A Study on Real-Time Edge Computed Occupancy Estimation in an Indoor Environment
01 Jan 2020-pp 527-530
TL;DR: This work aims to formulate thehuman occupancy estimation in an indoor environment as a multi-class problem and proposes a edge-based data management framework for human occupancy estimation that is low-cost and light-weight in addition to being capable of performing real-time inference.
Abstract: Sensing the presence of occupants and estimating the occupancy level in an indoor environment are the fundamental requirements for various applications performing remote monitoring, home automation and optimal resource planning. Data generated from a set of passive heterogeneous sensors deployed for this purpose are multimodal and streaming in nature. This work aims to formulate the human occupancy estimation in an indoor environment as a multi-class problem and proposes a edge-based data management framework for human occupancy estimation. The proposed framework is low-cost and light-weight in addition to being capable of performing real-time inference. Also testbed experimentation results is provided to justify the performance of the proposed scheme.
Citations
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TL;DR: In this article , the authors present an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed.
Abstract: The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed.
4 citations
TL;DR: In this article , a model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity.
Abstract: The handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity-based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi-criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM-AI, PCDM-SI, PCDM-AN, and PCDM-SN are developed and compared against the five baseline existing strategies namely IoT-P, Edge-P, Cloud-P, Random-P, and Probabilistic-P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.
1 citations
TL;DR: In this paper , the authors proposed a hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity.
References
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TL;DR: Interestingly, using only one predictor (temperature) the LDA model was able to estimate the occupancy with accuracies of 85% and 83% in the two testing sets.
455 citations
"A Study on Real-Time Edge Computed ..." refers background or methods in this paper
...Some used a single type [3] of sensor whereas, others have opted for multiple sensors in combination [6] for the study....
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...Usage of multiple versions of HMM [7], SVM [10], LDA [6], ANN, random forest etc. can be seen in various works that resulted in decent to good accuracy....
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...Usage of multiple versions of HMM [7], SVM [10], LDA [6], ANN, random forest etc....
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...Other data sources like light[6], noise, wifi [9] etc....
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TL;DR: The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments).
Abstract: The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.
294 citations
TL;DR: A framework based on understanding two important ways that people leave their signature on the transmitted signal: blocking the line of sight (LOS) and scattering effects is proposed and can estimate the total number of people with a good accuracy with only a pair of WiFi cards and the corresponding RSSI measurements.
Abstract: In this paper, we are interested in counting the total number of people walking in an area based on only WiFi received signal strength indicator (RSSI) measurements between a pair of stationary transmitter/receiver antennas. We propose a framework based on understanding two important ways that people leave their signature on the transmitted signal: blocking the line of sight (LOS) and scattering effects. By developing a simple motion model, we first mathematically characterize the impact of the crowd on blocking the LOS. We next probabilistically characterize the impact of the total number of people on the scattering effects and the resulting multipath fading component. By putting the two components together, we then develop a mathematical expression for the probability distribution of the received signal amplitude as a function of the total number of occupants, which will be the base for our estimation using Kullback-Leibler divergence. To confirm our framework, we run extensive indoor and outdoor experiments with up to and including nine people and show that the proposed framework can estimate the total number of people with a good accuracy with only a pair of WiFi cards and the corresponding RSSI measurements.
236 citations
"A Study on Real-Time Edge Computed ..." refers background in this paper
...Other data sources like light[6], noise, wifi [9] etc....
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TL;DR: In this article, an algorithm for the detection of occupants in the indoor environment is presented, validated and evaluated among different scenarios, based on the concentration of carbon dioxide in indoor air.
125 citations
"A Study on Real-Time Edge Computed ..." refers background in this paper
...The works like [5] consider l = 0 for CO2 concentration by addressing the scenario memory-less....
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TL;DR: In this paper, the authors presented a plug-and-play occupancy detection method based on the trajectory of various indoor climate sensor data, which was used to test the efficacy of the method.
93 citations
"A Study on Real-Time Edge Computed ..." refers background in this paper
...Most of the works have defined occupancy detection as a binary classification problem [12]....
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