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In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful. [ABSTRACT FROM AUTHOR]

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology. [ABSTRACT FROM AUTHOR]

Spectral clustering (SC) algorithms have been successful in discovering meaningful patterns since they can group arbitrarily shaped data structures. Traditional SC approaches typically consist of two sequential stages, i.e., performing spectral decomposition of an affinity matrix and then rounding the relaxed continuous clustering result into a binary indicator matrix. However, such a two-stage process could make the obtained binary indicator matrix severely deviate from the ground true one. This is because the former step is not devoted to achieving an optimal clustering result. To alleviate this issue, this paper presents a general joint framework to simultaneously learn the optimal continuous and binary indicator matrices for multi-view clustering, which also has the ability to tackle the conventional single-view case. Specially, we provide theoretical proof for the proposed method. Furthermore, an effective alternate updating algorithm is developed to optimize the corresponding complex objective. A number of empirical results on different benchmark datasets demonstrate that the proposed method outperforms several state-of-the-arts in terms of six clustering metrics. [ABSTRACT FROM AUTHOR]

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply $K$ -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset. [ABSTRACT FROM AUTHOR]

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- $k$ k spatial textual cluster retrieval ($k$ k -STC) query that returns the top- $k$ k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance. [ABSTRACT FROM AUTHOR]

This paper presents an operating model of a station that charges a single-depot and homogeneous fleet of electric vehicles (EV) performing deliveries. Both the charging station and the fleet of EVs are owned by a single delivery company. The operating model, whose goal is to find the optimal EV battery charging schedule, is based on a clustering technique that keeps track of the number of EVs with a specific battery state of energy (SoE), while considering battery degradation, variable (dis)charging efficiency and nonlinear charging speed. The proposed operating model can be used both for the day-ahead scheduling and for the intraday model-predictive-control-based adjustments. Due to its relatively low capacity as compared to other market participants, the charging station is considered to be a price taker in both markets. The price uncertainty is considered using the robust uncertainty budget. The paper also evaluates inefficiency of a commonly used charging policy, i.e., the baseline model, where every delivery vehicle is charged to at least a predetermined SoE before its departure. The presented model is evaluated using multiple case studies and sensitivity analysis. As opposed to the non-clustered baseline model, the proposed approach scales well for very large fleets. Our analysis confirms that the results of the baseline model depend on the preset SoE at departure, while the proposed model provides optimal solution without assumptions on the departing SoE. [ABSTRACT FROM AUTHOR]

This paper presents a methodology and set of validation criteria for the systematic creation of synthetic power system test cases. The synthesized grids do not correspond to any real grid and are, thus, free from confidentiality requirements. The cases are built to match statistical characteristics found in actual power grids. First, substations are geographically placed on a selected territory, synthesized from public information about the underlying population and generation plants. A clustering technique is employed, which ensures the synthetic substations meet realistic proportions of load and generation, among other constraints. Next, a network of transmission lines is added. This paper describes several structural statistics to be used in characterizing real power system networks, including connectivity, Delaunay triangulation overlap, dc power flow analysis, and line intersection rate. The paper presents a methodology to generate synthetic line topologies with realistic parameters that satisfy these criteria. Then, the test cases can be augmented with additional complexities to build large, realistic cases. The methodology is illustrated in building a 2000 bus public test case that meets the criteria specified. [ABSTRACT FROM AUTHOR]

Cloud radio networks coordinate transmission among base stations (BSs) to reduce the interference effects, particularly for the cell-edge users. In this paper, we analyze the performance of a cloud network with static clustering where geographically close BSs form a cloud network of cooperating BSs. Due to finite cooperation, the interference in a practical cloud radio cannot be removed, and in this paper, the distance-based interference is taken into account in the analysis. In particular, we consider centralized zero forcing equalizer and dirty paper precoding for canceling the interference. Bounds are developed on the signal-to-interference ratio distribution and achievable rate with full and limited channel feedback from the cluster users. The adverse effect of finite clusters on the achievable rate is quantified. We show that the number of cooperating BSs is more crucial than the cluster area when full channel state information from the cluster is available for precoding. Also, we study the impact of limiting the channel state information on the achievable rate. We show that even with a practically feasible feedback of about five to six channel states from each user, significant gain in mean rate and cell edge rate compared with conventional cellular systems can be obtained. [ABSTRACT FROM PUBLISHER]

