Routing Management Research Articles

Hierarchical Affinity Propagation for Reliable Integrated Multilayer V2X Networks

The integration of IEEE802.11p and 5G New Radio for the backbone transmission of V2X (vehicle-to-everything) technology has served as a novel type of intelligent transportation system with an efficient low-cost and high-coverage area. This integrated scheme implements a V2X end-to-end data delivery system in a multi-layer mechanism. However, the dynamic nature of vehicular networks leads to numerous network outages and frequent network renewal requests, which considerably diminishing the overall performance. Hence, efficient routing management is required to maximize reliable communication. Optimizing routing management is categorized as a non-linear problem with a very high computational load, making a practical solution very challenging. To cope with the complicated routing problem, we develop a low-complexity distributed algorithm based on a hierarchical affinity propagation framework. The proposed algorithm exchanges small-sized messages among local vehicles to determine data routing more efficiently. The distributive optimization of the proposed algorithm requires a small computational load for each node. The main advantage of the proposed algorithm is that each vehicle autonomously determines its best routing options without the help of any network coordinators. Extensive simulation results verify that the proposed algorithm outperforms conventional algorithms in terms of vehicle mobility, network scalability, the level of network overhead, and the algorithm convergence rate.

Comparative analysis of entropy and environmental impacts of shipping operations on arctic and international routes

With the increasing interest in Arctic shipping operations mostly due to global warming, while increasing the sectoral effects in route management, possible environmental sustainability effects are noteworthy. This study aims to comparatively analyze the entropy and environmental impacts of ships operating in the Arctic and international shipping routes. In this study, the entropy productions caused by 11 container ships with different DWTs were calculated and their environmental impacts were examined using two basic indices. According to the results, the Transpolar Sea Route operations without icebreaker escort were forecasted to be all-year-around by 2050 and Northwest Passage Deep Water Route by 2050 routes, produce entropy at approximately 52% higher than the Suez Canal route operations, and their environmental impact was 40.51% higher than the Suez Canal route. Moreover, according to the predicted environmental impact indices for current routes, the Suez Canal route had an average of 34.43% higher environmental pollution compared to the other two routes. However, the average improvement potential for reducing fuel-related environmental effects was found to be 56.35% for the Suez Canal route. Finally, recommendations were presented for the energy, economic potential effects, and sustainability of the Transpolar Sea Route and Northwest Passage Route for future shipping operations.

Dealing With Changes: Resilient Routing via Graph Neural Networks and Multi-Agent Deep Reinforcement Learning

The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. The traditional best-effort nature of Internet connections allows a single link to be shared among multiple flows competing for network resources, often without consideration of in-network states. In particular, due to the recent successes in other applications, Reinforcement Learning has seen steady growth in network management and, more recently, routing. However, if there are changes in the network topology, retraining is often required to avoid significant performance losses. This restriction has chiefly prevented the deployment of Reinforcement Learning-based routing in real environments. In this paper, we approach routing as a reinforcement learning problem with two novel twists: minimize flow set collisions, and construct a reinforcement learning policy capable of routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, with respect to various Quality-of-Service metrics, and we report our lesson learned.

Delegating Data Plane With Cloud-Assisted Routing

Data centers are embracing the software-defined networking (SDN) as it is evident that this technology of completely separating the data plane from the control plane gives more flexibility for their internal routing management and provides better Quality-of-Service (QoS) to the users. Implementing a similar solution to serve the purpose of software-defined wide-area networking using public Internet routing is also gaining popularity. Although, instead of having a complete separation, a hybrid approach to keep most of the control plane along with the least of the data plane in the remote and vice-versa for the local platform may be more fitting. To this end, we propose a new hybrid SDN approach, Cloud-Assisted Routing (CAR), that utilizes the high computational services that cloud offers at a lower price by splitting both control and data plane functions between a local router and a remote cloud computing platform. Such delegation of data plane to a third-party authority requires proper control plane management policies and handling or avoiding possible loops and failures. We detail the architectural view of CAR, address its associated challenges, and present prototype-based evaluations of it for reducing routing table sizes.

