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Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model. [ABSTRACT FROM AUTHOR]

Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient CTL algorithms. However, developing a convergent and accelerated CTL algorithm with accurate representations simultaneously with proper sparsity is an open problem. This article presents a new CTL framework with a $log$ regularizer that can not only obtain accurate representations but also yield strong sparsity. To efficiently address our nonconvex composite optimization, we propose to employ the proximal difference of the convex algorithm (PDCA) which relies on decomposing the nonconvex regularizer into the difference of two convex parts and then optimizes the convex subproblems. Furthermore, we introduce the extrapolation technology to accelerate the algorithm, leading to a fast and efficient CTL algorithm. In particular, we provide a rigorous convergence analysis for the proposed algorithm under the accelerated PDCA. The experimental results demonstrate that the proposed algorithm can converge more stably to desirable solutions with lower approximation error and simultaneously with stronger sparsity and, thus, learn filters efficiently. Meanwhile, the convergence speed is faster than the existing CTL algorithms. [ABSTRACT FROM AUTHOR]

Multiview data processing has attracted sustained attention as it can provide more information for clustering. To integrate this information, one often utilizes the non-negative matrix factorization (NMF) scheme which can reduce the data from different views into the subspace with the same dimension. Motivated by the clustering performance being affected by the distribution of the data in the learned subspace, a tri-factorization-based NMF model with an embedding matrix is proposed in this article. This model tends to generate decompositions with uniform distribution, such that the learned representations are more discriminative. As a result, the obtained consensus matrix can be a better representative of the multiview data in the subspace, leading to higher clustering performance. Also, a new lemma is proposed to provide the formulas about the partial derivation of the trace function with respect to an inner matrix, together with its theoretical proof. Based on this lemma, a gradient-based algorithm is developed to solve the proposed model, and its convergence and computational complexity are analyzed. Experiments on six real-world datasets are performed to show the advantages of the proposed algorithm, with comparison to the existing baseline methods. [ABSTRACT FROM AUTHOR]

With growing data volumes and the scaling of data center clusters, communication resources often become a bottleneck in service provisioning for many MapReduce applications (e.g., training machine learning models). Therefore, data placements that bring data blocks closer to data consumers (e.g., MapReduce applications) are seen as a promising solution. In this article, we propose an efficient data-placement technique that considers network traffic reduction as well as QoS guarantees for the data blocks to optimize the communication resources. We first formulate the joint optimization of the data-placement problem, propose a generic model for minimizing communication costs, and show that the joint data-placement problem is NP-hard. To solve this problem, we propose a heuristic algorithm considering traffic flows in the network topology of data centers by first seeking optimal QoS-aware data placement based on golden division on a Zipflike replica distribution, then transforming the joint data-placement problem into a block-dependence tree (BDT) construction problem, and finally reducing the BDT construction to a graph-partitioning problem. The experimental results demonstrate that our data-placement approach could effectively improve the performance of MapReduce jobs with lower communication costs and less job execution time for big-data processing. [ABSTRACT FROM AUTHOR]

This article investigates the throughput stability of nonsaturated 802.11 networks with distributed coordination function (DCF). We find that this throughput stability exhibits distinct characteristics in two different regions (or scopes) of parameter settings: the maximum stable throughput is higher than the saturation throughput and lasts for a long duration (say, in order of months) in one region, while the former is bounded by the latter in another region. Our core contribution is an innovative approach (called SatOpt partition) that can identify these two regions quickly and accurately, by a comparison between the saturated and optimal attempt rates. Our partition approach is suitable for a wide range of 802.11 DCF contention window settings, while our partition result is unique. Further, for 802.11 DCF standard settings, we predict the maximum stable throughput successfully, and reveal that it is often higher than the saturation throughput. We verify our approach via the widely adopted 802.11 DCF ns2 simulator (mostly with typical 802.11 DCF standard settings). We speculate that this two-region stability property should be inherent for any random-access networks. This suggests that the research on the throughput-stability problem should differentiate regions of parameter settings and failing to do so might produce inconsistent results. This study is expected to take a critical step toward solving the throughput-stability problem. [ABSTRACT FROM AUTHOR]

