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Wi-Fi fingerprinting is widely applied for indoor localization due to the widespread availability of Wi-Fi devices. However, traditional methods are not ideal for multi-building and multi-floor environments due to the scalability issues. Therefore, more and more researchers have employed deep learning techniques to enable scalable indoor localization. This paper introduces a novel semi-supervised learning framework for neural networks based on wireless access point selection, noise injection, and Mean Teacher model, which leverages unlabeled fingerprints to enhance localization performance. The proposed framework can manage hybrid in/outsourcing and voluntarily contributed databases and continually expand the fingerprint database with newly submitted unlabeled fingerprints during service. The viability of the proposed framework was examined using two established deep-learning models with the UJIIndoorLoc database. The experimental results suggest that the proposed framework significantly improves localization performance compared to the supervised learning-based approach in terms of floor-level coordinate estimation using EvAAL metric. It shows enhancements up to 10.99% and 8.98% in the former scenario and 4.25% and 9.35% in the latter, respectively with additional studies highlight the importance of the essential components of the proposed framework., Comment: 12 pages, 10 figures, under preparation for a journal publication

In this paper, we present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks, where each neural network is dedicated to a sub-problem and trained under a hierarchical stage-wise training framework. When the measured data from sensors have a hierarchical representation as in multi-building and multi-floor indoor localization, it is important to exploit the hierarchical nature in data processing to provide a scalable solution. In this regard, the hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks by training a lower-hierarchy network based on the prior knowledge gained from the training of higher-hierarchy networks. The experimental results with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi RSSI fingerprint database demonstrate that the linked neural networks trained under the proposed hierarchical stage-wise training framework can achieve a three-dimensional localization error of 8.19 m, which, to the best of the authors' knowledge, is the most accurate result ever obtained for neural network-based models trained and evaluated with the full datasets of the UJIIndoorLoc database, and that, when applied to a model based on hierarchical convolutional neural networks, the proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m., Comment: 9 pages, 5 figures, under review for journal publication

Wi-Fi fingerprinting has emerged as the most popular approach to indoor localization. The use of ML algorithms has greatly improved the localization performance of Wi-Fi fingerprinting, but its success depends on the availability of fingerprint databases composed of a large number of RSSIs, the MAC addresses of access points, and the other measurement information. However, most fingerprint databases do not reflect well the time varying nature of electromagnetic interferences in complicated modern indoor environment. This could result in significant changes in statistical characteristics of training/validation and testing datasets, which are often constructed at different times, and even the characteristics of the testing datasets could be different from those of the data submitted by users during the operation of localization systems after their deployment. In this paper, we consider the implications of time-varying Wi-Fi fingerprints on indoor localization from a data-centric point of view and discuss the differences between static and dynamic databases. As a case study, we have constructed a dynamic database covering three floors of the IR building of XJTLU based on RSSI measurements, over 44 days, and investigated the differences between static and dynamic databases in terms of statistical characteristics and localization performance. The analyses based on variance calculations and Isolation Forest show the temporal shifts in RSSIs, which result in a noticeable trend of the increase in the localization error of a Gaussian process regression model with the maximum error of 6.65 m after 14 days of training without model adjustments. The results of the case study with the XJTLU dynamic database clearly demonstrate the limitations of static databases and the importance of the creation and adoption of dynamic databases for future indoor localization research and real-world deployment., Comment: 10 pages, 5 figures, Invited paper with Excellent Paper Award to be presented at ICAIIC 2024, Osaka, Japan, Feb. 19--22, 2023

