• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.98-106
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• 2009

Process Control Systems (PCSs) or Supervisory Control and Data Acquisition (SCADA) systems have recently been added to the already wide collection of wireless sensor networks applications. The PCS/SCADA environment is somewhat more amenable to the use of heavy cryptographic mechanisms such as public key cryptography than other sensor application environments. The sensor nodes in the environment, however, are still open to devastating attacks such as node capture, which makes designing a secure key management challenging. In this paper, a key management scheme is proposed to defeat node capture attack by offering both forward and backward secrecies. Our scheme overcomes the pitfalls which Nilsson et al.'s scheme suffers from, and is not more expensive than their scheme.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.225-229
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• 2007

Recently, wireless sensor networks are deployed in various applications range from simple environment monitoring systems to complex systems, which generate large amount of information, like motion monitoring, military, and telematics systems. Although wireless sensor network nodes are operated with low-power 8bit processor to execute simple tasks like environment monitoring, the nodes in these complex systems have to execute more difficult tasks. Generally, MAC protocols for wireless sensor networks attempt to reduce the energy consumption using duty cycling mechanism which means the nodes periodically sleep and wake. However, in the duty cycling mechanism. a node should wait until the target node wakes and the sleep latency increases as the number of hops increases. This sleep latency can be serious problem in complex and sensitive systems which require high speed data transfer like military, wing of airplane, and telematics. In this paper, we propose a solution for reducing transmission latency through distributed duty cycling (DDC) in wireless sensor networks. The proposed algorithm is evaluated with real-deployment experiments using CC2420DBK and the experiment results show that the DDC algorithm reduces the transmission latency significantly and reduces also the energy consumption.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.39-43
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• 2006

In this paper, we proposed TASL-MAC, a medium-access control (MAC) protocol for wireless sensor networks. In wireless sensor networks, sensor nodes are usually deployed in a special environment, are assigned with long-term work, and are supported by a limited battery. As such, reducing the energy consumption becomes the primary concern with regard to wireless sensor networks. At the same time, reducing the latency in multi-hop data transmission is also very important. In the existing research, sensor nodes are expected to be switched to the sleep mode in order to reduce energy consumption. However, the existing proposals tended to assign the sensors with a fixed Sleep/Listening schedule, which causes unnecessary idle listening problems and conspicuous transmission latency due to the diversity of the traffic-load in the network. TASL-MAC is designed to dynamically adjust the duty listening time based on traffic load. This protocol enables the node with a proper data transfer rate to satisfy the application's requirements. Meanwhile, it can lead to much greater power efficiency by prolonging the nodes' sleeping time when the traffic. We evaluate our implementation of TASL-MAC in NS-2. The evaluation result indicates that our proposal could explicitly reduce packet delivery latency, and that it could also significantly prolong the lifetime of the entire network when traffic is low.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.123-126
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• 2005

Wireless Sensor Networks will revolutionize applications such as environmental monitoring, home automation, and logistics. We developed forest fire surveillance system. In this paper, Considering the fact that in Korea, during November to May, forest fires occur very frequently causing catastrophic damages on the valuable environment, Although exists other forest fire surveillance system such as surveillance camera tower, infrared ray sensor system and satellite system. Preexistence surveillance system can't real-time surveillance, monitoring, database and automatic alarm. But, forest fire surveillance system(FFSS) support above. In this paper, we describes a system development approach for a wireless sensor network based FFSS that is to be used to measure temperature and humidity as well as being fitted with a smoke detector. Such a device can be used as an early warning fire detection system and real-time surveillance in the area of a bush fire or endangered public infrastructure. Once the system has being development, a mesh network topology will be implemented with the chosen sensor node with the aim of developing a sophisticated mesh network.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.51-58
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• 2008

Wireless sensor networks are used by various application areas to implement smart data processing and ubiquitous system. In the recent research of parking management system based on wireless sensor networks, adaptive sensing and efficient data processing are not considered. The effectiveness of implementing these distributed computing devices affects the performance of the applications in parking management. This paper proposes an adaptive sensing using fuzzy wireless sensor for the ubiquitous networks of parking management system. The fuzzy inference system is encoded in the sensor for efficient car presence detection. Moreover, a rule base adaptive module is proposed which wirelessly transmit the new values to each sensor for adapting the environment of car park area. The result of experiments shows that the fuzzy wireless sensor provides more throughputs and less time delays compared to a normal method of data gathering by wireless sensors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1B
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• pp.68-77
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• 2010

Wireless sensor networks are installed in various environments and collect sensing data through wireless links. The quality of a wireless link may be unstable due to environment causes and hardware performance in wireless sensor networks. Since the change of the link quality may cause data loss, sensor nodes need to adaptively estimate the change of the link quality. Also, the routing protocol should deal with this situation. In this paper, the adaptive link quality estimation and routing scheme in the large-scale wireless sensor networks are proposed. When the quality of a link is unstable, sensor nodes agilely estimate the quality of links, and the new route is selected. When quality of a link is stable, the link quality is occasionally estimated so that the energy consumption is reduced. Moreover, sensor nodes exchange less beacons in order to reduce an overhead in dense networks. In the case of sparse network, the sensor nodes exchange more beacons for finding a better route. We prove that the proposed scheme can improve the energy efficiency and reliability.

• Journal of KIISE
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• v.41 no.11
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• pp.958-966
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• 2014

Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.55-66
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• 2012

In wireless sensor networks, a positioning scheme is one of core technologies for sensor applications such as disaster monitoring and environment monitoring. The One of the most positioning scheme, called DV-HOP does not consider non-uniform sensor networks that are actual distributed environments. Therefore, the accuracy of the existing positioning scheme is low in non-uniform network environments. Moreover, because it requires many anchor nodes for high accuracy in non-uniform network environments, it is expensive to construct the network. To overcome this problem, we propose a novel sensor positioning scheme using density probability models in non-uniform wireless sensor networks. The proposed scheme consists of the density probability model using the deployment characteristics of sensor nodes and the distance refinement algorithm for high accuracy. By doing so, the proposed scheme ensures the high accuracy of sensor positioning in non-uniform networks. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme improves about 44% accuracy of sensor positioning over the existing scheme on average even in non-uniform sensor networks.