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1. Performance of the Latest Generation Powerline Networking for Green Building Applications

   https://doi.org/10.1145/2528282.2528298  

   Roy, Nirmalya ; Kleinschmidt, David ; Taylor, Joseph ; Shirazi, Behrooz ( January 2013 , BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings)

   Green building applications need to efficiently communicate fine-grained power consumption patterns of a wide variety of consumer-grade appliances for an effective adaptation and percolation of demand response models in the home environment. A key hurdle to the widespread adoption of such demand response policies in these appliances is the lack of efficient connectivity to a local area network. One solution is delivering telemetry data over existing electrical infrastructure to which the devices are already connected. The use of existing wiring produces a simple and cost-effective solution, avoiding many issues observed with wireless mesh networks (such as islands and bottlenecks), while helping to vacate increasingly congested spectrum. In this paper we explore the feasibility and efficacy of Power-line Communications (PLC) as a backbone of wireless communications in a home environment. We evaluate the behavior of several state-of-the art PLC modems using end-to-end measurements to establish their performance and throughput characteristics. Our preliminary results suggest that PLC is a promising technology for low-bandwidth hungry green building applications but more in depth study is required before making large-scale smart grid deployment. 

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2. Learning from Non-Experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes

   https://doi.org/10.1145/3491241  

   Codispoti, Jackson ; Khamesi, Atieh R. ; Penn, Nelson ; Silvestri, Simone ; Shin, Eura ( February 2022 , ACM Transactions on Cyber-Physical Systems)

   With the acceleration of ICT technologies and the Internet of Things (IoT) paradigm, smart residential environments , also known as smart homes are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems, and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this paper, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency. 

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3. P-HIP: A Lightweight and Privacy-Aware Host Identity Protocol for Internet of Things

   https://doi.org/10.1109/JIOT.2020.3009024  

   Hossain, Mahmud ; Hasan, Ragib ( July 2020 , IEEE Internet of Things Journal)

   null (Ed.)

   The Host Identity Protocol (HIP) has emerged as the most suitable solution to uniquely identify smart devices in the mobile and distributed Internet of Things (IoT) systems, such as smart cities, homes, cars, and healthcare. The HIP provides authentication methods that enable secure communications between HIP peers. However, the authentication methods provided by the HIP cannot be adopted by the IoT devices with limited processing power because of the computation-intensive cryptographic operations involved in hash generation, signature validation, and session key establishment. Moreover, IoT devices cannot utilize the HIP as is to communicate securely in the low power and lossy networks as there is a considerable communication overhead, such as packet fragmentation and reassembly, for exchanging certificates over a lossy link. Additionally, the use of static host identifiers makes IoT devices vulnerable to cyber espionage and user-targeted attacks. In this article, we propose an authentication scheme, P-HIP, that protects the identity privacy of an IoT device by enabling the device to compute and use unique host identifiers from networks to networks and sessions to sessions. To make the HIP suitable for resource-constrained IoT devices, P-HIP provides methods that unburden IoT devices from computation-intensive operations, such as modular exponentiation, involved in authentication and session-key exchange. Additionally, P-HIP minimizes the communication overheads for exchanging certificates in lossy networks. We implement a prototype of P-HIP on Contiki enabled IoT that shows P-HIP can reduce computation costs, communication overheads, and the session-key establishment time when used by low-powered devices in a lossy network. 

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4. Visible Light Communication Cyber-Physical Systems-on-Chip for Smart Cities

   Zijin, Pan ; Tian, Lang ; Cheng, Li ; Mengfu, Di ; Gang, Chen ; Yehuda, Kalay ; Ramdas, Pai ; Albert, Wang ( December 2019 , Proc. 2nd IEEE International Conference on Information Communication and Signal Processing (ICSP))

   null (Ed.)

   This paper reviews recent developments of Light Emitting Diode (LED) based Visible Light Communication (VLC) technologies and related cyber-physical systems-on-chip (CPSoC) for smart city applications. Critical aspects of LED VLC cyber-physical systems are discussed. Designs of LEDbased VLC CPSoC Integrated Circuits (IC) are depicted. LED VLC technology, as a viable internet of things (IoT) solution, has the potential for various applications for smart cities including smart hospitals, smart homes, smart communities and smart traffics in near future. 

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5. Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems

   https://doi.org/10.3390/en13236399  

   Almassalkhi, Mads ; Brahma, Sarnaduti ; Nazir, Nawaf ; Ossareh, Hamid ; Racherla, Pavan ; Kundu, Soumya ; Nandanoori, Sai Pushpak ; Ramachandran, Thiagarajan ; Singhal, Ankit ; Gayme, Dennice ; et al ( December 2020 , Energies)

   null (Ed.)

   Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO. 

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