Reliable and energy-efficient data collection in sparse sensor networks with mobile elements

Sparse wireless sensor networks (WSNs) are emerging as an effective solution for a wide range of applications, especially for environmental monitoring. In many scenarios, a moderate number of sparsely deployed nodes can be sufficient to get the required information about the sensed phenomenon. To this end, special mobile elements, i.e. mobile data collectors (MDCs), can be used to get data sampled by sensor nodes. In this paper we present an analytical evaluation of the data collection performance in sparse WSNs with MDCs. Our main contribution is the definition of a flexible model which can derive the total energy consumption for each message correctly transferred by sensors to the MDC. The obtained energy expenditure for data transfer also accounts for the overhead due to the MDC detection when sensor nodes operate with a low duty cycle. The results show that a low duty cycle is convenient and allows a significant amount of correctly received messages, especially when the MDC moves with a low speed. When the MDC moves fast, depending on its mobility pattern, a low duty cycle may not always be the most energy efficient option.     

A framework for Resource-Aware Data Accumulation in sparse wireless sensor networks

Wireless sensor networks (WSNs) have become an enabling technology for a wide range of applications. In contrast with traditional scenarios where static sensor nodes are densely deployed, a sparse WSN architecture can also be used in many cases. In a sparse WSN, special mobile data collectors (MDCs) are used to gather data from ordinary sensor nodes. In general, sensor nodes do not know when they will be in contact with the MDC, hence they need to discover its presence in their communication range. To this end, discovery mechanisms based on periodic listening and a duty-cycle have shown to be effective in reducing the energy consumption of sensor nodes. However, if not properly tuned, such mechanisms can hinder the data collection process. In this paper, we introduce a Resource-Aware Data Accumulation (RADA), a novel and lightweight framework which allows nodes to learn the MDC arrival pattern, and tune the discovery duty-cycle accordingly. Furthermore, RADA is able to adapt to changes in the operating conditions, and is capable of effectively supporting a number of different MDC mobility patterns. Simulation results show that our solution obtains a higher discovery efficiency and a lower energy consumption than comparable schemes.     

Energy-efficient routing for mobile data collectors in wireless sensor networks with obstacles

This paper proposes an energy-efficient routing mechanism by introducing intentional mobility to wireless sensor networks (WSNs) with obstacles. In the sensing field, Mobile Data Collectors (MDCs) can freely move for collecting data from sensors. An MDC begins its periodical movement from the base station and finally returns and transports the data to the base station. In physical environments, the sensing field may contain various obstacles. A research challenge is how to find an obstacle-avoiding shortest tour for the MDC. Firstly, we obtain the same size grid cells by dividing the network region. Secondly, according to the line sweep technique, the spanning graph is easily constructed. The spanning graph composed of some grid cells usually includes the shortest search path for the MDC. Then, based on the spanning graph, we can construct a complete graph by Warshall-Floyd algorithm. Finally, we present a heuristic tour-planning algorithm on the basis of the complete graph. Through simulation, the validity of our method is verified. This paper contributes in providing an energy-efficient routing mechanism for the WSNs with obstacles.     

无线传感器网络延迟约束的MDC分布式轨道规划算法

针对引入移动元素后无线传感器网络数据面临的收集延时问题,提出了一种分布式的移动数据收集器(MDC)轨道规划算法.首先给出基于k跳支配集的MDC最小时延规划问题定义,并证明它是NP-hard.在基于集结的数据收集模式汇总,k跳支配节点作为集结点缓存传感节点收集的数据并在MDC到达时上传.然后,提出了一种高效的基于k跳支配集的MDC轨迹构建算法.算法通过分布式的k-跳支配集算法找出网络中的支配节点,进而通过Prim算法和Christofides近似算法对MDC的移动轨道进行规划.算法的正确性、k-跳支配集的界、时间复杂性和消息交换复杂性通过理论进行分析.最后,通过仿真实验验证了算法的有效性.仿真结论表明,与同类算法相比,所提出算法能够显著缩短MDC的移动轨迹,因而可以降低网络延迟.     