We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slot-wise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme can bring performance improvements, at high spectral efficiencies, as compared to a state-of-the-art technique that investigates the use of large-scale antenna arrays in the context of massive URA. [ABSTRACT FROM AUTHOR]

Motivated by the potentially high downlink traffic demands of commuters in future autonomous vehicles, we study a network architecture where vehicles use Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side Units (RSUs). Such cluster-based multihoming offers improved performance, e.g., in coverage and per user shared rate, but depends on the penetration of V2V+V2I capable vehicles and possible blockage, by legacy vehicles, of line of sight based V2V links, such as those based on millimeter-wave and visible light technologies. This paper provides a performance analysis of a typical vehicle’s connectivity and throughput on a highway in the free-flow regime, exploring its dependence on vehicle density, sensitivity to blockages, number of lanes and heterogeneity across lanes. The results, backed up by simulations of realistic vehicular traffic, show that even with moderate vehicle densities and penetration of V2V+V2I capable vehicles, such architectures can achieve substantial improvements in connectivity and reduction in per-user rate variability as compared to V2I based networks. The typical vehicle’s performance is also shown to improve considerably in the multilane highway setting as compared to a single lane road. This paper also sheds light on how the network performance is affected when vehicles can control their relative positions, by characterizing the connectivity-throughput tradeoff faced by the clusters of vehicles. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have become an indistinguishable part of any Internet of Things (IoT) based system. In an IoT-based system, WSN monitors different physical objects and forwards the collected data to the base station or gateway node for further processing. Existing routing protocols designed for WSNs are not suitable for IoT-based systems. An intelligent and high-performance routing protocol is essential for real-time data analysis and decision-making. Therefore, this paper proposes a mobile sink-based intelligent data routing scheme for WSNs. Initially, Particle Swarm Optimization is used for optimal cluster formation. After cluster formation, the optimal number of rendezvous points are selected, and a data-gathering tour is planned for the mobile sink. Furthermore, a mobile sink-based intelligent data gathering scheme is proposed to collect the data from the cluster heads. Extensive simulations and mathematical analysis are performed to verify the effectiveness of the proposed scheme. The simulation results are compared with state-of-the-art algorithms in terms of stability period, network lifetime, throughput, average energy consumption, fairness index, data transmission latency, and message overhead. [ABSTRACT FROM AUTHOR]

