Test-bed Implementation Research Articles

Systematic Review of Best Management Practice Implementation for Agricultural Phosphorus Management in Florida: A 30-Year Overview

Highlights BMPs have been proven to be efficient in managing P loads in Florida. BMP efficiencies vary between 20% and 80%, with few higher than 50%. Integration of modeling strategies and field demonstrations could improve BMP efficiencies. Further implementation of testbeds and pilot studies could improve adoption and enhance our understanding of BMP efficiency. Abstract. In Florida, the implementation of best management practices (BMPs) has significantly reduced phosphorous (P) loads from the Everglades Agricultural Area (EAA) into the Lake Okeechobee watershed over the past two decades. While the removal of over 6,165 metric tons of total phosphorus entering the Everglades Protection Area is a notable achievement, some freshwater sources continue to exceed established limits (Naja et al., 2017), demanding a critical examination of BMP efficiency at managing low P concentrations. In this systematic literature review, we analyzed peer-reviewed journal manuscripts published over the past 30 years, focusing on the intersection of phosphorus management, water, Florida, and the environment. The dataset synthesized from our review provides insights into the efficiency of BMPs in terms of P management over time. Our findings show a predominant focus on stormwater treatment areas (STAs) and constructed wetlands as BMPs with demonstrated good efficiency. However, the variability in reported efficiencies underscores the complexity of phosphorus pollution and its impacts. Treatment trains ranged from 20% to 39% for the lower range of efficiency in phosphorus removal and from 60% to 79% for the higher range of efficiency. Focusing on strategies with higher P load reduction efficiencies could enhance future management strategies in Florida. Field-based pilot studies with well-defined control settings can facilitate long-term evaluations of P management programs that allow the implementation of BMPs. Similarly, the evaluation of new technologies, including materials, precision-smart practices, and the integration of modeling strategies with field-scale studies suggest a promising approach to engaging stakeholders in achieving higher BMP efficiencies. This systematic review highlights current BMP strategies that have demonstrated high P load reduction efficiency. There is a need for continued research that simultaneously assesses strategies to reduce P pollution before it forms and employs a multidimensional approach to P management. This approach should integrate multiple successful BMPs through field and modeling strategies. Keywords: , Agriculture, Best Management Practices (BMP), Florida, Non-point source pollutants, Phosphorus loads.

Seamless Wireless Communication Platform for Internet of Things Applications

The rapid growth of the Internet of Things (IoT) devices resulted in the proliferation of wireless technologies to cater to their increasing data rate requirements and support multiple applications. However, such ever-increasing wireless technologies present numerous challenges such as incompatible wireless standards, increased energy consumption, and insecure communication. The traditional gateways proposed in the literature suffers from limitations such as computational complexity, resource requirements, increased cost, and device size. We vision an era of seamless wireless communication to alleviate the aforementioned challenges in IoT applications. through three inter-dependent functionalities namely detection and identification of wireless technologies, energy-efficient transmit power control, and secure end-to-end communication. To prove the concept, a new gateway is proposed to achieve these three functionalities with only physical layer measurements so that the different communication protocols in the higher layers can be avoided. Novel schemes are conceptualized for resource-limited seamless IoT applications. Moreover, the conceptual seamless IoT platform is validated through software-based computer simulation and software-defined radio-based testbed implementation. The preliminary analysis demonstrates that the proposed platform has great potential in advancing seamless IoT applications.

Taxonomic Exploration of Healthcare IoT: Challenges, Solutions, and Future Frontiers

An Internet of things (IoT) ecosystem is a fast-developing network in which users can connect a heterogeneity of physical and virtual devices, including customized healthcare areas. As medical resources are scarce, populations are aging with chronic diseases and require remote monitoring, medical expenses are rising, and telemedicine is being demanded in developing nations, the IoT is an attractive topic in healthcare. Through the IoT, people can enjoy better health and diminish pressure on sanitary systems. In this study, previously published studies in Healthcare IoT (HIoT) systems are detailed, analyzed, and taxonomically classified. By categorizing the articles according to the types of HIoT systems, we dispense a detailed taxonomical study. In addition, different evaluation methodologies, tools, and metrics are discussed, along with their advantages and disadvantages. The studies indicate that power management, trust, privacy, fog computing, and resource management are among the open issues. The future of the Internet includes tactile networks, social networks, big data analytics, software-defined networking, network function virtualization, the Internet of nano things (IoNT), and blockchain. It would be beneficial to study and research HioT systems further in terms of interoperability, the implementation of real-world test beds, scalability, and mobility.

