Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware ...approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.
Initially appearing as an abstract object frequently used in math and physics, tensors have been attracting increasing interest in a broad range of research fields, such as engineering and data ...science. However, a few studies have addressed their application in wireless scenarios. In this paper, we investigate the wide applications of tensor techniques with an emphasis on the tensor voting method, which serves as an artificial intelligence approach for automatic inference and perceptual grouping. To illustrate the efficiency of the tensor voting approach, we tackle the tracking problem of inferring human mobility traces, which can provide key location information of networking objects. The trace inferring problem is considered under the circumstance that the recorded location information exhibits missing data and noise. Based on the tensor voting theory, we propose a sparse tensor voting algorithm and an implementation scheme with computational efficiency. The model is constructed based on the geometric connections between the input signals and encodes the structure information in the tensor matrix. The missing location information and noise can be distinguished via tensor decomposition. Once the trace information has been completed, further analysis of the inferred trace can be performed based on feature extraction to differentiate different objects. Moreover, we propose several feature extraction methods to characterize the inferred trace, including the scale invariant feature obtained from the fractal analysis. The proposed methods for trace completion and pattern analysis are applied to real human mobility traces. The results show that our proposed approach effectively recovers human mobility trace from the incomplete and noisy data input, and discovers meaningful patterns of inferred traces from various objects.
The emergence of smart meters has enabled the new energy efficiency services in an automatic fashion. With the information and communication technology, the smart meters are devised to gather and ...communicate the information of electricity suppliers and residential electricity consumers to ameliorate the efficiency of power distribution as well as the sustainability of the power resources. Due to the enormous amount of electricity consumers, the analysis of the big data produced by the smart meters is a crucial challenge faced by the electricity companies and researchers. In this paper, we analyze the big data based on the smart meter readings collected in the Houston area. The statistical properties of the data is investigated such that the behaviors of the consumers can be better understood. Moreover, the kernel PCA analysis and non-parametric clustering of the data gives a comprehensive guidance on what are the potential clusters of the customers and how to allocate the power more efficiently.
With the advances of the information and communications technology, and smart meters in particular, fine grained user electricity usage of households is available for analyzing electricity usage ...behaviors. The information makes it possible for utility companies to provide differentiated user services from the time-of-use perspective, i.e., different pricing for users based upon when and how users consume power. In this paper, we present a methodology on differentiated user services based on extracted characteristic consumer load shapes (usage profiles as a function of time) from a large smart meter data set. We identify distinct user subgroups based upon their actual historic usage patterns, which are represented by the proposed electricity usage distributions. Since the big electricity user data cover millions of users and for each user the data are multi-dimensional and in fine-time granularity, we thus propose a sublinear algorithm to make the computation of the differentiated user service model efficient. The algorithm requests an input of only a small portion of users, and a sublinear amount of the electricity data from each of these selected users. We prove that the algorithm provides performance guarantees. Our simulated evaluation demonstrates the effectiveness of our algorithm and the differentiating user service model.
Device-to-device (D2D) communication has seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, we propose a social-aware ...approach for optimizing D2D communication by exploiting two layers: the social network and the physical wireless layers. First we formulate the physical layer D2D network according to users' encounter histories. Subsequently, we propose an approach, based on the so-called Indian Buffet Process, so as to model the distribution of contents in users' online social networks. Given the social relations collected by the Evolved Node B (eNB), we jointly optimize the traffic offloading process in D2D communication. In addition, we give the Chernoff bound and approximated cumulative distribution function (CDF) of the offloaded traffic. In the simulation, we proved the effectiveness of the bound and CDF. The numerical results based on real traces show that the proposed approach offload the traffic of eNB's successfully.
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can ...be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for various applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying structure of simulated data.
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can ...be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for various applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying structure of simulated data.
As the growth of wireless network, tremendous interests have been focused on statistically tracking the user equipment as well as the performance evaluation of motion tracking. In this paper, we ...tackle the problem of inferring human mobility trace under the circumstance that the recorded location information exhibits missing data. Based on the tensor voting theory, we propose an efficient sparse tensor voting algorithm and a specified implementation scheme. The model is constructed based on the geometric connections between the input signals and encodes the structure information in the tensor matrix. Thus, the computation is carried out in the form of matrix, which reduces the computation load since most the calculation involves only with matrix addition and multiplication. The proposed method is applied to real human mobility trace. The results show that our proposed approach effectively recovers human mobility trace from the incomplete data input.
Receiving a gift can create an impulse to reciprocate, even when doing so may be inefficient and potentially harmful to a third party. This paper provides a theoretical framework for a pure gift ...effect on reciprocity impulses and experimental evidence that such an effect exists: that is, a gift receiver will favor an actual gift giver over an intended gift giver, even if the intended gift giver incurred the same costs and signaled the same intention to give. This result contrasts with the predictions of existing theories on social preferences. We also show that the pure gift effect is present even when it leads to a less efficient outcome, or when the gift is given without the expectation of future returns. Our findings suggest that when reciprocating a gift becomes socially inefficient, it may be more advantageous to guard against gift receiving or to keep donations “secret” than to try to control the intent to give.
•This paper provides both a theoretical framework and experimental evidence for a pure gift effect.•A gift receiver will favor an actual gift giver over an intended gift giver even if the intended gift giver incurred the same costs and signaled the same intention to give.•The pure gift effect is present even if favoring the actual gift giver leads to an inefficient outcome•The pure gift effect is present even if the gift is given without expectation of future returns.
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