Chongmyung Park

Traffic-Aware Routing Protocol for Wireless Sensor Networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.

Energy Effective Time Synchronization inWireless Sensor Network

Advance in processor, memory and wireless communication technique have led to an increase of economical and small wireless sensor nodes. To provide the right responses quickly for the diverse events, wireless sensor nodes have cooperation with together. For successful cooperation, the time synchronization among sensor nodes is an important requirement for application execution. In wireless sensor networks, message packets are used for the time synchronization. However, the transmission of message packets dissipates the battery energy of wireless sensor nodes. Since wireless sensor nodes works on the limited battery capacity, the excessive use of message packets has a negative impact upon their lifetime. In this paper, reference interpolation protocol is proposed for reducing the number of message packets for the time synchronization. The proposed method performs time interpolation between the time of reference packets and the global time of the base station. The proposed method completes the synchronization operation with only two message packets. Due to the simple synchronization procedure, our method greatly reduces the number of synchronization messages. From the decrease in the transmission of message packets, the convergence time among wireless sensor nodes is shortened and the lifetime of wireless sensor nodes is also prolonged as much as the amount of saved battery energy.

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.

Cooperative Processing Model for Wireless Sensor Networks

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 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.

Reference interpolation protocol for time synchronization in wireless sensor networks

Advance in processor, memory and wireless communication technique have led to an increase of economical and small wireless sensor nodes. To provide the right responses quickly for the diverse events, wireless sensor nodes have cooperation with together. For successful cooperation, the time synchronization among sensor nodes is an important requirement for application execution. In wireless sensor networks, message packets are used for the time synchronization. However, the transmission of message packets dissipates the battery energy of wireless sensor nodes. Since wireless sensor nodes works on the limited battery capacity, the excessive use of message packets has a negative impact upon their lifetime. In this paper, reference interpolation protocol is proposed for reducing the number of message packets for the time synchronization. The proposed method performs time interpolation between the time of reference packets and the global time of the base station. The proposed method completes the synchronization operation with only two message packets. Due to the simple synchronization procedure, our method greatly reduces the number of synchronization messages. From the decrease in the transmission of message packets, the convergence time among wireless sensor nodes is shortened and the lifetime of wireless sensor nodes is also prolonged as much as the amount of saved battery energy.

Distributed Computing Models for Wireless Sensor Networks

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.