Route Discovery Algorithm Research Articles

A Dynamic Addressing Hybrid Routing Mechanism Based on Static Configuration in Urban Rail Transit Ad Hoc Network

With the rapid development of urban rail transit, the traditional urban rail wireless network based on fixed infrastructure is not in a position to meet the increasingly complex communication demand. At the same time, Ad Hoc network, as a special wireless mobile network, is developing rapidly. Applying this self-organized networking architecture to the urban rail vehicle–ground communication network can overcome the problems existing in the traditional urban rail communication system. The routing protocols that can achieve low delay and highly reliable data transmission are important in the urban rail transit scenario. Therefore, combined with the wireless Ad Hoc network and the characteristics of the urban rail transit scenario, this paper proposes a dynamic addressing hybrid routing mechanism based on static configuration. Using an improved AODV routing discovery algorithm and then writing the routing table into the router in advance for static configuration not only reduces network overhead but also prolongs the network’s lifetime. It also saves the delay of routing discovery. Then, the cluster head node dynamically monitors the link status, dynamically finds the path when the link needs to be replaced, and selects different update paths according to different types of communication services. Finally, each algorithm’s network performance parameters, like the routing discovery overhead, residual link lifetime, packet delivery rate, throughput, and end-to-end delay, are analyzed and compared.

Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

AbstractQoS aware vehicular ad‐hoc network (VANET) routing protocols address the increasing demand for delay‐sensitive vehicular applications to establish intelligent transportation. Primary challenge with ad‐hoc nature of VANET is that the communication between vehicle‐to‐vehicle and vehicle‐to‐infrastructure is prone to link failure. Bio‐inspired algorithms offer robust solutions to secure VANET links. Accordingly in this article, novel canine olfactory route‐finding algorithm (CORFA) is proposed for VANET to achieve the best possible route with the minimum transmission of packets, which ensures enhanced QoS. The underlying architecture utilizes the exceptional ability of canines to evaluate and memorize the environment and pass the message to neighbors. It makes canines a great choice to model their behavior for VANET routing. RSUs are fundamental components of VANET that supply contents to the proceeding vehicles from their cache. As RSUs carry out information dissemination tasks efficiently, the already traversed and discovered routing paths in the recent past can be cached in RSU's storage and through back‐haul can be circulated to neighboring RSUs. Vehicles request the pre‐cached route and use them for data transmission. Thereby avoiding the route discovery and maintenance procedure reduces the routing overhead. In absence of a pre‐cached route, the fittest vehicle from each hop is selected as next forwarder for packet transmission. The proposed routing algorithm forms multiple routes during route discovery to handle link failure scenario. Traffic simulator, SUMO is used to generate the mobility model and integrated with MATLAB for analysing the performance of CORFA with related geographical and topological routing protocols that confirm the performance superiority of CORFA under several metrics such as packet delivery ratio, network latency, throughput, and overhead.

An enhanced encryption-based security framework in the CPS Cloud

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

Safe route-finding: A review of literature and future directions

While road navigation systems seek to determine the shortest routes between a given set of origin and destination points, there are certain situations in which the fastest route increases the risk of being involved in road crashes. This implies the necessity of integrating safe route-finding into road navigation systems. This study is designed to synthesize the literature on safe route-finding and identify the gaps in the literature for future research. Specifically, a scoping literature review methodology is applied to understand how safety is incorporated in route-finding, even beyond motor vehicle navigation systems. Three databases (Scopus, Web of Science, and IEEE Xplore) are explored, and controlling for inclusion criteria, 40 studies are included in this review. The findings of this review indicated five areas through which safety was considered in route-finding: motor vehicle navigation, public safety, public health, pedestrian and cyclist navigation, and hazardous material transportation. The measurement of safety was found challenging with inconsistencies in safety quantification approaches. The safe route-finding algorithms were investigated based on their predictive/reactive, static/dynamic, and centralized/decentralized characteristics. Based on the critical review of the safe route-finding algorithms, availability of real-time data sources, accurate real-time and disaggregated crash risk prediction models, trade-off between time and safety in road navigation tools, and centralized safe route-finding are highlighted as the requirements and challenges in considering safety in road navigation systems. This study outlines a research agenda to address the identified challenges in safe route-finding.

Path Planning and Trajectory Tracking for Automatic Guided Vehicles.

