• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.131-147
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• 2024

A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2805-2826
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• 2021

In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.155-166
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• 2008

During last two decades the transportation system has developed into very intelligent system with GIS, GPS and ITS. The practical transportation management system provides real time response module to manage the customer's order. We have surveyed research papers on the real time vehicle routing problem in last two decades to figure out the dynamic vehicle routing problem. The papers are classified by basic routing algorithms and by managing the dynamic events which are the order management, the routing re-optimization, the routing post-optimization and the waiting strategy.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.193-204
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• 2013

The growing logistics strategy of a company is to optimize their vehicle route scheduling in their supply chain system. It is very important to analyze for continuous pickups and delivery vehicle scheduling. This paper is a computational study to investigate the effectiveness of continuous pickups and delivery vehicle routing problems. These scheduling problems have 3 subproblems; Inbound Vehicle Routing Problem with Makespan and Pickup, Line-haul Network Problem, and Outbound Vehicle Routing Problem with Delivery. In this paper, we propose 5 heuristic Algorithms; Selecting Routing Node, Routing Scheduling, Determining Vehicle Type with Number and Quantity, and Modification Selecting Routing Node. We apply these Algorithms to S vehicle company. The results of computational experiments demonstrate that proposed methods perform well and have better solutions than other methods considering the basic time and due-date.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3104-3123
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• 2022

The development of Vehicular Ad hoc Network (VANET) has greatly improved the efficiency and safety of social transportation, and the routing strategy for VANET has also received high attention from both academia and industry. However, studies on dynamic matching of routing policies with the message types of VANET are in short supply, which affects the operational efficiency and security of VANET to a certain extent. This paper studies the message types in VANET and fully considers the urgency and reliability requirements of message forwarding under various types. Based on the diversified types of messages to be transmitted, and taking the diversified message forwarding strategies suitable for VANET scenarios as behavioral candidates, an adaptive routing method for the VANET message types based on reinforcement learning (RL) is proposed. The key parameters of the method, such as state, action and reward, are reasonably designed. Simulation and analysis show that the proposed method could converge quickly, and the comprehensive performance of the proposed method is obviously better than the comparison methods in terms of timeliness and reliability.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.167-178
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• 2019

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1992-2008
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• 2018

Mobile opportunistic network is a kind of ad hoc networks, which implements the multi-hop routing communication with the help of contact opportunity brought about by the mobility of the nodes. It always uses opportunistic data transmission mode based on store-carry-forward to solve intermittent connect problem of link. Although many routing schemes have been proposed, most of them adopt the greedy transmission mode to pursue a higher delivery efficient, which result in unfairness extremely among nodes. While, this issue has not been paid enough attention up to now. In this paper, we analyzed the main factors that reflect fairness among nodes, modeled routing selection as a multiple attribute decision making problem, and proposed our Fairness-aware Routing Strategy, named FARS. To evaluate the performance of our FARS, extensive simulations and analysis have been done based on a real-life dataset and a synthetic dataset, respectively. The results show that, compared with other existing protocols, our FARS can greatly improve the fairness of the nodes when ensuring the overall delivery performance of the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2020-2037
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• 2017

In order to improve the energy efficiency of n-Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.99-106
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• 2014

Weather routing of a ship provides an optimal route to the destination by using minimal time or fuel in a given sea condition. These days, weather routing came into a spotlight with soaring fuel price and the environmental regulations of IMO and several countries. This study presents three scenarios of voyaging strategies for a ship and compared them in terms of the fuel consumption. The first strategy fixes the speed of a ship as a constant value for entire sailing course, the second fixes the RPM of the ship as constant for entire course, and the third determines the RPMs of the ship for each segment of the course. For each strategy, a ship route is optimized by using the $A^*$ search method. Wind, ocean current and wave are considered as ocean environment factors when seeking the optimal routes. Based on 7000 TEU container ship's sea trial records, simulation has been conducted for three scenarios, and the most efficient routing scenario is determined in the view of fuel consumption.