• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.255-261
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• 2022

Routing Protocol for Low-Power and Lossy Networks (RPLs) in Internet of Things (IoT) is currently one of the most popular wireless technologies for sensor communication. RPLs are typically designed for specialized applications, such as monitoring or tracking, in either indoor or outdoor conditions, where battery capacity is a major concern. Several routing techniques have been proposed in recent years to address this issue. Nevertheless, the expansion of the network lifetime in consideration of the sensors' capacities remains an outstanding question. In this research, aANN-CUCKOO based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IOT-RPL. The proposed method uses time constraints to minimise the distance between source and sink with the objective of a low-cost path. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. MATLAB software is used to simulate the proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1585-1609
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• 2013

Over recent years, enormous amounts of research in wireless sensor networks (WSNs) have been conducted, due to its multifarious applications such as in environmental monitoring, object tracking, disaster management, manufacturing, monitoring and control. In some of WSN applications dependent the energy-efficient and link reliability are demanded. Hence, this paper presents a routing protocol that considers these two criteria. We propose a new mechanism called Reliable Routing Scheme for Energy-Balanced (RRSEB) to reduce the packets dropped during the data communications. It is based on Swarm Intelligence (SI) using the Ant Colony Optimization (ACO) method. The RRSEB is a self-adaptive method to ensure the high routing reliability in WSNs, if the failures occur due to the movement of the sensor nodes or sensor node's energy depletion. This is done by introducing a new method to create alternative paths together with the data routing obtained during the path discovery stage. The goal of this operation is to update and offer new routing information in order to construct the multiple paths resulting in an increased reliability of the sensor network. From the simulation, we have seen that the proposed method shows better results in terms of packet delivery ratio and energy efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.550-556
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• 2008

Swam intelligence refers that a large group of simple and unsophisticated entities work together to achieve complex and significant tasks. Researches using such swarm intelligence has been performed in the network routing area. Expecially, it has been well known that routing in mobile ad-hoc networks whose features are dynamic topology and routing based on the local information is one of the applications of swarm intelligence. In this paper, we propose an ant-based routing method for MANET. The proposed method sets its goals to reduce overheads by managing ants efficiently, and to reduce route set up time. The results obtained from simulations proved that the proposed method provides shorter path set-up time and end-to-end delay and less overhead while providing comparable packet delivery ratio.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.179-186
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• 2011

Wireless Mesh Network technology refers to the technology which establishes wireless network whose transmission speed is similar to that of the wire system, and provides more enhanced flexibility in the building of network, compared to the existing wired network. In addition, it has the feature of less mobility and less restriction from the energy effect. However, there follow many considerations such as system overhead in the case of setting or the selection of multi-path. Accordingly, the focus is on the design and optimization of network which can reflect this network feature and the technology to establish path. This paper suggests the methods on the programming of path in Wireless Mesh Network routing by applying the evaluation value of node service, making use of the loss rate of data, the hop count of bandwidth and link and the traffic status of node, considering the performance of link and load in the fitness evaluation function, in order to respond to the programming of multi-path effectively.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1347-1360
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• 2018

Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.67-76
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• 1999

The study of the optimization of operating policy of AGV system, which is used in many factory automation environments has been proceeded by many researchers. The major operating policy of AGV system consists of routing and scheduling policy. AGV routing is composed with collision avoidance and minimal cost path find algorithm. To allocate jobs to the AGV system, AGV scheduling has to include AGV selection rules, parking rules, and recharging rules. Also in these rules, the key time parameters such as processing time of the device, loading/unloading time and charging time should be considered. In this research, we compare and analyze several operating policies of multiple loop-multiple AGV system by making a computer model and simulating it to present an appropriate operating policy.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.30-45
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• 2014

Recently, multimedia application users who demand for ubiquitous computing environment are rapidly increasing, and wireless mesh network is receiving attention as a cost-effective key technology for next generation wireless networking. When multiple flows are transmitting data at the same time in the network, routing for path selection of each flow and link resource allocation for data transmission of each flow are one of the key factors that influence to the effectiveness of the network directly. In this paper, we consider problems for path discovery and resource allocation of links at the same time and we propose an algorithm based on mathematical modeling using a technique for cross-layer optimization design in STDMA-based wireless mesh networks that can enhance transfer performance for each flow. We show by performance analysis that the proposed algorithm can enhance the throughput performance by maximally utilizing given bandwidth resources when the number of flows increase in multi-hop wireless mesh networks.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.559-566
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• 2016

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multipath routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.