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Abstract
Bin-ary is a self-contained gas detector that analyzes organic trash odor compounds and releases subtle burst of scent when bad odor is detected. The prototype is meant to be used as a plugin to make trash bins and dumpsters smarter and prevent insalubrity in cities and villages.
... The majority of instances within this category explored the integration of optical camera-based modules for object recognition, particularly for e-waste classification (Antora et al., 2022;Sampedro et al., 2021) and recognition of recyclables (Wahyutama & Hwang, 2022). Amores et al. (2015) presented the implementation of a multiple gas sensor module to monitor organic waste degradation, detect odor-specific compounds, and dispense a masking fragrance to mitigate bad odors until trash collection. Kokoulin and Kiryanov (2019) demonstrated the integration of an optical subsystem on a reverse vending machine (RVM) dedicated to classifying recyclable containers using computer vision and neural networks. ...
... Regarding the integration of gas detectors, different approaches were investigated. In some instances, the assessment of carbon monoxide buildup was employed as a measure to evaluate the deterioration of organic waste (Amores et al., 2015). Moreover, Ahmed et al. (2022) and Misra et al. (2018) employed sensors capable of detecting ammonia levels to promptly seal the lid and prevent air contamination. ...
... stands for Capacitive and Ind. refers to Inductive. (Ali et al., 2020) Ultrasound Alsbou et al., 2018) Ultrasound Alwis et al., 2022) Ultrasound Amores et al., 2015) N (Antora et al., 2022) Ultrasound Azarudeen et al., 2021 ...
Waste collection bins with smart capabilities play a crucial role in contemporary urban landscapes, forming integral components of efficient and sustainable waste management practices worldwide. This paper conducts a thorough review of state-of-the-art smart bins, emphasizing advanced features such as material separation and integrated automation. The analysis, based on 79 selected articles from a pool of over 1400 publications, underscores critical gaps in the existing literature. While a widely accepted common ground that outlines the definition for smart bins exists, in the form of waste disposal containers incorporating functionalities such as fill-level monitoring and IoT connectivity, this research identifies substantial areas for improvement. Specifically, material separation practices, including automated waste segregation, and the overall maturity of conceptual frameworks present significant challenges, hindering the optimization of resource recovery essential for pioneering circular economy models. Moreover, a lack of uniformity and standardization in automation techniques is apparent across studies. Various articles propose divergent approaches, each with distinct advantages and drawbacks. Notably, no unified solution emerges that could harness collective strengths and mitigate shortcomings. Identifying these issues is paramount for advancing the field and tackle sustainability-related challenges within modern smart cities.
... An olfactory interface provides a computer-controlled smell to an user [46]. For example, Bin-ary by Amores et al. uses hydrogen sulfide (H2S), ethylene (C2H4), carbon dioxide (CO2), carbon monoxide (CO), and hydrogen (H) sensors to detect bad odors from trash and releases a scent in response [4]. In addition, Amores et al. 's study on Essence introduces a necklace that releases a fragrance based on biometric and contextual data [3]. ...
... The state of the atmosphere can be perceived through atmospheric interfaces that mediate forecasts, current or historical information [e.g., 4,30,39]. In addition, users are able to manipulate sensor values through their direct activities (e.g., exhalation to increase CO2 sensor levels) or indirect activities (e.g., using a vaporizer to increase particle sensor levels; see Figure 2). ...
... Statistical methods are foundational in anomaly detection due to their efficiency and simplicity. Thresholding is a widely used approach where predefined limits indicate anomalies, as demonstrated by Amores et al. [55], who used an intelligent trash bin with gas sensors to monitor food quality. Another standard method, Analysis of Variance, determines significant differences between groups and is valuable for identifying unusual patterns [56]. ...
In recent years, there has been a concerted effort to improve anomaly detection techniques, particularly in the context of high‐dimensional, distributed clinical data. Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy, personalising treatment plans, and optimising resource allocation to enhance clinical outcomes. Nonetheless, this domain faces unique challenges, such as irregular data collection, inconsistent data quality, and patient‐specific structural variations. This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges. The strategy combines HPO‐based optimal Density‐Based Spatial Clustering of Applications with Noise for clustering patient exercise data, facilitating efficient anomaly identification. Subsequently, a stochastic method based on the Interquartile Range filters unreliable data points, ensuring that medical tools and professionals receive only the most pertinent and accurate information. The primary objective of this study is to equip healthcare professionals and researchers with a robust tool for managing extensive, high‐dimensional clinical datasets, enabling effective isolation and removal of aberrant data points. Furthermore, a sophisticated regression model has been developed using Automated Machine Learning (AutoML) to assess the impact of the ensemble abnormal pattern detection approach. Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML. Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhancement in AutoML performance, with an average improvement of 0.041 in the R2 score, surpassing the effectiveness of traditional regression models.
