Data-driven optimizations in IoT: a new frontier of challenges and opportunities

Abstract: Recent advances in electric metering infrastructure have given rise to the generation of gigantic chunks of data. Transmission of all of these data certainly poses a significant challenge in bandwidth and storage constrained Internet of Things (IoT), where smart meters act as sensors. In this work, a novel multivariate data compression scheme is proposed for smart metering IoT. The proposed algorithm exploits the cross correlation between different variables sensed by smart meters to reduce the dimension of data. Subsequently, sparsity in each of the decorrelated streams is utilized for temporal compression. To examine the quality of compression, the multivariate data is characterized using multivariate normal–autoregressive integrated moving average modeling before compression as well as after reconstruction of the compressed data. Our performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver. The proposed algorithm is tested in a real smart metering setup and its time complexity is also analyzed.

Abstract: This article presents a new paradigm for channel dynamics adaptive transmission of intermittent data in smart grid IoT communication networks, wherein novel channel prediction frameworks using stochastic modeling as well as data-driven learning of channel variability are proposed. A probing-based transmission is also proposed as a benchmark. These prediction frameworks are complemented with an adaptive channel coding scheme to increase the transmission reliability of time-critical grid monitoring data over a wireless channel. Through analyzing the prediction and packet loss performance at varying SNR and fading conditions, it is noted that the stochastic modeling framework is efficient when the fading correlation in the channel is high while the learning-based approach is more adaptive to channel dynamics as the correlation reduces. The proposed frameworks are easily implementable on low-cost end nodes, owing to the optimal selection of parameters for low runtime complexity. When compared to probing-based data transmission for a given fading in the channel, the packet loss probability of the learning-based transmission closely matches while with stochastic model loss probability is found to be 12.3% higher. However, their respective signaling overheads are 38% and 98% lower with respect to the probing-based approach, which is a significant gain at the cost of marginally additional computation complexity.

Abstract: The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

Abstract: With wide scale sensor deployments in smart grid IoT networks, there has been a manyfold increase in the variety and quantity of data generated in the network. In this work, the problem of data reduction in smart grid IoT network is addressed to enhance the resource utilization without hampering the required quality of service. A novel versatile algorithm for multivariate data pruning at the edge devices in smart grid IoT networks is presented. This is achieved via a two stage data reduction mechanism which first exploits the inter-variable correlation to cut down on the number of transmitted variables, followed by adaptive data compression in temporal domain using adaptive compressive sampling. It is shown that with the application of the proposed algorithm at the edge nodes, around 23% savings in bandwidth requirement can be achieved with minimum loss of information.

Abstract: For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

Abstract: Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors’ recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.

Abstract: This paper studies the fundamental dimensionality of synchrophasor data, and proposes an online application for early event detection using the reduced dimensionality. First, the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions is analyzed. This suggests an extremely low underlying dimensionality despite the large number of the raw measurements. An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed. Theoretical justification for the algorithm is provided using linear dynamical system theory. Numerical simulations using both synthetic and realistic PMU data are conducted to validate the proposed algorithm.

Abstract: The increasing pervasiveness of wireless sensor networks (WSNs) in diverse application domains including critical infrastructure systems, sets an extremely high security bar in the design of WSN systems to exploit their full benefits, increasing trust while avoiding loss. Nevertheless, a combination of resource restrictions and the physical exposure of sensor devices inevitably cause such networks to be vulnerable to security threats, both external and internal. While several researchers have provided a set of open problems and challenges in WSN security and privacy, there is a gap in the systematic study of the security implications arising from the nature of existing communication protocols in WSNs. Therefore, we have carried out a deep-dive into the main security mechanisms and their effects on the most popular protocols and standards used in WSN deployments, i.e., IEEE 802.15.4, Berkeley media access control for low-power sensor networks, IPv6 over low-power wireless personal area networks, outing protocol for routing protocol for low-power and lossy networks (RPL), backpressure collection protocol, collection tree protocol, and constrained application protocol, where potential security threats and existing countermeasures are discussed at each layer of WSN stack. This paper culminates in a deeper analysis of network layer attacks deployed against the RPL routing protocol. We quantify the impact of individual attacks on the performance of a network using the Cooja network simulator. Finally, we discuss new research opportunities in network layer security and how to use Cooja as a benchmark for developing new defenses for WSN systems.

Abstract: Residential buildings contribute significantly to the overall energy usage across the world. Real deployments, and collected data thereof, play a critical role in providing insights into home energy consumption and occupant behavior. Existing datasets from real residential deployments are all from the developed countries. Developing countries, such as India, present unique opportunities to evaluate the scalability of existing research in diverse settings. Building upon more than a year of experience in sensor network deployments, we undertake an extensive deployment in a three storey home in Delhi, spanning 73 days from May-August 2013, measuring electrical, water and ambient parameters. We used 33 sensors across the home, measuring these parameters, collecting a total of approx. 400 MB of data daily. We discuss the architectural implications on the deployment systems that can be used for monitoring and control in the context of developing countries. Addressing the unreliability of electrical grid and internet in such settings, we present Sense Local-store Upload architecture for robust data collection. While providing several unique aspects, our deployment further validates the common considerations from similar residential deployments, discussed previously in the literature. We also release our collected data- Indian data for Ambient Water and Electricity Sensing (iAWE), for public use.