Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Artificial immune system based wastewater parameter estimation

The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed by principal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the output parameters are successfully predicted using the AIS model with real data. The classifier's performance ratios are studied separately using the coefficient of determination (R-2) and the mean squared error of prediction (MSEP), and their rates are given in this study

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios

Elastic hybrid MAC protocol for wireless sensor networks

The future is moving towards offering multiples services based on the same technology. Then, billions of sensors will be needed to satisfy the diversity of these services. Such considerable amount of connected devices must insure efficient data transmission for diverse applications. Wireless sensor network (WSN) represents the most preferred technology for the majority of applications. Researches in medium access control (MAC) mechanism have been of significant impact to the application growth because the MAC layer plays a major role in resource allocation in WSNs. We propose to enhance a MAC protocol of WSN to overcome traffic changes constraints. To achieve focused goal, we use elastic hybrid MAC scheme. The main interest of the developed MAC protocol is to design a medium access scheme that respect different quality of services (QoS) parameters needed by various established traffic. Simulation results show good improvement in measured parameters compared to typical protocol

Developing Effective Fraud Detection Methods for Online Auction

[[abstract]]The past decade has witnessed the rapid growth of online auctions. However, the low cost and anonymity in joining online auctions provided an easy path for fraudsters. The simple binary reputation system promoted by the auction site is clearly not enough to protect consumers from fraud. In view of this, many fraud detection methods have been proposed. Nevertheless, there are still many weaknesses needed to be improved. To help secure the online trading environment, this study aims at developing more effective methods to identify the fraudsters in online auctions. First, a novel selection method is proposed for deriving a concise attribute set used to build efficient detection models, which allow a reduction in detection costs while improving detection accuracy. In addition, a two-stage detection procedure is proposed wherein multiple mutual-complement models are combined for promoting overall detection accuracy. To evaluate the proposed methods, actual auction transaction histories were collected for testing. The experimental results show that these methods can outperform those in the previous work.[[notice]]補正完

Goods Relocation Management in City Fashion Retailers with an Incentive System

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Supply chain integration in the industry 4.0 era: a systematic literature review: Integração da cadeia de suprimentos na era industrial 4.0: uma revisão sistemática da literatura

From an Industry 4.0 perspective, supply chain actors (suppliers, manufacturers, retailers, and third-party logistics operators) are integrated into a collaborative network based on information-sharing to improve the overall supply chain performance. Real data can be captured and systematically processed into information, hence dealing with uncertainty. Poor integration may lead to supply chain disruptions. This paper examines the role of Industry 4.0 (I4.0) for integrating the supply chain, to which a systematic literature review (SLR) has been applied. First, according to the research questions, the I4.0 technologies adopted for supply chain integration were identified. Second, the approaches for integrating the supply chain at I4.0 were examined and classified by strategy (vertical, horizontal, and end-to-end integration). Third, the functional and cross-functional approaches for supply chain integration at I4.0 were also examined. Finally, it was discussed which traditional SCI approaches can be upgraded to the Industry 4.0 era and the future research directions

Congestion-Aware Routing and Fuzzy-based Rate Controller for Wireless Sensor Networks

In this paper, congestion-aware routing and fuzzy-based rate controller for wireless sensor networks (WSNs) is proposed. The proposed method tries to make a distinction between locally generated data and transit data by using a priority-based mechanism which provides a novel queueing model. Furthermore, a novel congestion-aware routing using greedy approach is proposed. The proposed congestion-aware routing tries to find more affordable routes. Moreover, a fuzzy rate controller is utilized for rate controlling which uses two criteria as its inputs, including congestion score and buffer occupancy. These two parameters are based on total packet input rate, packet forwarding rate at MAC layer, number of packets in the queue buffer, and total buffer size at each node. As soon as the congestion is detected, the notification signal is sent to offspring nodes. As a result, they are able to adjust their data transmission rate. Simulation results clearly show that the implementation of the proposed method using a greedy approach and fuzzy logic has done significant reduction in terms of packet loss rate, end-to-end delay and average energy consumption