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In order to effectively adjust reservoir heterogeneity and further exploit the remaining oil, a new type of low-viscosity gel was prepared by adding a regulating agent, retarder, and reinforcing agent on the basis of a polymer + Cr3+ crosslinking system. The new gel has the advantages of low initial viscosity, a slow gel formation rate, and high strength after gel formation. The effectiveness of the gel was verified through three-layer core displacement experiments, and the injection scheme was optimized by changing the slug combination of the polymer and the gel. The results showed that the gel can effectively block the high-permeability layer and adjust reservoir heterogeneity. An injection of 0.1 pore volume (PV) low-initial-viscosity gel can improve oil recovery by 5.10%. By changing the slug combination of the gel and polymer, oil recovery was further increased by 3.12% when using an injection of 0.07 PV low-initial-viscosity gel +0.2 PV high-concentration polymer +0.05 PV low-initial-viscosity gel +0.5 PV high-concentration polymer. [ABSTRACT FROM AUTHOR]

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively. [ABSTRACT FROM AUTHOR]

Recently, a considerable research interest has grown up in the millimetre wave wireless system as the most promising technologies in the next generation communication. Since high-frequency channels of the millimetre wave are easily attenuated in space, beamforming technology relying on the massive multi-input-multi-output system is introduced to transmit the millimetre wave in a very narrow directional beam, so as to greatly improve the transmit efficiency. Then a challenging problem lies in that how to optimise the overall throughput by allocating the antenna resources to different mobile users in the massive MIMO antenna system. In this study, the authors handle such a difficult problem in two different cases. They first begin with the one-direction case, i.e. all sub-arrays are deployed in several parallel rows along the edge of a rectangle antenna array. They decompose the problem and solve it gradually. Then they generalise the authors' result to the two-dimensional case, where the sub-arrays can be deployed in orthogonal directions. They apply the similar scheme, decompose the problem and solve each sub-problem progressively. Both NP-hard problems are solved with time efficient approximation algorithms. Simulation results demonstrate the efficiency of the proposed algorithms in different cases. [ABSTRACT FROM AUTHOR]

Wireless charging technology has drawn great attention of both academia and industry in recent years, due to its potential of significantly improving the system performance of sensor networks. The emergence of an open-source experimental platform for wireless rechargeable sensor networks, Powercast, has made the theoretical research closer to reality. This pioneering platform is able to recharge sensor nodes much more efficiently and allows different communication protocols to be implemented upon user's demands. Different from the RFID-based model widely used in the existing works, Powercast designs the charger and sink station separately. This leads to a new design challenge of cooperatively deploying minimum number of chargers and sink stations in wireless rechargeable sensor networks. Such a co-deployment issue is extremely challenging, since the deployments of chargers and sink stations are coupled and each subproblem is known to be NP-hard. The key to the design is to understand the intrinsic relationship between data flow and energy flow, which is interdependent. In this article, we tackle this challenge by dividing it into two subproblems and optimizing charger and sink station deployment iteratively. Specifically, we first transform each subproblem to a max-flow problem. With this, we are able to select chargers or sink stations according to their contributions to the total flow rate. We design greedy-based algorithms with a guaranteed worst-case bound ln R ξ for the subproblems of charger deployment and sink station deployment, respectively. Further, we address the original problem by designing an iterative algorithm that solves two subproblems alternatively to achieve a near optimal performance. We corroborate our analysis by extensive simulations under practical coefficient settings and demonstrate the advantage of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication has recently attracted much research attention because of its potential to increase the capacity of cellular networks. Most existing works aim to maximise the overall system throughput (systemcentric), which ignores the actual traffic demands of D2D users. In this study, the authors consider user-centric relay assisted D2D communications where D2D users have different evaluations for the significance of every unit of increased data rate. By considering the traffic demands of D2D users, the authors propose a Vickrey-Clarke-Groves auction based relay allocation mechanism (ARM) in which every D2D user submits a bid to the basestation (BS). The submitted bids indicate D2D users' valuation on every unit of the increased data rate. The BS then allocates relays to D2D users by maximising the social welfare of D2D users while maintaining a predefined data rate requirement for cellular users. A payment scheme to charge D2D users for using relays is designed, and the authors show that the auction is truthful. The authors also extend the results to a general case and provide a general ARM accordingly. Extensive simulation results are provided to demonstrate the performance of the proposed mechanisms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), trap coverage has recently been proposed to trade off between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large-scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, practical issues, such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment, are still left untouched. In this article, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. We design Localized Trap Coverage Protocol as the localized implementation of TCO. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution. [ABSTRACT FROM AUTHOR]

A mobile sensor is used to cover a number of points of interest (PoIs), where dynamic events appear and disappear according to the given random processes. The sensor, which is of sensing range r, visits the PoIs in a cyclic schedule and gains information about any event that falls within its range. We consider the temporal dimension of the sensing as given by a utility function, which specifies how much information is gained about an event as a function of the cumulative sensing or observation time. The quality of monitoring (QoM), i.e., the fraction of information captured about all events, depends on the speed of the sensor and has been analyzed in an earlier paper for different utility functions. The prior work, however, does not consider the energy of motion, which is an important constraint for mobile sensor coverage. In this paper, we analyze the expected Information captured Per unit of Energy consumption (IPE) as a function of the event type (in terms of the utility function), the event dynamics, and the speed of the mobile sensor. Our analysis uses a realistic energy model of motion, and it allows the sensor speed to be optimized for information capture. The case of multiple sensors will also be discussed. Extensive simulation results verify and illustrate the analytical results. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Many real-world systems are composed of interdependent networks that rely on one another. Such networks are typically designed and operated by different entities, who aim at maximizing their own payoffs. There exists a game among these entities when designing their own networks. In this paper, we study the game investigating how the rational behaviors of entities impact the system robustness. We first introduce a mathematical model to quantify the interacting payoffs among varying entities. Then we study the Nash equilibrium of the game and compare it with the optimal social welfare. We reveal that the cooperation among different entities can be reached to maximize the social welfare in continuous game only when the average degree of each network is constant. Therefore, the huge gap between Nash equilibrium and optimal social welfare generally exists. The rationality of entities makes the system inherently deficient and even renders it extremely vulnerable in some cases. We analyze our model for two concrete systems with continuous strategy space and discrete strategy space, respectively. Furthermore, we uncover some factors (such as weakening coupled strength of interdependent networks, designing a suitable topology dependence of the system) that help reduce the gap and the system vulnerability. [ABSTRACT FROM AUTHOR]

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results. [ABSTRACT FROM AUTHOR]