Blockchain-Assisted Privacy-Preserving Traffic Route Management Scheme for Fog-Based Vehicular Ad-Hoc Networks

Traffic route management is essential for reducing traffic jams and enhancing driving safety becauese of the growing number of vehicles and frequent occurrence of traffic accidents. However, in vehicular ad-hoc networks (VANETs), real-time messages are transmitted via wireless channels, which can result in security and privacy concerns. Existing proposals for traffic route management exist security vulnerabilities, as well as high calculation and communication costs. Encouraged by this fact, we design a lightweight traffic route management scheme for fog-based VANETs. In this scheme, vehicles utilize homomorphic encryption to encrypt their driving routes and then send the encrypted information to a fog node. The traffic management center (TMC) decrypts the received ciphertexts that are aggregated by the fog node and performs traffic management according to the decrypted data, without knowing individual route of each vehicle. Furthermore, blockchain is used in the scheme to conduct public keys management of vehicles. Our detailed security proof and analysis indicate that our proposal can meet the security objectives of VANETs. Further, to demonstrate the feasibility of the scheme, we also implement it in the Ethereum test network (i.e.,Rinkeby). Significantly, the performance analysis demonstrates that our proposal achieves a better performance than other relevant representative schemes.

Fast Continuous and Integer L-Shaped Heuristics Through Supervised Learning

We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim at solving problems where the second stage is demanding. Our core idea is to gain large reductions in online solution time, while incurring small reductions in first-stage solution accuracy by substituting the exact second-stage solutions with fast, yet accurate, supervised ML predictions. This upfront investment in ML would be justified when similar problems are solved repeatedly over time—for example, in transport planning related to fleet management, routing, and container yard management. Our numerical results focus on the problem class seminally addressed with the integer and continuous L-shaped cuts. Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi-knapsack (SMKP) problems available in the literature. The proposed method can solve the hardest instances of SSLP in less than 9% of the time it takes the state-of-the-art exact method, and in the case of SMKP, the same figure is 20%. Average optimality gaps are, in most cases, less than 0.1%. History: Accepted by Alice Smith, Area Editor (for this paper) for Design and Analysis of Algorithms–Discrete. Funding: Financial support from the Institut de Valorisation des Données (IVADO) Fundamental Research Project Grants [project entitled “Machine Learning for (Discrete) Optimization”]; Canada Research Chairs; the Natural Sciences and Engineering Research Council of Canada [Collaborative Research and Development Grant CRD-477938-14]; and the Canadian National Railway Company Chair in Optimization of Railway Operations at Université de Montréal is gratefully acknowledged. E. Frejinger holds a Canada Research Chair. Computations were made on the supercomputer Béluga, managed by Calcul Québec and Digital Research Alliance of Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation; the Ministère de l’Économie, de la Science et de l’Innovation du Québec; and the Fonds de Recherche du Québec – Nature et Technologies.

EVALUATING THE BUSINESS VIABILITY OF SUSTAINABLE TRANSPORTATION INITIATIVES: A CASE STUDY OF GREEN LOGISTICS IMPLEMENTATION

Transportation is an important part of this era of globalization. But at the same time transportation, especially in the field of logistics, often harms the environment. This research will then look at the impact of sustainable transportation and the implementation of green logistics on the environment. This research will be carried out using a descriptive qualitative approach. The data used in this study comes from various relevant previous research results or studies. The results of this study then found that the negative impact of conventional transportation led to the importance of changes in transportation management. Through the adoption of innovative technology, smart route management, and environmentally friendly vehicles, a company will be able to reduce operational costs, increase efficiency, and significantly reduce the negative impact it has on the environment. Sustainable transportation and green logistics are strategic opportunities for future businesses.

