Optimal Route Selection Research Articles

Ship's response to low-sulfur regulations: From the perspective of route, speed and refueling strategy

Optimizing speed, routing, and refueling strategy are three ways to reduce ship operating costs without modifying on ship. Most past studies have simultaneously optimized one or two variables, resulting in a local optimal solution. To limit sulfur emissions, some governments have established Emission Control Areas (ECAs) in recent years, where ships must use low sulphur fuels. The constraints created by ECAs makes the simultaneous optimization of the three variables even more challenging. This study developed a nonlinear mixed integer programming model to address the challenge of simultaneously optimizing the three variables. After transforming the nonlinear discrete mixed integer programming model into a convex nonlinear continuous programming model, the study proposed a solution algorithm for the proposed model. The new model in this study was applied to the Europe-United States (US) and Southeast Asia service route. The results show that the optimal refueling policy, route, and speed selection can be determined simultaneously using the proposed model and solution method. The study investigated the impact of fuel price on ship refueling strategy, route, and speed; and developed insights from the example analyses.

ENERGY EFFICIENT PIGEON INSPIRED OPTIMIZATION ALGORITHM BASED CLUSTERING AND ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS

Owing to the advancement in recent technologies, Wireless Sensor Networks (WSNs) plays a vital role in today's real-world application. In WSN, energy efficiency is considered as the crucial process to conserve the energy and lengthen network lifetime. Most of the research has aroused in preserving energy with various enriched energy-efficient clustering and routing approaches. Therefore, the proper selection of cluster heads (CHs) and optimal route selection plays a vital role to achieve energy efficiency and prolong the network lifetime. In this work, we propose an optimistic approach for energy-efficient Pigeon Optimization Algorithm based Clustering and Dempster-Shafer (DS)for Routing called(PIOA-DS) Protocol for WSN. The PIOA-DS model involves two major phases namely PIO based clustering algorithm and DS evidence theory based Routing (DSET-R). The PIO algorithm derives a fitness function using residual energy, distance to base station (BS), and distance to neighbors. In addition to that, the non-cluster head nodes will join the respective CHs using the weight functions. Besides, the DSET-R algorithm finds an optimal set of routes based on three measures namely transmission power efficiency ratio, Idleness degree, and Energy density factor. The performance of the presented algorithm is tested extensively on various scenarios of WSNs. The observed results are compared with some existing algorithms to illustrate the superiority of the PIOA-DS work.

Information theory inspired optimization algorithm for efficient service orchestration in distributed systems.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled—physically and virtually interconnected—data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with benchmark heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms, Ant Colony, Cross Entropy, Neural Networks, 2opt and Simulated Annealing. The proposed algorithm can be used by applications to deploy services across large cluster of nodes by making better decision in the route design. The findings in this paper unifies critical areas in Computer Science, Mathematics and Statistics that many researchers have not explored and provided a new interpretation that advances the understanding of the role of entropy in decision problems encoded in Turing Machines.

Chemical data intelligence for sustainable chemistry.

This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

Optimal route selection model in freight transport with customer collection approach using genetic and fuzzy algorithms

Optimal route selection model in freight transport with customer collection approach using genetic and fuzzy algorithms

An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

Flying ad hoc networks (FANETs) present a challenging environment due to the dynamic and highly mobile nature of the network. Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission. Several different techniques are adopted to address the issues arising in FANETs, from game theory to clustering to channel estimation and other statistical schemes. These approaches mostly employ traditional concepts for problem solutions. One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes. Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes. One such species that resembles the dynamicity of FANETs are Bats. In this paper, the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBAT-COOP (Improved BAT Algorithm using Cooperation technique). We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements. Moreover, we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs. This paper employs cooperation for an optimal route selection and reflects on distance, Signal to Noise Ratio (SNR), and link conditions to an efficient level to deal with FANET’s routing. By way of simulations, the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio, end-to-end delay, and transmission loss by 81%, 21%, and 82% respectively. Furthermore, the average link duration is improved by 25% compared to the BAT-FANET protocol.

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

Wireless sensor networks (WSNs) is one of the hottest research areas, owing to its wider applicability. Due to its effectiveness and inexpensiveness, most of the domains enjoy the benefits of WSN. ...