Optimal computing resource allocation for edge-cloud-assisted Internet of things (IoT) in blockchain network is attracting increasing attention. Auction is a classical algorithm which guarantees that the computing resources are allocated to the buyers of the computing resource. However, the traditional auction algorithm only guarantees the revenue gains for the sellers of the computing resource. How to guarantee the seller and the buyer of the computing resource when both are willing to trade and moreover, bid truthfully, is still an open problem in computing resource trading for edge-cloud-assisted IoT. In this paper, we introduce a broker with sparse information to manage and adjust the trading market. We then propose an iterative double-sided auction scheme for computing resource trading, where the broker solves an allocation problem to determine how much computing resource is traded and designs a specific price rule to induce the buyers and sellers of the computing resource to submit bids in a truthful way. Thus, hidden information can be extracted gradually to obtain optimal computing resource allocation and trading prices. Hence, the proposed algorithm can achieve the maximum social welfare meanwhile protecting the privacies of the buyers and the sellers. Our theoretical analysis and simulations demonstrate that the proposed algorithm is efficient, i.e., it achieves the maximum social welfare. In addition, the proposed algorithm can provide effective trading strategies for the buyers and sellers of the computing resource, leading to the proposed algorithm satisfying incentive compatibility, individual rationality, and budget balance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a method to solve the device-free localization (DFL) problem that is able to detect spatial obstruction via wireless network. A dictionary learning approach with difference of convex (DC) programming and DC algorithm is proposed to indicate target location based on learning data. By measuring the variation in the received signal strength of the wireless links indicating the locations of the obstructions, the physical target in the monitoring area can be estimated through a learned dictionary. We show that the DFL problem can be efficiently formulated as a non-convex optimization problem. We adopt a penalty function called the minimax concave penalty, which possesses good properties in terms of seeking sparsity, and solve the non-convex optimization problem using DC programming. Furthermore, the localization accuracy achieved during the path-tracking task is further improved by the proposed tracking neighborhood rule. The rule provides a solution for increasing the localization accuracy under time-varying conditions generated by sampling channels of sensor networks under noisy conditions. The proposed approach is validated on a real-world dataset and has the potential to be adopted flexibly in DFL applications. [ABSTRACT FROM PUBLISHER]

The virtual machine (VM) allocation problem in cloud computing has been widely studied in recent years, and many algorithms have been proposed in the literature. Most of them have been successfully applied to batch processing models such as MapReduce; however, none of them can be applied to streaming workflow well because of the following weaknesses: 1) failure to capture the characteristics of tasks in streaming workflow for the short life cycle of data streams; 2) most algorithms are based on the assumptions that the price of VMs and traffic among data centers (DCs) are static and fixed. In this paper, we propose a streaming workflow allocation algorithm that takes into consideration the characteristics of streaming work and the price diversity among geo-distributed DCs, to further achieve the goal of cost minimization for streaming big data processing. First, we construct an extended streaming workflow graph (ESWG) based on the task semantics of streaming workflow and the price diversity of geo-distributed DCs, and the streaming workflow allocation problem is formulated into mixed integer linear programming based on the ESWG. Second, we propose two heuristic algorithms to reduce the computational space based on task combination and DC combination in order to meet the strict latency requirement. Finally, our experimental results demonstrate significant performance gains with lower total cost and execution time. [ABSTRACT FROM PUBLISHER]

We propose a new optimal data placement technique to improve the performance of MapReduce in cloud data centers by considering not only the data locality but also the global data access costs. We first conducted an analytical and experimental study to identify the performance issues of MapReduce in data centers and to show that MapReduce tasks that are involved in unexpected remote data access have much greater communication costs and execution time, and can significantly deteriorate the overall performance. Next, we formulated the problem of optimal data placement and proposed a generative model to minimize global data access cost in data centers and showed that the optimal data placement problem is NP-hard. To solve the optimal data placement problem, we propose a topology-aware heuristic algorithm by first constructing a replica-balanced distribution tree for the abstract tree structure, and then building a replica-similarity distribution tree for detail tree construction, to construct an optimal replica distribution tree. The experimental results demonstrated that our optimal data placement approach can improve the performance of MapReduce with lower communication and computation costs by effectively minimizing global data access costs, more specifically reducing unexpected remote data access. [ABSTRACT FROM PUBLISHER]