Phaeohyphomycosis caused by Corynespora cassiicola, a plant pathogen worldwide

ABSTRACT Although rare, trans-kingdom infection features an interesting infection biology concept, in which highly versatile pathogenic attributes allow successful infections in evolutionarily highly divergent species. Corynespora cassiicola is a phytopathogenic fungus and occasionally causes human infections. Herein, we report a phaeohyphomycosis case caused by C. cassiicola. Given that sporadic reports may contribute to a lack of awareness of the transmission route, clinical manifestations, and diagnostic and clinical management, we systematically reviewed the cases reported thus far. Nine patients were identified and included in the pooled analysis, 88.9% (8/9) of whom were reported after 2010. All patients were from Asian, African, and Latin American countries, among whom 77.8% (7/9) were farmers or lived in areas with active agriculture. Exposed body parts were the major affected infection area, and clinical manifestations were mainly non-specific inflammatory reactions. Although biochemical and morphological examinations confirmed the presence of fungal infection, molecular analysis was used for the final diagnosis, with 77.8% (7/9) being identified by internal transcribed spacer sequencing. Whereas voriconazole, terbinafine, and AmB, either alone or in combination, resulted in successful infection resolution in most cases (5/9; 55.5%), those suffering from invasive facial infections and CARD9 deficiency showed poor outcomes. Our patient is the third case of invasive facial infection caused by C. cassiicola and was successfully treated with intravenous LAmB followed by oral voriconazole combined with topical antifungal irrigation. Molecular identification of fungus and prompt antifungal treatment is pivotal in the clinical success of patients suspected to have phaeohyphomycosis. Moreover, as evidenced by our data, itraconazole treatment is not recommended.

Joint Controller Placement and Control-Service Connection in Hybrid-Band Control

By separating the forwarding and control planes, Software-Defined Networking (SDN) facilitates flexible traffic routing and network management for a service network. Because of the impact of controller deployment on message transmission distances and network latency, controller placement problems have drawn many researchers' attention. However, assumptions in most existing research that all control packets are either transmitted in the service network (i.e., in-band control) or through predetermined control-service connection (i.e., out-of-band control) are not reasonable due to bandwidth resources occupation on the service network or high construction costs. In this paper, we are the first to jointly discuss the controller placement and control-service connection problem for latency minimization in the hybrid-band control mode, which is essentially a bi-level programming optimization problem. Specifically, we introduce auxiliary variables to simplify the above NP-hard problem. Next, Generalized Benders decomposition is used to obtain an optimal solution in theory. In addition, we propose a time-efficient fireworks algorithm with a little latency increment for large-scale networks. Extensive evaluations show that the two proposed algorithms accomplish the desired objectives and respectively achieve up to 35% and 25% latency decrement than greedy algorithms.

Artificial Intelligence in Transportation Systems A Critical Review

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities.
 Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data.
 Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel.
 Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.

Collaborative Learning-Based Network Resource Scheduling and Route Management for Multi-Mode Green IoT

The multi-mode green internet of things (IoT) provides a communication support for social assets of smart park connecting to power grid for low-carbon operation. Software defined networking (SDN) and network function virtualization (NFV) can flexibly integrate heterogeneous communication modes through network resource scheduling and route management. However, the joint optimization of virtual network functions (VNF) embedding and flow scheduling faces several challenges of differentiated QoS guarantee, coupling and externality of VNF embedding, and route selection conflicts. In this work, a multi-timescale VNF Embedding and floW Scheduling algorithm named NEWS is proposed to maximize throughput while reducing VNF embedding cost and energy consumption. Specifically, the joint optimization problem is transformed into three subproblems, i.e., large-timescale VNF embedding, small-timescale admission control, small-timescale route selection and computation resource allocation. A swap matching-based low-cost VNF embedding algorithm is proposed for the first subproblem. Then, a queue backlog threshold-based admission control strategy is proposed for the second subproblem. Next, the third subproblem is decomposed into two stages, where a collaborative Q-learning-based backpressure-aware algorithm is presented in the first stage, and a greedy-based computation resource allocation algorithm is given in the second stage. Simulations demonstrate that NEWS performs superior in throughput, embedding cost, and energy consumption.

