Kentaro Yanagihara

Reaction-diffusion based autonomous control of wireless sensor networks

Taking into account requirements of sensor networks, we need fully-distributed and self-organising control mechanisms which are scalable to the size of a network, robust to failures of sensor nodes, and adaptive to different and dynamically changing topology and changes in wireless communication environment. To accomplish this goal, our research group focuses on behaviour of biological systems, which inherently are scalable, adaptive and robust. In this paper, we first verify the practicality of control mechanisms adopting a reaction-diffusion equation, which explains emergence of patterns on the surface of body of fishes and mammals, and then propose two methods for faster pattern generation to save energy consumption. Prom simulation and practical experiments on a prototype, it was shown that a stable pattern could be generated in a wireless sensor network in several minutes, even when packets were lost for collisions in wireless communication.

A Joint Estimation of Target Location and Channel Model Parameters in an IEEE 802.15.4-based Wireless Sensor Network

Many target localization techniques based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and its parameters for an area where a target node is localized. This is a limiting factor since an intensive pre- measurement campaign is required to determine the model and its parameters. Basing on channel measurement campaigns in different areas we confirmed that the variation in the RSSIs of the IEEE 802.15.4 signal can be described by a two-layer model. We therefore propose a novel target localization method with no prior knowledge of model parameter values. The method is based on a joint maximum likelihood target localization and channel model parameters estimation. The proposed method was experimentally verified in real environments and the results show that the location estimation accuracy outperforms the accuracy of the conventional method where the values of the channel model parameters are known in advance.

RSSI-based Localization without a Prior Knowledge of Channel Model Parameters

In target node localization problem, conventional methods based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and values of its parameters specific for an environment. This limits the conventional localization system to be set up quickly and effectively due to a necessary pre-measurement step to determine both the channel model and the values of its parameters. To address the limitation, a two-stage iterative algorithm which allows to localize a target node without any prior knowledge of the parameter values has been propose. Each stage of the algorithm can be implemented using different estimation methods, such as maximum likelihood (ML) and least square (LS) estimation which provides four different combinations. To determine the best combination, the location estimation performance for all four combinations is evaluated using experimental data collected in measurement campaigns on various indoor locations. The results reveal that the combination of ML estimation method implemented in both stages provides the best location estimation accuracy and the fastest convergence rate.

EACLE: Energy-Aware Clustering Scheme with Transmission Power Control for Sensor Networks

In this paper, we propose a new energy efficient clustering scheme with transmission power control named “EACLE” (Energy-Aware CLustering scheme with transmission power control for sEnsor networks) for wireless sensor networks, which are composed of the following three components; “EACLE clustering” is a distributed clustering method by means of transmission power control, “EACLE routing” builds a tree rooted at a sink node and sets the paths from sensor nodes taking energy saving into consideration, and “EACLE transmission timing control” changes the transmission timing with different levels of transmission power to avoid packet collisions and facilitates packet binding.With an indoor wireless channel model which we obtained from channel measurement campaigns in rooms and corridors and an energy consumption model which we obtained from a measurement of a chipset, we performed computer simulations to investigate the performance of EACLE in a realistic environment. Our simulation results indicate that EACLE outperforms a conventional scheme such as EAD (Energy-Aware Data-centric routing) in terms of communication success rate and energy consumption. Furthermore, we fully discuss the impact of transmission power and timing control on the performance of EACLE.

A cooperative transmission scheme for real-time data gathering in a Wireless Body Area Network

Wireless Body Area Network (WBAN) has drawn considerable attention as a means to gather vital data from on-body sensors. In WBAN, however, a star network topology is mainly supported, connecting a central coordinator to vital sensor nodes put on different positions of a human body, so the links between the coordinator and the sensor nodes are often blocked by parts of the human body when the man takes different postures and motions. Therefore, to support real-time vital data gathering in WBANs, a scheme for mitigating such link blockings is essential. This paper proposes a cooperative scheme for ensuring reliable data transmission in a WBAN. For each sensor node on a human body, the proposed scheme autonomously assigns a sensor node as a cooperator out of other sensor nodes and the cooperator retransmits packets from the sensor node for a coordinator instead of the sensor node when the direct link between them is blocked. After presenting the cooperator selection algorithm, using the Received Signal Strength Indication (RSSI) data obtained from the experiment in an anechoic chamber, the paper evaluates the performance of the proposed scheme in terms of average and worst outage rates.

