• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.115-122
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• 2017

In this paper, we propose an adaptive data aggregation and compression scheme for wireless sensor networks with energy-harvesting nodes, which increases the amount of data arrived at the sink node by efficient use of the harvested energy. In energy-harvesting wireless sensor networks, sensor nodes can have more than necessary energy because they harvest energy from environments continuously. In the proposed scheme, when a node judges that there is surplus energy by estimating its residual energy, the node compresses and transmits the aggregated data so far. Conversely, if the residual energy is estimated to be depleted, the node turns off its transceiver and collects only its own sensory data to reduce its energy consumption. As a result, this scheme increases the amount of data collected at the sink node by preventing the blackout of relay nodes and facilitating data transmission. Through simulation, we show that the proposed scheme suppresses the occurrence of blackout nodes and collect the largest amount of data at the sink node compared to previous schemes.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3151-3168
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• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.998-1013
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• 2013

Wireless wearable sensor networks have emerged as a promising technique for human motion tracking due to the flexibility and scalability. In such system several wireless sensor nodes being attached to human limb construct a wearable sensor network, where each sensor node including MEMS sensors (such as 3-axis accelerometer, 3-axis magnetometer and 3-axis gyroscope) monitors the limb orientation and transmits these information to the base station for reconstruction via low-power wireless communication technique. Due to the energy constraint, the high fidelity requirement for real time rendering of human motion and tiny operating system embedded in each sensor node adds more challenges for the system implementation. In this paper, we discuss such challenges and experiences in detail during the implementation of such system with wireless wearable sensor network which includes COTS wireless sensor nodes (Imote 2) and uses TinyOS 1.x in each sensor node. Since our system uses the COTS sensor nodes and popular tiny operating system, it might be helpful for further exploration in such field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1359-1375
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• 2015

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.159-173
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• 2009

Locations of positioned nodes as well as gathered data from nodes are very important because generally multiple nodes are deployed randomly and data are gathered in wireless sensor network. Since the nodes composing wireless sensor network are low cost and low performance devices, it is very difficult to add specially designed devices for positioning into the nodes. Therefore in wireless sensor network, technology positioning nodes precisely using low cost is very important and valuable. This research proposes Cooperative Positioning System, which raises accuracy of location positioning and also can find positions on multiple sensors within limited times. And this research verifies this technology is excellent in terms of performance, accuracy, and scalability through simulation.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.207-210
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• 2005

Ubiquitous computing is one of the key technology areas in the "Project on Development of Ubiquitous computing and network technology" promoted by the Ministry of Science and Technology as a frontier business of the $21^{st}$ century in Korea, which is based on the new concept merging physical space and computer-based cyber space. With recent advances in Micro Electro Mechanical System (MEMS) technology, low cost and low-power consumption wireless micro sensor nodes have been available. Using these smart sensor nodes, there are many activities to monitor real world, for example, habitat monitoring, earthquake monitoring and so on. In this paper, we introduce web-based real environment monitoring system incorporating wireless sensor nodes. It collects sensing data produced by some wireless sensor nodes and stores them into a database system to analyze. Our environment monitoring system is composed of a networked camera and environmental sensor nodes, which are called Mica2 and developed by University of California at Berkeley. We have modified and ported network protocols over TinyOS and developed a monitoring application program using the MTS310 and MTS420 sensors that are able to observe temperature, relative humidity, light and accelerator. The sensed data can be accessed user-friendly because our environment monitoring system supports web-based user interface. Moreover, in this system, we can setup threshold values so the system supports a function to inform some anomalous events to administrators. Especially, the system shows two useful pre-processed data as a kind of practical uses: a discomfort index and a septicity index. To make both index values, the system restores related data from the database system and calculates them according to each equation relatively. We can do enormous works using wireless sensor technologies, but just environment monitoring. In this paper, we show just one of the plentiful applications using sensor technologies.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.195-202
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• 2006

For applying sensor networking technology for our daily life, security service is essential, and pairwise key establishment is the key point for security. In this paper, we propose fairwise key establishment mechanism for secure coumunication in sensor networks. In the mechanism, we cluster the network field before deployment and predistribute key materials to normal sensor nodes and clusterheads. For clusterheads, more key materials are predistributed, and after deployment, sensor nodes which need to establish pairwise keys with other sensor nodes in different clusters make request for related key materials to their own clusterheads. Our proposal reduces the memory requirements for normal sensor nodes by distributing more information to clusterheads, and it raises the security level and resilience against node captures. In addition, it guarantees perfect pairwise key establishments for every pair of neighboring nodes and provides efficient and secure sensor communications.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.492-498
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• 2015

In general, data packets from sensor nodes are transferred to the sink node in a wireless sensor networks. So many data packets are gathered around the sink node, resulting in significant packet collision and delay. In this paper, we propose an efficient packet transmission mechanism for multi-hop wireless sensor networks. The proposed mechanism is composed of two modes. One mode works between sink node and 1-hop nodes from sink. In this mode, data packets are transmitted in predefined time slots to reduce collisions. The other mode works between other nodes except sink node. In this mode, duplicated packets from neighbor nodes can be detected and dropped using some control signals. Our numerical analysis and simulation results show that our mechanism outperforms X-MAC and RI-MAC in terms of energy consumption and transmission delay.

• Journal of information and communication convergence engineering
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• v.10 no.4
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• pp.349-358
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• 2012

Over a wireless sensor network (WSN), accurate localization of sensor nodes is an important factor in enhancing the association between location information and sensory data. There are many research works on the development of a localization algorithm over three-dimensional (3D) space. Recently, the complexity-reduced 3D trilateration localization approach (COLA), simplifying the 3D computational overhead to 2D trilateration, was proposed. The method provides proper accuracy of location, but it has a high computational cost. Considering practical applications over resource constrained devices, it is necessary to strike a balance between accuracy and computational cost. In this paper, we present a novel 3D localization method based on the received signal strength indicator (RSSI) values of four anchor nodes, which are deployed in the initial setup process. This method provides accurate location estimation results with a reduced computational cost and a smaller number of anchor nodes.