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper.

Towards understanding network topology and robustness of logistics systems

Advanced integration of logistics systems has been promoted for competitiveness and sustainability. Interconnection of transport operations increases complexity at a network level, which reduces the predictability of the response of the system to disruptions. However, our understanding of the behavior of such systems is still limited. In particular, the topology of the network, which changes as the systems are integrated, is an important factor that affects the performance of the entire system. Knowledge of such mechanisms would be useful in the design and evaluation of integrated logistics. Here, we developed a simple simulation framework for logistics networks that extracts the essence of the problem. We performed extensive numerical experiments for three scenarios that mimic changes in demand: (i) locally and temporally increased traffic demand, (ii) globally and temporally increased traffic demand, and (iii) permanent change in demand pattern, under various conditions on the type of route-finding algorithm, network structure, and transportation capacity. Adaptive route-finding algorithms were more effective in square lattice and random networks, which contained many bypass routes, than in hub-and-spoke networks. Furthermore, the square lattice and random networks were robust to the change in the demand. We suggest that such preferable properties are only present in networks with redundancy and that the bypass structure is an important criterion for designing network logistics. We also performed a realistic case study that mimics interregional truck transport in Japan and confirmed that our conclusions are applicable to a practical problem.

GAR: Gradient assisted routing for topology self-organization in dynamic mesh networks

Modern mobile handheld devices, such as smartphones and tablets, feature multiple wireless interfaces, some of which can support device-to-device communications, which enable mesh networks on even when the infrastructure is unavailable. One of the key technological challenges hampering the use of multi-hop mesh networks is the extremely high communication overhead of route discovery and maintenance algorithms. The problem is especially pronounced under dynamic network conditions caused by user mobility and nodes joining and leaving the network. In this paper, we propose a fully distributed algorithm for constructing a virtual coordinate system used for geo-like routing by approximating the physical network nodes coordinate. The proposed algorithm, called gradient assisted routing (GAR), builds upon two-hop neighbors’ information exchanged in beacons in contrast to conventional geographic routing protocols which rely on external positioning information. We evaluate the proposed solution using algorithmic, topological, and routing-related metrics of interest. We further numerically quantify how the node mobility increases the time needed for topology stabilization, and how network size affects the route discovery success rate. Our comparison also shows that for small to mid-size mesh networks (up to 60 nodes), the performance of the proposed routing procedure is similar to the conventional geographic routing protocols that exploit external positioning information. The proposed solution may efficiently supplement the traditional on-demand routing in small to mid-size mesh systems by independently establishing 50 to 70% of paths and thereby reducing the discovery overheads.

Relieving Bottlenecks during Evacuations Using IoT Devices and Agent-Based Simulation

Most of the existing studies on relieving bottlenecks have aimed to develop route-finding algorithms that consider structural factors such as passages and stairs, as well as human factors such as density and speed. However, the methods in providing evacuation routes are as important as the route-making algorithms because a secondary bottleneck could occur continuously during evacuations. Even if an evacuation system provides the same routes to all evacuees regardless of their locations, secondary bottlenecks could happen following the initial bottlenecks due to people rushing toward uncrowded exits all together. To address this issue, we developed a location-based service (LBS) evacuation system prototype that provides optimized-alternative routes to evacuees in real time considering their locations in indoor space. The system was designed to relieve continuous bottlenecks, which relies on installed IoT sensors and beacon machines which detect bottlenecks and provide updated routes, separately. Next, we conducted agent-based simulations to measure the system’s effectiveness (evacuation time reduction and dispersion of evacuees) by changing the system parameters. Simulation results show the evacuation time decreased from 100 to 65 s, and the number of people who took a detour to avoid bottlenecks increased by 28.66% out of the total evacuees with this system. Since this system provides the theoretical solution for distributing evacuees, it can be flexibly employed to a disaster situation in a large and complex indoor environment by applying to other evacuation systems. Moreover, by adjusting parameters, we can derive maximum evacuation effectiveness in other indoor spaces. Future work will consider demographic features of population and multilayer building structure to draw a more accurate pedestrian flow.

Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network and gated recurrent unit for intrusion detection system

AbstractWireless sensor network (WSN) is a collection of a huge number of autonomous sensor nodes having capabilities such as sensing, processing, and manipulation. In any WSN, routing protocols are the backbone for performing all type tasks such as sensing, controlling, and transmission of packets in ubiquitous environment. In this article, a LEACH protocol with Levenberg‐Marquardt neural network (LEACH‐LMNN) is considered to analyze the overall network lifetime. The aim of LEACH‐LMNN protocol comprises two parts: selection of cluster head node using LMNN approach and the second part is to locate the shortest path from the cluster‐head node to base‐station node adopting various route discovery algorithms, that is, breadth‐first search, Bellman‐Ford, and Dijkstra. The simulation result shows that the LEACH‐LMNN protocol with the Dijkstra shortest path algorithm outperforms other route discovery algorithms. In addition to this, this work also analyzes normal and anomaly detection based on intrusion detection system in wireless sensor networks using gated mechanism, that is, long short‐term memory (LSTM) and gated recurrent unit (GRU) in deep learning models. The proposed model achieves the highest detection rate of 97.84% for GRU and 97.85% for LSTM as well as improves the false positive rate (FPR) of 5.87% and 3.88% FPR for GRU and LSTM, respectively.

Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks

Wireless sensor networks (WSNs) are generically self-configuring and organizing networks with constrained communicational ability and energy supply. One of the crucial crises in WSN is the employment of energy effectual routing and load balancing protocol to improve network lifetime. Therefore, this work anticipated an effectual load balancing and routing strategies using the Glowworm swarm optimization approach (LBR-GSO). This LBR-GSO employs a pseudo-random route discovery algorithm and an enhanced pheromone trail-based updating strategy to handle the energy consumption of sensor nodes. It utilizes an effectual heuristic updating algorithm based on cost effectual energy measure to optimize route establishment. At last, to eliminate energy consumption that causes due to control overhead, LBR-GSO cast-off energy-based broadcasting strategy has been proposed. Here, WSNs environment is simulated in MATLAB for various application scenarios to compute LBR-GSO along with metrics such as energy efficiency, energy consumption and prolonging network lifetime. Outcomes derived from this comprehensive analysis determine that LBR-GSO offers an effectual enhancement in contrary to prevailing approaches like ACO, EE-ACO and s-Ant approaches.

Ant-Behavior Inspired Intelligent NanoNet for Targeted Drug Delivery in Cancer Therapy.

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real "active" drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis.

A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

This study proposed a context-aware ontology-based route finding algorithm for self-driving tourists. In this algorithm, two ontologies—namely drivers’ experiences and required tourist services—were used according to tourist requirements. Trips were classified into business and touristic. The algorithm was then compared with Google Maps in terms of travel time and travel length for evaluation. The results showed that the proposed algorithm performed similarly to Google Maps in some cases of business trips and better in other cases, with a maximum 10-min travel time difference. In touristic trips, the capabilities of the proposed algorithm were far better than those of Google Maps.

Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO.

An Improved Route‐Finding Algorithm Using Ubiquitous Ontology‐Based Experiences Modeling

Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology‐based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology‐based route‐finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology‐based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs.

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

INDOOR ROUTE PLANNING UNDER HEXAGON NETWORK CONSIDERING MULTI-CONSTRAINS

Abstract. Indoor route planning, affected by many constraints, needs to take consideration of both the spatial geometry, environmental attribute information of the scene and the application preferences. Thus, it requires a data model that can integrate multiple constraints to model the indoor scene; meanwhile, it’s also necessary to take into account the isotropic features of the indoor pathfinding behavior and the superposition analysis of multi-constraint conditions by the route-finding algorithm. Based on this, this paper proposes a multi-factor constrained A* algorithm model based on the hexagonal grid, that is using an isotropic regular hexagon to model an indoor scene, and taking constraint conditions as heuristic factors to guide the route-finding algorithm so as to get the route. Based on this model, distance, recognition and pedestrian density are used as examples to illustrate the impact of constraints on route planning and their organic combination with scene modeling and route-finding algorithms. The experimental results show that this scheme can effectively take into account the constraints such as distance, landmark strength, and pedestrian heat, thus providing a route that is more in line with the application preferences.

