Real Test Bed Research Articles

An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters.

This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.

Air-to-ground real-time multimedia delivery: A multipath testbed

In this work, we focus our attention on real-time multimedia flows from Unmanned Aerial Vehicless (UAV) to the ground, presenting and analysing the data collected in field trials during a real testbed. The objective is assessing whether a video feed of reasonable quality can be provided to the pilot of an UAV to enable Beyond Visual Line of Sight (BVLoS) operations, by exploiting the multiple cellular operators available in the area. Three cellular networks have been jointly used in a multihoming/multipath setup, leveraging the variable coverage offered in both urban and suburban environments. Taking into account both Quality of Service (QoS) and Quality of Experience (QoE) metrics, the target parameters measured in this testbed are: latency, packet error rate, and video quality, which accounts for frames integrity, continuity, and fluidity. Data collected on the field allow to evaluate both QoS and QoE in the presence of a multipath architecture, showing how the latter, in the presence of network diversity, offer the possibility to improve the QoE at the receiver. We also design a framework to characterize the error model and to map it into a QoE model, therefore providing an analytical characterisation of a multipath channel.

Subgraph matching over graph federation

Many real-life applications require processing graph data across heterogeneous sources. In this paper, we define the graph federation that indicates that the graph data sources are temporarily federated and offer their data for users. Next, we propose a new framework FedGraph to efficiently and effectively perform subgraph matching, which is a crucial application in graph federation. FedGraph consists of three phases, including query decomposition, distributed matching, and distributed joining. We also develop new efficient approximation algorithms and apply them in each phase to attack the NP-hard problem. The evaluations are conducted in a real test bed using both real-life and synthetic graph datasets. FedGraph outperforms the state-of-the-art methods, reducing the execution time and communication cost by 37.3 × and 61.8 ×, respectively.

Evaluation of a Blockchain-Enabled Resource Management Mechanism for NGNs

A new era in ICT has begun with the evolution of Next Generation Networks (NGNs) and the development of human-centric applications. Ultra-low latency, high throughput, and high availability are a few of the main characteristics of modern networks. Network Providers (NPs) are responsible for the development and maintenance of network infrastructures ready to support the most demanding applications that should be available not only in urban areas but in every corner of the earth. The NP’s must collaborate to offer high- quality services and keep their overall cost low. The collaboration among competitive entities can in principle be regulated by a trusted 3rd party or by a distributed approach/technology which can guarantee integrity, security, and trust. This paper examines the use of blockchain technology for resource management and negotiation among NPs and presents the results of experiments conducted in a dedicated real testbed. The implementation of the resource management mechanism is described in a Smart Contract (SC) and the testbeds use the Raft and the IBFT consensus mechanisms respectively. The goal of this paper is two-fold: to assess its performance in terms of transaction throughput and latency so that we can assess the granularity at which this solution can operate (e.g. support resource re-allocation among NPs on micro-service level or not) and define implementation-specific parameters like the consensus mechanism that is the most suitable for this use case based on performance metrics.

Cluster-Based Antiphishing (CAP) Model for Smart Phones

Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

Device-independent cellular-based indoor location tracking using deep learning

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Cellular-based systems are a good candidate to provide such ubiquitous service due to their wide availability worldwide. One of the main barriers to the accuracy of such services is the large number of models of cell phones, which results in variations of the measured received signal strength (RSS), even at the same location and time. In this paper, we propose OmniCells++, a deep learning-based system that leverages cellular measurements from one or more training devices to provide consistent performance across unseen tracking phones. Specifically, OmniCells++ uses a novel approach to multi-task learning based on LSTM encoder–decoder models that allows it to learn a rich and device-invariant RSS representation without any assumptions about the source or target devices. OmniCells++ also incorporates different modules to boost the system’s accuracy with RSS relative difference-based features and improve the deep model’s generalization and robustness.Evaluation of OmniCells++ in two realistic testbeds using different Android phones with different form factors and cellular radio hardware shows that OmniCells++ can achieve a consistent median localization accuracy when tested on different phones. This is better than the state-of-the-art indoor cellular-based systems by at least 148%.

Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

Long-term rate control for concurrent multipath real-time video transmission in heterogeneous wireless networks

Concurrent multipath transmission provides an effective solution for streaming high-quality mobile videos in heterogeneous wireless networks. Rate control is commonly adopted in multimedia communication systems to fully utilize the available network bandwidth. This paper proposes a novel rate control for concurrent multipath video transmission. The existing rate control algorithms mainly adapt bit rate in the short-term pattern, i.e., without considering the long-term video transmission quality. We propose a long-term rate control scheme that takes into account the status of both the transmission buffer and video frames. First, a mathematical model is developed to formulate the non-convex problem of long-term quality maximization. Second, we develop a dynamic programming solution for online encoding bit rate control based on buffer status. The performance evaluation is conducted in a real test bed over LTE and Wi-Fi networks. Experimental results demonstrate that the proposed long-term rate control scheme achieves appreciable improvements over the short-term rate control schemes in terms of video quality and delay performance.

