Scholarly Works, Electrical and Computer Engineering

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  • Measuring Broadband America: A Retrospective on Origins, Achievements, and Challenges
    Burger, Eric W.; Krishnaswamy, Padma; Schulzrinne, Henning (ACM, 2023-04)
    The "Measuring Broadband America" program, run by the United States Federal Communications Commission (FCC), continually measures and releases data on the performance of consumer broadband access networks in the US. This paper presents a retrospective on the program, from its beginnings in 2010 to the present. It also reviews the underlying measurement approaches, philosophies, distinguishing features, and lessons learned over the program's duration thus far. We focus on fixed broadband access since it is the program component with the longest history. We also discuss future directions and challenges.
  • Covert and Quantum-Safe Tunneling of Multi-Band Military-RF Communication Waveforms Through Non-Cooperative 5G Networks
    Alwan, Elias; Volakis, John; Islam, Md Khadimul; De Silva, Udara; Madanayake, Arjuna; Sanchez, Jose Angel; Sklivanitis, George; Pados, Dimitris A.; Beckwith, Luke; Azarderakhsh, Reza; Muralkrishan, Madhuvanti; Rastogi, Rishabh; Hore, Aniruddha; Burger, Eric W. (IEEE, 2023)
    We have built a prototype universal radio adapter which furnishes seamless and secure wireless communication through non-cooperative indigenous 5G networks for military and government users. The adapter consists of a waveform-agnostic hardware add-on that tunnels DoD terrestrial and satellite data. The adapter uses secure protocols for cross-connecting military-grade wireless RF communications equipment using spectrum in the range from UHF to Ka-band. A 5G data transport channel replaces the captured spectrum for transporting information at the IQ-sample level. In a sense, we replace the antenna-air interface and wireless channel with a transparent 5G data network. A plurarity of legacy military systems can operate through modern 5G networks in a seamless way without any knowledge of the characteristics of military waveforms. The adapter incorporates AI/ML based methods for smart spectrum sensing and autonomous radio reconfiguration. This enables intelligent interconnection of a number of military radios through non-cooperative (potentially adversarial) 5G commercial cellular networks. The adapter is built on four technical pillars: 1) ultra-wideband apertures for multi-functional and flexible software-defined radios (SDRs) with agile, wideband, and dual-band tunable RF transceivers for FR1/FR2 bands; 2) physical layer operation that involve device authentication via deep-learning based RF fingerprinting and compression of acquired IQ data; 3) secure and reconfigurable cryptographic co-processors employing the new quantum-safe algorithms selected by NIST to achieve authentication, key exchange, and encryption with focus on resource-constrained low size, weight, power, and cost (SWaP-C) devices; and 4) generative artificial intelligence and spread-spectrum steganography to hide DoD traffic passed through 5G networks and improve resiliency against real-time traffic analysis by nation-state carriers and intelligence agencies.
  • Spectrum Sharing of the 12 GHz Band with Two-Way Terrestrial 5G Mobile Services: Motivations, Challenges, and Research Road Map
    Hassan, Zoheb; Heeren-Moon, Erika; Sabzehali, Javad; Shah, Vijay K.; Dietrich, Carl; Reed, Jeffrey H.; Burger, Eric W. (IEEE, 2023-07)
    Telecommunication industries and spectrum regulation authorities are increasingly interested in unlocking the 12 GHz band for two-way 5G terrestrial services. The 12 GHz band has a much larger bandwidth than the current sub-6 GHz band and better propagation characteristics than the millimeter-wave (mmWave) band. Thus, the 12 GHz band offers great potential for improving the coverage and capacity of terrestrial 5G networks. However, interference issues between incumbent receivers and 5G radio links present a major challenge in the 12 GHz band. If one could exploit the dynamic contexts inherent to the 12 GHz band, one could reform spectrum sharing policy to create spectrum access opportunities for 5G mobile services. This article makes three contributions. First, it presents the characteristics and challenges of the 12 GHz band. Second, we explain the characteristics and requirements for spectrum sharing at a variety of levels to resolve those issues. Lastly, we present several research opportunities to enable harmonious coexistence of incumbent licensees and 5G networks within the 12 GHz band.