Efficient monte carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.     

Feature engineering for semantic place prediction

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We have found that feature engineering, or in other words, constructing features using human heuristics, is very effective for this task. In particular, we have proposed a novel feature engineering technique, Conditional Feature (CF), a general framework for domain-specific feature construction. In total, we have generated 2,796,200 features and in our final five submissions we use feature selection to select 100 to 2000 features. One of our key findings is that features conditioned on fine-granularity time intervals, e.g. every 30 min, are most effective. Our best 10-fold CV accuracy on training set is 75.1% by Gradient Boosted Trees, and the second best accuracy is 74.6% by L1-regularized Logistic Regression. Besides the good performance, we also report briefly our experience of using F# language for large-scale (～70 GB raw text data) conditional feature construction.     

A visual exploration of mobile phone users,land cover,time, and space

A series of maps were produced that together form a type of atlas of the Nokia Mobile Data Challenge (MDC). Like in a traditional geographic atlas, a limited number of base map configurations is generated, onto which various thematic elements are then overlaid. Two of those base maps are themselves derived from MDC data; the third is referenced in geographic space. Thematic overlays serve several purposes, including elaborating different elements from which the base map geometry had been derived, as well as linking other data to it. The core of the study presented here is an intersection of high-dimensional concepts, dimensionality reduction, geographic analysis, and visualization, intended as a point of departure towards an integrated, attribute-centered understanding of people’s movement patterns. Among the advances put forth is a new time-weighted kernel density model approach derived from journey vertices captured via GPS and WLAN.     

一种适合掌上电脑GIS矢量的栅格存储数据结构的研究

提出了一种适应于以掌上电脑为硬件平台的GIS数据存储数据结构设计技术 ,即基于矢量的栅格化存储结构 ,该数据结构适应于以掌上电脑为代表的小型嵌入式智能移动终端图形显示。     

混合型异构传感器网络中移动数据收集器的路径优化

针对周期工作-事件驱动混合型异构传感器网络, 设计一种基于静态Sink 搭配移动数据收集器(MDC) 的数据收集策略. 为了解决MDC访问规划问题, 提出一种最小能耗访问节点集搜索算法. 首先, 基于节点相对边缘度从整体层面去除适量边缘节点; 然后, 依据节点排除优先度, 迭代排除当前节点集中相对能效最低的节点, 从而逐步获得优化的访问节点集. 仿真实验结果表明, 所提出的新数据收集策略具有优异的能效性、负载均衡性和实时性.

     

多描述编码研究现状

研究开发实时、鲁棒的流媒体编码与传输技术已成为当前信息技术的一个热点问题.多描述编码作为解决该问题的一种方法,因其在满足数据传输实时性要求的同时可减少数据失真,受到广泛关注.文中首先介绍了多描述编码的历史和评价性能指标,然后详述了多描述编码的研究和进展状况,包括各类多描述编码方法的思想、框架和特点,最后对现有研究中存在的难点问题进行了探讨与展望.     

Establishing connectivity among disjoint terminals using a mix of stationary and mobile relays

In multiple application scenarios, need arises to connect a set of disjoint nodes or segments. Examples include connecting a sparsely located data sources, repairing a partitioned network topology after failure, and federating a set of standalone networks to serve an emerging event. Contemporary solutions either deploy stationary relay nodes (RN) to form data paths or employ one or multiple mobile data carriers (MDCs) that pick packets from sources and transport them to destinations. In this paper we investigate the interconnection problem when the number of available RNs is insufficient for forming a stable topology and a mix of RNs and MDCs is to be used. We present a novel algorithm for determining where the RNs are to be placed and planning optimized travel paths for the MDCs so that the data delivery latency as well as the MDC motion overhead are minimized. The performance of the algorithm is validated through simulation.     

