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The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, a unified data-driven system to enhance IoT computing at the network edge. The EdgeLSTM leverages the grid long short-term memory (Grid LSTM) to provide an agile solution for both deep and sequential computation, therefore can address important features such as large-scale, variety, time dependency and real time in IoT data. Our system exploits the advantages of Grid LSTM network and extends it with a multiclass support vector machine by rigorous regularization and optimization approaches, which not only has strong prediction capability of time series data, but also achieves fine-grained multiple classification through the predictive error. We deploy the EdgeLSTM into four IoT applications, including data prediction, anomaly detection, network maintenance and mobility management by extensive experiments. Our evaluation results of real-world time series data with different short-term and long-term time dependency from these typical IoT applications show that our EdgeLSTM system can guarantee robust performance in IoT computing. [ABSTRACT FROM AUTHOR]

The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this article, we propose a long short-term memory (LSTM)-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in the IIoT. In a nutshell, the LSTM-NN builds a model on normal time series. It detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of the Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on three real-life datasets that involve both long-term and short-term time dependence. Empirical studies demonstrate that our proposed techniques outperform the best-known competitors, which is a preferable choice for detecting anomalies. [ABSTRACT FROM AUTHOR]

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average. [ABSTRACT FROM AUTHOR]

Broadband mobile networks utilize a radio resource control (RRC) state machine to allocate scarce radio resources. Current implementations introduce high latencies and cross-layer degradation. Recently, the RRC enhancements, continuous packet connectivity (CPC), and the enhanced forward access channel (Enhanced FACH), have emerged in UMTS. We measure the availability and performance of these enhancements on a network serving a market with a population in the millions. We demonstrate that these enhancements offer significant reductions in latency, mobile device energy consumption, and improved end user experience. We develop new over-the-air measurements that resolve existing limitations in measuring RRC parameters. We find CPC provides significant benefits with minimal resource costs, prompting us to rethink past optimization strategies. We examine the cross-layer performance of CPC and Enhanced FACH, concluding that CPC provides reductions in mobile device energy consumption for many applications. While the performance increase of HS-FACH is substantial, cross-layer performance is limited by the legacy uplink random access channel (RACH), and we conclude full support of Enhanced FACH is necessary to benefit most applications. Given that UMTS growth will exceed LTE for several more years and the greater worldwide deployment of UMTS, our quantitative results should be of great interest to network operators adding capacity to these networks. Finally, these results provide new insights for application developers wishing to optimize performance with these RRC enhancements. [ABSTRACT FROM AUTHOR]

With the development of WDM techniques and cloud computing, incorporating the optical backbone into data centers has received extensive attention. Virtual network (VN) planning has been proposed for this new networking paradigm by taking advantage of network virtualization. VN planning is essentially a united virtualization of optical and server resources in a collaborative manner. In this article, we first elaborate the advantages and problems of existing VN planning schemes through a comprehensive survey. Next, we provide a VN planning scheme. The simulation results demonstrate the effectiveness of our strategy, particularly in the context of power outage and evolving recovery. Finally, the future opportunities and challenges are also discussed. [ABSTRACT FROM PUBLISHER]

In this paper, we study the intertwined propagation of two competing "memes" (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI_1I_2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected. [ABSTRACT FROM AUTHOR]