This work presents two explanatory mathematical models explaining how network traffic features that display Gaus-sian tendencies in single devices and small networks aggregate to alpha-stable processes in larger networks. The first model shows how self-similarity originates from an impulsive-noise-based representation of individual processes. A second model uses renewal processes to justify impulsive process aggregation to alpha-stable or Gaussian end states and permits estimating network traffic alpha-stable rates of convergence. We develop a model based on this first method to empirically validate this aggregation approach.
Antenna consolidation on the topside of shipboard platforms reduces the amount of different antennas needed to perform various capabilities at different frequencies. Parabolic antenna stacking consolidates antenna capabilities and saves space on shipboard platforms. This paper presents the design and simulated radiation pattern results of a tri-band parabolic stacked antenna that reduces the number of antennas required on shipboard platforms. This tri-band parabolic stacked antenna is focused on providing a Q, X and Ka band capable solution on a single antenna pedestal.
We describe an optical computation technology that can be used for optical communication, sensing and imaging. We demonstrate intelligent optical computation for online signal processing in optical coherence tomography.
Applying the Cognitive Radio paradigm to short-range systems inside buildings, it is intended to control two major drawbacks. The first one refers to controlling the efficiency in the use of the spectrum in interior scenarios, and the second is to overcome the inconvenience in the quality of service that arises when there are large quantities of short-range systems interfering with each other. Estimating the effective spectrum available inside a smart building is a big challenge. To do so, we must consider the deployment of users of the primary system, whose band would be reused. Large amounts of spectral opportunities have been found for the primary band, especially on the lower floors of the building. However, when considering the effect of shadowing on the interior of the building, it is shown that effective spectrum availability decreases.
Bitcoin is a well-known cryptocurrency in which records of transactions are maintained by a P2P network to create a distributed ledger. Due to the complex nature of maintaining highly efficient, transparent and speed transactions between nodes, security is one of the primary concerns of this system. The most prominent modern-day attacks to this system are the selfish mining and double spending attacks. This survey is organized around the aspects pertaining to countermeasures of selfish mining and double spending attacks. We selected a total of 20 primary studies from recent years as a result of a systematic analysis approach. In this study we will aim to classify, analyze, and evaluate these proposed methods in order to create a secure blockchain system. These countermeasures are being developed and are being experimented with in order to mitigate and/or even eliminate these attacks from occurring. These proposed frameworks and research papers will outline the keys to improving the blockchain architecture with code scripts and design parameters to increase the functionality of the blockchain. We aim to identify implications of these countermeasures to address vulnerabilities in the blockchain network for future research on this topic.
With the proliferation of the delay sensitive applications and services in cellular networks, the outage probability becomes an important metric that should be minimized to support the low latency requirement of the 5G network. In this paper, the analytical model is proposed to calculate the system outage probability as a closed form expression under the Rayleigh fading channel for the NOMA downlink system. We utilize the model to obtain the optimum power allocation that minimizes the system outage probability as a closed form expression. The accuracy of the proposed analytical model and optimum power allocation is validated by the Monte Carlo simulations. The numerical results show that the outage probability depends on the power allocation and the outage probability of OMA with the fractional power allocation is lower than NOMA with the optimum power allocation. The results indicate that the trade-off between the outage and spectral efficiency in NOMA should be carefully controlled to meet higher throughput and lower latency objectives of 5G.
Internet of Things (IoT) applications are anticipated to bloom in the future, and two popular industry areas related to Internet of Things (IoT) are smart houses and smart factories. To make a home or factory smart, hundreds or thousands of sensors need to work and communicate together with simple, secure and stable connectivity. Thus, the design of smart homes and smart factories relies on wireless sensor networks, whose transmission is often characterized to be sporadic. Classical approaches to multiuser communications rely on tight time synchronization achieved by employing coordinating infrastructure, but in a mass sensor transmission system, sensors may communicate with each other directly without relying on coordinating mechanisms. Our study explores the possibilities of simple and reliable multiuser detection in DS-CDMA system for few data and short distance mass sensor connectivity with joint synchronization of data. Furthermore, we discussed a scenario where spreading sequences are overused. We show that our system has strong potential for excellent mass sensors detection performance without synchronizing infrastructure.
