Matrix Factorization Techniques for Recommender Systems

Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

RMST: Reliable Data Transport in Sensor Networks

Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers’ energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home’s power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RA ${k}$ EL), and a bespoke multi-label classification algorithm (ML ${k}$ NN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like Count , Sum , and Average . Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

/pdf/a-survey-of-distributed-data-aggregation-algorithms-16eiyu3imz.pdf

A Survey of Distributed Data Aggregation Algorithms

There is provided an information processing device including a display section configured to display a first object in a virtual three-dimensional space having a depth direction of a display screen, an operation section configured to acquire an operation for moving the first object in at least the depth direction, and a controller configured to move the first object on the display screen in accordance with the acquired operation, to execute, when a region of the first object overlaps a first overlap determination region, a first process to one or both of the first and second objects, and to execute, when the region of the first object overlaps a second overlap determination region, a second process to one or both of the first and second objects. The first overlap determination region may be a region obtained by extending the second overlap determination region in at least the depth direction.

Information processing device, information processing method, and program

Sensor networks have received considerable attention in recent years, and are often employed in the applications where data are difficult or expensive to collect. In these applications, in addition to individual sensor readings, statistical aggregates such as Min and Count over the readings of a group of sensor nodes are often needed. To conserve resources for sensor nodes, in-network strategies are adopted to process the aggregates. One primitive in-network aggregation strategy is the tree-based aggregation, where the aggregates are computed from leaves to the root of a spanning tree over a sensor network. However, a shortcoming with the tree-based aggregation is that it is not robust against communication failures, which are common in sensor networks. One of the solutions to overcome this shortcoming is to enable multipath routing, by which each node broadcasts its reading or a partial aggregate to multiple neighbors. However, multipath routing-based aggregation typically suffers from the problem of overcounting sensor readings. In this study, we propose two schemes based on the linear counting technique to deal with the overcounting problem. These two schemes process aggregates by statically and dynamically, respectively, allocating space for the use of the linear counting technique. Both schemes provide the same accuracy guarantee but involve different communication costs. Through extensive experiments with real-world and synthetic data, we demonstrate the efficiency and effectiveness of using these two schemes as solutions for processing aggregates in a sensor network. The experiments also show that the scheme that dynamically allocates the space often outperforms the other one in terms of energy conservation since it requires less space to satisfy an accuracy constraint.

http://web.nchu.edu.tw/~yfan/index.files/YFan-TPDS.pdf

Efficient and Robust Schemes for Sensor Data Aggregation Based on Linear Counting

Sensor networks have received considerable attention in recent years, and are often employed in the applications where data are difficult or expensive to collect. In these applications, in addition to individual sensor readings, statistical aggregates such as Min and Count over the readings of a group of sensor nodes are often needed. To conserve resources for sensor nodes, in-network strategies are adopted to process the aggregates. One primitive in-network aggregation strategy is the tree-based aggregation, where the aggregates are computed along a spanning tree over a sensor network. However, a shortcoming with the tree-based aggregation is that it is not robust against communication failures, which are common in sensor networks. One of the solutions to overcome this shortcoming is to enable multi-path routing, by which each node broadcasts its reading or a partial aggregate to multiple neighbors. However, multi-path routing based aggregation typically suffers from the problem of overcounting sensor readings. In this study, we propose using the linear counting sketches for multi-path routing based in-network aggregation. We claim that the use of the linear counting sketches makes our approach considerably more accurate than previous approaches using the same sketch space. Our approach also enjoys low variances in term of the aggregate accuracy, and low overheads either in computations or sketch space. Through extensive experiments with real-world and synthetic data, we demonstrate the efficiency and effectiveness of using the linear counting sketches as a solution for the in- network aggregation.

http://web.nchu.edu.tw/~yfan/index.files/ipdps.pdf

Efficient and robust sensor data aggregation using linear counting sketches

A wireless sensor network and data sensing method thereof are provided. A prediction model is established according to sensed data. When statistical value of the sensed data is within a user allowable range, the average value of the sensed data is returned to a user based on the prediction model. Alternatively, when the statistical value of the sensed data is beyond the user allowable range, actually sensed data is returned to the user based on the prediction model. In addition, the prediction model may further be dynamically updated according to subsequently received sensed data.

Wireless sensor network and data sensing method thereof

Sensor networks have received considerable attention in recent years, and are employed in many applications. In these applications, statistical aggregates such as Sum over the readings of a group of sensor nodes are often needed. One challenge for computing sensor data aggregates comes from the communication failures, which are common in sensor networks. To enhance the robustness of the aggregate computation, multipath-based aggregation is often used. However, the multipath-based aggregation suffers from the problem of overcounting sensor readings. The approaches using the multipath-based aggregation therefore need to incorporate techniques that avoid overcounting sensor readings. In this paper, we present a novel technique named scalable counting for efficiently avoiding the overcounting problem. We focus on having an (e, δ) accuracy guarantee for computing an aggregate, which ensures that the error in computing the aggregate is within a factor of e with probability (1 - δ). Our schemes using the scalable counting technique efficiently compute the aggregates under a given accuracy guarantee. We provide theoretical analyses that show the advantages of the scalable counting technique over previously proposed techniques. Furthermore, extensive experiments are made to validate the theoretical results and manifest the advantages of using the scalable counting technique for sensor data aggregation.

http://web.nchu.edu.tw/~yfan/index.files/YFan-TKDE.pdf

Energy Efficient Schemes for Accuracy-Guaranteed Sensor Data Aggregation Using Scalable Counting

Nowadays, mobile devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for commons. In this study, we investigate using Wi-Fi logs from a mobile device to discover user preferences. The core ideas are two folds. First, every Wi-Fi access point is with a network name, normally a human-readable string, called SSID (Service Set Identifier). Since SSIDs are often with semantics, from which we can infer the place where the user stayed. Second, a Wi-Fi log is produced when the user is near a Wi-Fi access point. A high frequency of a consecutively observed SSID implies a long stay duration at a place. To the best of our knowledge, our work is the first attempting to understand users from the collected Wi-Fi logs from mobile devices. However, Wi-Fi logs are essentially of various information types and with noises. How to assess the information types, eliminate irrelevant information, and clean up the noises within partial-informative SSIDs are therefore keys for profiling user preferences over Wi-Fi logs. In this paper, we propose a data cleaning and information enrichment framework for enabling the user preference understanding through collected Wi-Fi logs, and introduce a data clean framework for cleaning, correcting, and refining Wi-Fi logs. In addition, a comprehensive experiment with data collected from users is made to verify the effectiveness of the proposed techniques for cleaning noisy Wi-Fi data for user preferences profiling. The experiment results demonstrate the effectiveness of the proposed framework for profiling user preferences through Wi-Fi logs.

Yao-Chung Fan

Papers

Efficient and Robust Schemes for Sensor Data Aggregation Based on Linear Counting

Efficient and robust sensor data aggregation using linear counting sketches

Wireless sensor network and data sensing method thereof

Energy Efficient Schemes for Accuracy-Guaranteed Sensor Data Aggregation Using Scalable Counting

A Framework for Enabling User Preference Profiling through Wi-Fi Logs