Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006669
K. Fukuoka, Miyuki Yamamoto, T. Yokotani, M. Saito, Y. Terashima
In autonomous cars that use wireless ad hoc communication, a network abnormality may adversely affect the safety of the vehicle. Therefore, network monitoring technology is required that can be used to detect attacks and realize reliable networks. In this study, we propose a method to estimate network behavior by analyzing data transmission traffic for ad hoc networks used in autonomous cars. We estimate the communication route by proposing two algorithms for estimating the route terminals and the route switching time. The verification of these algorithms is performed using the QualNet network simulator. The results show that the route terminal estimation accuracy is 90 percent and the accuracy of the route switching time estimation is about 50 percent.
{"title":"Network Behavior Estimation Method for Wireless Ad-Hoc Networks by Analyzing Data Transmission Traffic","authors":"K. Fukuoka, Miyuki Yamamoto, T. Yokotani, M. Saito, Y. Terashima","doi":"10.23919/ICMU48249.2019.9006669","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006669","url":null,"abstract":"In autonomous cars that use wireless ad hoc communication, a network abnormality may adversely affect the safety of the vehicle. Therefore, network monitoring technology is required that can be used to detect attacks and realize reliable networks. In this study, we propose a method to estimate network behavior by analyzing data transmission traffic for ad hoc networks used in autonomous cars. We estimate the communication route by proposing two algorithms for estimating the route terminals and the route switching time. The verification of these algorithms is performed using the QualNet network simulator. The results show that the route terminal estimation accuracy is 90 percent and the accuracy of the route switching time estimation is about 50 percent.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006642
Kouki Takahashi, Yuki Takazawa, Hiromu Kamei, J. Mitsugi, H. Ichikawa, Yuusuke Kawakita
Authors propose a method for improving channel reservations in multi-subcarrier multiple-access wireless communications schemes. This scheme is designed for use with wide-area synchronous sensing zones that use functionally extended multi-subcarrier multiple-access subcarrier communication schemes for passive radio-frequency identification (RFID). When the sensing target is larger than the communication distance of the sensor, the sensing zone must be divided. Overlapping zones invariably occur in this case. Sensors in overlapping zones transmit signals to their respective receivers over the same channels and interference follows. If three or more zones are placed side by side, middle zone's communication capacity is lower than else zones. Because, middle zone can not use nearly center frequency. However, the communication capacity and its variations have not been examined for how they are affected by the zones in which overlapping sensors are used. This study investigated communications between sensors in overlapping zones by assigning channels to receivers in odd zones. The channel reservation method and the conventional method were simulated in MATLAB an compared. By allocating channels to sensors in odd zones, communication capacity can be improved and variations suppressed.
{"title":"Channel Reservation by Even-odd Separation in Multiple Subcarrier Multiple Access","authors":"Kouki Takahashi, Yuki Takazawa, Hiromu Kamei, J. Mitsugi, H. Ichikawa, Yuusuke Kawakita","doi":"10.23919/ICMU48249.2019.9006642","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006642","url":null,"abstract":"Authors propose a method for improving channel reservations in multi-subcarrier multiple-access wireless communications schemes. This scheme is designed for use with wide-area synchronous sensing zones that use functionally extended multi-subcarrier multiple-access subcarrier communication schemes for passive radio-frequency identification (RFID). When the sensing target is larger than the communication distance of the sensor, the sensing zone must be divided. Overlapping zones invariably occur in this case. Sensors in overlapping zones transmit signals to their respective receivers over the same channels and interference follows. If three or more zones are placed side by side, middle zone's communication capacity is lower than else zones. Because, middle zone can not use nearly center frequency. However, the communication capacity and its variations have not been examined for how they are affected by the zones in which overlapping sensors are used. This study investigated communications between sensors in overlapping zones by assigning channels to receivers in odd zones. The channel reservation method and the conventional method were simulated in MATLAB an compared. By allocating channels to sensors in odd zones, communication capacity can be improved and variations suppressed.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128646332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006651
Tsukasa Kudo
With the recent development and expansion of the Internet of Things, fog computing has been proposed to solve the problem of transferring large quantities of sensor data to a cloud server. Primary processing is performed at fog nodes installed near sensors and only its results are transferred to server to be shared and used by various analytical processing. When any missing or defective data is detected, the corrected data is retransferred to the server and the related analytical processing are executed again. And, when the results of the analytical and primary processing have a relationship, such as summaries and details, the analytical processing results must be updated with maintaining consistency between them. However, to maintain consistency at all times, analytical processing must be performed with every re-transfer thereby degrading the efficiency. In this paper, I propose a method to reflect many sequential corrections in a lump while maintaining consistency by using the temporal database concept and show its effectiveness.
{"title":"Data Correction Management Method Using Temporal Data in Fog Computing","authors":"Tsukasa Kudo","doi":"10.23919/ICMU48249.2019.9006651","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006651","url":null,"abstract":"With the recent development and expansion of the Internet of Things, fog computing has been proposed to solve the problem of transferring large quantities of sensor data to a cloud server. Primary processing is performed at fog nodes installed near sensors and only its results are transferred to server to be shared and used by various analytical processing. When any missing or defective data is detected, the corrected data is retransferred to the server and the related analytical processing are executed again. And, when the results of the analytical and primary processing have a relationship, such as summaries and details, the analytical processing results must be updated with maintaining consistency between them. However, to maintain consistency at all times, analytical processing must be performed with every re-transfer thereby degrading the efficiency. In this paper, I propose a method to reflect many sequential corrections in a lump while maintaining consistency by using the temporal database concept and show its effectiveness.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124679660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006630
Yuki Kohara, M. Nakazawa
Human recognition using single laser range finder (LRF) is utilized for the task of following a target person such as a cargo transport robot. In these recognition methods, the approach is applied in which human-crafted features is inputted to the one-class classification model to identify whether it is a human or not. In this paper, we propose a method that introduce features extracted by deep learning. In this method, we create an encoder that can extract features from input data using PointNet-based autoencoder. In its experiment, the features extracted by encoder is compared with the human-crafted features, and these extraction process length of time is measured.
{"title":"Human Tracking of Single Laser Range Finder Using Features Extracted by Deep Learning","authors":"Yuki Kohara, M. Nakazawa","doi":"10.23919/ICMU48249.2019.9006630","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006630","url":null,"abstract":"Human recognition using single laser range finder (LRF) is utilized for the task of following a target person such as a cargo transport robot. In these recognition methods, the approach is applied in which human-crafted features is inputted to the one-class classification model to identify whether it is a human or not. In this paper, we propose a method that introduce features extracted by deep learning. In this method, we create an encoder that can extract features from input data using PointNet-based autoencoder. In its experiment, the features extracted by encoder is compared with the human-crafted features, and these extraction process length of time is measured.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129112629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006673
K. Kaji, Masaaki Kaneko, Nobuyuki Ito, K. Naito, N. Chujo, T. Mizuno
PDR is mentioned as a powerful indoor position estimation method. In general PDR, the direction of movement is determined from the value of the angular velocity sensor, so it is difficult to estimate behavior such as walking sideways or backwards without changing the angular velocity value. In order to solve this problem, we proposed a method to estimate the traveling direction of each step using the acceleration plane component. Although this research has made it possible to estimate the behavior in which the value of angular velocity does not change, there is a problem that the estimated trajectory becomes zigzag when going straight. The main reason for the zigzag trajectory is that the rotation of the waist is not taken into consideration. In this research, in addition to the acceleration plane component, the angular velocity plane component is used in combination, and the motion direction estimation for each step is made more accurate by canceling the waist rotation. As a experimental result, the error of the method of the previous research is about 14.2 degrees on average, and the method of this research is about 9.1 degrees on average, and the accuracy improvement of about 5.1 degrees on average was confirmed. In addition, the proposed method is implemented as an Android application with real-time processing enabled.