Wi-Fi fingerprinting becomes a dominant solution for large-scale indoor localization due to its major advantage of not requiring new infrastructure and dedicated devices. The number and the distribution of Reference Points (RPs) for the measurement of localization fingerprints like RSSI during the offline phase, however, greatly affects the localization accuracy; for instance, the UJIIndoorLoc is known to have the issue of uneven spatial distribution of RPs over buildings and floors. Data augmentation has been proposed as a feasible solution to not only improve the smaller number and the uneven distribution of RPs in the existing fingerprint databases but also reduce the labor and time costs of constructing new fingerprint databases. In this paper, we propose the multidimensional augmentation of fingerprint data for indoor localization in a large-scale building complex based on Multi-Output Gaussian Process (MOGP) and systematically investigate the impact of augmentation ratio as well as MOGP kernel functions and models with their hyperparameters on the performance of indoor localization using the UJIIndoorLoc database and the state-of-the-art neural network indoor localization model based on a hierarchical RNN. The investigation based on experimental results suggests that we can generate synthetic RSSI fingerprint data up to ten times the original data -- i.e., the augmentation ratio of 10 -- through the proposed multidimensional MOGP-based data augmentation without significantly affecting the indoor localization performance compared to that of the original data alone, which extends the spatial coverage of the combined RPs and thereby could improve the localization performance at the locations that are not part of the test dataset., Comment: 10 pages, 6 figures, under review for journal publication

A clock skew compensation algorithm was recently proposed based on the extension of Bresenham's line drawing algorithm (Kim and Kang, IEEE Commun. Lett., vol. 26, no. 4, pp. 902--906, Apr. 2022), which takes into account the discrete nature of clocks in digital communication systems and mitigates the effect of limited floating-point precision on clock skew compensation. It lacks, however, a theoretical analysis of the range of the initial value of skew-compensated clock, which is also an initial condition for the proposed algorithm. In this letter, we provide practical as well as theoretical bounds on the initial value of skew-compensated clock based on a systematic analysis of the errors of floating-point operations, which replace the approximate bounds in Theorem 1 of the prior work., Comment: 4 pages, under review for journal publication

Location fingerprinting based on RSSI becomes a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of AI/ML technologies like DNNs makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building; unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments based on the state-of-the-art RNN indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database, where the RNN model trained with the UJIIndoorLoc database augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building) outperforms the other two augmentation methods as well as the RNN model trained with the original UJIIndoorLoc database, resulting in the mean three-dimensional positioning error of 8.42 m., Comment: 6 pages, 5 figures

There has been an increasing tendency to move from outdoor to indoor lifestyle in modern cities. The emergence of big shopping malls, indoor sports complexes, factories, and warehouses is accelerating this tendency. In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those indoor facilities. One of the most economical and practical approaches to indoor localization is Wi-Fi fingerprinting, which exploits the widely-deployed Wi-Fi networks using mobile devices (e.g., smartphones) without any modification of the existing infrastructure. Traditional Wi-Fi fingerprinting schemes rely on complicated data pre/post-processing and time-consuming manual parameter tuning. In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data pre/post-processing and with less parameter tuning. The RNN in the proposed scheme estimates locations in a sequential manner from a general to a specific one (e.g., building->floor->location) in order to exploit the hierarchical nature of the localization in multi-building and multi-floor environments. The experimental results with the UJIIndoorLoc dataset demonstrate that the proposed scheme estimates building and floor with 100% and 95.24% accuracy, respectively, and provides three-dimensional positioning error of 8.62 m, which outperforms existing deep neural network-based schemes., Comment: 4 pages, 3 figures, the 6th International Workshop on GPU Computing and AI (GCA'21)

We propose a novel clock skew compensation algorithm based on Bresenham's line drawing algorithm. The proposed algorithm can avoid the effect of limited floating-point precision (e.g., 32-bit single precision) on clock skew compensation and thereby provide high-precision time synchronization even with resource-constrained sensor nodes in wireless sensor networks., Comment: 5 pages; 3 figures; under review for journal publication

Message bundling is an effective way to reduce the energy consumption for message transmissions in wireless sensor networks. However, bundling more messages could increase both end-to-end delay and message transmission interval; the former needs to be maintained within a certain value for time-sensitive applications like environmental monitoring, while the latter affects time synchronization accuracy when the bundling includes synchronization messages as well. Taking as an example a novel time synchronization scheme recently proposed for energy efficiency, we propose an optimal message bundling approach to reduce the message transmissions while maintaining the user-defined requirements on end-to-end delay and time synchronization accuracy. Through translating the objective of joint maintenance to an integer linear programming problem, we compute a set of optimal bundling numbers for the sensor nodes to constrain their link-level delays, thereby achieve and maintain the required end-to-end delay and synchronization accuracy while the message transmission is minimized., Comment: 6 pages, 6 figures

The ever-increasing number of WSN deployments based on a large number of battery-powered, low-cost sensor nodes, which are limited in their computing and power resources, puts the focus of WSN time synchronization research on three major aspects, i.e., accuracy, energy consumption and computational complexity. In the literature, the latter two aspects have not received much attention compared to the accuracy of WSN time synchronization. Especially in multi-hop WSNs, intermediate gateway nodes are overloaded with tasks for not only relaying messages but also a variety of computations for their offspring nodes as well as themselves. Therefore, not only minimizing the energy consumption but also lowering the computational complexity while maintaining the synchronization accuracy is crucial to the design of time synchronization schemes for resource-constrained sensor nodes. In this paper, focusing on the three aspects of WSN time synchronization, we introduce a framework of reverse asymmetric time synchronization for resource-constrained multi-hop WSNs and propose a beaconless energy-efficient time synchronization scheme based on reverse one-way message dissemination. Experimental results with a WSN testbed based on TelosB motes running TinyOS demonstrate that the proposed scheme conserves up to 95% energy consumption compared to the flooding time synchronization protocol while achieving microsecond-level synchronization accuracy., Comment: 12 pages, 16 figures

Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into account an innate hierarchy among target classes (e.g., cats and dogs in animal image classification) or additional information that can be easily derived from the data (e.g.,numbers larger than five in the recognition of handwritten digits), thereby resulting in scalability issues when the number of target classes is large. Combining two related but slightly different ideas of hierarchical classification and logical learning by auxiliary inputs, we propose a new learning framework called hierarchical auxiliary learning, which not only address the scalability issues with a large number of classes but also could further reduce the classification/recognition errors with a reasonable number of classes. In the hierarchical auxiliary learning, target classes are semantically or non-semantically grouped into superclasses, which turns the original problem of mapping between an image and its target class into a new problem of mapping between a pair of an image and its superclass and the target class. To take the advantage of superclasses, we introduce an auxiliary block into a neural network, which generates auxiliary scores used as additional information for final classification/recognition; in this paper, we add the auxiliary block between the last residual block and the fully-connected output layer of the ResNet. Experimental results demonstrate that the proposed hierarchical auxiliary learning can reduce classification errors up to 0.56, 1.6 and 3.56 percent with MNIST, SVHN and CIFAR-10 datasets, respectively.

In wireless sensor networks (WSNs), implementing a high-precision time synchronization scheme on resource-constrained sensor nodes is a major challenge. Our investigation of the practical implementation on a real testbed of the state-of-the-art WSN time synchronization scheme based on the asynchronous source clock frequency recovery and the reverse two-way message exchange, which can compensate for both propagation delay and clock skew for higher precision, reveals that its performance on battery-powered, low-complexity sensor nodes is not up to that predicted from simulation experiments due to the limited precision floating-point arithmetic of sensor nodes. Noting the lower computational capability of typical sensor nodes and its impact on time synchronization, we propose an asymmetric high-precision time synchronization scheme that can provide high-precision time synchronization even with resource-constrained sensor nodes in multi-hop WSNs. In the proposed scheme, all synchronization-related computations are done at the head node equipped with abundant computing and power resources, while the sensor nodes are responsible for timestamping only. Experimental results with a testbed based on TelosB motes running TinyOS demonstrate that the proposed time synchronization scheme can avoid time synchronization errors resulting from the single-precision floating-point arithmetic of the resource-constrained sensor nodes and achieve microsecond-level time synchronization accuracy in multi-hop WSNs., Comment: 20 pages, 10 figures