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of spectrum sharing in mmWave cellular networks, where multiple network operators are granted access to the same spectrum resources. Prior spectrum sharing schemes for mmWave cellular networks have two limitations: high coordination overhead and high computational complexity. In this paper, we propose two spectrum sharing schemes for the mobile scenario and the stationary scenario, respectively. In the mobile scenario, we propose a Spectrum sharing scheme using Clustering Algorithms to find the Optimal Positions of Mobile BSes (SCA-OPM). In the stationary scenario, we propose a Spectrum sharing scheme using Clustering Algorithms to Select the most appropriate Subset from all BSes (SCA-SSB). Moreover, for each newly-arriving mobile terminal (MT), we propose two online spectrum sharing schemes based on our SCA-OPM scheme and SCA-SSB scheme, which effectively saves the overall overhead and complexity. Our experimental results show that the SCA-OPM scheme has the best performance, while the SCA-SSB scheme has the same performance as that of the prior scheme but with lower overhead and lower complexity. When the MT density is 200 MTs/km2, for the sum-rate with 10−2 bits/s/Hz, the performance gap between the SCA-OPM scheme and the remaining schemes achieves about 19 dB. Moreover, our online schemes have the same performance as those of their counterpart schemes but with lower overhead and lower complexity for the scenario of a few newly-arriving MTs. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of phase reference estimation with noise, introduced by the imperfect phase-locked loop (PLL) circuit, or the imperfect channel estimation, or both. Prior solutions for suppressing phase noise focus on improving the accuracy of phase reference estimation. The accuracy of phase reference estimation is not high enough due to the following two limitations. First, since the PLL circuit works in radio-frequency (RF), a PLL circuit with high accuracy leads to high cost and high complexity, which makes the deployment difficult. Second, as data rates increase and wireless channels become more complex, the receiver is more difficult to obtain an ideal channel estimation and the negative effect of phase noise becomes more apparent. In this paper, we propose a machine learning approach to mitigate the negative effect of phase noise by using clustering algorithms. The key intuition of our approach is that the clustering algorithm can adaptively trace the shifted constellation point due to the phase noise. Our approach is adaptive because it can adaptively find each received symbol belongs to its original constellation point if the phase noise is not too large, e.g., no larger than $0.25 \pi $. While the shifted distance is not too large, we can map the received symbols into the correct constellation point to mitigate the negative effect of phase noise. Instead of directly using conventional clustering algorithms into the proposed machine learning approach, we propose a new weighted ensemble clustering algorithm to further improve the performance of our approach. In comparison with prior approaches based on RF circuits, our approach has comparable reception performance but with low complexity and low cost. Our experimental results show that, for a QPSK system, our approach improves the demodulation performance and the decoding performance about 10 dB, 8 dB under BCH codes, and 3 dB under Turbo codes, respectively. Even the demodulation performance of our approach without channel coding is better than the decoding performance of the system with channel coding about 5 dB under BCH codes. [ABSTRACT FROM AUTHOR]

This paper proposes and compares feature construction and calibration methods for clustering daily electricity load curves. Such load curves describe electricity demand over a period of time. A rich body of the literature has studied clustering of load curves, usually using temporal features. This limits the potential to discover new knowledge, which may not be best represented as models consisting of all time points on load curves. This paper presents three new methods to construct features: 1) conditional filters on time-resolution-based features; 2) calibration and normalization; and 3) using profile errors. These new features extend the potential of clustering load curves. Moreover, smart metering is now generating high-resolution time series, and so the dimensionality reduction offered by these features is welcome. The clustering results using the proposed new features are compared with clusterings obtained from temporal features, as well as clusterings with Fourier features, using household electricity consumption time series as test data. The experimental results suggest that the proposed feature construction methods offer new means for gaining insight in energy-consumption patterns. [ABSTRACT FROM PUBLISHER]

In order to improve low voltage (LV) network visibility without extensive monitoring and integrate low carbon technologies (LCTs) in a cost-effective manner, this paper proposes a novel three-stage network load profiling method. It uses real-time information monitored from selective representative areas to develop network templates. The three stages are: clustering, classification and scaling. It can be used to identify the loading conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. In the clustering stage, hierarchical clustering and K-means are used to cluster substations into groups based on the shape of the monitored load profiles. The classification tool designed with multinomial logistic regression maps an unmonitored LV substation into the most probable templates by using routinely available fixed data. Finally, clusterwise weighted constrained regression is employed to estimate peak for individual LV substations and the developed templates. The three-stage profiling is demonstrated on a practical system in the U.K. under the umbrella of a smart grid trail project. Ten LV templates are developed by using the metered data from 800 monitored LV substations. A comprehensive comparison between the estimated peaks using the three-stage process and the metered peaks suggests that the methodology can achieve superior accuracy. This is part I of the paper, introducing clustering and classification. The scaling (peak estimation) process will be introduced in part II of the paper. [ABSTRACT FROM PUBLISHER]

Household consumer demand response (DR) is an important research and industry problem, which seeks to categorize, predict, and modify consumer’s energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it difficult to classify consumers into stable representative groups and to predict individual energy consumption patterns. In this paper, we present a shape-based approach that better classifies and predicts consumer energy consumption behavior at the household level. The method is based on the dynamic time warping (DTW). DTW seeks an optimal alignment between energy consumption patterns reflecting the effect of hidden patterns of regular consumer behavior. Using real consumer 24-hour load curves from Opower Corporation, our method results in a 50% reduction in the number of representative groups and an improvement in prediction accuracy measured under DTW distance. We extend the approach to estimate which electrical devices will be used and in which hours. [ABSTRACT FROM AUTHOR]

After deploying a large number of smart meters, utilities are challenged with managing a massive set of interval energy consumption data and decoding the information into meaningful measures that can help them. These new tasks need a more detailed understanding of customers than was previously sought because customers vary widely in their usage, needs, and suitability for different programs and pricing packages. Defining and describing different customer segments will equip decision makers with information to advance not only in pricing and program marketing but also resource allocation and program development. Therefore, among many applications, customer lifestyle segmentation can unlock potential energy-savings and can help utilities understand operating requirements and better coordinate energy resources for grid management. This paper focuses on how to segment customers’ lifestyles based on their electricity consumption data and provides suggestions on which lifestyle groups can be good candidates for certain energy programs based on the segmentation result. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate that a consumer’s marginal system impact is determined only by their demand profile, rather than by their demand level. Demand-profile clustering is identical to clustering consumers according to their marginal impacts on system costs. A profile-based, uniform-rate price is economically efficient as real-time pricing. We develop a criteria system to evaluate the economic efficiency of an implemented retail price scheme in a distribution system by comparing profile clustering and daily average clustering. Our criteria system can examine the extent of a retail price scheme’s inefficiency, even without information about the distribution system’s daily cost structure. For this work, we analyze data from a real distribution system in China. In this system, we find that targeting each consumer’s high-impact days is more efficient than targeting high-impact consumers. [ABSTRACT FROM PUBLISHER]

Subspace clustering aims to cluster a collection of data points lying in a union of subspaces. Based on the assumption that each point can be approximately represented as a linear combination of other points, extensive efforts have been made to compute an affinity matrix in a self-expressive framework for describing the similarity between points. However, the existing clustering methods consider the average feature solutions, which would not be powerful enough to capture the intrinsic relationship between points. In this paper, we present the $k$ -support norm subspace clustering (KSC) method by utilizing k-$ support norm regularization. The k-$ support norm trades off the sparsity of norm and the uniform shrinkage of \ell _{2} norm to yield better predictive performance on the data connection. The theoretical analysis of KSC makes up a large proportion of paper. In the noise-free case, we provide the kEBD condition, which ensures the coefficient matrix to be block diagonal. If the data are corrupted, we prove the incompletion-grouping effect for KSC. Moreover, we provide the statistical recovery guarantee for both noise-free and noise cases. The theory analyses show the validity and feasibility of KSC, and the experimental results on multiple challenging databases demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The optimal sizing of an energy storage system (ESS) in a power generation system that incorporates photovoltaic (PV) generation is crucial in a power grid for which the reduction of CO2 emissions is important. This problem is particularly challenging when it relates to the power system of a ship because it involves uncertain meteorological and load data along a navigation route. This paper proposes a novel method for multi-objective minimization of investment/replacement cost, fuel cost, and CO2 emissions, to find the optimal size of the ESS considering life-span of the ESS. The generation of power by PV modules on a ship is affected by temporal and geographical variations of irradiation along the navigation route. In particular, operating load conditions and irradiation are uncertain. This paper proposes a novel algorithm for partitioning high-dimensional uncertain data into tractable clusters solved by deterministic optimization method. Case studies of an all-electric ship along a route from Dalian in China to Aden in Yemen are shown to demonstrate the applicability of the proposed clustering-based stochastic optimization method. [ABSTRACT FROM PUBLISHER]