{"title":"A PDR Smartphone Application Considering Side/Backward Steps","authors":"K. Kaji, Masaaki Kaneko, Nobuyuki Ito, K. Naito, N. Chujo, T. Mizuno","doi":"10.23919/ICMU48249.2019.9006673","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006673","url":null,"abstract":"PDR is mentioned as a powerful indoor position estimation method. In general PDR, the direction of movement is determined from the value of the angular velocity sensor, so it is difficult to estimate behavior such as walking sideways or backwards without changing the angular velocity value. In order to solve this problem, we proposed a method to estimate the traveling direction of each step using the acceleration plane component. Although this research has made it possible to estimate the behavior in which the value of angular velocity does not change, there is a problem that the estimated trajectory becomes zigzag when going straight. The main reason for the zigzag trajectory is that the rotation of the waist is not taken into consideration. In this research, in addition to the acceleration plane component, the angular velocity plane component is used in combination, and the motion direction estimation for each step is made more accurate by canceling the waist rotation. As a experimental result, the error of the method of the previous research is about 14.2 degrees on average, and the method of this research is about 9.1 degrees on average, and the accuracy improvement of about 5.1 degrees on average was confirmed. In addition, the proposed method is implemented as an Android application with real-time processing enabled.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130244761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006647
Tuan Anh Nguyen Gia, Qingqing Li, J. P. Queralta, H. Tenhunen, Zhuo Zou, Tomi Westerlund
The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression are run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications.
{"title":"Lossless Compression Techniques in Edge Computing for Mission-Critical Applications in the IoT","authors":"Tuan Anh Nguyen Gia, Qingqing Li, J. P. Queralta, H. Tenhunen, Zhuo Zou, Tomi Westerlund","doi":"10.23919/ICMU48249.2019.9006647","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006647","url":null,"abstract":"The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression are run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128813124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006652
Qingqing Li, J. P. Queralta, Tuan Anh Nguyen Gia, H. Tenhunen, Zhuo Zou, Tomi Westerlund
A recent trend in the IoT is to shift from traditional cloud-centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as realtime analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system's latency and poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multivehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.
{"title":"Visual Odometry Offloading in Internet of Vehicles with Compression at the Edge of the Network","authors":"Qingqing Li, J. P. Queralta, Tuan Anh Nguyen Gia, H. Tenhunen, Zhuo Zou, Tomi Westerlund","doi":"10.23919/ICMU48249.2019.9006652","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006652","url":null,"abstract":"A recent trend in the IoT is to shift from traditional cloud-centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as realtime analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system's latency and poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multivehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121771689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006637
T. Kondo, Kaori Maeda
Mobile devices such as mobile phones and tablets are daily devices as personal viewing of various streaming. They have potentials as a big virtual screen by a tiling display. It is important to keep video quality among tiled video displays in the case of cloud video streaming through the Internet. To solve this issue, we design and develop a mobile tiled display system which supports dynamic tiling adapting to the situation of mobile devices and network status. The proposed system has a dynamic grouping mechanism by devices properties and network status using attribute-based cryptography. Only authorized devices can join the group of the tiled display. Since the video resource is assumed to be on clouds, we introduce edge computing in the architecture of the system and it enables effective processing of video streaming for real-time adapting to variation of distinction of devices.
{"title":"Cloud-based Dynamic Tiled Display Adapting to Grouping by Distinction of Mobile Devices","authors":"T. Kondo, Kaori Maeda","doi":"10.23919/ICMU48249.2019.9006637","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006637","url":null,"abstract":"Mobile devices such as mobile phones and tablets are daily devices as personal viewing of various streaming. They have potentials as a big virtual screen by a tiling display. It is important to keep video quality among tiled video displays in the case of cloud video streaming through the Internet. To solve this issue, we design and develop a mobile tiled display system which supports dynamic tiling adapting to the situation of mobile devices and network status. The proposed system has a dynamic grouping mechanism by devices properties and network status using attribute-based cryptography. Only authorized devices can join the group of the tiled display. Since the video resource is assumed to be on clouds, we introduce edge computing in the architecture of the system and it enables effective processing of video streaming for real-time adapting to variation of distinction of devices.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122516332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Indoor location estimation has long been researched to realize location-based services. In this paper, we propose an indoor location estimation method for Bluetooth Low Energy (BLE) devices using end-to-end LSTM neural network. We focus on large-scale exhibition where is a tough environment for wireless indoor location estimation due to signal strength instability. To achieve higher accuracy, deep learning based methods are proposed rather than trilateration or fingerprint. Existing deep learning based methods estimate the location from the probabilities using the difference of query signal strength and autoencoder-reconstruction of it. Proposed method adopts end-to-end location estimation, which means the neural network takes a time-series of signal strength and outputs the estimated location at the latest time in the input time-series. We also build a loss function which takes how a person walks into account. Considering the difficulty of data collection within a short preparation term of an exhibition, the data generated by a simple simulation is used in the training phase before training with a small amount of real data. As a result, the estimation accuracy is average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. Proposed method outperforms our previous trilateration based method's 4.51m average.
