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

Collaborative data collection scheme based on optimal clustering for wireless sensor networks

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison

Design And Implementation Of An Autonomous Wireless Sensor-Based Smart Home

The Smart home has gained widespread attentions due to its flexible integration into everyday life. This next generation of green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this thesis, a smart home design is put forward that can classify and predict the state of the home utilizing historical data of the home. A wireless sensor network was setup in a home to gather and send data to a sink node. The collected data was utilized to train and test a classification model achieving high accuracy with Support Vector Machine (SVM). SVM was further utilized as a predictor of future home states. Based on the data collection, classification and prediction models, a system was designed that can learn, run with minimal human supervision and detect anomalies in a home. The aforementioned attributes make the system an asset for senior care scenarios

Indoor Localization Based on Wireless Sensor Networks

Indoor localization techniques based on wireless sensor networks (WSNs) have been increasingly used in various applications such as factory automation, intelligent building, facility management, security, and health care. However, existing localization techniques cannot meet the accuracy requirement of many applications. Meanwhile, some localization algorithms are affected by environmental conditions and cannot be directly used in an indoor environment. Cost is another limitation of the existing localization algorithms. This thesis is to address those issues of indoor localization through a new Sensing Displacement (SD) approach. It consists of four major parts: platform design, SD algorithm development, SD algorithm improvement, and evaluation. Platform design includes hardware design and software design. Hardware design is the foundation for the system, which consists of the motion sensors embedded on mobile nodes and WSN design. Motion sensors are used to collect motion information for the localizing objects. A WSN is designed according to the characteristics of an indoor scenario. A Cloud Computing based system architecture is developed to support the software design of the proposed system. In order to address the special issues in an indoor environment, a new Sensing Displacement algorithm is developed, which estimates displacement of a node based on the motion information from the sensors embedded on the node. The sensor assembly consists of acceleration sensors and gyroscope sensors, separately sensing the acceleration and angular velocity of the localizing object. The first SD algorithm is designed in a way to be used in a 2-D localization demo to validate the proposal. A detailed analysis of the results of 2-D SD algorithm reveals that there are two critical issues (sensor’s noise and cumulative error) affecting the measurement results. Therefore a low-pass filter and a modified Kalman filter are introduced to solve the issue of sensor’s noises. An inertia tensor factor is introduced to address the cumulative error in a 3-D SD algorithm. Finally, the proposed SD algorithm is evaluated against the commercial AeroScout (WiFi-RFID) system and the ZigBee based Fingerprint algorithm

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN

Approaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols