Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition using Deep Convolutional Neural Networks

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train deep convolutional neural network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy

Vector OFDM Transmission over Non-Gaussian Power Line Communication Channels

Most of the recent power line communication (PLC) systems and standards, both narrow-band and broadband, are based on orthogonal frequency-division multiplexing (OFDM). This multiplexing scheme, however, suffers from the high peak-to-average power ratio (PAPR), which can considerably impact the energy efficiency, size, and cost of PLC modems as well as cause electromagnetic compatibility (EMC) issues. This paper investigates the performance of vector OFDM (VOFDM), which has inherently better PAPR properties, over non-Gaussian broadband PLC channels equipped with two nonlinear preprocessors at the receiver. In addition, the low PAPR property of the VOFDM system is exploited to further enhance the efficiency of the nonlinear preprocessors. The achievable gains are studied in terms of the complementary cumulative distribution function of the PAPR, probability of noise detection error, and the signal-to-noise ratio at the output of the nonlinear preprocessors. For comparison’s sake, the performance of conventional OFDM systems is also presented throughout this paper. Results reveal that the proposed system is able to provide up to 2-dB saving in the transmit power relative to the conventional OFDM under same system conditions, which eventually also translates into a system that is more resilient to EMC limits, reduced cost, and size of PLC modems. It is also shown that the achievable gains become more significant as the vector block size of the VOFDM system is increased

Multi-commodity optimization of peer-to-peer energy trading resources in smart grid

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others

Spectrum management of power line communications networks for industrial applications

Power line communications (PLC) reuse the existing power-grid infrastructure for the transmission of data signals. As power line communication technology does not require a dedicated network setup, it can be used to connect a multitude of sensors and Internet of Things (IoT) devices. Those IoT devices could be deployed in homes, streets, or industrial environments for sensing and to control related applications. The key challenge faced by future IoT-Oriented Narrowband PLC Networks is to provide a high quality of service (QoS). In fact, the power line channel has been traditionally considered too hostile. Combined with the fact that spectrum is a scarce resource and interference from other users, this requirement calls for means to increase spectral efficiency radically and to improve link reliability. However, the research activities carried out in the last decade have shown that it is a suitable technology for a large number of applications. Motivated by the relevant impact of PLC on IoT, this paper proposed a cooperative spectrum allocation in IoT-Oriented Narrowband PLC Networks using an iterative water-filling algorithm

A multi-agent framework for electric vehicles charging power forecast and smart planning of urban parking lots

Abstract This paper proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state of charge (SOC), the type of the day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro-level and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Further, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions. The simulation has been carried out in an agent-based modeling software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time