{"title":"An End-to-End BLE Indoor Location Estimation Method Using LSTM","authors":"Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi","doi":"10.23919/ICMU48249.2019.9006638","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006638","url":null,"abstract":"Indoor location estimation has long been researched to realize location-based services. In this paper, we propose an indoor location estimation method for Bluetooth Low Energy (BLE) devices using end-to-end LSTM neural network. We focus on large-scale exhibition where is a tough environment for wireless indoor location estimation due to signal strength instability. To achieve higher accuracy, deep learning based methods are proposed rather than trilateration or fingerprint. Existing deep learning based methods estimate the location from the probabilities using the difference of query signal strength and autoencoder-reconstruction of it. Proposed method adopts end-to-end location estimation, which means the neural network takes a time-series of signal strength and outputs the estimated location at the latest time in the input time-series. We also build a loss function which takes how a person walks into account. Considering the difficulty of data collection within a short preparation term of an exhibition, the data generated by a simple simulation is used in the training phase before training with a small amount of real data. As a result, the estimation accuracy is average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. Proposed method outperforms our previous trilateration based method's 4.51m average.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124173233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.23919/ICMU48249.2019.9006659
Tymoteusz Cejrowski, J. Szymański, Andrzej Sobecki, David Gil, H. Mora
The paper presents a universal low-power system for biosensory data acquisition in scope of bees monitoring. We describe the architecture of the system, energy-saving components as well as we discuss the selection of used sensors. The work focuses on energy optimization in a scope of wireless communication. A custom protocol was implemented, which is the basis for presented energy-efficient devices. Data exchange process during network initialization and measurement collection was presented. The core principles are devices synchronization and parallel clocks. Devices wake up in their time slot in order to exchange data. For the energy consumption tests, Keysight N6705B power analyzer was used to draw accurate current intake characteristic. The most demanding operations in terms of power consumption were communication and microphone recordings. The performance of devices in case of non-optimized operation, i.e. when optimization techniques are switched off, was compared. It has been shown that devices equipped with 2200 mAh battery can operate continuously for 2 years and 7 months, which gives 3600% increase in operating days compared to the non-optimized case.
{"title":"Low-Power WSN System for Honey Bee Monitoring","authors":"Tymoteusz Cejrowski, J. Szymański, Andrzej Sobecki, David Gil, H. Mora","doi":"10.23919/ICMU48249.2019.9006659","DOIUrl":"https://doi.org/10.23919/ICMU48249.2019.9006659","url":null,"abstract":"The paper presents a universal low-power system for biosensory data acquisition in scope of bees monitoring. We describe the architecture of the system, energy-saving components as well as we discuss the selection of used sensors. The work focuses on energy optimization in a scope of wireless communication. A custom protocol was implemented, which is the basis for presented energy-efficient devices. Data exchange process during network initialization and measurement collection was presented. The core principles are devices synchronization and parallel clocks. Devices wake up in their time slot in order to exchange data. For the energy consumption tests, Keysight N6705B power analyzer was used to draw accurate current intake characteristic. The most demanding operations in terms of power consumption were communication and microphone recordings. The performance of devices in case of non-optimized operation, i.e. when optimization techniques are switched off, was compared. It has been shown that devices equipped with 2200 mAh battery can operate continuously for 2 years and 7 months, which gives 3600% increase in operating days compared to the non-optimized case.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127684119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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