The blockchain technology has been changing our daily lives since Bitcoin - i.e., the first decentralized cryptocurrency - was invented and released as open-source software by an unidentified person or a group called Satoshi Nakamoto in 2009. Of many applications which can be implemented based on the blockchain, storage is an important one, a notable example of which is the InterPlanetary File System (IPFS). IPFS is a distributed web based on a peer-to-peer hypermedia protocol to make the web faster, safer, and more open and focuses on public accessible files. To provide a solution for private file storage in the blockchain way, in this paper we propose a Merkle-tree-based File System (MTFS). In MTFS, the blockchain is more than a trust machine; it is an abstract of a cluster system. Distributed random nodes form a tree network cluster without a central controller to provide a secure private storage service and faster message propagation. Advance proxy re-encryption algorithm is applied to guarantee secure file exchanges under permission. Merkle tree will make sure that the files are distributed among the service nodes in a balanced way. The proposed MTFS can be used not only for personal file storage and exchange but also for industry requiring mutual trust in file uploading and downloading in making contracts like insurances., Comment: 4 pages, conference

Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the transformed space, while preserving the topological properties of the original space, and investigate the effect of the latent space transformation on learning generative models and denoising corrupted data. The experimental results demonstrate that our generative and denoising models based on the proposed framework can provide better performance than conventional variational and denoising autoencoders due to the transformation, where we evaluate the performance of generative and denoising models in terms of the Hausdorff distance between the sets of training and processed i.e., either generated or denoised images, which can objectively measure their differences, as well as through direct comparison of the visual characteristics of the processed images.

In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajectory estimation based on convolutional neural network (CNN) and long short-term memory (LSTM) network with this database. For localization, we map RSS data for a reference point to an image-like, two-dimensional array and then apply CNN which is popular in image and video analysis and recognition. For trajectory estimation, we use a modified random way point model to efficiently generate continuous step traces imitating human walking and train a stacked two-layer LSTM network with the generated data to remember the changing pattern of geomagnetic field intensity against (x,y) coordinates. Experimental results demonstrate the usefulness of our new database and the feasibility of the CNN and LSTM-based localization and trajectory estimation with the database., Comment: 7 pages, 16 figures, 3rd International Workshop on GPU Computing and AI (GCA'18)

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors., Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18)

One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Experimental results for the performance of building/floor estimation and floor-level coordinates estimation of a given location demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN, for the implementation with lower complexity and energy consumption at mobile devices., Comment: 9 pages, 6 figures

One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus Information and Visitor Service System. To take into account the hierarchical nature of the building/floor classification problem, we propose a new DNN architecture based on a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification with argmax functions to convert multi-label classification results into multi-class classification ones. We also describe the demonstration of a prototype DNN-based indoor localization system for floor-level location estimation using real received signal strength (RSS) data collected at one of the buildings on the XJTLU campus. The preliminary results for both building/floor classification and floor-level location estimation clearly show the strengths of DNN-based approaches, which can provide near state-of-the-art performance with less parameter tuning and higher scalability., Comment: 5 pages, 6 figures, FOAN 2017 (Munich, Germany, Oct. 2017)

We present the multi-hop extensions of the recently proposed energy-efficient time synchronization scheme for wireless sensor networks, which is based on the asynchronous source clock frequency recovery and reversed two-way message exchanges. We consider two hierarchical extensions based on packet relaying and time-translating gateways, respectively, and analyze their performance with respect to the number of layers and the delay variations through simulations. The simulation results demonstrate that the time synchronization performance of the packet relaying, which has lower complexity, is close to that of time-translating gateways., Comment: 6 pages, 7 figures, Smart Grid Technology and Data Processing (SGTDP 2017), Feb.16-18, 2017, Suzhou, China