Route Master

The developer of a new route-finding algorithm aims to give EV drivers the ability to optimize travel across the public charging network

Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique

In Mobile Ad-hoc networks (MANET), power conservation and utilization is an acute problem and has received significant attention from academics and industry in recent years. Nodes in MANET function on battery power, which is a rare and limited energy resource. Hence, its conservation and utilization should be done judiciously for the effective functioning of the network. In this paper, a novel protocol namely Energy Reduction Multipath Routing Protocol for MANET using Recoil Technique (AOMDV-ER) is proposed, which conserves the energy along with optimal network lifetime, routing overhead, packet delivery ratio and throughput. It performs better than any other AODV based algorithms, as in AOMDV-ER the nodes transmit packets to their destination smartly by using a varying recoil off time technique based on their geographical location. This concept reduces the number of transmissions, which results in the improvement of network lifetime. In addition, the local level route maintenance reduces the additional routing overhead. Lastly, the prediction based link lifetime of each node is estimated which helps in reducing the packet loss in the network. This protocol has three subparts: an optimal route discovery algorithm amalgamation with the residual energy and distance mechanism; a coordinated recoiled nodes algorithm which eliminates the number of transmissions in order to reduces the data redundancy, traffic redundant, routing overhead, end to end delay and enhance the network lifetime; and a last link reckoning and route maintenance algorithm to improve the packet delivery ratio and link stability in the network. The experimental results show that the AOMDV-ER protocol save at least 16% energy consumption, 12% reduction in routing overhead, significant achievement in network lifetime and packet delivery ratio than Ad hoc on demand multipath distance vector routing protocol (AOMDV), Ad hoc on demand multipath distance vector routing protocol life maximization (AOMR-LM) and Source routing-based multicast protocol (SRMP) algorithms. Hence, the AOMDV-ER algorithm performs better than these recently developed algorithms.

Towards realistic estimation of ship excessive motions in heavy weather. A case study of a containership in the Pacific Ocean

Synchronous roll and parametric roll resonance are critical phenomena that lead a ship, under certain conditions, to quickly develop large roll amplitudes. This will result in excessive accelerations that can be dangerous for a ship and her cargo. Ships with large bow and stern flare (i.e. container carriers and RoPax) are especially prone to this type of hazards. Therefore, it is of utmost importance for ship operators to be aware of a set of conditions under which these two unwanted phenomena happen. This will help implementing necessary counter measures, either operational (speed or course alterations) or tactical (route finding).In this context, the development of a fast and reliable method for screening the conditions leading to excessive accelerations would improve ship safety. Therefore, in this study, we propose a method detecting critical scenarios for a container ship in heavy weather conditions. The method utilizes an original, six-degree numerical model of ship motions, called LaiDyn, to investigate large amplitude motions and the associated longitudinal and transverse accelerations that a ship is experiencing.Finally, the accelerations that are calculated for a set of operational conditions are checked against their predefined threshold values associated with the cargo lashing system.The proposed method can be used for operational purposes, at the stage of route planning, where the expected accelerations could be considered as another objective function in the route-finding algorithm.

A MODIFIED ROUTE DISCOVERY APPROACH FOR DYNAMIC SOURCE ROUTING (DSR) PROTOCOL IN MOBILE AD-HOC NETWORKS

Mobile Ad-hoc networks (MANETs) involved in many applications, whether commercial or military because of their characteristics that do not depend on the infrastructure as well as the freedom movement of their elements, but in return has caused this random mobility of the nodes many of the challenges, where the routing is considered one of these challenges. There are many types of routing protocols that operate within MANET networks, which responsible for finding paths between the source and destination nodes with the modernization of these paths which are constantly changing due to the dynamic topology of the network stemming from the constant random movement of the nodes. The DSR (Dynamic Source Routing) routing protocol algorithm is one of these routing protocols which consist of two main stages; route discovery and maintenance, where the route discovery algorithm operates based on blind flooding of request messages. blind flooding is considered as the most well known broadcasting mechanism, it is inefficient in terms of communication and resource utilization, which causing increasing the probability of collisions, repeating send several copies of the same message, as well as increasing the delay. Hence, a new mechanism in route discovery stage and in caching the routes in DSR algorithm according to the node's location in the network and the direction of the broadcast is proposed for better performance especially in terms of delay as well as redundant packets rate. The implementation of proposed algorithms showed positive results in terms of delay, overhead, and improve the performance of MANETs in general. Keywords: MANET; DSR Protocol; Threshold, Broadcasting