Mitigation of Black Hole and Selective Forwarding Attacks through Active Trust Mechanisms in Wireless Sensor Networks

In modern times, security remains a significant concern in wireless networks. Wireless sensor networks are widely employed in different practical situations. WSNs struggle with numerous internal and external threats, and detecting and defending against insider attacks is difficult. Insider attacks, in which intruders discard received data packets selectively, pose a significant risk to WSNs. The presence of black hole nodes within the network causes this issue. This research paper presents a secure and reliable route management methodology for WSNs that selects nodes and routing paths based on trust metrics. The proposed mechanism employs intelligent routing and node selection to defend against various types of assaults encountered during the routing process, including selective forwarding and black hole attacks. As a result, the paper identifies secure routes and establishes secure network routing. This scheme uses throughput, latency, and packet drop ratio as performance metrics. Experimentation demonstrates that the proposed system effectively increases the probability of secure network routing paths while extending the network lifecycle.

A Bayesian Multi-Armed Bandit Algorithm for Dynamic End-to-End Routing in SDN-Based Networks with Piecewise-Stationary Rewards

To handle the exponential growth of data-intensive network edge services and automatically solve new challenges in routing management, machine learning is steadily being incorporated into software-defined networking solutions. In this line, the article presents the design of a piecewise-stationary Bayesian multi-armed bandit approach for the online optimum end-to-end dynamic routing of data flows in the context of programmable networking systems. This learning-based approach has been analyzed with simulated and emulated data, showing the proposal’s ability to sequentially and proactively self-discover the end-to-end routing path with minimal delay among a considerable number of alternatives, even when facing abrupt changes in transmission delay distributions due to both variable congestion levels on path network devices and dynamic delays to transmission links.

Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems

With the increasing penetration level of electric vehicles (EVs) in the transportation network, it is necessary to control the charging and discharging process of EVs to avoid operational issues in the power grid. Therefore, in this paper, a two-stage framework is presented for EVs optimal routing and their active and reactive power control in the distribution grid. In the first stage, the EVs mobility in the transportation system is taken into consideration. On this basis, EV daily trips in the transportation network are modelled using the trip chain method. Also, the Dijkstra algorithm is utilized for optimal routing with the shortest travel time between the origin and the destination node. Furthermore, the effects of ambient temperature, traffic congestion and road type on the EVs energy consumption are assessed. In the second stage, optimal active and reactive power exchange of EVs with the distribution grid is carried out. Thus, a mixed-integer linear programming (MILP) model is proposed that simultaneously considers EV owners’ and the distribution network operator's benefits as the optimization goals. The EV battery degradation cost due to the discharging of active power is also integrated into the objective function of the optimization problem. The proposed framework is implemented on a standard IEEE 33-bus system coupled with a 30-node transportation network. The simulation results show that the presented method can reduce the loss cost of the distribution grid and benefit EV owners compared to different charging strategies. In addition, with the battery technology development, the proposed framework can significantly improve the distribution system operation, decrease environmental issues, and reduce EV owner’s costs.

Smart Route Management for Bin Truck

Smart Route Management for Bin Truck

VORTEX: Network-Driven Opportunistic Routing for Ad Hoc Networks

The potential of ad hoc networks, which enable flexible and dynamic network establishment only by mobile terminals equipped with wireless communication devices, has recently attracted attention for the coming IoT era. Although the nature of ad hoc networks shows the advantages of their autonomous and distributed network management, a manifestation of drawbacks owing to the nature of wireless communication and the mobility of terminals are inevitable. Many routing protocols have already been proposed to address the issues by adapting to nature and achieving a certain level of improvement. However, the routing protocols still suffer from difficulties in information collection for routing and adaptive route management during communication. Moreover, there is another issue that end pair-based routing procedures prevent other end pairs from reusing the routing information effectively. To address the drawbacks of conventional routing protocols, this paper proposes VORTEX, a novel routing protocol that employs an opportunistic routing strategy using hierarchization. One of the characteristic features of VORTEX is its network-driven opportunistic forwarding, in which packets travel toward destination terminals using hierarchy as a guide without conventional route discovery procedures. Moreover, another characteristic feature of VORTEX is that the hierarchical structure also contributes to adapting to communication environment changes in an autonomous manner. In other words, VORTEX enables flexible network-wide information-based routing only with the locally collected information. The simulation results show that the proposed VORTEX could provide better service quality and reliability with improved efficiency compared to the conventional routing protocols. Furthermore, the most significant contribution is not only in the communication performance but also VORTEX omits route discovery or route maintenance from routing protocols, and formed networks themselves have a function to deliver packets toward destination terminals.