The current practice of shaping subscriber traffic using a token bucket filter by Internet service providers may result in a severe waste of network resources in shared access networks; except for a short period of time proportional to the size of a token bucket, it cannot allocate excess bandwidth among active subscribers even when there are only a few active subscribers. To better utilize the network resources in shared access networks, therefore, we recently proposed and analyzed the performance of access traffic control schemes, which can allocate excess bandwidth among active subscribers proportional to their token generation rates. Also, to exploit the excess bandwidth allocation enabled by the proposed traffic control schemes, we have been studying flexible yet practical service plans under a hybrid traffic control architecture, which are attractive to both an Internet service provider and its subscribers in terms of revenue and quality of service. In this paper we report the current status of our modeling of the hybrid traffic control schemes and service plans with OMNeT++/INET-HNRL based on IEEE standard 802.1Q stacked VLANs., Comment: Published in: A. Foerster, V. Vesely, A. Virdis, M. Kirsche (Eds.), Proc. of the 3rd OMNeT++ Community Summit, Brno University of Technology - Czech Republic - September 15-16, 2016

We report preliminary results of a simulation study on the multi-hop extension of the recently-proposed energy-efficient wireless sensor network time synchronization scheme based on time-translating gateways. Unlike the single-hop case, in multi-hop time synchronization a sensor node sends measurement data to a head node through gateways which translate the timestamp values of the received measurement data. Through simulations for two-hop time synchronization, we analyze the impact of the addition of a gateway and its translation of timestamp values on the clock frequency and measurement time estimation and thereby demonstrate the feasibility of the multi-hop extension of the energy-efficient WSN time synchronization based on time-translating gateways., Comment: 4 pages, 4 figures, ICISCA2016, Chiang Mai, Thailand, July 13-16, 2016

In this paper we report preliminary results of a simulation study on an energy-efficient time synchronization scheme based on source clock frequency recovery (SCFR) at sensor nodes in asymmetric wireless sensor networks (WSNs), where a head node --- serving as a gateway between wired and wireless networks --- is equipped with a powerful processor and supplied power from outlet, and sensor nodes --- connected only through wireless channels --- are limited in processing and battery-powered. In the SCFR-based WSN time synchronization scheme, we concentrate on battery-powered sensor nodes and reduce their energy consumption by minimizing the number of message transmissions from sensor nodes to the head node. Through simulation experiments we analyze the performance of the SCFR-based WSN time synchronization scheme, including the impact of SCFR on time synchronization based on two-way message exchange, and demonstrate the feasibility of the proposed time synchronization scheme., Comment: 4 pages, 2 figures, 6th International Symposium on Advanced Engineering (ISAE 2015), Pukyong National University, Busan, Korea, 22-24 Oct. 2015. arXiv admin note: substantial text overlap with arXiv:1508.02708

We consider energy-efficient time synchronization in a wireless sensor network where a head node (i.e., a gateway between wired and wireless networks and a center of data fusion) is equipped with a powerful processor and supplied power from outlet, and sensor nodes (i.e., nodes measuring data and connected only through wireless channels) are limited in processing and battery-powered. It is this asymmetry that our study focuses on; unlike most existing schemes to save the power of all network nodes, we concentrate on battery-powered sensor nodes in minimizing energy consumption for time synchronization. We present a time synchronization scheme based on asynchronous source clock frequency recovery and reverse two-way message exchanges combined with measurement data report messages, where we minimize the number of message transmissions from sensor nodes, and carry out the performance analysis of the estimation of both measurement time and clock frequency with lower bounds for the latter. Simulation results verify that the proposed scheme outperforms the schemes based on conventional two-way message exchanges with and without clock frequency recovery in terms of the accuracy of measurement time estimation and the number of message transmissions and receptions at sensor nodes as an indirect measure of energy efficiency., Comment: 11 pages, 8 figures, submitted to IEEE Transactions on Communications