Recently, multiple research teams aimed to address the scalability and fault-tolerance issues of Network Functions (NFs) by implementing a storage system (or distributed shared object) to share state across NF instances. Each system demonstrated promising performance under specific scenarios. However, there are many more technical challenges to be addressed to build a production-ready storage system for NFs.In this paper, we enumerate these challenges, explain how some of them were addressed by previous systems, and discuss alternative promising solutions to tackle them. Most challenges remain as open problems, while others deserve more thorough study. The first goal of the paper is to provide a broad research agenda around this topic, by discussing more problems than solutions. Moreover, we advocate that the first step towards a production-ready system is to build a realistic yet extensible benchmarking tool to deploy, test, and analyze "network function storages" comprehensively. Toward this end, we present our tool - Network Function Storage Benchmarking (NFSB). Finally, we present some preliminary results using NFSB.
Multipath TCP (MPTCP) can improve overall throughput of an end-to-end connection by leveraging different network paths. However, the heterogeneity of these paths can significantly hamper MPTCP’s performance. In this paper, we propose to send acknowledgments (ACKs) along the lowest-latency path. This can help improve the performance of MPTCP when subflow throughput is constrained by packet loss by reacting to loss events faster. An active probing module is also developed to dynamically select the lowest-latency path against the potential change of path condition. Experiments demonstrate that overall throughput improvement generally ranges from 10% to 50%.
We consider an $M/M/1$ update-and-decide system where Poisson distributed decisions are made based on the received updates. We propose to characterize the freshness of the received updates at decision epochs with Age upon Decisions (AuD). Under the first-come-first-served policy (FCFS), the closed form average AuD is derived. We show that the average AuD of the system is determined by the arrival rate and the service rate, and is independent of the decision rate. Thus, merely increasing the decision rate does not improve the timeliness of decisions. Nevertheless, increasing the arrival rate and the service rate can decrease the average AuD efficiently.
Routing and Spectrum Allocation (RSA) is the key problem in Spectrum-Sliced Elastic Optical Path (SLICE) networks. The difficulty of RSA problem lies on three factors: first, the allocated sub-carriers have to be continuously available along each established spectrum path; second, the allocated sub-carriers have to be consecutive in the spectrum domain as implied by the OFDM technology of SLICE networks; and third, sub-carriers of spectrum paths sharing the same fiber have to be separated by the guard-band that is determined at run-time. As a decision problem, the RSA has been proven to be NP-Complete. In this work, we study an optimization version of the RSA problem with the goal of maximizing the revenue from the accommodated requests. We present Integer Liner Programming (ILP) formulations for the problem, namely Routing and Spectrum Allocation with Optimal Revenue (ROR). Also, we present detailed design of a framework that utilizes techniques of relaxation, decomposition and auxiliary graphs, which can be employed to obtain a near optimal solution that has a per-instance guarantee on the closeness to the optimal solution.
E-science supports inter-disciplinary research that requires processing highly data-intensive workflows. These workflows require compute resources for processing, and storage resources to save data generated on computation. Additionally, there may be a need to make this data available to researchers at distinct and disparate locations, which requires network resources. Current-generation networks cannot scale to meet the demands of tomorrow's unpredictable application workflow scenarios. Elastic Optical Network (EON) is a cutting-edge technology which supports more data and faster data, without the need to construct larger networks. The challenge of joint scheduling of computational, storage and EON network resources to e-science workflows is a complex problem known as co-scheduling. In this paper, heuristics are proposed to investigate sequential and parallel co-scheduling. Simulation results are presented to demonstrate the effectiveness of the proposed approaches.
A major challenge for network providers is to design a network that can cater to differentiated quality-of-service (QoS) for different traffic classes in a cost-effective manner. Traditional differentiated services (“DiffServ”) is for IP networks where differentiation is done in IP packet headers through code points. However, this does not by itself land in architecting a network from a traffic engineering point of view. In an SDN environment, with more control over flows or a collection of flows, differentiated QoS traffic classes can be considered and accordingly a network can be optimized. In this work, we consider network optimization in a software-defined network (SDN) environment where differentiated QoS traffic is provided through different latency bounds. We present a mixed integer linear programming (MILP) formulation for this problem. The study shows that our approach can maintain more stringent QoS requirement by reducing the value of maximum allowable latency without a significant increment in bandwidth cost.
Applications of the Kappa distribution, which is used to describe the particle velocity distribution when a system is in thermal anti-equilibrium, to statistical mechanics and high frequency (HF) communication are considered. In particular, the tail distribution of the sum of Kappa random variables (RVs) is studied. First, an approximation for the sum of unequally weighted, uncorrelated Kappa RVs is found that provides results for the tail distribution of that sum. Correlated Kappa RVs are then examined by using the Cholesky decomposition to obtain similar results. Simulation results are shown to agree with this development.