  • TriSAS: Toward Dependable Inter-SAS Coordination with Auditability
    Shi, Shanghao; Xiao, Yang; Du, Changlai; Shi, Yi; Wang, Chonggang; Gazda, Robert; Hou, Y. Thomas; Burger, Eric W.; Dasilva, Luiz; Lou, Wenjing (ACM, 2024-07-01)
    To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem — the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize faulttolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms. In this work, we propose TriSAS—a novel inter-SAS coordination mechanism to facilitate secure, efficient, and dependable spectrum allocation that is fully compatible with the existing SAS infrastructure. TriSAS decomposes the coordination process into two phases including input synchronization and decision finalization. The first phase ensures participants share a common input set while the second one fulfills a fair and verifiable spectrum allocation selection, which is generated efficiently via SAS proposers’ proprietary allocation algorithms and evaluated by a customized designed allocation evaluation algorithm (AEA), in the face of no more than one-third of malicious participants. We implemented a prototype of TriSAS on the AWS cloud computing platform and evaluated its throughput and latency performance. The results show that TriSAS achieves high transaction throughput and low latency under various practical settings.
  • Secure Data-Binding in FPGA-based Hardware Architectures utilizing PUFs
    Frank, Florian; Schmid, Martin; Klement, Felix; Palani, Purushothaman; Weber, Andreas; Kavun, Elif Bilge; Xiong, Wenjie; Arul, Tolga; Katzenbeisser, Stefan (ACM, 2024-07-01)
    In this work, a novel FPGA-based data-binding architecture incorporating PUFs and a user-specific encryption key to protect the confidentiality of data on external non-volatile memories is presented. By utilizing an intrinsic PUF derived from the same memory, the confidential data is additionally bound to the device. This feature proves valuable in cases where software is restricted to be executed exclusively on specific hardware or privacy-critical data is not allowed to be decrypted elsewhere. To improve the resistance against hardware attacks, a novel method to randomly select memory cells utilized for PUF measurements is presented. The FPGA-based design presented in this work allows for low latency as well as small area utilization, offers high adaptability to diverse hardware and software platforms, and is accessible from bare-metal programs to full Linux kernels. Moreover, a detailed performance and security evaluation is conducted on five boards. A single read or write operation can be executed in 0.58 𝜇𝑠 when utilizing the lightweight PRINCE cipher on an AMD Zync 7000 MPSoC. Furthermore, the entire architecture occupies only about 10% of the FPGA’s available space on a resource-constrained AMD PYNQ-Z2. Ultimately, the implementation is demonstrated by storing confidential user data on new generations of network base stations equipped with FPGAs
  • A Family of Fast and Memory Efficient Lock- and Wait-Free Reclamation
    Nikolaev, Ruslan; Ravindran, Binoy (ACM, 2024-06-20)
    Historically, memory management based on lock-free reference counting was very inefficient, especially for read-dominated workloads. Thus, approaches such as epoch-based reclamation (EBR), hazard pointers (HP), or a combination thereof have received significant attention. EBR exhibits excellent performance but is blocking due to potentially unbounded memory usage. In contrast, HP are non-blocking and achieve good memory efficiency but are much slower. Moreover, HP are only lock-free in the general case. Recently, several new memory reclamation approaches such as WFE and Hyaline have been proposed. WFE achieves wait-freedom, but is less memory efficient and performs suboptimally in oversubscribed scenarios; Hyaline achieves higher performance and memory efficiency, but lacks wait-freedom. We present a family of non-blocking memory reclamation schemes, called Crystalline, that simultaneously addresses the challenges of high performance, high memory efficiency, and wait-freedom. Crystalline can guarantee complete wait-freedom even when threads are dynamically recycled, asynchronously reclaims memory in the sense that any thread can reclaim memory retired by any other thread, and ensures (an almost) balanced reclamation workload across all threads. The latter two properties result in Crystalline's high performance and memory efficiency. Simultaneously ensuring all three properties requires overcoming unique challenges. Crystalline supports ubiquitous x86-64 and ARM64 architectures, while achieving superior throughput than prior fast schemes such as EBR as the number of threads grows. We also accentuate that many recent approaches, unlike HP, lack strict non-blocking guarantees when used with multiple data structures. By providing full wait-freedom, Crystalline addresses this problem as well.
  • Implementation of Numerical Model for Prediction of Temperature Distribution for Metallic-Coated Firefighter Protective Clothing
    Naeem, Jawad; Mazari, Adnan; Kus, Zdenek; Havelka, Antonin; Abdelkader, Mohamed (MDPI, 2024-05-21)
    The aim of this study is to predict the distribution of temperature at various positions on silver-coated firefighter protective clothing when subjected to external radiant heat flux. This will be helpful in the determination of thermal protective performance. Firefighter clothing consists of three layers, i.e., the outer shell, moisture barrier and thermal liner. The outer shell is the exposed surface, which was coated with silver particles through a physical vapor deposition process called magnetron sputtering. Afterwards, these uncoated and silver-coated samples were exposed to radiant heat transmission equipment at 10 kW/m2 as per the ISO 6942 standard. Silver-coated samples displayed better thermal protective performance as the rate of temperature rise in silver-coated samples slowed. Later, a numerical approach was employed, contemplating the impact of metallic coating on the exterior shell. The finite difference method was utilized for solving partial differential equations and the implicit method was employed to discretize the partial differential equations. The numerical model displayed a good prediction of the distribution of temperature at different nodes with respect to time. The comparison of time vs. temperature graphs at different nodes for uncoated and silver-coated samples acquired from numerical solutions showed similar patterns, as witnessed in the experimental results.
  • An Analysis of Radio Frequency Transfer Learning Behavior
    Wong, Lauren J.; Muller, Braeden; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-06-03)
    Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.
  • Granular retrosplenial cortex layer 2/3 generates high-frequency oscillations dynamically coupled with hippocampal rhythms across brain states
    Arndt, Kaiser C.; Gilbert, Earl T.; Klaver, Lianne M.F.; Kim, Jongwoon; Buhler, Chelsea M.; Basso, Julia C.; McKenzie, Sam; English, Daniel Fine (CellPress, 2024-03-26)
    The granular retrosplenial cortex (gRSC) exhibits high-frequency oscillations (HFOs; ~150 Hz), which can be driven by a hippocampus-subiculum pathway. How the cellular-synaptic and laminar organization of gRSC facilitates HFOs is unknown. Here, we probe gRSC HFO generation and coupling with hippocampal rhythms using focal optogenetics and silicon-probe recordings in behaving mice. ChR2-mediated excitation of CaMKII-expressing cells in L2/3 or L5 induces HFOs, but spontaneous HFOs are found only in L2/3, where HFO power is highest. HFOs couple to CA1 sharp wave-ripples (SPW-Rs) during rest and the descending phase of theta. gRSC HFO current sources and sinks are the same for events during both SPW-Rs and theta oscillations. Independent component analysis shows that high gamma (50–100 Hz) in CA1 stratum lacunosum moleculare is comodulated with HFO power. HFOs may thus facilitate interregional communication of a multisynaptic loop between the gRSC, hippocampus, and medial entorhinal cortex during distinct brain and behavioral states.
  • Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
    Smith, Alana J.; Tasnim, Noor; Psaras, Zach; Gyamfi, Daphne; Makani, Krishna; Suzuki, Wendy A.; Basso, Julia C. (MyJove Corporation, 2024-01-26)
    Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.
  • Wrist-bound Guanxi, Jiazu, and Kuolie: Unpacking Chinese Adolescent Smartwatch-Mediated Socialization
    Liu, Lanjing; Zhang, Chao; Lu, Zhicong (ACM, 2024-05-11)
    Adolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, is reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as “Bump-to-Connect” and exclusive social platforms. Nonetheless, how these devices infuence adolescents’ peer experience remains unknown. Addressing this, we interviewed 18 Chinese adolescents (age: 11—16), discovering a smartwatch-mediated social ecosystem. Our fndings highlight the ice-breaking role of smartwatches in friendship initiation and their use for secret messaging with local peers. Within the online smartwatch community, peer status is determined by likes and visibility, leading to diverse pursuit activities (i.e., chu guanxi, jiazu, kuolie) and negative social dynamics. We discuss the core afordances of smartwatches and Chinese cultural factors that infuence adolescent social behavior, and ofer implications for designing future wearables that responsibly and safely support adolescent socialization.
  • GPU-based Private Information Retrieval for On-Device Machine Learning Inference
    Lam, Maximilian; Johnson, Jeff; Xiong, Wenjie; Maeng, Kiwan; Gupta, Udit; Li, Yang; Lai, Liangzhen; Leontiadis, Ilias; Rhu, Minsoo; Lee, Hsien-Hsin S.; Reddi, Vijay Janapa; Wei, Gu-Yeon; Brooks, David; Suh, Edward (ACM, 2024-04-27)
    On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than 20× over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over 5× additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to 100, 000 queries per second—a > 100× throughput improvement over a CPU-based baseline—while maintaining model accuracy.
  • Enforcing C/C++ Type and Scope at Runtime for Control-Flow and Data-Flow Integrity
    Ismail, Mohannad; Jelesnianski, Christopher; Jang, Yeongjin; Min, Changwoo; Xiong, Wenjie (ACM, 2024-04-27)
    Control-flow hijacking and data-oriented attacks are becoming more sophisticated. These attacks, especially dataoriented attacks, can result in critical security threats, such as leaking an SSL key. Data-oriented attacks are hard to defend against with acceptable performance due to the sheer amount of data pointers present. The root cause of such attacks is using pointers in unintended ways; fundamentally, these attacks rely on abusing pointers to violate the original scope they were used in or the original types that they were declared as. This paper proposes Scope Type Integrity (STI), a new defense policy that enforces all pointers (both code and data pointers) to conform to the original programmer’s intent, as well as Runtime Scope Type Integrity (RSTI) mechanisms to enforce STI at runtime leveraging ARM Pointer Authentication. STI gathers information about the scope, type, and permissions of pointers. This information is then leveraged by RSTI to ensure pointers are legitimately utilized at runtime. We implemented three defense mechanisms of RSTI, with varying levels of security and performance tradeoffs to showcase the versatility of RSTI. We employ these three variants on a variety of benchmarks and real-world applications for a full security and performance evaluation of these mechanisms. Our results show that they have overheads of 5.29%, 2.97%, and 11.12%, respectively.
  • Save the Bruised Striver: A Reliable Live Patching Framework for Protecting Real-World PLCs
    Zhou, Ming; Wang, Haining; Li, Ke; Zhu, Hongsong; Sun, Limin (ACM, 2024-04-22)
    Industrial Control Systems (ICS), particularly programmable logic controllers (PLCs) responsible for managing underlying physical infrastructures, often operate for extended periods without interruption. Thus, it is challenging to patch security vulnerabilities of ICS in a timely manner after disclosure because it often necessitates waiting for a rare downtime window. While live patching has been introduced to avoid downtime and maintenance costs, conventional live patching methods are not viable for closed-source PLCs. Without the source code, it is difficult to understand the system behaviors and determine binary patch equivalence. To address these challenges, we present a Reliable Live Patching framework called RLPatch for applying live patches to third-party binary without source code.We design RLPatch to capture real-time conditions and dynamic behaviors of PLCs, which enables DevOps engineers to identify major non-recoverable fault (MNRF) vulnerabilities and generate hot patches. The core of RLPatch is an update agent that inserts breakpoints over the original MNRF code and then directs execution to the patches. To ensure system reliability, we use the unique constraints of PLCs to integrate the update processes with the scan cycle. We leverage RLPatch to patch 20 real vulnerabilities in three widely used Rockwell PLCs. We evaluate RLPatch in a real-world gas pipeline, demonstrating its reliability and effectiveness in practice.
  • Seneca: Taint-Based Call Graph Construction for Java Object Deserialization
    Santos, Joanna C. S.; Mirakhorli, Mehdi; Shokri, Ali (ACM, 2024-04-29)
    Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization.
  • Magnetic Field Sensing via Acoustic Sensing Fiber with Metglas® 2605SC Cladding Wires
    Dejneka, Zach; Homa, Daniel; Buontempo, Joshua; Crawford, Gideon; Martin, Eileen; Theis, Logan; Wang, Anbo; Pickrell, Gary R. (MDPI, 2024-04-10)
    Magnetic field sensing has the potential to become necessary as a critical tool for long-term subsurface geophysical monitoring. The success of distributed fiber optic sensing for geophysical characterization provides a template for the development of next generation downhole magnetic sensors. In this study, Sentek Instrument’s picoDAS is coupled with a multi-material single mode optical fiber with Metglas® 2605SC cladding wire inclusions for magnetic field detection. The response of acoustic sensing fibers with one and two Metglas® 2605SC cladding wires was evaluated upon exposure to lateral AC magnetic fields. An improved response was demonstrated for a sensing fiber with in-cladding wire following thermal magnetic annealing (~400 °C) under a constant static transverse magnetic field (~200 μT). A minimal detectable magnetic field of ~500 nT was confirmed for a sensing fiber with two 10 μm cladding wires. The successful demonstration of a magnetic field sensing fiber with Metglas® cladding wires fabricated via traditional draw processes sets the stage for distributed measurements and joint inversion as a compliment to distributed fiber optic acoustic sensors.
  • Securing Your Airspace: Detection of Drones Trespassing Protected Areas
    Famili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry); Gerdes, Ryan (MDPI, 2024-03-22)
    Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.
  • A Logical Circuit Optimization in Balancing Delay and Energy Consumption
    Shan, Qihang (ACM, 2023-11-03)
    The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit’s purpose and hope to bring some insights on further optimization.
  • Application of Distributed Ledger Technology in Distribution Networks
    Zhou, Yue; Manea, Andrei Nicolas; Hua, Weiqi; Wu, Jianzhong; Zhou, Wei; Yu, James; Rahman, Saifur (IEEE, 2022-06-24)
    In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO-DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., 'planning,' 'operation,' 'market,' 'asset,' and 'connection') with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.
  • Optimal Scheduling of Integrated Energy Systems With Multiple CCHPs for High Efficiency and Low Emissions
    Xie, Haimin; Liu, Hui; Wan, Can; Goh, Hui Hwang; Rahman, Saifur (IEEE, 2023-08-14)
    In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.