The generalization of the maximum-likelihood-like estimator for clock skew by Leng and Wu in the above paper is erroneous because the correlation of the noise components in the model is not taken into account in the derivation of the maximum likelihood estimator, its performance bound, and the optimal selection of the gap between two subtracting time stamps. This comment investigates the issue of noise correlation in the model and provides the range of the gap for which the maximum likelihood estimator and its performance bound are valid and corrects the optimal selection of the gap based on the provided range., Comment: 2 pages, 1 figure, submitted to IEEE Transactions on Vehicular Technology

In this paper we study energy-efficient time synchronization schemes with focus on asymmetric wireless sensor networks, where a head node, which is connected to both wired & wireless networks, is equipped with a powerful processor and supplied power from outlet, and sensor nodes, which are connected only through wireless channels, are limited in processing and battery-powered. It is this asymmetry that we focus our study on; unlike existing schemes saving the power of all sensor nodes in the network (including the head node), we concentrate on battery-powered sensor nodes in minimizing energy consumption for synchronization. Specifically, we discuss a time synchronization scheme based on source clock frequency recovery, where we minimize the number of message transmissions from sensor nodes to the head node, and its extension to network-wide, multi-hop synchronization through gateway nodes., Comment: 2 pages, 2 figures, extended abstract of the presentation at International Conference on Information, System and Convergence Applications (ICISCA) 2015 (awarded Best Paper Award)

The current formulation of the optimal scheduling of appliance energy consumption uses as optimization variables the vectors of appliances' scheduled energy consumption over equally-divided time slots of a day, which does not take into account the atomicity of appliances' operations (i.e., the unsplittable nature of appliances' operations and resulting energy consumption). In this paper, we provide a new formulation of atomic scheduling of energy consumption based on the optimal routing framework; the flow configurations of users over multiple paths between the common source and destination nodes of a ring network are used as optimization variables, which indicate the starting times of scheduled energy consumption, and optimal scheduling problems are now formulated in terms of the user flow configurations. Because the atomic optimal scheduling results in a Boolean-convex problem for a convex objective function, we propose a successive convex relaxation technique for efficient calculation of an approximate solution, where we iteratively drop fractional-valued elements and apply convex relaxation to the resulting problem until we find a feasible suboptimal solution. Numerical results for the cost and peak-to-average ratio minimization problems demonstrate that the successive convex relaxation technique can provide solutions close to, often identical to, global optimal solutions., Comment: 8 pages; 6 figures

We study the design of pricing schemes for a group of consumers with smart meters (e.g., in a Greenfield area) who are connected through a gateway to a traditional electricity greed with a progressive tariff. Because the progressive tariff cannot take into account the time aspect of electricity demands, we apply it to consumers in both an individual and a group basis over a shorter time period, which can flatten the overall demand over time and thereby reduce peak load. This scenario for the coexistence of traditional and smart girds and the pricing schemes under this scenario can enable smooth migration to a future smart grid., Comment: 4 pages, 3 figures, to be presented at International Conference on Information and Convergence Technology for Smart Society (ICICTS) 2015

Shaping subscriber traffic based on token bucket filter (TBF) by Internet service providers (ISPs) results in waste of network resources in shared access when there are few active subscribers, because it cannot allocate excess bandwidth in the long term. New traffic control schemes have been recently proposed to allocate excess bandwidth among active subscribers proportional to their token generation rates. In this paper we report the current status of our research on designing flexible yet practical service plans exploiting excess bandwidth allocation enabled by the new traffic control schemes in shared access networks, which are attractive to both ISP and its subscribers in terms of revenue and quality of service (QoS) and serve as a stepping stone to fully-shared access in the future., Comment: 4 pages, 2 figures, ICTC 2014 (Oct. 2014, Busan, Korea)

Passive Optical Network (PON)-based Fiber-To-The-Home (FTTH) are promising solutions that can break through the economic barrier of traditional point-to-point solutions. Once fibers are deployed with PON-based FTTH solutions, it becomes critical how to migrate to Wavelength Division Multiplexing (WDM)-PON because Time Division Multiplexing (TDM) used in current PON solutions cannot exploit the huge bandwidth of the optical fibers and therefore will not be able to meet ever-increasing demands for higher bandwidth by future network applications. In this paper we review and compare the current PON-based FTTH solutions, ATM-PON (APON) and Ethernet PON (EPON), and provide a possible evolution scenario to future WDM-PON., Comment: 4 pages; 6 figures; (Invited paper) Proc. of JCIS 2002, Research Triangle Park, NC, USA, pp. 1402-1405, Mar. 2002

Ethernet-based Passive optical network (E-PON) is the key for next generation access networks. It must have the property of high efficiency, low cost, and support quality of service (QoS). We present a novel media access control (MAC) protocol that maximizes network efficiency by using dynamic bandwidth allocation (DBA) algorithm suitable for E-PON. This protocol minimizes packet delay and delay variation for high priority traffic to ensure QoS. Simulation results show excellent network throughput. Simulation results also show low packet delay and packet delay variation for high priority traffic compare with traditional MAC protocol of E-PON. When the network performs ranging, this protocol ensures zero interruption of high priority traffic, such as audio or video applications., Comment: Proc. of IASTED International Conference on Wireless and Optical Communications (WOC 2003), Banff, Canada, Jul. 2003

Current practice of shaping subscriber traffic based on token bucket by Internet service provider (ISP) allows short-term fluctuations in its shaped rate and thereby enables a subscriber to transmit traffic at a higher rate than a negotiated long-term average. The traffic shaping, however, results in significant waste of network resources, especially when there are only a few active subscribers, because it cannot allocate excess bandwidth to active subscribers in the long term. In this letter we investigate the long-term aspect of resource sharing in ISP traffic control for shared access networks. We discuss major requirements for the excess bandwidth allocation in shared access networks and propose ISP traffic control schemes based on core-stateless fair queueing (CSFQ) and token bucket meters. Simulation results demonstrate that the proposed schemes allocate excess bandwidth among active subscribers in a fair and efficient way, while not compromising the service contracts specified by token bucket for conformant subscribers.

In the above letter, Chin and Chen proposed an IEEE 1588 clock synchronization method based on dual slave clocks, where they claim that multiple unknown parameters --- i.e., clock offset, clock skew, and master-to-slave delay --- can be estimated with only one-way time transfers using more equations than usual. This comment investigates Chin and Chen's dual clock scheme with detailed models for a master and dual slave clocks and shows that the formulation of multi-parameter estimation is invalid, which affirms that it is impossible to distinguish the effect of delay from that of clock offset at a slave even with dual slave clocks., Comment: 2 pages, 1 figure

In shared access shaping subscriber traffic based on token bucket by ISPs wastes network resources when there are few active subscribers, because it cannot allocate excess bandwidth in the long term. To address it, traffic control schemes based on core-stateless fair queueing (CSFQ) and token bucket meters (TBMs) have been proposed, which can allocate excess bandwidth among active subscribers proportional to their token generation rates. Using FIFO queue for all packets, however, degrades the short-term performance of conformant traffic due to the presence of non-conformant packets already in the queue. Also, the rate estimation based on exponential averaging makes it difficult to react to rapid changes in traffic conditions. In this paper we propose a new traffic control scheme based on deficit round-robin (DRR) and TBMs to guarantee the quality of service of conformant packets in all time scales while allocating excess bandwidth among active subscribers proportional to their token generation rates, whose advantages over the CSFQ-based schemes are demonstrated through simulation results., Comment: 5 pages, 6 figures, submitted to ICC 2015, Jul. 26, 2014