Bridging the app gap: An examination of a professional development initiative on mobile learning in urban schools

In this study we present a professional development initiative aimed at helping urban teachers in low-income underserved schools in the U.S. learn how to utilize iPads (a representative mobile device) and educational apps (software programs that run on mobile devices) to support teaching and learning. Subsequently, we examine the ways in which four case study teachers utilized iPads and educational apps in their classrooms to support their students' learning experiences. Data included observations of professional development activities, classroom observations, teacher interviews, and student focus groups. Findings revealed that students used iPads and educational apps to (a) access online content, (b) create learning artifacts, and (c) reinforce content learning through personalized instruction. Findings also indicated that use of iPads and educational apps supported student academic growth and empowerment. Results have implications for mobile learning researchers, practitioners, and policy makers, particularly those charged with the design and implementation of professional development programs.     

Semantics,intelligent processing and services for big data

With the continuous increase of data, scaling up to unprecedented amounts, generated by Internet-based systems, Big Data has emerged as a new research field, coined as “Big Data Science”. The core of Big Data Science is the extraction of knowledge from data as a basis for intelligent services and decision making systems, however, it encompasses many research topics and investigates a variety of techniques and theories from different fields, including data mining and machine learning, information retrieval, analytics, and indexing services, massive processing and high performance computing. Altogether the aim is the development of advanced data-aware knowledge based systems.This special issue presents advances in Semantics, Intelligent Processing and Services for Big Data and their applications to a variety of domains including mobile computing, smart cities, forensics and medicine.     

Transfer learning for the efficient detection of COVID-19 from smartphone audio data

Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users’ mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L³-Net (including 12 different configurations) evaluated through user-independent experiments on 4 different datasets (13,447 samples in total). Results clearly show the advantages of L³-Net in all the experimental settings as it overcomes the other solutions by 12.3% in terms of Precision–Recall AUC as features extractor, and by 10% when the model is fine-tuned. Moreover, we note that to fine-tune only the fully-connected layers of the pre-trained models generally leads to worse performances, with an average drop of 6.6% with respect to feature extraction. Finally, we evaluate the memory footprints of the different models for their possible applications on commercial mobile devices.     

Rollover-free navigation for a mobile agent in an unstructured environment.

This paper introduces a navigation method for a teleoperated mobile agent (or robot) moving in an unstructured environment that includes unknown obstacles and uneven terrain, based on a guided-navigation algorithm (GNA) and a rollover-prevention algorithm (RPA). Although the mobile agent is primarily driven by an operator at a remote site, it reacts autonomously for avoiding collision with obstacles and for preventing rollover when it suspects/detects possible collision or rollover. The autonomous reactive motion is normally unexpected, thus there exists the inconsistency between the intended motion and the controlled motion of the agent from the operator. A force-reflection technique utilizing a force-feedback joystick is developed to manipulate this inconsistency. To verify the feasibility and effectiveness of the proposed navigation method, experiments with the Robot for Hazardous Application-Double Tracks (ROBHAZ-DT) (actual mobile agent) are successfully carried out.     

以“数据+能力”的IT 管理模式支撑千万级用户的迁转维系和发展创新研究#br#

IT集约化建设、强化IT 赋能是集团信息化工作的主线。在这个思路下,一方面需要强化CBSS核心能力,提高支撑效能;另一方面,需要加速BSS 向CBSS的迁转,这对移动企业的网络建设及管理能力提出了更高的标准和要求。根据现有研究资料,以广东联通迁转CBSS工作的执行落地为例,针对以“数据+能力”的IT 管理方式实现过程进行了分析和研究,阐述 了具体技术方案及其创新点。通过研究,希望能够对其它移动通信企业的用户迁转维系和发展创新提供一些帮助和启示。     

A study of the model and algorithms for handling location-dependent continuous queries

Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example, mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly, which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints, considering various types of queries. Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in 1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model checking. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications, mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data replication and placement issues in network-centric systems. Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems, especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and high-assurance systems engineering. Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools, the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford University Press High Integrity Systems Journal. I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted to high-assurance systems.