2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)最新文献_第3页

Decentralized Slicing in Mobile Low-Power Wireless Networks 移动低功耗无线网络中的分散切片

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.17

Piotr Jaszkowski, Pawel Sienkowski, K. Iwanicki

引用次数: 1

Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures 基于共识的随机链路故障无线传感器网络自适应分布卡尔曼滤波

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.38

D. Alonso-Roman, B. Beferull-Lozano

{"title":"Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures","authors":"D. Alonso-Roman, B. Beferull-Lozano","doi":"10.1109/DCOSS.2016.38","DOIUrl":"https://doi.org/10.1109/DCOSS.2016.38","url":null,"abstract":"Wireless Sensor Networks have emerged as a very powerful tool for the monitoring and control, over large areas, of diverse phenomena. One of the most appealing properties of these networks is their potentiality to perform complex tasks in a total distributed fashion, without requiring a central entity. In this scenario, where nodes are constrained to use only local information and communicate with one-hop neighbors, iterative consensus algorithms are extensively used due to their simplicity. In this work, we propose the design of a consensus-based distributed Kalman filter for state estimation, in a sensor network whose connections are subject to random failures. As a result of this unreliability, the agreement value of the consensus process is a random variable. Under these conditions, we ensure that the estimator is unbiased, and adaptively compute the gain of the filter by considering the statistical properties of the consensus process. To the best of our knowledge, this is the first time that the design of a consensus-based distributed Kalman filter is addressed by considering the random error introduced by the consensus process. We present some numerical results that confirm the validity of our approach.","PeriodicalId":217448,"journal":{"name":"2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132417156","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}

引用次数: 10

Interactive Wireless Charging for Weighted Energy Balance 用于加权能量平衡的交互式无线充电

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.41

S. Nikoletseas, Theofanis P. Raptis, C. Raptopoulos

引用次数: 5

TransTrack: Tracking Multiple Targets by Sensing Their Zone Transitions TransTrack:通过感知他们的区域转换来跟踪多个目标

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.27

Avinash Kalyanaraman, Erin Griffiths, K. Whitehouse

引用次数: 10

Years-Long Binary Image Broadcast Using Bluetooth Low Energy Beacons 使用蓝牙低能量信标进行长达数年的二进制图像广播

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.20

Chong Shao, S. Nirjon, Jan-Michael Frahm

{"title":"Years-Long Binary Image Broadcast Using Bluetooth Low Energy Beacons","authors":"Chong Shao, S. Nirjon, Jan-Michael Frahm","doi":"10.1109/DCOSS.2016.20","DOIUrl":"https://doi.org/10.1109/DCOSS.2016.20","url":null,"abstract":"This paper describes the first 'image beacon' system that is capable of broadcasting binary images over a very long period (years, as opposed to days or weeks) using a set of cheap, low-power, memory-constrained Bluetooth Low Energy (BLE) beacon devices. We design a patch-based image encoding algorithm to produce encoded images of reasonably high quality, having sizes of as low as 16 bytes -- without any prior knowledge of the test images. We test our system with different types of images that contain hand-written alphanumeric characters, geometric shapes, and arbitrary binary images having complex shapes and curves. We empirically determine the tradeoffs between the system lifetime and the quality of broadcasted images, and determine an optimal set of parameters for our system, under user-specified constraints such as the number of available beacon devices, maximum latency, and life expectancy. We develop a smartphone application that takes an image and user-requirements as inputs, shows previews of different quality output images, writes the encoded image into a set of beacons, and reads the broadcasted image back. Our evaluation shows that a set of 2 -- 3 beacons is capable of broadcasting high-quality images (75% -- 90% structurally similar to original images) for a year-long continuous broadcasting, and both the lifetime and the image quality improve when more beacons are used.","PeriodicalId":217448,"journal":{"name":"2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127350947","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}

引用次数: 8

Unsupervised Interesting Places Discovery in Location-Based Social Sensing 基于位置的社会感知中的无监督有趣地点发现