Near-Optimal and Collaborative Service Caching in Mobile Edge Clouds

In this paper, we study the problem of service caching in an MEC network under a service market with multiple network service providers competing for both computation and bandwidth resources in terms of Virtual Machines (VMs) in the MEC network. We first propose an Integer Linear Program (ILP) solution and a randomized rounding algorithm, for the problem without VM sharing among different network service providers. We then devise a distributed and stable game-theoretical mechanism for the problem with VM sharing among network service providers, with the aim to minimize the social cost of all network service providers, through introducing a novel cost sharing model and a coalition formation game. We also analyze the performance guarantee of the proposed mechanism, Strong Price of Anarchy (SPoA). We thirdly consider the cost- and delay-sensitive service caching problem with temporal VM sharing, and propose a mechanism with provable SPoA. We finally evaluate the performance through extensive simulations and a real world test-bed implementation. Experimental results demonstrate that the proposed algorithms outperform existing approaches by achieving at least 40% lower social cost via service caching and resource sharing among different network service providers.

Calibration reinforcement regularizations for optimized snapshot spectral imaging.

Optical coding is a fundamental tool in snapshot computational spectral imaging for capturing encoded scenes that are then decoded by solving an inverse problem. Optical encoding design is crucial, as it determines the invertibility properties of the system sensing matrix. To ensure a realistic design, the optical mathematical forward model must match the physical sensing. However, stochastic variations related to non-ideal characteristics of the implementation exist; therefore, these variables are not known a priori and have to be calibrated in the laboratory setup. Thus, the optical encoding design leads to suboptimal performance in practice, even if an exhaustive calibration process is carried out. This work proposes an algorithm to speed up the reconstruction process in a snapshot computational spectral imaging, in which theoretically optimized coding design is distorted by the implementation process. Specifically, two regularizers are proposed that perform the gradient algorithm iterations of the distorted calibrated system in the direction of the originally, theoretically optimized system. We illustrate the benefits of the reinforcement regularizers for several state-of-the-art recovery algorithms. For a given lower bound performance, the algorithm converges in fewer iterations due to the effect of the regularizers. Simulation results show an improvement of up to 2.5dB of peak signal-to-noise ratio (PSNR) when fixing the number of iterations. Furthermore, the required number of iterations reduces up to 50% when the proposed regularizers are included to obtain a desired performance quality. Finally, the effectiveness of the proposed reinforcement regularizations was evaluated in a test-bed implementation, where a better spectral reconstruction was evidenced when compared with a non-regularized system's reconstruction.

On the Feasibility of Remote Driving Applications Over mmWave 5G Vehicular Communications: Implementation and Demonstration

This paper presents a study conducted for the feasibility validation of a remote driving application, which is one of the use cases of 5G Vehicle-to-everything (V2X), over millimeter-wave (mmWave) 5G New Radio (NR) vehicular communications. First, the overall network architecture of a typical mmWave 5G NR communication system and design details of essential system components for remote driving applications are provided. Then, we discuss key technologies, including beam switching, automatic gain control, uplink transmit power control, and hybrid automatic repeat request with soft combining, enabling the system to offer reliable communication links for remote driving support. Lastly, we describe the implementation and deployment of testbeds and provide the experimental results obtained from field trials. The field trial results reveal that leveraged by the key enabling technologies and mmWave providing a wider spectrum and shortened latency, the mmWave 5G NR vehicular communication system has the capability to provide broadband, reliable, and low-latency communication links for the realization of remote driving applications, which eventually paves the road towards expanding the applicability of mmWave communication systems.

Contention-Based Proportional Fairness (CBPF) Transmission Scheme for Time Slotted Channel Hopping Networks

Time slotted channel hopping (TSCH) is a mediumaccess control (MAC) protocol introduced in IEEE802.15.4e standard, addressing low-power requirements of the Internet of Things and low-power and lossy networks. The 6TiSCH operation (6top) sublayer of IEEE802.15.4e defines the schedule that includes sleep, transmit, and receive routines of the nodes. However, the standard does not specify the design of the schedule. In this article, we propose a contention-based proportional fairness transmission scheme for TSCH networks to maximize the system throughput addressing the fair allocation of resources to the nodes. We propose a convex programming-based method to achieve the fairness and throughput objectives. We model TSCH MAC as a multichannel slotted ALOHA and analyze it for a schedule given by the 6top layer. Performance metrics, such as throughput, delay, and energy spent per successful transmission, are derived and validated through simulations and real-time testbed implementations.

Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

A top–down approach for building realistic reference scenarios and simulation framework for LTE C-V2X communications

Many vehicle-to-everything (V2X) applications and use cases require their feasibility to be simulated, tested, and validated in realistic traffic scenarios and under various network conditions before real-time testbed implementation. As cellular-V2X (C-V2X) becomes a superior technology for future connected and autonomous vehicles, the need for a simulation framework, which integrates traffic and network simulators with a realistic channel model, becomes more evident. The challenge is to overcome existing simulation platforms’ weaknesses and improve simulation results’ accuracy while preserving flexibility with manageable implementation complexity. This paper proposed a top–down approach for building reference scenarios with macroscopic and microscopic layers, which interweaves traffic, network, and channel simulators. The basis for the proposed simulation framework is realistic scenario data, which provides the input to the road traffic simulator and the radio channel simulator. The core of the whole simulator is the network simulator interacting with the two other simulators via dedicated interfaces. The road traffic simulator generates the vehicles’ positions and is forwarded to the network simulator, which requests the radio channel simulator for the path loss values on the transmitter–receiver (TX-RX) link. As a proof of concept, the simulation results focused on the load and interference analysis in the absence and presence of V2X traffic.

IoT-based communal garbage monitoring system for smart cities

In Malaysia, approximately 38,000 metric tons of garbage are generated due to human daily activities. This is due to the growing population in the urban area, hence increasing the tendency of overflowing garbage due to the insufficient space in the garbage container. In addition, the tight schedule of the garbage collection allows the spreading of the toxic odor as the garbage start to rotten up, hence leading to air pollution. Therefore, a systematic waste management system is important to provide a healthy and clean environment to the community. In this work, a communal garbage monitoring system has been developed to notify the administrator of the status of the container. Besides monitoring the level of garbage, the system is also designed to monitor the temperature, humidity, and air quality of the garbage container. These monitored data will be uploaded at the cloud for real-time monitoring. Compared to the other work, a real test-bed implementation has been conducted considering different types of waste including food waste, paper, bottles and metal; to determine the accuracy of the developed system. The results show that the system has high reliability and high accuracy with 96% for food waste and 98% for other types of waste.

X-ray Compton backscattering imaging via structured light.

Compton backscattering imaging (CBI) is a technique that uses ionizing radiation to detect the presence of low atomic number materials on a given target. Unlike transmission x-ray imaging, the source and sensor are located on the same side, such that the photons of interest are scattered back after the radiation impinges on the body. Rather than scanning the target pixel by pixel with a pencil-beam, this paper proposes the use of cone-beam coded illumination to create the compressive x-ray Compton backscattering imager (CXBI). The concept was developed and tested using Montecarlo simulations through the Geant4 application for tomography emissions (GATE), with conditions close to the ones encountered in experiments, and posteriorly, a test-bed implementation was mounted in the laboratory. The CXBI was evaluated under several conditions and with different materials as target. Reconstructions were run using denoising-prior-based inverse problem algorithms. Finally, a preliminary dose analysis was done to evaluate the viability of CXBI for human scanning.

Design and Evaluation of a Heterogeneous Lightweight Blockchain-Based Marketplace.

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.

Development of real-time indoor human tracking system using LoRa technology

<p>Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios.</p>

Real-time traffic sign detection and recognition using Raspberry Pi

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

Designing Fine-Grained Access Control for Software-Defined Networks Using Private Blockchain

Emerging next-generation Internet yields proper administration of a wide-ranging dynamic network to assist rapid ubiquitous resource accessibility, whilst providing higher channel bandwidth. Since its inception, the traditional static network infrastructure-based solutions involve manual configuration and proprietary controls of networked devices. It then leads to improper utilization of the overall resources, and hence experiences various security threats. Although transport layer security (TLS)-based solution is presently advocated in the said framework, it is vulnerable to many security threats like man-in-the-middle, replay, spoofing, privileged insider, impersonation, and denial-of-service attacks. Moreover, the current settings of the said tool do not facilitate any secure and reliable mechanisms for data forwarding, application flow routing, new configuration deployment, and network event management. Also, it suffers from the single point of controller failure issue. In this article, we propose a new private blockchain-enabled fine-grained access control mechanism for the SDN environment. In this regard, attribute-based encryption (ABE) and certificate-based access control protocol are incorporated. This proposed solution can resist several well-known security threats, and alleviate different system-level inconveniences. The formal and informal security inspections and performancewise comparative study of the proposed scheme endorse better qualifying scores as compared to the other existing competing state-of-the-art schemes. Besides, the experimental testbed implementation and blockchain simulation show the implementation feasibility of the proposed mechanism.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

A Framework for Exploring Churches/Monuments/Museums of Byzantine Cultural Influence Exploiting Immersive Technologies in Real-Time Networked Environments