An Effect of Anchor Nodes Placement on a Target Location Estimation Performance

Many indoor localisation and tracking techniques in wireless sensor networks assume placing anchor nodes on a ceiling. However, placing the nodes on the ground can improve target localisation estimation accuracy based on received signal strength indicator as we show in this work. The presented results are based on an experiment conducted in a hall under two sets of conditions. In one case, people were present in the hall and in the other one, the hall was empty. For the case when people were present in the hall, the location estimation performance improved from 3.7 meters root-mean square error (RMSE) when using only the anchor nodes fixed to the ceiling to 2.2 meters RMSE when using only the anchor nodes placed on the ground. For the case when the hall was empty, the location estimation performance improved from 4.1 meters RMSE when using only the anchor nodes fixed to the ceiling to 2.4 meters RMSE when using only the anchor nodes placed on the ground. The target location estimation accuracy improved by 40% for both conditions

Experiments and considerations on Reaction-Diffusion based Pattern Generation in a Wireless Sensor Network

Taking into account requirements of sensor networks, we need fully-distributed and self-organizing control mechanisms which are scalable to the size of a network, robust to failures of sensor nodes, and adaptive to different and dynamically changing topology and changes in wireless communication environment. To accomplish this goal, our research group focuses on behavior of biological systems, which inherently are scalable, adaptive, and robust. In this paper, we first verify the practicality of control mechanisms adopting a reaction diffusion equation, which explains emergence of patterns on the surface of body of fishes and mammals, and then propose two methods for faster pattern generation to save energy consumption. From simulation and practical experiments on a prototype, it was shown that a stable pattern could be generated in a wireless sensor network in several minutes, even when packets were lost for collisions in wireless communication.

A Sensor Network Protocol for Automatic Meter Reading in an Apartment Building

A wireless sensor network for automatic meter reading needs to satisfy two contradicting requirements, i.e., long lifetime and prompt detection and notification of emergency. We propose a sensor network protocol for this purpose, in which sensor nodes operate on a low duty cycle while the latency of transmission is guaranteed to be less than a certain bound. In this protocol, each node is assigned a time slot in which it receives messages from other nodes. To accomplish slot assignment where nodes further from a BS are assigned earlier time slots for a packet to be transmitted to the BS in one cycle, we propose a slot assignment function with which a node can determine its own slot in a distributed way. We explore several slot assignment functions to find one which gives low and homogeneous contention over a grid network. The simulation results show that our protocol performs well close to the optimal case.

Effect of Walking People on Target Location Estimation Performance in an IEEE 802.15.4 Wireless Sensor Network

Target location estimation is one of many promising applications of wireless sensor networks. However, until now only few studies have examined location estimation performances in real environments. In this paper, we analyze the effect of walking people on target location estimation performance in three experimental locations. The location estimation is based on received signal strength indicator (RSSI) and maximum likelihood (ML) estimation, and the experimental locations are a corridor of a shopping center, a foyer of a conference center and a laboratory room. The results show that walking people have a positive effect on the location estimation performance if the number of RSSI measurements used in the ML estimation is equal or greater than 3, 2 and 2 in the case of the experiments conducted in the corridor, foyer and laboratory room, respectively. The target location estimation accuracy ranged between 2.8 and 2.3 meters, 2.5 and 2.1 meters, and 1.5 and 1.4 meters in the case of the corridor, foyer and laboratory room, respectively.

A Traffic Reduction Method for Centralized RSSI-Based Location Estimation in Wireless Sensor Networks

In a centralized localization scenario, the limited throughput of the central node constrains the possible number of target node locations that can be estimated simultaneously. To overcome this limitation, we propose a method which effectively decreases the traffic load associated with target node localization, and therefore increases the possible number of target node locations that can estimated simultaneously in a localization system based on received signal strength indicator (RSSI) and maximum likelihood estimation. Our proposed method utilizes a threshold which limits the amount of forwarded RSSI data to the central node. As the threshold is crucial to the method, we further propose a method to theoretically determine its value. We experimentally verified the proposed method in various environments and the experimental results revealed that the method can reduce the load by 32-64% without significantly affecting the estimation accuracy.