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.12

Chao Huang, Dong Wang

{"title":"Unsupervised Interesting Places Discovery in Location-Based Social Sensing","authors":"Chao Huang, Dong Wang","doi":"10.1109/DCOSS.2016.12","DOIUrl":"https://doi.org/10.1109/DCOSS.2016.12","url":null,"abstract":"This paper presents an unsupervised approach to accurately discover interesting places in a city from location-based social sensing applications, a new sensing application paradigm that collects observations of physical world from Location-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests (POI) recommendation systems, they used supervised learning approaches that did not work for users who have little or no historic (training) data. In this paper, we focused on an interesting place discovery problem where the goal is to accurately discover the interesting places in a city that average people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. In particular, we develop a new Physical-Social-aware Interesting Place Discovery (PSIPD) scheme which jointly exploits the location's physical dependency and the visitor's social dependency to solve the interesting place discovery problem using an unsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significant performance improvements compared to all baselines in terms of both estimation accuracy and ranking performance.","PeriodicalId":217448,"journal":{"name":"2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132722321","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}

引用次数: 24

Round Trip Time Based Adaptive Congestion Control with CoAP for Sensor Network 基于往返时间的传感器网络CoAP自适应拥塞控制

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.35

Jung June Lee, S. Chung, B. Lee, K. Kim, H. Youn

引用次数: 12

Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data 基于传感器数据监督网络分类的节能数据收集

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.24

Lorenzo A. Rossi, B. Krishnamachari, C.-C. Jay Kuo

{"title":"Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data","authors":"Lorenzo A. Rossi, B. Krishnamachari, C.-C. Jay Kuo","doi":"10.1109/DCOSS.2016.24","DOIUrl":"https://doi.org/10.1109/DCOSS.2016.24","url":null,"abstract":"In wireless sensor networks, data collection (or gathering) is the task of transmitting rounds of measurements of physical phenomena from the sensor nodes to a sink node. We study how to increase the efficiency of data collection via supervised in-network classification of rounds of measurements. We assume that the end users of the data are interested only in rounds characterized by certain patterns. Hence the wireless sensor network uses classification to select the rounds of measurements that are transmitted to the base station. The energy consumption is potentially reduced by avoiding the transmission of rounds of measurements that are not of interest to the end users. In-network classification requires distributed feature extraction and transmission. Such tasks can be less or more energy expensive than the transmission of measurements without classification. We provide analytical results and simulations on real data to show requirements and key trade-offs for the design of in-network data classification systems that can improve the collection efficiency. Besides, we study the impact of spatial subsampling of the sensor data (a way to further decrease energy consumption) on the classification performance.","PeriodicalId":217448,"journal":{"name":"2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"25 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133719897","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}

引用次数: 4

Tree-Structured Linear Approximation for Data Compression over WSNs 基于树结构线性逼近的无线传感器网络数据压缩

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.37

Chu-Ming Wang, Chia-Cheng Yen, Wen-Yen Yang, Jia-Shung Wang

{"title":"Tree-Structured Linear Approximation for Data Compression over WSNs","authors":"Chu-Ming Wang, Chia-Cheng Yen, Wen-Yen Yang, Jia-Shung Wang","doi":"10.1109/DCOSS.2016.37","DOIUrl":"https://doi.org/10.1109/DCOSS.2016.37","url":null,"abstract":"In wireless sensor networks (WSNs), how to reduce the power consumption thus lengthen the system life time is one of the key issues to sustain the services. According to the radio model, packet transmission depletes a much more substantial amount of the energy budget when compared to sensing and processing. Therefore, it is desirable to compress or filter the sensing data effectively in order to save the transmission power eventually. Recently, the model-based scheme is proved to be a promising solution, which usually approximate temporal data by a piecewise linear function. In this paper, a tree-structured linear approximation scheme is proposed to compress sensing data according to an optimal rate-distortion (R-D) relationship. The main design goals are two: (1) providing a bottom-up procedure to explore the best-fit piecewise partition for modeling globally, (2) considering the heterogeneity of sensors simultaneously using our proposed rate-distortion adjustment. That is, a distortion allocation procedure is designed to allocate the distortions to sensor nodes for aware of the heterogeneous properties. Thus the proposed spatio-temporal scheme is adaptable to heterogeneous sensors, various sampling rate, and outliers of data. A real-world dataset simulation is applied to demonstrate the effectiveness. For nearly all combinations with distortion requirements, the proposed method shows better performance than the earlier approaches in terms of data reduction.","PeriodicalId":217448,"journal":{"name":"2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123266395","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}

引用次数: 2

LLSF: Low Latency Scheduling Function for 6TiSCH Networks LLSF: 6TiSCH网络的低延迟调度功能

2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2016-05-26 DOI: 10.1109/DCOSS.2016.10

Tengfei Chang, T. Watteyne, Qin Wang, Xavier Vilajosana

引用次数: 55