The unique art that was developed in Byzantine times is widely accepted as a precursor to the Renaissance and is still evident in monuments either from or influenced by the Byzantium. Visiting such a site is a unique experience due to the lavishly painted interior for both tourists and scholars. Taking advantage of the emerging 5G technologies, cloud/fog computing, and Augmented/Mixed Reality mechanisms, a common smart device (e.g., smartphone, tablet) could be employed to give a better experience to the end-users. The proposed framework is intended to provide visitors with interpretative information regarding the visited monuments and their paintings. Under the framework introduced in this paper, camera input is uploaded to a cloud/fog computing infrastructure where appropriate algorithms and services provide monument and painting recognition and the mobile application retrieves and projects the related information. In addition, the designed immersive user interfaces assist visitors in contributing in cases of monuments and paintings for which no available information exists. This paper presents the state of the art in approaches for immersive experiences in Digital Culture, reviews the current image recognition approaches and their suitability for Byzantine paintings, proposes interaction techniques appropriately designed for observation and interaction of paintings, and discusses the overall framework architecture that is needed to support the described functionality while stressing the challenging issues of the above aspects thus paving the road for future work and providing guidelines for a test-bed implementation.

Internet of Things-Based Smart Farming Monitoring System for Bolting Reduction in Onion Farms

According to the Pakistan Bureau of Statistics, Pakistan is amongst the top ten onion-producing countries in the world. Though in Pakistan, most of the districts of Khyber Pakhtunkhwa produce onions, Malakand division lonely contributes 60% of the total onion production of the country. In onion farming, bolting is an insidious phenomenon that occurs in onion plants due to fluctuations in environmental factors such as temperature, humidity, and light intensity. Due to bolting, the flowering stem of an onion plant is produced before the crop is harvested, resulting in a poor-quality harvest and yield. Therefore, from a farmer’s perspective, it is highly desirable to monitor and control the environmental factors to avoid bolting. In this paper, we propose and design a new prototype, namely, a smart farming monitoring system (SFMS) for bolting reduction, which is based on the generic three-layered IoT architecture. By using IoT (Internet of things) technology and careful remote monitoring, a more favorable environment can be provided to reduce and avoid onion bolting. To analyze the efficacy and performance of the proposed SFMS, a real test-bed implementation was carried out. The SFMS prototype was installed both in the open and in a greenhouse environment to monitor onion crops. Based on the data received via sensors, the percentage of onion bolting was recorded as 16.7% in the open environment while 3% in the closed environment. In the closed environment, optimal temperature, humidity, and light intensity were provided to the onion crops using the SFMS. For this reason, the percentage of onion bolting was reduced from 16.7% to 3%, consequently yielding better onion production. Moreover, the SFMS is a low-cost, easy-to-install solution that is developed with locally available hardware and resources, and we believe that this new solution can transform conventional onion farming into a more productive and convenient smart farming in the region.

Implementation and Evaluation of Physical, Hybrid, and Virtual Testbeds for Cybersecurity Analysis of Industrial Control Systems

Industrial Control Systems are an essential part of our daily lives and can be found in industries such as oil, utilities, and manufacturing. Rapid growth in technology has introduced industrial components with network capabilities that allow them to communicate with traditional computer networks, thus increasing their exposure to cyber-attacks. Current research on Industrial Control Systems suffer from lack of technical information as these systems are part of critical infrastructures. To overcome this, researchers have employed different types of testbeds to develop their mechanisms of cyber-attack detection and prevention. This manuscript describes, implements, and evaluates physical, hybrid, and virtual application of a clean water supply system developed for cybersecurity research. The results show that physical testbeds allow an understanding of the behaviour and dynamics of control components like sensors and actuators, which might be affected by external influences such as noise, vibration, temperature, and non-ideal device behaviour. Although, hybrid testbeds reduce the cost of implementation, they ignore the physical dynamics of the system as explained above. Virtual testbeds are the cheapest option in comparison with physical and hybrid testbeds; however, they provide a limited view of the control system operation that could have negative consequences when developing a detection/prevention system.

Universal inverters for flexible power electronic test-bed implementation

As the rapid development of electric grids, power electronic equipment has been widely deployed in modern power systems. The flexible operation of power electronic equipment has significantly complicated the grid operation, which enables different types of grid operation conditions that need to be emulated in the lab to facilitate the testing and development of advanced control diagram. Note that the fully controllable power electronic converters make it feasible to establish and simulate different operation conditions, which greatly diversifies the flexibility of conventional test-beds used for power electronics and power system research. In this paper, a generic three-leg converter is used and further functional expansion is implemented to make it a controllable unit for simulating different grid assets, including photovoltaics (PVs), batteries, etc. Hence, a universal inverter-based grid simulator is developed. Advanced control diagram is designed to achieve the flexible operation of the power-electronics-based grid simulators. Rather than relying on conventional synchronous-frame-based proportional-integral (PI) controllers, proportional-resonant (PR) controllers in stationary frame is used to avoid the coordinate transformation from a-b-c framework to d-q-0 framework. In other words, the coordinate transformation from stationary frame to rotational frame is eliminated. Test cases are established to test and validate the developed universal grid simulator.