We propose a scheme for calculation of indoor evacuation routes of a single-floor building in the event of a fire that uses selection of a potentially safe exit before evacuation starts. We refer to potentially safe exits to those that have high probability of being accessible at evacuation time. The scheme pre-calculates whether an exit may be reached by an occupant before being reached by the fire. Among those exits, the one with the shortest distance to the occupant is selected. This exit pre-selection improves evacuation success ratio by avoiding destination changes during evacuation. This approach is applicable to a building where the floor plan is known and analyzable. With such an evacuation routing scheme, we study the applicability of our approach to cases where the floor plan has not been characterized and yet we may be able to perform route calculation in a fast manner. To do so, we use machine learning with data obtained from a floor plan where the evacuation success ratio has been analyzed and apply it on the new floor plan. This approach indicates floor plan similarities and it is used to rapidly estimate evacuation routes with high probability of a successful evacuation. We show how floor similarity accuracy estimation increases with the use of data from an increasing number of analyzed floor plans.
The main utility of blockchain networks stem from their ability to offer a trusted platform for execution of processes. The default blockchain implementation, where an immutable log of all transactions is maintained by every participant, is i) inefficient, and ii) does not scale well. This paper outlines several strategies to improve the scope and scale of processes that can be executed in a blockchain.
Modern computing devices such as smartphones and personal computers provide multiple network interfaces such as WiFi and 3G/LTE cellular network. Multipath TCP (MPTCP) is a commonly proposed method of aggregating the bandwidth of these interfaces. However, how MPTCP affects the quality of experience of existing services, especially to variances in bandwidth and latency among the individual paths over the wireless networks is not well-known. In this work, we explore the quality of service (QoS) and quality of experience (QoE) of adaptive video streaming using MPTCP over wireless network given its vast popularity and significant bandwidth demand. Unlike prior works, we conduct systematic measurements over three mobile network operators, AT&T, Verizon Wireless (VzW), and T-Mobile, along with WiFi. Based on extensive measurements, we show that MPTCP can improve the QoS and QoE of video streaming only if the network interfaces have the roughly similar bandwidth and latency. Our studies also show that MPTCP can perform worse than TCP in case of extreme differences between the network interfaces.
Statistical level crossings in a communications paradigm, are typically used in conjunction with estimating a signal's probability of outage over a given channel model. Here the level crossing is examined in the frequency domain of a signal within the context of estimating the Signal-to-Noise Ratio (SNR) for linear digital signals. It is shown within that when compared to the Mean-Squared-Error estimator, which uses the same information to calculate the estimate, the Level Crossing approach performs better in reasonable to high SNR and can be cheaper in terms of computation expense to calculate.
Distributed Denial of Service (DDoS) attack is the most common type of attack faced by today's data centers (DC). Such attacks can have a devastating impact on the system as it consumes resources like network bandwidth, hard disk storage, and CPU processing resources. As a consequence, the legitimate customers face more service blocking due to a major portion of the resources being occupied by the illegitimate traffic generated by the attackers. In this paper, we proposed a novel monitoring scheme based on the sliding window to detect and prevent the DDoS attack in DCs that serve enterprise customers that has low computational complexity. Compared to a benchmark scheme (without attack monitoring and preventing), our scheme ensures service provisioning for the legitimate customers with no false alarm. We also measure the robustness of our scheme in terms of the time taken to detect and prevent attack traffic by varying the traffic intensities of illegitimate traffic. Simulation results show that our scheme can successfully detect the attack even if the attack traffic intensity is not too much higher than the projected legitimate traffic intensity.
Unmanned aerial vehicles (UAVs) are now widely used as backup base stations for the areas which lack of wire-less/cellular access. Since UAV does not depend on fundamental infrastructure, it plays an important role in emergency response and search & rescue. In the prior studies of the UAV-aided wireless coverage extension problem, it typically considers an outdoor scenario with Air-to-Ground path loss model. In this paper, we specify the problem with the use case of UAV-aided emergency rescue. In the new problem formulation, both indoor and outdoor path loss models are considered and the goal is to find an efficient deployment of minimum number of UAVs that guarantees the connection requirements. To solve this problem, we propose a heuristic approach which contains genetic based algorithm to arrange UAVs. During evaluation, our approach is compared with the brute-force search on randomly simulated emergencies. The results show that our approach could find efficient solution with much lower computation.