Hybridization of Metaheuristic Algorithm for Dynamic Cluster-Based Routing Protocol in Wireless Sensor Networksx

Energy efficiency is considered the major design issue in wireless sensor networks (WSN), which can be addressed using clustering and routing techniques. They are treated as Non-deterministic Polynomial (NP)-hard optimization problems and are solved using metaheuristic algorithms to identify the optimal or near-optimal solutions. With this motivation, this paper develops a hybridization of the metaheuristic cluster-based routing (HMBCR) technique for WSN. The HMBCR technique initially involves a brainstorm optimization with levy distribution (BSO-LD) based clustering process using a fitness function incorporating four parameters such as energy, distance to neighbors, distance to the base station, and network load. Besides, a water wave optimization with a hill-climbing (WWO-HC) based routing process is carried out for optimal route selection. Extensive experimentation analysis is performed to ensure the energy efficiency and network lifetime performance of the HMBCR technique. The experimental outcome ensured the superior results of the HMBCR technique over the compared methods under different aspects.

Reinforcement Learning Framework for Delay Sensitive Energy Harvesting Wireless Sensor Networks

A multi-hop energy harvesting wireless sensor network (EH-WSNs) is a key enabler for future communication systems such as the internet-of-things. Optimal power management and routing selection are important for the operation and successful deployment of EH-WSNs. Characterizing the optimal policies increases significantly with the number of nodes in the network. In this paper, optimal control policy is devised based on minimum-delay transmission in a multi-hop EH-WSN using reinforcement learning (RL). The WSN consists of M EH sensor nodes aiming to transmit their data to a sink node with a minimum delay. Each sensor node is equipped with a battery of limited capacity to save the harvested energy and a data buffer of limited size to store both the sensed and relayed data from neighboring nodes. Centralized and distributed RL algorithms are considered for EH-WSNs. In the centralized RL algorithm the control action is taken at a central unit using the state information of all sensor nodes. In the distributed RL algorithm the control action is taken locally at each sensor node using its state of information and the state information of neighboring nodes. The proposed RL algorithms are based on the state-action-reward-state-action (SARSA) algorithm. Simulation results demonstrate the merits of the proposed algorithms.

Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

A Selection of Bandung City Travel Route Using The FLOYD-WARSHALL Algorithm

The rapid development of information and technology, the city of Bandung tourism has also increased. However, tourists who visit the city of Bandung have problems with a limited time when visiting Bandung tourist attractions. Traffic congestion, distance, and the number of tourist destinations are the problems for tourists travel. The optimal route selection is the solution for those problems. Congestion and distance data are processed using the Simple Additive Weighting (SAW) method. Route selection uses the Floyd-Warshall Algorithm. In this study, the selection of the best route gets the smallest weight with a value of 5.127 from the Algorithm process. Based on testing, from two to five tourist attractions get an average calculation time of 3 to 5 seconds. This application is expected to provide optimal solutions for tourists in the selection of tourist travel routes.

Multi-objective cost optimization for geological risk oriented rail transit route selection

China’s rail transit has entered a period of rapid development, but related safety and risk management fail to match with high-speed construction. The complex and uncertain underground space leads to frequent risk accidents of urban rail transit. As the primary task of rail transit development, the reasonable route selection can not only avoid a lot of geological risks, but also greatly save the duration and cost. In order to guide the rail transit route selection work scientifically and reasonably, the cost problems related to geological risk factors by constructing the objective function of risk assessment, section construction cost and duration are analyzed in this study. Combining with heuristic algorithms such as ant algorithm and particle swarm optimization algorithm, an evaluation method of cost optimization of rail transit route selection for the geological risk are put forward. Taking the geological data of a certain section of a proposed rail transit route in Guangzhou as an example, based on this proposed method, the optimal route selection scheme with relatively low risk, short construction duration and low cost is found, which provides theoretical basis and guidance for the route selection and construction of rail transit, and also verifies the rationality of the application of the cost optimization method for the geological risk to multi-objective engineering management.

Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

With the incoming of 5G communications, Vehicular Networks have the hope to achieve ultra-high data transmission rate with extremely low end-to-end delay. However, the dynamic nature of transportation traffic and increased data bandwidth demands are the major obstacles to achieve high transmission rate in Vehicular-to-Anything (V2X) Networks. To overcome these obstacles, this paper presents a novel Software Defined Networking (SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz). To improve the network management, Road Side units (RSUs) are used to segregate the V2X network into different clusters. Stability-aware clustering (SAC) scheme is also used for cluster formations. Our intelligent V2X is based on three features enabled machine learning approach: (1) To predict future 3D positions of the vehicles in the Cluster Heads (CHs) using Deep Neural Network with Extended Kalman Filter (DNN-EKF) algorithm for real-time, high-resolution prediction. (2) For THz communications, 0.3 THz to 3 THz band is selected for short-distance super-fast data transmissions. The THz band detection is performed by the CR-enabled Road Side Units (cRSUs). A Genetic Algorithm (GA)-based Improved Fruit Fly (GA-IFF) scheme is proposed to achieve an optimal route selection in THz communications. (3) In mmWave-based V2X communications, optimal beam selection is performed by the multi-type2 fuzzy inference system (M-T2FIS). By using these three intelligent designs approaches, we are able to achieve ultrahigh- rate and minimized transmission delay for short-range (in THz bands) and middle-range (in mmWave) communications. Finally, the proposed SDN-controlled, CR-enabled V2X Network is modeled and tested for performance evaluations with the metrics of delivery ratio, routing delay, protocol overhead, and data rate.

CS‐MAODV: Cuckoo search and M‐tree‐based multiconstraint optimal Multicast Ad hoc On‐demand Distance Vector Routing Protocol for MANETs

SummaryMobile ad hoc networks (MANETs) are a subclass of wireless ad hoc networks, having amazing qualities of dynamic network topology and moving nodes. As MANET changes its topology, routing is difficult at some moment. Routing protocol controls the stream of information and selects the efficient way to transmit the data. There are a variety of routing protocols existing in MANET. Accordingly, this paper presents the multicast routing protocol, named cuckoo search and M‐tree‐based multicast ad hoc on‐demand distance vector (CS‐MAODV) protocol for MANET. The proposed routing protocol includes a two‐step process, namely, M‐tree construction and optimal multicast route selection. Here, the divisional‐based cluster (DIVC) technique is used for building the M‐tree using three constraints: destination flag, path‐inclusion factor, and multifactor. Then, cuckoo search algorithm is used for selecting the optimal route by considering the multiple objectives, such as energy, link lifetime, distance, and delay. The performance of the proposed multicast routing protocol is analyzed, and the comparative analysis is performed with the existing routing protocols, such as MAODV, QoS‐based multicast routing protocol using reliable neighbor nodes selection (QMRPRNS), efficient fuzzy‐based multiconstraint protocol (EFMMRP), and energy‐efficient lifetime aware multicast route selection (EELAM). From the simulation results, it can be shown that the proposed CS‐MAODV protocol attains the maximum energy of 90.3513%, LLT of 158.7708 s, the throughput of 86.2226%, and PDR of 87.1606, respectively. Also, the proposed protocol has the minimum control overhead of 2.1415% and PDD of 0.0563 s.

Development of information collection scheme in internet of things environment for intelligent radiation monitoring systems

The new revolution in computing and wireless communications technologies led to Internet of Things (IoT). Information collection scheme performs an important role for energy efficient utilization and latency awareness in IoT environments. The enhancement of an effectual information collection scheme is crucial to improve the overall performance of the internet of things applications. In this paper, the proposed information collection scheme aimed to enhance the confidence regarding any captured measurements under IoT environment. The scheme can verify the selection of optimal information collection routes through using the Dijkstra algorithm. It depends on selecting the preferable IoT devices (collectors) with optimal paths in efficient energy utilization. The IoT devices (sources) have to elect whichever a preferable collector can deliver the captured information with an endeavour to sense the latest contextual information. They elect the optimal collectors through implicit and explicit solutions. Also, it considers different failure conditions to specify the optimal collection time for furtherance scalability of IoT environment. The simulation results show the ability of our scheme to improve the performance in terms of network lifetime, energy consumption, and reliability.

SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

In WSN-assisted IoT, energy efficiency and security which play pivotal role in Quality of Service (QoS) are still challenging due to its open and resource constrained nature. Although many research works have been held on WSN-IoT, none of them is able to provide high-level security with energy efficiency. This paper resolves this problem by designing a novel Secure Deep Learning (SecDL) approach for dynamic cluster-based WSN-IoT networks. To improve energy efficiency, the network is designed to be Bi-Concentric Hexagons along with Mobile Sink technology. Dynamic clusters are formed within Bi-Hex network and optimal cluster heads are selected by Quality Prediction Phenomenon (QP2) that ensure QoS and also energy efficiency. Data aggregation is enabled in each cluster and handled with a Two-way Data Elimination then Reduction scheme. A new One Time-PRESENT (OT-PRESENT) cryptography algorithm is designed to achieve high-level security for aggregated data. Then, the ciphertext is transmitted to mobile sink through optimal route to ensure high-level QoS. For optimal route selection, a novel Crossover based Fitted Deep Neural Network (Co-FitDNN) is presented. This work also concentrates on IoT-user security since the sensory data can be accessed by IoT users. This work utilizes the concept of data mining to authenticate the IoT users. All IoT users are authenticated by Apriori based Robust Multi-factor Validation algorithm which maps the ideal authentication feature set for each user. In this way, the proposed SecDL approach achieves security, QoS and energy efficiency. Finally, the network is modeled in ns-3.26 and the results show betterment in network lifetime, throughput, packet delivery ratio, delay and encryption time.

A crowdsourcing-based optimal route selection for drug delivery in low- and middle-income countries

The timely delivery of life-saving products such as medicines from the pharmacy to the patient’s location requires availability of an adequate road infrastructure and reliable directions to the patient’s home. However, in many regions of low- and middle-income countries (LMIC), the road infrastructure is in poor state, and medicine delivery is affected by flooded roads, unsafe routes, congestion, traffic disruptions due to accidents, and lack of reliable navigation directions. Owing to the proliferation of smartphones and increasing mobile connectivity, these days the vehicle users rely heavily on routing software apps to select and follow shortest or fastest routes for reaching the destination and deliver life-saving medicinal products. However, routing software apps such as Google and Waze are not able to detect flooded roads or insecure and unsafe roads in various locations in LMIC countries, which causes disruption in drug delivery. Hence, this paper proposes a crowdsourcing-based approach to select optimal drug delivery routes with the objective to prevent drug delivery disruption and to guarantee the required delivery time-widow. The novelty of the proposed approach is that it determines optimal drug delivery routes based on real-time crowdsourced data and using communication services such as SMS. Furthermore, it overcomes the limitation of routing software apps. The tests conducted using the proposed approach show promised results with no drug delivery disruption. In the rainy season, 71% all selected drug delivery routes recommended by proposed system were optimal compared with 89% in the dry season. The similar tests using Google Maps are less successful, where in the rainy season only 11% and in the dry season 49% of the selected routes were found to be optimal.

Study on the Optimal Travel Route Selection Based on Ant Colony Algorithm Model

Based on the perspective of travel agencies, this paper focuses on the optimization of tourist traffic routes, and divides the optimization of tourist traffic routes into “one day tour”, the optimization of tourist traffic routes and the optimization of “multi day tour” tourist traffic routes. The objective function of tourism traffic route optimization is improved by using principal component analysis. Finally, the ant colony algorithm is used to model, which completes the research based on the ant colony algorithm model to select the best tourism route, so that the travel agency get the most benefits and the tourists enjoy the most humane experience.

RETRACTED ARTICLE: A distributed fuzzy multicast routing protocol (DFMCRP) for maximizing the network lifetime in mobile ad-hoc networks

An ad hoc wireless network is formed whenever multiple devices or nodes that wish to make communication, connect among themselves wirelessly based on their radio range and distribute network information with each other without any central administrator. In a MANET, the devices are mobile in nature and multi-hop communication is carried out between the mobile devices through some intermediate mobile devices. Due to the mobile nature of nodes, unsteady links, and restricted resources, routing is a difficult issue in MANET. Several routing techniques are already in use and multicast routing is used to conserve the network resources. In this paper, the optimal path is selected for data transmission using fuzzy logic. Distributed fuzzy decision making is obtained by running fuzzy inference engine in every node to handle the uncertainties in the MANET environment. The proposed work also suggests an optimized local route repair to be used in case of route failure. The ultimate objectives of both fuzzy based optimal route selection and route repair result in increased network lifetime. The fuzzy rules are developed using MATLAB and they are used in the NS2 network simulation for route generation and performance analysis. The proposed protocol performs better and also gives good network lifetime compared with the existing protocol MAODV.