347 research outputs found
Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation
Wireless network emulators are being increasingly used for developing and
evaluating new solutions for Next Generation (NextG) wireless networks.
However, the reliability of the solutions tested on emulation platforms heavily
depends on the precision of the emulation process, model design, and parameter
settings. To address, obviate or minimize the impact of errors of emulation
models, in this work we apply the concept of Digital Twin (DT) to large-scale
wireless systems. Specifically, we demonstrate the use of Colosseum, the
world's largest wireless network emulator with hardware-in-the-loop, as a DT
for NextG experimental wireless research at scale. As proof of concept, we
leverage the Channel emulation scenario generator and Sounder Toolchain (CaST)
to create the DT of a publicly-available over-the-air indoor testbed for sub-6
GHz research, namely, Arena. Then, we validate the Colosseum DT through
experimental campaigns on emulated wireless environments, including scenarios
concerning cellular networks and jamming of Wi-Fi nodes, on both the real and
digital systems. Our experiments show that the DT is able to provide a faithful
representation of the real-world setup, obtaining an average accuracy of up to
92.5% in throughput and 80% in Signal to Interference plus Noise Ratio (SINR).Comment: 15 pages, 21 figures, 1 tabl
On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses
With the rapid evolution of wireless networking, it becomes vital to ensure transmission reliability, enhanced connectivity, and efficient resource utilization. One possible pathway for gaining insight into these critical requirements would be to explore the spatial geometry of the network. However, tractably characterizing the actual position of nodes for large wireless networks (LWNs) is technically unfeasible. Thus, stochastical spatial modeling is commonly considered for emulating the random pattern of mobile users. As a result, the concept of random geometry is gaining attention in the field of cellular systems in order to analytically extract hidden features and properties useful for assessing the performance of networks. Meanwhile, the large-scale fading between interacting nodes is the most fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel losses in general, and the inevitability of random networks in real-life situations, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the large-scale fading. Therefore, in exact closed-form notation, we generically derived the large-scale fading distributions between a reference base-station and an arbitrary node for uni-cellular (UCN), multi-cellular (MCN), and Gaussian random network models. In fact, we for the first time provided explicit formulations that considered at once: the lattice profile, the users’ random geometry, the spatial intensity, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. Overall, the results can be useful for analyzing and designing LWNs through the evaluation of performance indicators. Moreover, we conceptualized a straightforward and flexible approach for random spatial inhomogeneity by proposing the area-specific deployment (ASD) principle, which takes into account the clustering tendency of users. In fact, the ASD method has the advantage of achieving a more realistic deployment based on limited planning inputs, while still preserving the stochastic character of users’ position. We then applied this inhomogeneous technique to different circumstances, and thus developed three spatial-level network simulator algorithms for: controlled/uncontrolled UCN, and MCN deployments
Airborne Wireless Communication Modeling and Analysis with MATLAB
Over the past decade, there has been a dramatic increase in the use of unmanned aerial vehicles (UAV) for military, commercial, and private applications. Critical to maintaining control and a use for these systems is the development of wireless networking systems [1]. Computer simulation has increasingly become a key player in airborne networking developments though the accuracy and credibility of network simulations has become a topic of increasing scrutiny [2-5]. Much of the inaccuracies seen in simulation are due to inaccurate modeling of the physical layer of the communication system. This research develops a physical layer model that combines antenna modeling using computational electromagnetics and the two-ray propagation model to predict the received signal strength. The antenna is modeled with triangular patches and analyzed by extending the antenna modeling algorithm by Sergey Makarov, which employs Rao-Wilton-Glisson basis functions. The two-ray model consists of a line-of-sight ray and a reflected ray that is modeled as a lossless ground reflection. Comparison with a UAV data collection shows that the developed physical layer model improves over a simpler model that was only dependent on distance. The resulting two-ray model provides a more accurate networking model framework for future wireless network simulations
Robust Sensor Networks in Homes via Reactive Channel Hopping
Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy and assisted living. A key challenge faced in deploying robust wireless sensor networks (WSNs) for home automation applications is the need to provide long-term, reliable operation in the face of the varied sources of interference found in typical residential settings. To better understand the channel dynamics in these environments, we performed an in-depth empirical study of the performance of HANs in ten real-life apartments. Our empirical study leads to several key insights into designing robust HANs for residential environments. For example, we discover that there is not always a persistently good channel over 24 hours in many apartments; that reliability is strongly correlated across adjacent channels; and that interference does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through a small number of channel hops. Based on these insights, we propose Adaptive and Robust Channel Hopping (ARCH) protocol, a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. We evaluate our approach through a series of simulations based on real data traces as well as a testbed deployment in real-world apartments. Our results demonstrate that ARCH can reduce the number of packet retransmissions by a median of 42.3% compared to using a single, fixed wireless channel, and can enable up to a 2.2 X improvement in delivery rate on the most unreliable links in our experiment. Due to ARCH\u27s lightweight reactive design, this improvement in reliability is achieved with an average of 6 or fewer channel hops per link per day
Using Commercial Ray Tracing Software to Drive an Attenuator-Based Mobile WIreless Testbed
We propose and build a prototype architecture for a laboratory-based mobile wireless testbed that uses highly detailed, site-specific channel models to dynamically configure a many-to-many analog channel emulator. Unlike similar systems that have used abstract channel models with few details from the physical environment, we take advantage of commercial ray tracing software and high-performance hardware to make realistic signal power and characteristics predictions in a highly detailed environment. The ray tracing results are used to program a many-to-many analog channel emulator. Using this system, we can conveniently, repeatedly, and realistically subject real wireless nodes to the effects of mobility. We use our prototype system and a detailed CAD model of the University of Maryland campus to compare field test measurements to measurements made from the same devices in the same physical scenario in the testbed. This thesis presents the design, implementation, and validation phases of the proposed mobile wireless testbed
Evaluation of an OPNET Model for Unmanned Aerial Vehicle Networks
The concept of Unmanned Aerial Vehicles (UAVs) was first used as early as the American Civil War, when the North and the South unsuccessfully attempted to launch balloons with explosive devices. Since the American Civil War, the UAV concept has been used in all subsequent military operations. Over the last few years, there has been an explosion in the use of UAVs in military operations, as well as civilian and commercial applications. UAV Mobile Ad Hoc Networks (MANETs) are fast becoming essential to conducting Network-Centric Warfare (NCW). As of October 2006, coalition UAVs, exclusive of hand-launched systems, had flown almost 400,000 flight hours in support of Operations Enduring Freedom and Iraqi Freedom [1]. This study develops a verified network model that emulates UAV network behavior during flight, using a leading simulation tool. A flexible modeling and simulation environment is developed to test proposed technologies against realistic mission scenarios. The simulation model evaluation is performed and findings documented. These simulations are designed to understand the characteristics and essential performance parameters of the delivered model. A statistical analysis is performed to explain results obtained, and identify potential performance irregularities. A systemic approach is taken during the preparation and execution simulation phases to avoid producing misleading results
Sophisticated Batteryless Sensing
Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly
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