In radio frequency energy harvesting-cognitive radio sensor networks (RF EH-CRSNs), clustering routing protocols can help deliver monitored data towards the sink and they are vital for network performance. However, due to the impact of dynamic channel availability, limited node transmission range, and position-dependent energy arrival, existing clustering routing protocols for nonEH-CRSNs and EH nonCRSNs cannot be applied to RF EH-CRSNs. In order to solve above challenges, an RF EH-based multihop clustering routing protocol (RFMCRP) based on nonlinear EH model is proposed in this paper. Firstly, by leveraging curve fitting tool and statistical analysis, the most reasonable nonlinear EH model is identified and it is utilized by RFMCRP to measure the harvested energy accurately. Secondly, the optimal number of clusters is theoretically derived and its value is used as benchmark to evaluate the validity of our proposal. Thirdly, energy control mechanism is introduced to manage node state, and it can help improve the stability of cluster construction. In addition, energy level function-based selection criteria are defined to select high-quality cluster heads and relays, which can help improve the energy sustainability and connectivity of the whole network. Simulation results show that RFMCRP gains obvious advantages over existing protocols in extending network lifetime and improving network monitoring capability. [ABSTRACT FROM AUTHOR]

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals’ cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. The framework first segments app usage traces into short time windows and then applies a probabilistic topic model to infer users’ cyber activities in each window. By constructing and exploring the coherence of users’ activity sequences, the framework can identify individuals’ daily patterns. Next, the framework uses a hierarchical clustering algorithm to recognize the common patterns across diverse groups of individuals. We apply the framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually follow yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media. [ABSTRACT FROM AUTHOR]

With the extensive application of microelectromechanical systems (MEMS) in smartphone, indoor positioning based on smartphone has attracted immense attention over recent years. An indoor positioning method based on a single positioning information source has limitations, and it is difficult to balance the accuracy and cost. Therefore, it is worth further research to reduce the positioning error and improve the robustness of a system by integrating multiple sensors. The direct fusion of multiple information sources may introduce new errors. Therefore, the information sources should be screened to make their advantages complementary. In this paper, a hierarchical indoor positioning method integrating Wi-Fi, magnetic matching (MM), and pedestrian dead reckoning (PDR) is proposed. Firstly, a region is divided into several subregions by a cluster algorithm. Then a magnetic matching suitability (MMS) model is proposed to evaluate the subregional MMSs, and different strategies are adopted for the information fusion according to the evaluation results. Finally, an adaptive extended Kalman filter (AEKF) with innovation is proposed to integrate PDR and the absolute localization result. Experimental results show that the proposed positioning method can make full use the advantages of each positioning information source and has better positioning accuracy and stability. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only $k$ k -means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future. [ABSTRACT FROM AUTHOR]

Smart meters (SMs) measure and transmit fine-grained electricity consumption data to the data center at a certain high frequency (such as every 15 minutes). A data aggregation point (DAP) is a relay device between a cluster of SMs and data centers. The placement of DAPs significantly impacts the cost-effectiveness and quality of service (QoS) of the communication system. Nowadays, many newly installed SMs not only can send data to the DAPs but also receive/send data from/to other SMs. This paper proposes a novel approach for the optimal DAP placement in a multi-hop routing scenario, where SMs can serve as small relay devices so that communications among SMs can be leveraged to reduce the overall communication cost. To do so, we formulate a mixed-integer optimization problem that minimizes three types of cost: installation, transmission, and delay cost. Since the optimization problem involves too many binary variables to be efficiently solved to optimality, a heuristic approach is developed to obtain a desirable solution with a low computational burden. The heuristic iteratively implements clustering approximation, pole assignment, and optimal routing until convergence of the total cost value. Both an illustrative example and case studies verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption of appliances by analyzing the aggregated consumption signal. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer’s privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring, where appliances are partitioned into blocks and the consumption of each block is monitored via separate power smart meters. While a greater number of smart meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of “disaggregation difficulty metric (DDM),” which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer’s usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing smart meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off. [ABSTRACT FROM AUTHOR]

