A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Understanding and changing household energy consumption behavior are considered as effective ways to improve energy efficiency and promote energy conservation. With the increasing penetration of conventional and emerging information and communication technologies (ICTs) in energy sector, traditional energy systems are being digitized. The energy big data provides a new way to analyze and understand individuals׳ energy consumption behavior, and thus to improve energy efficiency and promote energy conservation. We first propose a framework of the interdisciplinary research of energy, social and information science, which includes energy social science, social informatics and energy informatics. Then, different dimensions and different research paradigms of household energy consumption behavior are presented. Household energy consumption behavior can be analyzed in time dimension, user dimension and spatial dimension. The economic paradigm (including demand response) and the behavior-oriented paradigm (including intervention strategies) are two major research streams of household energy consumption behavior. Finally, the “4V” characteristics (i.e., volume, velocity, variety and value) of energy big data are discussed.

Understanding household energy consumption behavior: The contribution of energy big data analytics

Keynote David Rose The New Vanguard for Business: Connectivity, Design, and the Internet of Things The Internet of Things is the hottest topic of the moment a shift predicted to be as momentous as the impact of the internet itself. The internet has allowed us to share ideas and data largely input by humans. What about a world where data from objects as diverse as umbrellas, fridges, and gas tanks all flows through the internet?

The Internet of things.

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource management techniques in wireless IoT networks and the techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

The proportion of residential electricity consumption in the total energy consumption has increased rapidly in the past decades all over the world. It is becoming increasingly important to promote household energy conservation for the sustainable development of a country in the case of resource constraints. This paper reviews and evaluates the existing research works which are related to the residential electricity consumption behavior. Particular attention is given to the following aspects. (1) Factors influencing residential electricity consumption in social psychology. (2) Theories of social psychology in understanding residential electricity consumption behavior. (3) Different interventions aiming at encouraging households to reduce electricity consumption. Finally, we discuss the challenges and opportunities of research on residential electricity consumption behavior in the big data era.

Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies

The modern automobile is a complex piece of technology that uses the Controller Area Network (CAN) bus system as a central system for managing the communication between the electronic control units (ECUs). Despite its central importance, the CAN bus system does not support authentication and authorization mechanisms, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways including Denial of Service (DoS), Fuzzing and Spoofing attacks. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We generate our own dataset by first extracting attack-free data from our experimental car and by injecting attacks into the latter and collecting the dataset. We use the dataset for testing and training our model. With our selected hyper-parameter values, our results demonstrate that our classifier is efficient in detecting the CAN bus network attacks, we achieved an overall detection accuracy of 99.995%. We also compare the proposed LSTM method with the Survival Analysis for automobile IDS dataset which is developed by the Hacking and Countermeasure Research Lab, Korea. Our proposed LSTM model achieves a higher detection rate than the Survival Analysis method.

/pdf/lstm-based-intrusion-detection-system-for-in-vehicle-can-bus-3t0qnsz55r.pdf

LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications

This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.

A DTN-Based Sensor Data Gathering for Agricultural Applications

Many message routing schemes have been proposed in the context of delay tolerant networks (DTN) and intermittently connected mobile networks (ICMN). Those routing schemes are tested on specific environments that involve particular mobility complexity whether they are random-based or sociologically organized. We, in this paper, propose community structured environment (CSE) and mobility entropy to discuss the effect of node mobility complexity on message routing performance. We also propose potential-based entropy adaptive routing (PEAR) that adaptively carries messages over the change of mobility entropy. According to our simulation, PEAR has achieved high delivery rate on wide range of mobility entropy, while link-state routing has worked well only at small entropy scenarios and controlled replication-based routing only at large entropy environments.

/pdf/mobility-entropy-and-message-routing-in-community-structured-1ovsqe1v3u.pdf

Mobility entropy and message routing in community-structured delay tolerant networks

Intelligent buildings are getting data-centric - they archive the historical records of motion detectors, power usages, HVAC statuses, weather, and any other information in order to improve their control strategies. The engineering cost of installation and maintenance of such systems should be minimized as the system owner has to operate them for several decades: i.e., the lifetime of the building. However, there are several design pitfalls that multiply such engineering costs, which make the operation heavy burden. This paper identifies those pitfalls and presents technical challenges that enable lightweight installation and maintenance. We, then, design facility information access protocol (FIAP) for data-centric building automation systems. We carried out FIAP-based system integration into a building of the University of Tokyo, and demonstrate that FIAP enables incremental installation for wide varieties of applications with small engineering costs.

/pdf/fiap-facility-information-access-protocol-for-data-centric-523pfk4eik.pdf

FIAP: Facility information access protocol for data-centric building automation systems

Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic patterns based on a handcrafted dataset of rules. Recently, the application of machine learning in NIDS helps alleviate the enormous effort of human observation. Federated learning (FL) is a collaborative learning scheme concerning distributed data. Instead of sharing raw data, it allows a participant to share only a trained local model. Despite the success of existing FL solutions, in NIDS, a network’s traffic data distribution does not always fit into the single global model of FL; some networks have similarities with each other but other networks do not. We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group. A comparison between FL and our method was conducted against a range of metrics including the weighted precision, recall, and F1 score, using a collected dataset from 20 massively distributed networks within 60 days. By studying the optimized hyperparameters of Segmented-FL and employing three evaluation methods, it shows that Segmented-FL has better performance in all three types of intrusion detection tasks, achieving validation weighted F1 scores of 0.964, 0.803, and 0.912 with Method A, Method B, and Method C respectively. For each method, this scheme shows a gain of 0.1%, 4.0% and 1.1% in performance compared with FL.

/pdf/adaptive-intrusion-detection-in-the-networking-of-large-4iej92e1wg.pdf

Hideya Ochiai

Papers

LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications

A DTN-Based Sensor Data Gathering for Agricultural Applications

Mobility entropy and message routing in community-structured delay tolerant networks

FIAP: Facility information access protocol for data-centric building automation systems

Adaptive Intrusion Detection in the Networking of Large-Scale LANs With Segmented Federated Learning