Dual Radio Networks: Are Two Disjoint Paths Enough?

Newer applications for Wireless Sensor Networks, such as for video and audio applications, require higher data rates compared to traditional low data rate sensor network applications. Platforms with two radios were proposed to address these new classes of applications: each radio can send data on different routes to achieve high data rates and energy efficiency. In this work, we show that in heterogeneous dual radio networks the use of two disjoint paths is not enough to achieve maximal throughput. We present the novel disjoint paths with the same parity problem and how our system called Two Path Protocol (TPP) can improve throughput in dual radio networks. Our algorithm maintains energy efficiency and uses all the hardware resources available to improve end-to-end data delivery. We compare our design with FastForward, the state-of-the-art protocol for dual-radio in a real testbed. Experimental results confirm that our approach doubled the throughput getting very close to the maximum theoretical limit value.

The LSST DESC DC2 Simulated Sky Survey

Abstract We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N-body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin’s LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep area of approximately 300 deg2, as well as a deep drilling field of approximately 1 deg2. We simulate 5 yr of the planned 10 yr survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the data set to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic test bed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time domain cosmology.

AI-Based Resource Management in Beyond 5G Cloud Native Environment

5G system and beyond targets a large number of emerging applications and services that will create extra overhead on network traffic. These industrial verticals have aggressive, contentious, and conflicting requirements that make the network have an arduous mission for achieving the desired objectives. It is expected to get requirements with close to zero time latency, high data rate, and network reliability. Fortunately, a ray of hope comes shining the way of telecom providers with the new progress and achievements in machine learning, cloud computing, micro-services, and the ETSI ZSM era. For this reason there is a colossal impetus from industry and academia toward applying these techniques by creating a new concept called CCN environment that can cohabit and adapt according to the network and resource state, and perceived KPIs. In this article, we pursue the aforementioned concept by providing a unified hierarchical closed-loop network and service management framework that can meet the desired objectives. We propose a cloud-na-tive simulator that accurately mimics cloud-native environments, and enables us to quickly evaluate new frameworks and ideas. The simulation results demonstrate the efficiency of our simulator for parroting the real testbeds in various metrics.

Autonomous Allocation and Scheduling of Minimal Cell in 6TiSCH Network

6TiSCH standardization helps the Industrial Internet of Things (IIoT) to achieve reliable and timed data delivery. It also deals with minimal resource allocation during network formation. However, faster network formation using minimum resources remains an active research issue. 6TiSCH minimal configuration standard (6TiSCH-MC) recommends to use only one cell per slotframe known as the minimal cell for all the nodes to transmit their network bootstrapping traffic. It is observed that, including 6TiSCH-MC, the existing schemes did not use all the available cells, and so, all the physical channels at the timeslot where this minimal cell resides, i.e., at timeslot zero. It results in underutilization of channel resources, and thus, higher network formation time. To leverage the available cells at timeslot zero of each slotframe, and thus to improve the network formation performance, an autonomous allocation and scheduling of minimal cell (TACTILE) is proposed. The main challenge is to utilize the available cells at timeslot zero as there is a minimal cell already scheduled for network bootstrapping. To address this issue, TACTILE distributes the location of the minimal cell as per nodes' EUI64 addresses along the different physical channels followed by scheduling them intelligently to avoid de-synchronization among nodes. Combined with Markov chain-based theoretical analysis, evaluation of TACTILE is done on the FIT IoT-LAB real testbed. The testbed results show that TACTILE can achieve 87% and 42% improvements in terms of joining time and energy consumption, respectively, compared to 6TiSCH-MC.

Sensors and Communication Simulation for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, and poorly repeatable. As a solution, in this paper we propose an agent-based simulation platform, implemented through a micro service architecture, which may simulate UTM information sources, such as flight plans, telemetry messages, or tracks from a surveillance network. The final objective of this simulator is to use these information streams to perform a system-level evaluation of UTM systems both in the pre-flight and in-flight stages. The proposed platform, with a focus on simulation of communications and sensors, allows to model UTM actors’ behaviors and their interactions. In addition, it also considers the manual definition of events to simulate unexpected behaviors/events (contingencies), such as communications failures or pilots’ actions. In order to validate our architecture, we implemented a simulator that considers the following actors: drones, pilots, ground control stations, surveillance networks, and communications networks. This platform enables the simulation of the drone trajectory and control, the C2 (command and control) link, drone detection by surveillance sensors, and the communication of all agents by means of a mobile communications network. Our results show that it is possible to truthfully recreate complex scenarios using this simulator, mitigating the disadvantages of real testbeds.

Blockchain-Based Fair and Secure Certified Electronic Mail Without a TTP

Certified mail is a special postal service that provides the sender with a proof that the mail was successfully received by the other party. However, when it comes to certified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electronic</i> mail (CEM), there has not been a widely accepted protocol yet. Most of the existing solutions rely on a Trusted Third Party (TTP) in order to achieve fairness, which can become a bottleneck and lead to more charges and computations. Other solutions that do not rely on a TTP have a relatively high computation and communication overhead. Blockchains have allowed the creation of TTP-free protocols for fair exchange; however, the existing Blockchain-based protocols are either too expensive or do not meet the necessary fairness and confidentiality guarantees. In this work, we have developed two TTP-free Blockchain-based protocols for certified electronic mail that achieve the required security properties of strong fairness, non-repudiation, timeliness, and confidentiality. The first protocol uses simple bitcoin transactions for a single receiver, whereas the second one implements smart contracts for one or multiple receivers. We have analyzed and compared both protocols to existing TTP-free Blockchain solutions and have demonstrated their superior properties and cost. Furthermore, we have implemented the two protocols in a realistic testbed, demonstrating the viability of our approach.

CPS Device-Class Identification via Behavioral Fingerprinting: From Theory to Practice

Cyber-Physical Systems (CPS) utilize different devices to collect sensitive data, communicate with other systems, and monitor essential processes in critical infrastructure applications. However, in the ecosystem of CPS, unauthorized or spoofed devices may danger or compromise the performance and security of the critical infrastructure. The unauthorized and spoofed devices may include tampered pieces of software or hardware components that can negatively impact CPS operations or collect vital CPS metrics from the network. Such devices can be outsider or insider threats trying to impersonate other real CPS devices via spoofing their legitimate identifications to gain access to systems, steal information, or spread malware. Device fingerprinting techniques are promising approaches to identify unauthorized or illegitimate devices. However, current fingerprinting solutions are not suitable as they disrupt critical real-time operations in CPS due to the nature of their extensive data analysis or too much overhead on the devices' computational resources. To address these concerns, in this work, we propose STOP-AND- FRISK (S&F), a novel fingerprinting framework to identify CPS device classes and complement traditional security mechanisms in CPS. S&F is based on a secure challenge/response mechanism that analyzes the behavior of the CPS devices at both the hardware and OS/kernel levels. Specifically, the proposed novel mechanism combines system and function call tracing techniques, signal processing, and hardware performance analysis to create specific device-class signatures. Then, the signatures are correlated against known behavioral ground-truth to identify the device types. To test the efficacy of S&F extensively, we implemented a realistic testbed that included different classes of CPS devices with a variety of computing resources, architectures, and configurations. Our experimental results reveal an excellent rate on the CPS device-class identification. Finally, extensive performance analysis demonstrates that the use of S&F yields minimal overhead on the CPS devices' computing resources.

Integrated resource management pipeline for dynamic resource-effective cloud data center

Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

WITHDRAWN: A study on single frequency network on high speed train

Abstract This paper presents a comprehensive study on millimetre wave (mmWave)-based mobile hotspot network (MHN) system for high-speed train (HST) communications, including system design, field trial, channel modeling based on measurement campaign, simulation and validation. Firstly, the physical layer specification and single frequency network (SFN) which includes MHN enhanced design (MHN-E) and the overview of the MHN system is provided. Secondly, For MHN conventional (MHN-C) and MHN-E systems, the prototypes and the overall design is presented and the MHN-C prototype and demonstrations with the MHN-E prototype are analysed with the help of results of field trial which conducted at Seoul subway line 8.Thirdly, the paper presents a characteristics of a measurement campaign investigating channel which is based on rural HST environment. Lastly, the performance of the MHNE system is validated by the help of computer simulation. The simulation is done at the speed of 500 km/h under the QuaDRiGa channel model which is also known as the good method for validation of HST communication systems, since it is impractical by real testbeds now. The simulation results revealed that with a peak data rate which exceeding 5 Gbps at a speed of 500 km/h the MHN-E system can able to provide a broadband mobile wireless backhaul (MWB).

Pushing the Limits of Transmission Concurrency for Low Power Wireless Networks

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

Simulation-Driven Platform for Edge-Based AAL Systems

The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which supports both Edge and Cloud Computing paradigm to develop innovative AAL services in scenarios of different scales. E-ALPHA flexibly combines Edge, Cloud or Edge/Cloud deployments, supports different communication protocols, and fosters the interoperability with other IoT platforms. Moreover, the simulation-based design helps in preliminary assessing (i) the expected performance of the service to be deployed according to the infrastructural characteristics of each specific small, medium and large scenario; and (ii) the most appropriate applications/platform configuration for a real deployment (kind and number of involved devices, Edge- or Cloud-based deployment, required connectivity type, etc). In this direction, two different use cases modeled according to realistic input (coming from past experience involving real testbed) are shown in order to demonstrate the potentials of the proposed simulation-driven AAL platform.

Enabling Multicast Slices in Edge Networks

Telecommunication networks are undergoing a disruptive transition toward distributed mobile edge networks with virtualized network functions (VNFs) [e.g., firewalls, intrusion detection systems (IDSs), and transcoders] within the proximity of users. This transition will enable network services, especially Internet-of-Things (IoT) applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this article, we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into integer linear programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a nontrivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on reinforcement learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real testbed. The experimental results demonstrate that the proposed algorithms are promising.