• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.189-196
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• 2010

The LEACH does not use flooding method for data transmission and this makes low power consumption. So performance of the WSN is increased. On the other hand, QoS based algorithm which use restricted flooding method in WSN also achieves low power consuming rate by reducing the number of nodes that are participated in routing path selection. But when the data is delivered to the sink node, the LEACH choose a routing path which has a small hop count. And it leads that the performance of the entire network is worse. In the paper we propose a QoS based energy efficient clustering and routing algorithm in WSN. I classify the type of packet with two classes, based on the energy efficiency that is the most important issue in WSN. We provide the differentiated services according to the different type of packet. Simulation results evaluated by the NS-2 show that proposed algorithm extended the network lifetime 2.47 times at average. And each of the case in the class 1 and class 2 data packet, the throughput is improved 312% and 61% each.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.97-103
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• 2020

Wireless sensor networks collect and analyze sensing data in a variety of environments without human intervention. The sensor network changes its lifetime depending on routing protocols initially installed. In addition, it is difficult to modify the routing path during operating the network because sensors must consume a lot of energy resource. It is important to measure the network performance through simulation before building the sensor network into the real field. This paper proposes a WSN model for a low-energy adaptive clustering hierarchy protocol using DEVS kernel models. The proposed model is implemented with the sub models (i.e. broadcast model and controlled model) of the kernel model. Experimental results indicate that the broadcast model based WSN model showed lower CPU resource usage and higher message delivery than the broadcast model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.29-43
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• 2019

In recent years, as the network traffic in the WSN(Wireless Sensor Network) has been increased by the growing number of IoT wireless devices, SDWSN(Software-Defined Wireless Sensor Network) and its security that aims a secure SDN(Software-Defined Networking) for efficiently managing network resources in WSN have received much attention. In this paper, we study on how to efficiently and securely design a PUF(Physical Unclonable Function)-assisted group key distribution scheme for the SDWSN environment. Recently, Huang et al. have designed a group key distribution scheme using the strengths of SDN and the physical security features of PUF. However, we observe that Huang et al.'s scheme has weak points that it does not only lack of authentication for the auxiliary controller but also it maintains the redundant synchronization information. In this paper, we securely design an authentication process of the auxiliary controller and improve the vulnerabilities of Huang et al.'s scheme by adding counter strings and random information but deleting the redundant synchronization information.

• 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 the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1165-1170
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• 2022

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.149-155
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• 2009

Attacks in wireless sensor networks (WSN), are similar to the attacks in ad-hoc networks because there are deployed on a wireless environment. However existing security mechanism cannot apply to WSN, because it has limited resource and hostile environment. One of the typical attack in WSN is setting up wrong route that using wormhole. To overcome this threat, Ji-Hoon Yun et al. proposed WODEM (WOrmhole attack DEfense Mechanism) which can detect and counter with wormhole. In this scheme, it can detect and counter with wormhole attacks by comparing hop count and initial TTL (Time To Live) which is pre-defined. The selection of a initial TTL is important since it can provide a tradeoff between detection ability ratio and energy consumption. In this paper, we proposed a fuzzy rule-based system for TTL determination that can conserve energy, while it provides sufficient detection ratio in wormhole attack.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.53-58
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• 2024

This paper presents a novel miniaturized 3-D cubic antenna for use in wireless sensor network (WSN) application. The geometry of this antenna is designed as a cube including a meander dipole antenna. A truly omnidirectional pattern is produced by this antenna in both E-plane and H-plane, which allows for non-intermittent communication that is orientation independent. The operating frequency lies in the ISM band (centered in 2.45 GHz). The dimensions of this ultra-compact cubic antenna are 1.25*1.12*1cm3 which features a length dimension λ/11. The coefficient which presents the overall antenna structure is Ka=0.44. The cubic shape of the antenna is allowing for smart packaging, as sensor equipment may be easily integrated into the cube hallow interior. The major constraint of WSN is the energy consumption. The power consumption of radio communication unit is relatively high. So it is necessary to design an antenna which improves the energy efficiency. The parameters considered in this work are the resonant frequency, return loss, efficiency, bandwidth, radiation pattern, gain and the electromagnetic field of the proposed antenna. The specificity of this geometry is that its size is relatively small with an excellent gain and efficiency compared to previously structures (reported in the literature). All results of the simulations were performed by CST Microwave Studio simulation software and validated with HFSS. We used Advanced Design System (ADS) to validate the equivalent scheme of our conception. Input here the part of summary.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.360-374
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• 2016

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.901-920
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• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.