Quantum Entanglement with Self-stabilizing Token Ring for Fault-tolerant Distributed Quantum Computing System

This paper shows how to construct quantum entanglement states of n qubits based on a self-stabilizing token ring algorithm. The entangled states can be applied to the fields of the quantum network, quantum Internet, distributed quantum computing, and quantum cloud. To the best of our knowledge, this is the first attempt to construct quantum entanglement based on the self-stabilizing algorithm. By the quantum circuit implementation based on the IBM Quantum Experience platform, it is demonstrated that the construction indeed can achieve specific n qubit entangled states, which in turn can be used to circulate a token in a quantum network or quantum Internet for building a distributed quantum computing system (DQCS). The built DQCS is fault-tolerant in the sense that it can tolerate transient faults such as occasional errors of entangled quantum states.Comment: 8 pages, 7 figure

The Extended Dijkstra’s-based Load Balancing for OpenFlow Network

This paper proposes load-balancing algorithm on the basis of the Extended Dijkstra’s shortest path algorithm for Software Defined Networking (SDN). The Extended Dijkstra’s algorithm considers not only the edge weights, but also the node weights to find the nearest server for a requesting client. The proposed algorithm also considers the link load in order to avoid congestion. We use Pyretic to implement the proposed algorithm and compare it with related ones under the Abilene network topology with the Mininet emulation tool. As shown by the comparisons, the proposed algorithm outperforms the others in term of the network end-to-end latency, throughput and response time at the expense of a little heavier computation load and more memory usage on the SDN controller

Strategies of Mobile Agents on Malicious Clouds

Cloud computing is a service model enabling resources limited mobile devices to remotely execute tasks on the clouds. The Mobile Agent is a software program on behalf of the software installed in the mobile device to negotiate with other mobile agents in the clouds, which provides a diversity of automated negotiation based applications in Mobile Commences. However, the negotiation plans carried by mobile agents are easily be eavesdropped by the malicious cloud platforms, since the codes of mobile agents are read and executed on the cloud platform. Thus, the sellers can take cheat actions to increase their profits, which is to tailor the negotiation plans to seize buyers’ profits after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations are modeled as a mathematical model, called the Eavesdropping and Resistance of Negotiation (ERN) Game. We develop a simulator to simulate an artificial market for analyzing the behaviors on ERN Game. The simulation results show buyers’ resistances deter sellers from cheating and cooperative strategies are adopted by buyers and sellers

Secure bootstrapping and routing in an IPv6-based ad hoc network

The mobile ad hoc network (MANET), which is characterized by an infrastructureless architecture and multi-hop communication, has attracted a lot of attention recently. In the evolution of IP networks to version 6, adopting the same protocol would guarantee the success and portability of MANETs. In this paper, we propose a secure bootstrapping and routing protocol for MANETs. Mobile hosts can autoconfigure and even change their IP addresses based on the concept of CGA (cryptographically generated address), but they can not hide their identities easily. The protocol is modified from DSR (dynamic source routing) to support secure routing. The neighbor discovery and domain name registration in IPv6 are incorporated and enhanced with security functions. The protocol is characterized by the following features: (i) it is designed based on IPv6, (ii) relying on a DNS server, it allows bootstrapping a MANET with little pre-configuration overhead, so network formation is light-weight, and (iii) it is able to resist a variety of security attacks

Pricing resources in LTE networks throughmultiobjective optimization,”The

The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn, " which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution

Scalable AOI-Cast for Peer-to-Peer Networked Virtual Environments

Networked virtual environments (NVEs) are computer-generated virtual worlds where users interact by exchang-ing messages via network connections. Each NVE user of-ten pays attention to only a limited visibility sphere called area of interest (AOI) where interactions occur. The dis-semination of messages to other users within the AOI (i.e., the AOI neighbors) thus is a fundamental NVE operation referred to as AOI-cast. Existing studies on NVE scala-bility have focused on system scalability, or the ability for the system to handle a growing number of total users, by using multicast or peer-to-peer (P2P) architectures. How-ever, another overlooked, yet important form of scalability relates to the handling of a growing number of users within the AOI (or AOI scalability). In this paper, we propose two AOI-cast schemes, called VoroCast and FiboCast, to im-prove the AOI scalability of P2P-based NVEs. VoroCast constructs a spanning tree across all AOI neighbors based on Voronoi diagrams, while FiboCast dynamically adjusts the messaging range by a Fibonacci sequence, so that AOI neighbors would receive updates at frequencies based on their hop counts from the message originator. Simulations show that the two schemes provide better AOI scalability than existing approaches. 1

Selection strategies for peer-to-peer 3D streaming

In multi-user networked virtual environments such as Sec-ond Life, 3D streaming techniques have been used to pro-gressively download and render 3D objects and terrain, so that a full download or prior installation is not necessary. As existing client-server architectures may not scale easily, 3D streaming based on peer-to-peer (P2P) delivery is recently proposed to allow users to acquire 3D content from other users instead of the server. However, discovering the peers who possess relevant data and have enough bandwidth to answer data requests is non-trivial. A naive query-response approach thus may be inefficient and could incur unnec-essary latency and message overhead. In this paper, we propose a peer selection strategy for P2P-based 3D stream-ing, where peers exchange information on content availabil-ity incrementally with neighbors. Requestors can thus dis-cover suppliers quickly and avoid time-consuming queries. A multi-level area of interest (AOI) request is also adopted to avoid request contention due to concentrated requests. Simulation results show that our strategies achieve better system scalability and streaming performance than a naive query-response approach

ADCA: An Asynchronous Duty Cycle Adjustment MAC Protocol for Wireless Sensor Networks

Abstract--In this paper, we propose an asynchronous duty cycle adjustment MAC protocol, called ADCA, for the wireless sensor network (WSN). ADCA is a sleep/wakeup schedule-based protocol to reduce power consumption without lowering network throughput or lengthening transmission delay. It is asynchronous; it allows each node in the WSN to set its own sleep/wakeup schedule independently. The media access is thus staggered and collisions are reduced. According to the statuses of previous transmissions, ADCA adjusts the duty cycle length for shortening transmission delay and increasing throughput. Simulation results show that ADCA outperforms related ones in terms of energy saving, network throughput and transmission delay

Fingerprint Feature Extraction for Indoor Localization

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods

Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning

Thanks to the advance of novel technologies, such as sensors and Internet of Things (IoT) technologies, big amounts of data are continuously gathered over time, resulting in a variety of time series. A semi-supervised anomaly detection framework, called Tri-CAD, for univariate time series is proposed in this paper. Based on the Pearson product-moment correlation coefficient and Dickey–Fuller test, time series are first categorized into three classes: (i) periodic, (ii) stationary, and (iii) non-periodic and non-stationary time series. Afterwards, different mechanisms using statistics, wavelet transform, and deep learning autoencoder concepts are applied to different classes of time series for detecting anomalies. The performance of the proposed Tri-CAD framework is evaluated by experiments using three Numenta anomaly benchmark (NAB) datasets. The performance of Tri-CAD is compared with those of related methods, such as STL, SARIMA, LSTM, LSTM with STL, and ADSaS. The comparison results show that Tri-CAD outperforms the others in terms of the precision, recall, and F1-score