Energy statistics was proposed by Székely in the 80’s inspired by Newton’s gravitational potential in classical mechanics and it provides a model-free hypothesis test for equality of distributions. In its original form, energy statistics was formulated in euclidean spaces. More recently, it was generalized to metric spaces of negative type. In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type. We show that this approach leads to a quadratically constrained quadratic program in the associated kernel space, establishing connections with graph partitioning problems and kernel methods in machine learning. To find local solutions of such an optimization problem, we propose kernel k-groups, which is an extension of Hartigan’s method to kernel spaces. Kernel k-groups is cheaper than spectral clustering and has the same computational cost as kernel k-means (which is based on Lloyd’s heuristic) but our numerical results show an improved performance, especially in higher dimensions. Moreover, we verify the efficiency of kernel k-groups in community detection in sparse stochastic block models which has fascinating applications in several areas of science. [ABSTRACT FROM AUTHOR]

Containerized deployment of microservices has gained immense traction across industries. To meet demand, traditional cloud providers offer container-as-a-service, where selection of the container and containerization of workloads remain developer’s responsibility. This task is arduous for a developer since the choice of containers across different cloud providers is many. Furthermore, there does not exist any mechanism using which one can compare and contrast the capabilities of containers across different providers. In this scenario, we envisage the need for a smart cloud broker that can automatically deploy a chosen IT service into the best-fit container environment mapped to performance requirements, from among the set of available underpinning brokered container hosting systems spread across multiple cloud providers. We propose a novel fitness-aware containerization-as-a-service to achieve this. We show why a best-fit container selection process is operationally complex and time consuming, and how we heuristically prune the associated decision tree in two phases so that it becomes viable to implement this as an on-demand service. We propose a new metric called fitness quotient ($FQ$ F Q ) to evaluate containers obtained from heterogeneous providers. We leverage machine learning techniques to inject automation into these two phases: unsupervised K-Means clustering in the first-level build-time phase to accurately classify IaaS cost and performance data, and polynomial regression during the second-level provisioning-time phase to discover relationships between SaaS performance and container strength. We also show that the utility of the framework that we propose is not limited to the container fitness use case that we analyze in this paper; rather it can be generalized to address a class of problems where overall time and cost complexity for provisioning-time decision making needs to be controlled under a given set of constraints. [ABSTRACT FROM AUTHOR]

Systematic defects within integrated circuits (ICs) are a significant source of failures in nanoscale technologies. Identification of systematic defects is therefore very important for yield improvement. This paper discusses a diagnosis-driven systematic defect identification methodology that we call layout analysis for systematic IC-defect identification using clustering (LASIC). By clustering images of the layout locations that correspond to diagnosed sites for a statistically large number of IC failures, LASIC uncovers the common layout features. To reduce computation time, only the dominant coefficients of a discrete cosine transform analysis of the layout images are used for clustering. LASIC is applied to an industrial chip and it is found to be effective. In addition, detailed simulations reveal that LASIC is both accurate and effective. [ABSTRACT FROM PUBLISHER]

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances. [ABSTRACT FROM PUBLISHER]

The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling, due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively. [ABSTRACT FROM AUTHOR]

