Wireless Sensor Network

 

Overview

In sensor network group we focus on realizing shared wireless sensor networks (WSN) with the goal of utilizing them as infrastructures to serve massive amounts of various data. Recently, many context-aware systems have been developed that use embedded sensors in mobile phones. Also, in order to determine the states of the real world more precisely, WSNs, which consist of small computers equipped with sensors (so called sensor nodes), gather a lot of attention and edge closer to practical use. However, existing WSNs are designed to be used for a single purpose solely, therefore in order to serve multiple applications we need to deploy WSNs for each one of them. This remains to be one of the sources that increase the development costs of such systems.

Our group focuses on developing shared WSNs that can be used by multiple context-aware systems. Our research focuses on self-adaptive WSN middleware.

Self-adaptive WSN middleware

A sensor node in a WSN has limited resources, e.g. CPU, memory, bandwidth, and battery. Still, a middleware for a shared WSN has to provide QoS for each application (e.g. accuracy of observation) under such resource constraints. For this reason, we have proposed self-adaptive methods to reduce redundant resource consumptions. For instance, we have presented a configurable sensor model that enables to adjust the quality of the observation. Also, we have proposed a sensor selection method to minimize the number of nodes participating in the observation along with a sensor allocation method to guarantee fairness among multiple applications. Since WSNs intrinsically have a dynamic nature, these methods are required to be self-adaptive.

Research Topics

Configurable Sensor Model for Target Tracking in Wireless Sensor Networks

In shared wireless sensor networks a vast amount of data is transferred between the nodes. In order to improve the scalability, the data transmissions caused by each application should be reduced to a minimum while upkeeping the required resolution. Existing works proposed a binary sensor model, which maps the sensory data to binary values to reduce the necessary data transmissions by sacrificing resolution at the same time. However, this binary sensor model cannot adjust the resolution adequately to the required resolution. Therefore, we proposed a N-ary sensor model by generalizing the binary sensor model. The N-ary sensor model maps sensory data to N-ary values. This way, the model can adjust the resolution of each sensor node by configuring the arity N. In addition, we proposed an arity configuration algorithm to assign an appropriate arity to each node in order to further reduce needless data transmissions while preserving the required resolution.

Geographic-based Adaptive Sensor Node Selection

In a sensor network, limiting the number of sensors used for observation purposes is an effective way to reduce the energy consumption of each sensor. In order to limit the number of sensors without sacrificing observation accuracy, an appropriate sensor combination must be selected by evaluating the observation effectiveness of various combinations. However, the computational workload for evaluating all the sensor combinations is quite large. In region-based sensor selection, a combination of sensors that are near to the observation target is selected. We define a parameter related to the optimal size of a region around an observation target by making a trade-off between accuracy and computational workload.

Sensor Node Assignment for Multiple Tasks

Wireless Sensor Networks are applied as a platform for ubiquitous computing. Since WSNs are deployed in steadily growing numbers and are used for many purposes, a huge amount of different tasks with a variety of priorities/requirements are deployed into such networks. In general, tasks are executed by assigning them to a concrete set of sensors. However, it remains unclear how to assign the sensors to the tasks such that both, the requirements of the tasks and the constraints on the network, are satisfied. In this research work, we first formulate this problem as an optimization problem by defining several constraints on the WSN. This way, we can thoroughly evaluate several algorithms by giving bounds for the problem. Furthermore, by extracting the features of the formulated problem, we can propose a new algorithm that has a better performance. In short, the goal of this research is that in the future the dwarf shown in the picture, who is responsible for the sensor allocation, makes smarter decisions.

 

Self-Corrective Fault Tolerant Wireless Sensor Networks

Ultimately, the goal of WSN is to provide accurate data about monitored phenomena efficiently over the maximum possible period. General characteristics of wireless sensor networks and the direct exposure to the environment cause frequent faults. Accumulation of these faults can lead to the progressive decrease of the reliability and accuracy of sensor readings. This leads to the shortening of effective lifetime, defined as the time of operation in which network reliably provides accurate data. An automated framework for fault tolerance would enable the network to be aware of faults and adapt and correct its behavior accordingly. Fault detection and classification based on continuity and frequency of occurrence is the starting point for discovery of observable and learnable patterns in faulty readings. Models of faults learned in this phase are used to correct readings from faulty sensors. In this way, network learns from observing its own behavior and learns how to maximize the utility of each sensor node.

 

Members

  • Kenji Tei (Assistant Professor)
  • Ryo Shimizu (D2)

Contact Information

Kenji Tei:

Research Results

Selected Publications

  1. Valentina Baljak, Tei Kenji and Shinichi Honiden: "Fault Classification and Model Learning from Sensory Readings - Framework for Fault Tolerance in Wireless Sensor Networks", IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP'13), April 2013.
  2. Valentina Baljak, Tei Kenji, Shinichi Honiden: "Faults in Sensory Readings: Classification and Model Learning", Sensors & Transducers, Vol.18, 177-187, January 2013.
  3. Valentina Baljak, Marius Tudor Benea, Amal El Fallah Seghrouchni, Cedric Herpson, , Shinichi Honiden, Thi Thuy Nga Nguyen, Andrei Olaru, Ryo Shimizu, Kenji Tei, Susumu Toriumi: "S-CLAIM: An Agent-based Programming Language for AmI, A Smart-Room Case Study", Procedia Computer Science, Vol.10, 30-37, 2012.
  4. Ehsan Warriach, Kenji Tei, Tuan Anh Nguyen, Marco Aiello: "A Hybrid Fault Detection Approach for Context-aware Wireless Sensor Networks", The 9th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS2012), October 2012.
  5. Valentina Baljak, Kenji Tei, and Shinichi Honiden: "Classification of Faults in Sensor Readings with Statistical Pattern Recognition", The Sixth International Conference on Sensor Technologies and Applications (SENSORCOMM 2012), August 2012.
  6. Valantina Baljak, Marius Tudor Benea, Amal El Fallah Seghrouchni, Cedric Herpson, Shinichi Honiden, Thi Thuy Nga Nguyen, Andrei Olaru, RyoShimizu, Kenji Tei and Susumu Toriumi: "S-CLAIM: An Agent-based Programming language for AmI, A Smart-Room Case Study", The 3rd International Conference on Ambient Systems, Networks and Technologies (ANT-2012), August 2012.
  7. Ehsan Warriach, Kenji Tei, Tuan Anh Nguyen, Marco Aiello: "Fault Detection in Wireless Sensor Networks: a Hybrid Approach", Poster session of the 11th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'12), April 2012.
  8. Susumu Toriumi, Shinichi Honiden: "Assignment of Sensors for Multiple Tasks Using Path Information", 9th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC-2011), 120-127, October 2011
  9. Themistoklis Bourdenas, Kenji Tei, Shinichi Honiden and Morris Sloman: "Autonomic Role and Mission Allocation Framework for Wireless Sensor Networks", Fifth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO'11), October 3 - October 7, 2011.
  10. Rey Abe, Shinichi Honiden: "Suppressing Redundancy in Wireless Sensor Network Traffic", In Proceedings of the 6th IEEE International Conference on Distributed Computing in Sensor Systems, Santa Barbara (DCOSS' 10), California, USA. Springer-Verlag, June 21-23, 2010.
  11. Valentina Baljak, Shinichi Honiden: "Discovery of Configurations for Indoor Wireless Sensor Networks Through Use of Simulation in Virtual Worlds", In Proceedings of the Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM 2010). July 18-25, 2010.