Eunchan Kim

Localization with a mobile beacon based on geometric constraints in wireless sensor networks

The location information of sensor nodes is needed for location-based services and network management in wireless sensor networks. Among the many kinds of localization schemes researched to find the location of sensor nodes, a few some schemes presume the use of mobile beacons because using mobile beacons has many potentialities. In particular, a localization scheme with mobile beacons, such as proposed by Ssu et al. [5], has fine-grained accuracy, scalability, and power efficiency even without distance or angle information. To improve the localization accuracy encountered in Ssus scheme, this paper proposes a localization scheme that estimates the location of a sensor node from a possible area where the sensor node is supposed to be, by using geometric constraints. Through simulation results, the proposed scheme is shown to provide higher localization accuracy and reduce the power consumption of sensor nodes by decreasing the communication overhead between sensor nodes and a mobile beacon in comparison to Ssus scheme.

Mobile Beacon-Based 3D-Localization with Multidimensional Scaling in Large Sensor Networks

Localization is essential in wireless sensor networks to handle the reporting of events from sensor nodes. For 3-D applications, we propose a mobile beacon-based localization using classical multidimensional scaling (MBL-MDS) by taking full advantage of MDS with connectivity and measurements. To further improve location performance, MBL-MDS adopts a selection rule to choose useful reference points, and a decision rule to prevent a failure case due to reference points placed on the same plane. Simulation results show improved performance of MBL-MDS in terms of location accuracy and computation complexity.

Distance Estimation With Weighted Least Squares for Mobile Beacon-Based Localization in Wireless Sensor Networks

In large-scale sensor networks, localization with mobile beacons is one of the most efficient ways to deploy sensor nodes as well as locate them. Direct communication with mobile beacons has an advantage of improvement in location accuracy by enabling sensor nodes to measure distances to the mobile beacons. Thus, it is important to improve the accuracy in the distance for high accurate positioning. In this letter, we propose a distance estimation scheme with weighted least squares in mobile beacon-based localization. First, we model distance measurements to a beacon node moving along the given linear tracks. Given our measurement model, the proposed scheme uses weighted least squares to minimize errors in distance measurements. Additionally we analyze the lower bound of errors in our distance estimation based on the Cramer-Rao bound. Simulation results show that our scheme can provide improved accuracy in both distance estimation and position estimation.

LaMSM: localization algorithm with merging segmented maps for underwater sensor networks

Underwater sensor networks (UWSNs) are considered a costeffective solution to ocean applications, such as the acquisition of natural resources in oceans, protection from underwater disasters, etc. These applications basically require location information of nodes to identify the venue of reported events. To locate more accurately the position of nodes, multidimensional scaling (MDS) is widely used because of its good tolerance to errors in measured distances. MDS requires measured distances between every pair of nodes but in practice, only distances between nodes within a communication range can be measured. Hence, the well-known MDS-MAP(P) [6] calculates unmeasured distances for MDS but these calculations result in large errors. In this paper, we proposed a localization algorithm with merging segmented maps (LaMSM) that constructs many reliable segmented maps composed of only nodes within a communication range, and then merges them together based on their common nodes. The segmented maps are built from only the measured distances and as a result, LaMSM provides more accurate node positions than MDS-MAP(P).

An Efficient RSS-Based Localization Scheme with Calibration in Wireless Sensor Networks

In wireless sensor networks (WSNs), localization using the received signal strength (RSS) method is famous for easy adaptation and low cost where measuring the distance between sensor nodes. However, in real localization systems, the RSS is strongly affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As a result, the proposed scheme shows that it can provide high precision.

Localization with a Mobile Beacon based on Geometric Constraints in Wireless Sensor Networks

The location information of sensor nodes is needed for location-based services and network management in wireless sensor networks. Among the many kinds of localization schemes researched to find the location of sensor nodes, a few some schemes presume the use of mobile beacons because using mobile beacons has many potentialities. In particular, a localization scheme with mobile beacons, such as proposed by Ssu et al. [5], has fine-grained accuracy, scalability, and power efficiency even without distance or angle information. To improve the localization accuracy encountered in Ssus scheme, this paper proposes a localization scheme that estimates the location of a sensor node from a possible area where the sensor node is supposed to be, by using geometric constraints. Through simulation results, the proposed scheme is shown to provide higher localization accuracy and reduce the power consumption of sensor nodes by decreasing the communication overhead between sensor nodes and a mobile beacon in comparison to Ssus scheme.

Long-Range Beacons on Sea Surface Based 3D-Localization for Underwater Sensor Networks

In underwater sensor networks (UWSNs), localization is an important issue and a challenging task due to harsh environments for people to access. In this paper, we propose a distributed algorithm to locate nodes deployed in 3-D space using long-range beacons floating on the sea surface. Long-range beacons allow underwater nodes directly to obtain reference positions of beacons and to measure distances to beacons. Because all beacons are placed on the same plane, i.e. the sea surface, the proposed algorithm estimates two candidates for a node position with multidimensional scaling and then determines the final position out of two candidates. Simulation results confirm that our proposed algorithm can achieve high location accuracy with respect to measurement errors.

Floating beacon-assisted 3-D localization for variable sound speed in underwater sensor networks

In this paper, we propose a floating beacon-assisted 3-D localization for variable sound speed in underwater sensor networks (FBL-VSS). Most underwater localization schemes have assumed that the sound speed is constant under water for simplicity; however, it is actually variable depending on depth and seasonal variation. Taking into account various sound speed, FBL-VSS utilizes novel beacons moored to the sea floor. Each beacon has a transmitter floating on the sea surface and a receiver placed on the sea floor, in order to obtain the average of the actual sound speed from the sea surface to the sea floor by measuring the time of flight (TOF) of broadcasted messages. For self-location, FBL-VSS uses multidimensional scaling techniques with distances computed from the average of the actual sound speed. Simulation result shows that our proposed scheme has stable location accuracy for variable sound speed profile under water.

Large-Signal Robustness of the Chair-Varshney Fusion Rule Under Generalized-Gaussian Noises

The Chair-Varshney rule (CVR) has been used to provide a large signal-to-noise ratio (SNR) approximation of the optimal fusion rule under Gaussian noise. For more practical use in sensor networks, this paper extends CVR to Generalized-Gaussian noise channels, along with verification of the suboptimality and robustness of CVR under the Generalized-Gaussian channel noise through the use of Monte Carlo simulations.

Energy Consumptlon Analysls of S-MAC Protocol in Single-Hop Wireless Sensor Networks

Recently, analytic models proposed to evaluate the energy consumption of sensor-medium access control (S-MAC) protocol, one of the well-known MAC protocols for wireless sensor networks (WSNs), are based on the model for the IEEE 802.11 MAC protocol. Since the S-MAC protocol has distinct differences from the IEEE 802.11 MAC protocol, however, those differences should be reflected in the analytic evaluation. For this, we first present a Markov chain model for the single-hop S-MAC protocol under un-saturated condition. Then, by considering competing sensors in shared channel, we derive the energy consumption of the S-MAC protocol. Finally, numerical results for a practical sensor mote are shown