Prioritizing management of high-risk routes and ports by vessel type to improve marine biosecurity efforts

The shipping industry constitutes the main vector of marine bioinvasions. Over 90,000 vessels world-wide create a highly complex shipping network that requires appropriate management tools. Here we characterized a novel vessel category, Ultra Large Container Vessels (ULCV), in terms of potential contribution to the dispersal of Non-Indigenous Species (NIS) in comparison to smaller vessels traveling similar routes. Such approach is essential for providing precise information-based risk analysis necessary to enforce biosecurity regulations and reduce the adverse global effects of marine NIS. We used Automatic Identification System (AIS) based websites to extract shipping data that will enable us to test for differences in two vessel behaviors linked to NIS dispersal: port visit durations and voyage sailing times. We then examined the geographic spread of ULCVs and small vessels, quantifying the accumulation of new port visits, countries, and ecoregions for each vessel category. Finally, Higher Order Network (HON) analysis revealed emergent patterns within shipping traffic, species flow, and invasion risk networks of these two categories. Compared to the smaller vessels, ULCVs spent significantly longer time in 20% of the ports and were more geographically constrained, with fewer port visits, countries, and regions. HON analysis revealed that the ULCV shipping species flow and invasion risk networks were more similar to each other than to those of the smaller vessels. However, HON port importance shifts were discernible for both vessel categories, with major shipping hubs not necessarily being major invasion hubs. Overall, compared to smaller vessels, ULCVs behave differently in ways that potentially increase biofouling risk, albeit in a smaller set of ports. Future studies using HON analysis of other dispersal vectors appears critical for prioritizing management of high-risk routes and ports.

Novel Framework for Secure Data Aggregation in Precision Agriculture with Extensive Energy Efficiency

Precision agriculture (PA) is the next generation of a technological revolution in smart farming, where sensing technology is the core technological player. Energy-efficient data transmission in PA via sensing technology is possible only when additional security measures are synchronized. Nevertheless, security considerations often introduce additional overhead. Thus, it is necessary to develop an efficient mechanism to achieve an optimal trade-off between security and resource efficiency. The prime purpose of the proposed study is to introduce a lightweight communication protocol that can ensure an adequate balance between energy efficiency and maximum-security demands to benefit the success of PA. This paper proposes a synchronized framework where unique public-key encryption has been used, unlike any existing approach to facilitate the participation of legitimate on-field sensors in PA. On the other hand, an algorithm for energy efficiency where unique structural management of routing is discussed concerning aggregator nodes. In contrast, the security algorithm discusses a uniquely progressive and noniterative mechanism to perform secure data aggregation with a parallel validation technique. The proposed logic is scripted in MATLAB, considering a suitable PA environment where comparative assessment is carried out on a uniform testbed. The study outcome exhibited the effectiveness of the proposed scheme concerning better energy efficiency and higher resiliency from threats in contrast to existing schemes.

Nx-IoT: Improvement of Conventional IoT Framework by Incorporating SDN Infrastructure

The rapid advancement of the Internet of Things (IoT) in real-world applications has attracted immense research endeavors in the last few years. It has got tremendous potential in industrial automation and various other fields. The use of IoT has changed the perspective of general applications in today’s world. In this article, a next-generation architecture, Nx-IoT, is proposed for conventional 6LoWPAN-based IoT. The proposed Nx-IoT architecture works in two modes: 1) single controller-based (6SSDx) and 2) multicontroller-based (6MSDx). The Nx-IoT architecture uses new algorithms for routing management and load distribution among the SDN controllers. The experimentation is carried out in a prototype testbed environment. The result shows improved performances in terms of round trip and packet drop, compared to the conventional 6LoWPAN and cloud system. The proposed Nx-IoT architecture reduces the latency and shows better throughput performances as compared to the existing state of the art.

Swarm Intelligence in Internet of Medical Things: A Review.

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.