Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Green compressive sampling reconstruction in IoT networks

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks

Agent-Based Computational Architectures for Distributed Data Processing in Wireless Sensor Networks.

As the structural health monitoring (SHM) community continues to develop algorithms for monitoring performance and detecting degradation in engineered systems, the importance of pervasive sensing and autonomous data processing methodologies will increase. Fortunately, the emergence of wireless sensor technologies at the forefront of SHM research has provided a platform on which problems related to both sensor density and processing autonomy can be addressed. By utilizing wireless communication links instead of expensive data cables, wireless monitoring systems can be deployed with much greater sensor density and at significantly lower costs than traditional SHM systems. Perhaps more importantly, because wireless sensing units typically integrate a traditional sensor with a low-power microprocessor, analog-to-digital converter, and wireless transceiver, wireless sensing networks (WSNs) have shown great promise in their ability to process sensor data in-network (i.e., without the need for a centralized data center). Over the past decade, the wireless SHM community has shown that it is possible to minimize problems associated with power efficiency, data loss, and finite communication range by processing data before transmitting it to a central repository. Recently, in an effort to further improve the efficiency and capability of in-network computation, researchers have started to move away from centralized processing frameworks (where no data is shared between nodes) towards more hierarchical data processing architectures. However, work to date in this area has yet to fully leverage the computational advantages provided in large networks of wireless sensors. In this dissertation, several distinct agent-based architectures are developed for distributed data processing in WSNs. Each of these agent-based architectures leverages the ad-hoc communication and pervasive nature inherent to wireless sensing technology, and can be viewed as a parallel computing system with an unknown and possibly changing number of processing nodes. As such, sophisticated data analysis can be performed while maintaining a scalable environment that is not only resistant to sensor failure, but that also becomes increasingly efficient at higher nodal densities. These agent-based architectures represent a significant step towards the creation of a fully autonomous WSN for application to SHM.Ph.D.Civil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75799/1/atzimmer_1.pd

A low-complexity turbo decoder architecture for energy-efficient wireless sensor networks

Turbo codes have recently been considered for energy-constrained wireless communication applications, since they facilitate a low transmission energy consumption. However, in order to reduce the overall energy consumption, Look-Up- Table-Log-BCJR (LUT-Log-BCJR) architectures having a low processing energy consumption are required. In this paper, we decompose the LUT-Log-BCJR architecture into its most fundamental Add Compare Select (ACS) operations and perform them using a novel low-complexity ACS unit. We demonstrate that our architecture employs an order of magnitude fewer gates than the most recent LUT-Log-BCJR architectures, facilitating a 71% energy consumption reduction. Compared to state-of- the-art Maximum Logarithmic Bahl-Cocke-Jelinek-Raviv (Max- Log-BCJR) implementations, our approach facilitates a 10% reduction in the overall energy consumption at ranges above 58 m

Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)

In this paper, we use statistical tools to analysis dependency between Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC). Our approach has two main phases: profiling, and effective parameter extraction. In former, a sensor network simulator is re-run 800 times with different values for eight WSN parameters to profile consumed energy in nodes; then in latter, three statistical analyses (p-value, linear and non-linear correlation) are applied to the outcome of profiling phase to extract the most effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference (http://www.pdcat2012.org/

A Survey on Communication Networks for Electric System Automation

Published in Computer Networks 50 (2006) 877–897, an Elsevier journal. The definitive version of this publication is available from Science Direct. Digital Object Identifier:10.1016/j.comnet.2006.01.005In today’s competitive electric utility marketplace, reliable and real-time information become the key factor for reliable delivery of power to the end-users, profitability of the electric utility and customer satisfaction. The operational and commercial demands of electric utilities require a high-performance data communication network that supports both existing functionalities and future operational requirements. In this respect, since such a communication network constitutes the core of the electric system automation applications, the design of a cost-effective and reliable network architecture is crucial. In this paper, the opportunities and challenges of a hybrid network architecture are discussed for electric system automation. More specifically, Internet based Virtual Private Networks, power line communications, satellite communications and wireless communications (wireless sensor networks, WiMAX and wireless mesh networks) are described in detail. The motivation of this paper is to provide a better understanding of the hybrid network architecture that can provide heterogeneous electric system automation application requirements. In this regard, our aim is to present a structured framework for electric utilities who plan to utilize new communication technologies for automation and hence, to make the decision making process more effective and direct.This work was supported by NEETRAC under Project #04-157

Atomic-SDN: Is Synchronous Flooding the Solution to Software-Defined Networking in IoT?

The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change. Recent research has extended this concept to IEEE 802.15.4 low-power wireless networks, which form a key component of the Internet of Things (IoT). However, the multiple traffic patterns necessary for SDN control makes it difficult to apply this approach to these highly challenging environments. This paper presents Atomic-SDN, a highly reliable and low-latency solution for SDN in low-power wireless. Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition: where SF solutions have consistently outperformed other entries. Using this approach, Atomic-SDN presents considerable performance gains over other SDN implementations for low-power IoT networks. We evaluate Atomic-SDN through simulation and experimentation, and show how utilizing SF techniques provides latency and reliability guarantees to SDN control operations as the local mesh scales. We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics