• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.469-472
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• 2014

S-MAC is hybrids of CSMA and TDMA approaches that use local sleep-wake schedules to coordinate packet exchanges and reduce idle listening. In this method, all the nodes are considered with equal priority which may lead to increased delay during heavy traffic. The method introduced in this paper provides high throughput and small end-to-end delay suitable for applications such as real-time voice streaming and its functionality is independent of underlying synchronization protocol. The novel idea behind our scheme is that it uses the priority concept with (m,k)-firm scheduling in order to achieve its objectives. The performance of our scheme is obtained through simulations for various packet sizes, traffic loads which show significant improvements in packet delivery ratio, and delay compared to existing protocols.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Journal of applied mathematics & informatics
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• v.41 no.1
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• pp.95-105
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• 2023

To make the network energy efficient and to protect the network from malignant user's energy efficient grid based secret key sharing scheme is proposed. The cost function is evaluated to select the optimal nodes for carrying out the data transaction process. The network is split into equal number of grids and each grid is placed with certain number of nodes. The node cost function is estimated for all the nodes present in the network. Once the optimal energy proficient nodes are selected then the data transaction process is carried out in a secured way using malicious nodes filtration process. Therefore, the message is transmitted in a secret sharing method to the end user and this process makes the network more efficient. The proposed work is evaluated in network simulated and the performance of the work are analysed in terms of energy, delay, packet delivery ratio, and false detection ratio. From the result, we observed that the work outperforms the other works and achieves better energy and reduced packet rate.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.49-54
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• 2016

Data delivery is very challenging in VANETs because of its unique characteristics, such as fast topology change, frequent disruptions, and rare contact opportunities. This paper tries to explore the scope of 3G-assisted data delivery in a VANET within a budget constraint of 3G traffic. It is started from the simple S_Random (Srand) and finally reached the 3GSDD, i.e., the proposed algorithm. The performance evaluation of different algorithms is done through the two metrics delivery ratio and average delay. A third function utility is created to reflect the above two metrics and is used to find out the best algorithm. A packet can either be delivered via multihop transmissions in the VANET or via 3G. The main challenge is to decide which set of packets should be selected for 3G transmissions and when to deliver them via 3G. The aim is to select and send those packets through 3G that are most sensitive and requiring immediate attention. Through appropriate communication mechanism, these sensitive information are delivered via VANET for 3G transmissions. This way the sensitive information which could not be transmitted through normal VANET will certainly find its destination through 3G transmission unconditionally and with top priority. The delivery ratio of the packets can also be maximized by this system.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.243-253
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• 2010

Multihop data delivery in vehicular ad hoc networks (VANETs) suffers from the fact that vehicles are highly mobile and inter-vehicle links are frequently disconnected. In such networks, for efficient multihop routing of road safety information (e.g. road accident and emergency message) to the area of interest, reliable communication and fast delivery with minimum delay are mandatory. In this paper, we propose a multihop vehicle-to-infrastructure routing protocol named Vertex-Based Predictive Greedy Routing (VPGR), which predicts a sequence of valid vertices (or junctions) from a source vehicle to fixed infrastructure (or a roadside unit) in the area of interest and, then, forwards data to the fixed infrastructure through the sequence of vertices in urban environments. The well known predictive directional greedy routing mechanism is used for data forwarding phase in VPGR. The proposed VPGR leverages the geographic position, velocity, direction and acceleration of vehicles for both the calculation of a sequence of valid vertices and the predictive directional greedy routing. Simulation results show significant performance improvement compared to conventional routing protocols in terms of packet delivery ratio, end-to-end delay and routing overhead.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.73-80
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• 2007

In conventional on-demand mobile ad hoc routing algorithms, an alternate path is sought only after an active path is broken. It incurs a significant cost in terms of money and time in detecting the disconnection and establishing a new route. In this thesis, we propose proactive route selection and maintenance to conventional mobile ad hoc on-demand routing algorithms. The key idea for this research is to only consider a path break to be likely when the signal power of a received packet drops below an optimal threshold value and to generate a forewarning packet. In other words, if a path is lost with high probability, the neighboring node that may easily be cut off notifies the source node by sending a forewarning packet. Then the source node can initiate route discovery early and switched to a reliable path potentially avoiding the disconnection altogether. For the simulational study, network simulator(NS2) is used. The result of simulation shows that the algorithm significantly improves the performance of networks comparing to conventional on-demand routing protocols based on DSR and AODV in terms of packet delivery ratio, packet latency and routing overhead.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1077-1088
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• 2013

As broadcasting is the process that a node sends a packet to all nodes in the network. it is basic process used for discovering of a routes to a node and disseminating of control information message in Mobile Ad hoc NETwork (MANET). In this paper, we propose dynamically adjusted probabilistic mechanism based on distance ratio and node density for broadcasting in MANETs. The distance ratio can be calculated as the ratio of the radio strength length to the distance from sender of a node, and node density can be get from 1-hop nodes of neighbours. A mobile node receiving broadcast packets determines the probability of rebroadcasting considering distance ratio and node density of itself. Rebroadcast probability will be set as low value to a node which is located in nearby area of sender and has high 1-hop node density, So it reduces packets transmission caused by the early die-out of rebroadcast packets. Compared with the simple flooding and fixed probabilistic flooding by simulation, our approach shows better performances results. Proposed algorithm can reduce the rebroadcast packet delivery more than 30% without scanting reachability, where as it shows up to 96% reachability compared with flooding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1390-1411
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• 2024

Underwater Wireless Sensors Networks (UWSNs) are deployed in remotely monitored environment such as water level monitoring, ocean current identification, oil detection, habitat monitoring and numerous military applications. Providing scalable and efficient routing is very challenging in UWSNs due to the harsh underwater environment. The biggest difficulties are the nodes inherent movement due to water current, long delay in data transmission, low bandwidth of the acoustic signal, high error rate and energy scarcity in battery powered nodes. Many routing protocols have been proposed to solve the aforementioned problems. There are three broad categories of routing protocols namely depth based, energy based and vector-based routing. Vector Based Forwarding protocols perform routing through virtual pipeline by defining their radius which give proper direction to packets communication. We proposed a routing protocol termed as Path-Oriented Energy Scaled Expanded Vector Based Forwarding (PESEVBF). PESEVBF takes into account all parameters; holding time, the source nodes packets routing path and void holes creation on the second hop; PESEVBF not only considers the packet upward advancement but also focus on density of the forwarded nodes in terms of number of potential forwarding and suppressed nodes for path selection. Node selection in resultant holding time is based on minimum Path Factor (PF) value. Moreover, the suppressed node will be selected for packet forwarding to avoid the void holes occurrences on the second hop. Performance of PESEVBF is compared with other routing protocols using matrices such as energy consumption, packet delivery ratio, packets dropping ratio and duplicate packets creation indicating considerable performance improvement.