Empowering the 6G Cellular Architecture with Open RAN

Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracing novel cellular architectures by transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.Comment: This paper is part of the IEEE JSAC SI on Open RAN. Please cite as: M. Polese, M. Dohler, F. Dressler, M. Erol-Kantarci, R. Jana, R. Knopp, T. Melodia, "Empowering the 6G Cellular Architecture with Open RAN," in IEEE Journal on Selected Areas in Communications, doi: 10.1109/JSAC.2023.333461

The Human in the loop in Cyber-Physical Systems: the case of Building Automation

Context: The world is facing environmental challenges due to carbon dioxide emissions. Building energy consumption accounts for thirty to forty-five per cent of global energy consumption. Changing these figures is imperative for achieving environmental sustainability. Building Automation Systems (BAS) can be considered a type of Cyber-Physical Systems (CPS) that have the objective of increasing energy efficiency while maximising human comfort. Problem: Automated systems usually do not consider human e↵ective participation as a tool that can be used to achieve the system’s goals Solution: Humans can assume several roles in the available building automation control loops. Building operators determine operating rules; building users can be the source of data used for automated decisions and also the system may require their actions to change the building environment. Gains or losses can be introduced in a BAS operation if humans are considered components of the system. To the best of our knowledge, no studies can be found that show evident gains or losses of integrating the human-in-theloop in system design. To assess the impact of having humans performing clear and predefined roles in a BAS Cyber-Physical System (CPS) operation, we implemented a BAS case study. Results: The initial results show that when the BAS consider humans more than CPS plant’s elements, the BAS is more energy efficient while providing conditions that promote the user’s health and productivity. With the experience gained with this work it will be possible to build in the future more resilient and e↵ective participatory BAS

Reducing the cost of operational water on military bases through modeling, optimization, and control

Military municipal water systems provide safe and clean water to the surrounding community while also supporting the intense and often unpredictable training schedules of the tenant units. Much like their civilian counterparts, military water systems are also consumers of great amounts of energy and capital. As a part of the Army Net Zero program in 2011, an annual water inventory conducted on eight U.S. Army installations concluded that consumption was 5.5 billion gallons. Using the Environmental Protection Agency’s average national estimate of 1,500 kWh of energy consumed for every 1,000 gallons of treated water, it is readily apparent that the department of defense is a heavy consumer of both water and energy. Because the scale of the military’s usage is so vast, so too is their waste. Waste in water systems is common and commonly neglected, as many were initially constructed decades ago and the commodity that they transport is relatively inexpensive. However, recent droughts affecting regions of the United States highlighted the need to conserve and avoid waste, regardless of the commodity price. The efficiency of water systems is highly dependent upon developing accurate models and using those models to accurately deal with disturbances such as demand and chlorine concentration. This work extends water distribution system modeling, optimization, and control to a military setting where constraints are tighter for resiliency purposes, demands are often unpredictable, and saving money and water improves defense capabilities. First, a discretized nonlinear, equation based model of a known system at an existing U.S. Army installation that accurately predicts system behavior under typical demand considerations. The model is calibrated for accuracy using actual system data from a military installation and employed in a nonlinear optimization program to study reduction of costs, minimizing waste, and improvements in energy efficiency. Demand profiles were constructed from residential data and scaled to better represent demand on military bases. With very little adjustment, this model can be used to optimize similar systems in the military inventory. Water and energy savings exceed 10% in the optimized system, which predicts the Army could save greater than $1.5 million per year in the continental United States if rigorous optimization was conducted on storage and pumping at every base. It is shown that a reduced order empirical model is a viable alternative to the computationally expensive equation based approach. The empirical model is used to implement model predictive control, providing the system protection against large and unpredictable disturbances. This method adds an additional manipulated variable, chlorine injection, to ensure efficient constraint compliance. Experimental results show this method further supports the aforementioned savings in the optimized system alone, while efficiently handling disturbances. This research closes previous gaps in research, particularly on military installations. First, it serves to minimize the system volume, or excess water on hand, while meeting all demands and strict system constraints dictated by resiliency and emergency preparedness. Secondly, this work uses a nonlinear model predictive control structure to deal with large and unpredictable disturbances that occur uniquely on military installations. The feedforward control action integrated into the controller is particularly effective at minimizing disturbances on inlet concentration.Chemical Engineerin

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Circular Economy of Advanced Prefabricated Buildings

This PhD documents the design, construction and assessment of a Circular economy building: The Legacy Living Lab. L3 is now an operational Curtin University - off campus - building located in Fremantle and it leading the field in circular economy in construction by incorporating 28 industry partners to merge industry academia and society. The results of this PhD include a circular economy index and seven peer reviewed publications

Virtualization-Based Resilience Approaches for Industrial Control Systems

Industrial Control Systems (ICS) and their components perform cyber-physical functions. In the context of critical infrastructure, these functions are vital to modern life. Programmable Logic Controllers (PLCs) are prominently found in ICS environments and execute the operational logic of the system. The continued escalation of cyberattacks targeting ICS and their PLCs serves as motivation for increasing system resilience. This dissertation analyzes domain cyber-threats and demonstrates novel approaches which utilize virtualization, containerization, input/output multiplexing, cryptographic attestation, software defined networking, security orchestration, and PLC runtimes to advance PLC trust and resilience while facilitating integration into past, present, and future systems. The research approaches were proven using physical ICS testbed environments with experimentation results showcasing enhanced control system trust and resilience

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

A Survey of Smart Grid Systems on Electric Power Distribution Network and Its Impact on Reliability

This paper presents an excerpt of a more comprehensive survey of smart grid systems on electric power distribution networks and its impact on reliability. The survey was carried out as part of the feasibility study in Nigeria to determine its enhance-ability on the smartness of a conventional (traditional) distribution network. A smart grid is not a single technology but multiplex technologies in which the combination of different areas of engineering, communication and energy management systems are done. Consequently, a comprehensive review of various approaches and their impact on reliability of the network is presented. Furthermore, this paper introduces the smart grid technology and its features, reliability impacts and emerging issues and challenges that arise from the smart grid system applications. The benefit of this comprehensive survey is to provide a reference point for educational advancement on the recently published articles in the areas of smart grid systems on electric power distribution network as well as to stimulate further research interest