids, connected in a local network to exchange their energy resources and collectively meet their load. A microgrid can import/export energy to the neighboring microgrid in the network based on energy deficit/availability. However, in executing such an operation, a well-established communication network is essential. This network must provide a reliable communication path between microgrids. In addition, the network must provide an optimal path between any two microgrids in the network to optimize immediate energy generation, import requirements, and export possibilities. To meet these requirements, different conventional research approaches have been used to provide reliable communication, such as backup/alternative/Hot Standby Router Protocol (HSRP)-based redundant path concepts, in addition to traditional/renowned Dijkstra algorithms, in order to find the shortest path between microgrids. The HSRP-based mechanism provides an additional path between microgrids, but may not completely solve the reliability issue, especially during multiple link failures and simultaneous failures of the actual path and redundant path. Similarly, Dijkstra algorithms discussed in the literature do not work for finding the shortest path during link failures. Thus, to enhance reliability, this paper proposes a refined network topology that provides more communication paths between microgrids, while retaining the same number of total links needed, as in conventional HSRP-based networks. In addition, this paper proposes an enhanced Dijkstra algorithm to find the optimum path during link failures. Simulations are executed using NetSimTM by implementing test cases such as single-link and multiple-link failures. The results prove that the proposed topology and method are superior to conventional approaches.

Research on automatic parking based on A* search algorithm

Automatic parking is one of the most landing scenarios in autonomous driving technology. Automatic parking refers to the automatic parking process of cars in parking lots. In this paper, we consider three parking models under vertical, horizontal and bending conditions. We first establish physical models according to different conditions, introducing A* search algorithm is d as the optimal path selection algorithm for practical cases. We also set up a constrained optimization problem for the situation of car occupancy in the garage. By solving this constraint, we can efficiently calculate the optimal parking path to avoid the situation of car occupancy.

An efficient stable node selection based on Garson's pruned recurrent neural network and MSO model for multipath routing in MANET

AbstractIn mobile ad hoc network, the routing task is considered to be more complicated issue. In order to find efficient route in MANET, the positions of each stable node in the network is identified initially. The main contribution of this paper is to identify the locations of neighboring nodes in MANET for the establishment of multi path routing in diverse mobility patterns. It also handles packet scheduling to balance the load as well as data forwarding with less communication time. The proposed work explains four phases such as stable node prediction, stability determination, route exploration, and packet dissemination. At first, the stable nodes are identified using recurrent neural network along with modified seagull optimization approach. By means of Garson's pruning based recurrent neural network accompanied with modified sea gull optimization (RMSG) algorithm, the stable neighbors are chosen. The network routing is formed by interconnecting the stable nodes from the source to the destination. When a routing link failure happens, the route recovery process will be initiated. Thus, the data packets are broadcasted in multi‐paths without any intervention. The measures namely packet delivery ratio, throughput, end‐to‐end delay, routing overhead, optimal path selection, and energy consumption are utilized to evaluate the performance of proposed approach. The experimental analysis proved that the proposed approach well performed than other compared existing approaches.

Modeling road traffic safety based on point patterns of wildlife-vehicle collisions

Wildlife-vehicle collisions represent one of the main coexistence problems that appear between human populations and the environment. In general terms, this affects road safety, wildlife management, and the building of road infrastructures. These accidents are a great danger to the life and safety of car drivers, cause property damage to vehicles, and affect wildlife populations. In this work, we develop a new approach based on algorithms used to obtain minimum paths between vertices in weighted networks to get the optimal (safest) route between two points (departure and destination points) in a road structure based on wildlife-vehicle collision point patterns together with other road variables such as traffic volume (traffic flow information), road speed limits, and vegetation density around roads. For this purpose, we have adapted the road structure into a mathematical linear network as described in the field of Graph Theory and added weights to each linear segment based on the intensity of accidents. Then, the resulting network structure allows us to consider some graph theory methodologies to manipulate and apply different calculations to analyze the network. This new approach has been illustrated with a real data set involving the locations of 491 wildlife-vehicle collisions in a square region (40 km × 40 km) around the city of Lleida, during the period 2010–2014, in the region of Catalonia, North-East of Spain. Our results show the usefulness of our new approach to model road traffic safety based on point patterns of wildlife-vehicle collisions. As such, optimal path selection on linear networks based on wildlife-vehicle collisions can be considered to find the safest path between two pairs of points, avoiding more dangerous routes and even routes containing hotspots of accidents.

A Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) are getting used in numerous applications nowadays. Many robust energy-efficient routing protocols have been proposed to handle multimedia traffic-intensive data like images and videos in WMSNs. It is a common trend in the literature to facilitate a WMSN with numerous sinks allowing cluster heads (CHs) to distribute the collected data to the adjacent sink node for delivery overhead mitigation. Using multiple sink nodes can be expensive and may incur high complexity in routing. There are many single-sink cluster-based routing protocols for WMSNs that lack in introducing optimal path selection among CHs. As a result, they suffer from transmission and queueing delay due to high data volume. To address these two conflicting issues, we propose a data aggregation mechanism based on reinforcement learning (RL) for CHs (RL-CH) in WMSN. The proposed method can be integrated to any of the cluster-based routing protocol for intelligent data transmission to sink node via cooperative CHs. Proposed RL-CH protocol performs better in terms of energy-efficiency, end-to-end delay, packet delivery ratio, and network lifetime. It gains 17.6% decrease in average end-to-end delay and 7.7% increase in PDR along with a network lifetime increased to 3.2% compared to the evolutionary game-based routing protocol which has been used as baseline.

An Adaptive Fuzzy C Means with Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field with IoT

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.

An improved Aquila optimization with fuzzy model based energy efficient cluster routing protocol for wireless sensor networks

Due to the latest advances in microelectronics, wireless sensor networking (WSN) has been introduced in many applications. The flow of event data in WSN applications requires timely and reliable distribution so that immediate response and appropriate action can be taken. However, the limited power supply to the sensor terminal causes a transmission between the delay on the way to the base station and the power consumption. Clustering techniques are essential in developing the WSN routing algorithm that improves network operating time and power efficiency. However, due to the unbalanced power consumption between the terminals, the WSN is the optimal, energy efficient routing. In this work, we propose an energy efficient cluster based routing protocol (EEC-HO) for WSN using hybrid optimization algorithm. We introduce an improved Aquila optimization with fuzzy (IAO-Fuzzy) model for optimal and efficient cluster formation and cluster head (CH) computation. The main objective of proposed IAO-Fuzzy model used to compute the trust degree of each node, the highest trust node is considered as CH. After that, the hybrid beetle search induced decision making (BSDM) algorithm for optimal path selection to transfer data transfer between two nodes. Finally, the simulation results of proposed EEC-HO routing protocol is compared with the existing routing protocols. For the impact of sensor node density case, we observed that the effectiveness of proposed EEC-HO routing protocol is 45.887%, 30.666%, 56.666%, 17.629% and 41.666% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and dead nodes respectively. For the impact of simulation rounds case, we observed that the effectiveness of proposed EEC-HO routing protocol is 21.216%, 35.417%, 41.667%, 18.568% and 40.000% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and number of dead nodes respectively.

Road Traffic Prediction and Optimal Alternate Path Selection Using HBI-LSTM and HV-ABC

Objectives: The objective of this work is to monitor and manage the traffic flow, so an Intelligent Transportation System (ITS) is developed that comprises the fundamental information of the real-time traffic flow. Methods: For reducing road Traffic Congestion (TC), this paper proffers an efficient traffic prediction framework and the optimal alternate route selection. The conversion of videos (from surveillance camera) into frames is done, and then pre-processing occurs. Then, for recognizing the traffic on the roadways, the background elimination utilizing Gaussian Mixture Model (GMM) is performed. Next, for identifying the vehicle motion, Motion Estimation (ME) utilizing the Virtual loop-based Lucas-Kanade (VLK) technique is performed. Utilizing the You Only Look Once (YOLO) technique, the frames are segmented centered on the estimated motion for identifying the type of objects on the road. Then, for classifying the traffic centered on the number of objects in the segmented frames, the H-detach optimized Bidirectional Long Short Term Memory (HBI-LSTM) is utilized. The traffic is classified by the classifier as heavy traffic, medium traffic, and low traffic. Findings: Utilizing the Horizontal Vertical cross-search appended Artificial Bee Colony (HV-ABC) optimization algorithm, the optimal alternate paths are chosen from different other routes if the traffic is heavy or medium. Novelty: The experimental outcomes demonstrate that the other top-notch models are outperformed by the proposed framework. Keywords: Traffic Congestion, Surveillance Videos, Noise Removal, Motion Estimation, Path Selection, Artificial Bee Colony (ABC) Optimization