Wireless energy transfer (WET) is a promising solution to enable massive machine-type communications (mMTC) with low-complexity and low-powered wireless devices. Given the energy restrictions of the devices, instant channel state information at the transmitter (CSIT) is not expected to be available in practical WET-enabled mMTC. However, because it is common that the terminals appear spatially clustered, some degree of spatial correlation between their channels to the base station (BS) is expected to occur. The paper considers a massive antenna array at the BS for WET that only has access to i) the first and second order statistics of the Rician channel component of the multiple-input multiple-output (MIMO) channel and also to ii) the line-of-sight MIMO component. The optimal precoding scheme that maximizes the total energy available to the single-antenna devices is derived considering a continuous alphabet for the precoders, permitting any modulated or deterministic waveform. This may lead to some devices in the clusters being assigned a low fraction of the total available power in the cluster, creating a rather uneven situation among them. Consequently, a fairness criterion is introduced, imposing a minimum amount of power allocated to the terminals. A piece-wise linear harvesting circuit is considered at the terminals, with both saturation and a minimum sensitivity, and a constrained version of the precoder is also proposed by solving a non-linear programming problem. A paramount benefit of the constrained precoder is the encompassment of fairness in the power allocation to the different clusters. Moreover, given the polynomial complexity increase of the proposed unconstrained precoder, and the observed linear gain of the system's available sum-power with an increasing number of antennas at the ULA, the use of massive antenna arrays is desirable. [ABSTRACT FROM AUTHOR]

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator. [ABSTRACT FROM AUTHOR]

Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16\thinspace000-bus real-world system model. [ABSTRACT FROM AUTHOR]

In Brazil, agribusiness has a considerable role in the country's GDP. Because of this, the State needs territorial planning to minimize the impacts on natural resources, especially in the Pantanal and Amazon biomes, where agribusiness has expanded. The lower the agricultural diversification, the lower the pattern of land use homogeneity, generally associated with agribusiness, especially when it occupies large areas with more technological productive units. This paper investigates the relationship between spatial diversification patterns and the dynamics of native vegetation in Brazil. We propose a feature engineering and clustering approach for 5570 Brazilian municipalities between 1999 and 2018. It was based on the unsupervised artificial neural network Self-Organizing Map (SOM) to divide the municipalities into homogeneous groups of agricultural products diversity trends. The results were compared with the change in vegetation area using data from the national land use-mapping project called Mapbiomas. The analysis allowed the identification of three different regimes of modification in native vegetation, particularly related to municipalities in Brazil's Midwest and North regions, indicating substantial changes in the Cerrado and Amazon biomes. [ABSTRACT FROM AUTHOR]

The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. First, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy $c$ c -means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets. [ABSTRACT FROM AUTHOR]

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes. [ABSTRACT FROM AUTHOR]

The distribution network has typically been the least observable and most dynamic and locally controlled element in the power grid. Complete information about the network topology is continuously changing and is not always readily available when needed. This makes the phase identification and network rebalancing a hard, costly, and time-consuming task for electric utilities, however, it is of great importance to future grid planning and advanced distribution management system (ADMS) type operation. Phase identification traditionally is executed manually, although there are existing voltage measurement based methods that are not always reliable. This paper develops a machine learning based data mining method for an accurate and efficient phase identification of residential customers in a distribution network by leveraging power consumption data collected through the advanced metering infrastructure (AMI). The proposed method uses a high-pass filter to remove the redundant and irrelevant parts of the power consumption time series, then identifies the residential customers’ phase connectivity by proposing a modified clustering algorithm. Simulation results show the effectiveness of the proposed method in phase identification in both small and large networks and under complete and incomplete data scenarios. [ABSTRACT FROM AUTHOR]

Due to the increasing number of car-centered connected services, making efficient use of limited radio resources is critical in vehicular communications. Hybrid vehicular networks dispose of multiple Radio Access Technologies (RATs) like cellular and vehicle-to-vehicle (V2V) networks, with complementary characteristics that allow for developing smarter network traffic distribution methods. This paper proposes a self-adaptive clustering system for ensuring a suitable trade-off between data aggregation (over the cellular network) and communication congestion due to cluster management (within the V2V network). The system’s algorithms use a distributive justice approach for selecting cluster heads, to improve fairness among car drivers and hence help the social acceptability of self-adaptive clustering. Simulation results show that this approach significantly improves fairness over time without affecting network performance. This solution can thus optimize the usage of radio resources, reducing cellular access costs, without the need for uniformization among different mobile operators’ access plans. [ABSTRACT FROM AUTHOR]