Ubiquitous Structural Monitoring using Wireless Sensor Networks

Kurata, Narito; Saruwatari, Shunsuke; Morikawa, Hiroyuki

doi:10.1109/ispacs.2006.364844

Cited by 41 publications

(38 citation statements)

References 4 publications

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“…Some applications in wireless sensor networks need to obtain data of sufficient quality to have real scientific value, and the applications include earthquake monitoring [10], volcano monitoring [3], and structural health monitoring [4]- [6]. The applications require high fidelity sampling.…”

Section: Requirementsmentioning

confidence: 99%

“…The applications require high fidelity sampling. For example, earthquake monitoring requires precise time-synchronized 100 Hz sampling, and tasks are periodically executed with strict deadlines [4], [10]. In addition, based on the success of TinyOS which is an event driven operating system, we know that compactness is an important factor when covering wide-area applications for wireless sensor networks because compactness is strongly related to power consumption over the entire sensor network.…”

Section: Requirementsmentioning

confidence: 99%

“…However, the event model does not allow preemption because the advantages are strongly related to the absence of preemption. For example, earthquake monitoring requires radio physical layer tasks and exact 100 Hz sensor sampling [4], [10]. The radio physical layer task has a 26 μs deadline and cycle, and a 12.5 μs computation time.…”

Section: Event Modelmentioning

confidence: 99%

“…To evaluate hard real-time tasks, we assume tasks in earthquake monitoring [4], [10], [23] as an actual application for wireless sensor networks. Earthquake monitoring requires precise 100 Hz sampling with radio communication because each sampling must be synchronized among sensor nodes.…”

Section: Hard Real-time Tasksmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) have a number of potential fields of applications, including habitat monitoring, military applications [1], wildland fire monitoring [2], volcano monitoring [3], and structural monitoring [4]- [6]. Each application has different requirements for communication protocols and sensing tasks, and sensor nodes have very limited physical resources because of their design requirements, namely, low power, low cost, and small size.…”

Section: Introductionmentioning

confidence: 99%

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PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

Saruwatari

Morikawa

2012

SICE Journal of Control, Measurement, and System Integration

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: The paper shows a compact hard real-time operating system for wireless sensor nodes called PAVENET OS. PAVENET OS provides hybrid multithreading: preemptive multithreading and cooperative multithreading. Both of the multithreading are optimized for two kinds of tasks on wireless sensor networks, and those are real-time tasks and best-effort ones. PAVENET OS can efficiently perform hard real-time tasks that cannot be performed by TinyOS. The paper demonstrates the hybrid multithreading realizes compactness and low overheads, which are comparable to those of TinyOS, through quantitative evaluation. The evaluation results show PAVENET OS performs 100 Hz sensor sampling with 0.01% jitter while performing wireless communication tasks, whereas optimized TinyOS has 0.62% jitter. In addition, PAVENET OS has a small footprint and low overheads (minimum RAM size: 29 bytes, minimum ROM size: 490 bytes, minimum task switch time: 23 cycles).

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Section: Requirementsmentioning

confidence: 99%

Section: Requirementsmentioning

confidence: 99%

Section: Event Modelmentioning

confidence: 99%

Section: Hard Real-time Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

Saruwatari

Morikawa

2012

SICE Journal of Control, Measurement, and System Integration

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Risk Monitoring of Civil Structures

Kurata

2008

Encyclopedia of Structural Health Monitoring

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Buildings and civil engineering structures are subjected to natural hazards such as severe earthquakes, tsunamis, floods, and hurricanes, as well as man‐made hazards such as fires, crime, and terrorist attacks, during their long‐term use. To mitigate these hazards, monitoring various risks in a building employing structural health monitoring (SHM) technologies is necessary. In this article, a concept of risk monitoring of buildings for disaster mitigation is presented and related sensing technologies are reviewed. As advanced examples of risk monitoring of buildings, a ubiquitous structural monitoring (USM) system and a risk information delivery (RID) system using wireless sensor networks are introduced.

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Parallel checking of content integrity in multi‐cloud storage of heterogeneous sensor systems

Mao

Chen

Yan

et al. 2016

Int J Communication

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Heterogeneous sensor systems collect a large amount of sensor data. Because of limited processing capability of sensor nodes, such data are typically stored and processed in a centralized environment in the cloud. However, when data are managed and processed by multiple cloud servers, users do not physically possess these data. The risk of unauthorized access to the cloud data increases dramatically. Therefore, enforcing the integrity of the outsourced data remotely becomes a challenge in the heterogeneous sensor system. Remote data possession auditing is a solution to guarantee the availability and integrity of data outsourced to the cloud. Most existing solutions only target at a single cloud service provider (CSP) environment without considering the multi-cloud scenario. How to audit the data possession among the distributed CSP servers effectively becomes a major concern. In this paper, we propose a parallel cloud data possession checking scheme for the multi-cloud environment. Our approach utilizes the homomorphic verification tag created by the Paillier cryptosystem to support unlimited query challenges and introduces the error-correction encoding method to ensure error localization and data correction. We prototype our solution and analyze the scheme comprehensively. Our evaluation results demonstrate the efficiency and security of our method. It also supports simultaneous verification of multiple CSPs. Figure 1. Overall architecture of heterogeneous sensor system.As another example, heterogeneous sensors are also used in earthquake or volcano monitoring, where different types of sensors, including temperature and acceleration, are used to obtain environmental data that are processed in cloud servers [5]. When such systems are integrated with cloud-storage infrastructure, users lose the physical possession of the out-sourced data. There is a dramatic increase in the potential risk in unauthorized access to the data, such as the incident in the S3 service of Amazon [6]. We will present the generalized architecture in Section 3.Intuitively, to ensure that data are stored correctly, we can verify data using general cryptographicbased approaches, for example, hash functions and digital signature schemes. Deswarte et al. [7,8] first make use of the RSA signature scheme combined with hash functions to verify the correctness of outsourced files. However, such a verification scheme needs to access or retrieve the whole outsourced data, which will cause high performance overhead (e.g., computation complexity and communication cost).The work of [9, 10], and [11] proposed the provable data possession model. To reduce the computation and communication cost, these schemes utilize the sampling technique combined with homomorphic verification instead of the whole-file retrieval verification. The approach proposed by Ateniese et al. [9] supports public data possession audit with unbounded times of challenge. Most previous schemes only verify the data correctness without error identification, localization, and correction. ...

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Ubiquitous Structural Monitoring using Wireless Sensor Networks

Cited by 41 publications

References 4 publications

PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

Risk Monitoring of Civil Structures

Parallel checking of content integrity in multi‐cloud storage of heterogeneous sensor systems

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