... The statistical methods are successfully applied to discover the anomalous behavior in many application domains. One of the simplest methods is thresholding [41], that specifies the behavior of the objects when a certain threshold is crossed during the monitoring phase of the smart objects. The experiments are performed on an intelligent trash bin equipped with gas sensor to detect the state of the food. ...
Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.
Recently, biosensor-based drones (BBD) have emerged and have proven to be highly influential in the convergence of modern technology and agriculture. These drones possess the capacity to bring about significant changes in the realm of sustainable agriculture development. This study presents a comprehensive insight into the crucial element of anomaly detection (AD) in integrating BBD and investigates their diverse uses in promoting sustainability in agriculture. With various advanced sensor technologies, BBD collects and transmits real-time data for accurate monitoring of agricultural crops, soil quality, and environmental parameters. The utilization of a diverse range of sensors, including multispectral, hyperspectral, thermal infrared, global positioning system (GPS), light detection and ranging (LiDAR), environmental , chemical, and crop health sensors, provides farmers with the capability to make informed decisions based on data. The management of extensive datasets produced by these sensors presents a considerable obstacle. The utilization of AD techniques is crucial to exploit the capabilities of drones equipped with biosensors fully. Machine learning (ML) algorithms and artificial intelligence (AI) systems 284 significantly impact the processing and interpretation of sensor data. They are essential in detecting deviations from anticipated trends and notifying farmers of abnormalities that could indicate crop stress, illnesses, or pest presence. Detecting issues early enables prompt action, decreasing crop production losses and reducing reliance on chemical treatments. This, in turn, supports the adoption of sustainable farming methods. The utilization of BBD in advancing sustainable agriculture encompasses a wide range of applications. The practices encompass precision irrigation management, targeted fertilization, disease and insect control, land optimization, and minimization of environmental impact. These applications collectively enhance resource efficiency, augment agricultural yields, mitigate environmental impact, and promote sustainable agriculture. Selected studies were obtained from six top academic research databases. The authors used an exhaustive data extraction technique, focusing on the study objectives and type of AD in a smart operation, such as smart agriculture, transportation and smart things settings. According to this analysis, several studies have shown that deep learning (DL) and ML are more employed in preventing point and collective anomalies. Statistical approaches are more applicable in contextual and collaborative AD. The study presents an AD summary of ML, DL, and statistical-based approaches. Conclusively, the study identifies AD's future research directions from a drone operations perspective.
Olfactory disorders can significantly affect a human’s quality of life. We sought to investigate whether there are opportunities for assistive technologies to support people with olfactory disorders in day-to-day life. To achieve this goal, we surveyed 70 people with olfactory disorders to understand their behaviors, experiences, technologies used, and challenges with olfaction. We then conducted 10 follow-up interviews to further discuss views on current technology for people with olfactory disorders and the design opportunities for assistive technologies for olfaction from their point of view. Our results illustrated participants’ varied
views on current technology, behaviors/technologies adopted when presented with particular scenarios, and potential solutions for designing assistive technologies for olfactory disorders. Through these findings, we discuss design considerations for assistive technology for olfaction.
Internet of Things (IoT) has attracted considerable attention, let alone acquaintance, in academia and industry in particular over the last few years. The factors behind this attraction are the new capabilities IoT is promising to deliver. On a human level, it creates an image of a future world in which everything in our ambient environment is hooked up to the internet and communicates seamlessly with each other in order to operate smartly. The ultimate aim is to allow objects around us to efficiently sense our environments, interact easily, and eventually create a better world for us: one where everyday objects behave on the basis of what we need and want without explicit orders. That being said, enabling devices to communicate to when they are not properly secured, the internet opens them up to a range of significant vulnerabilities. This research concentrates on security threats and vulnerabilities on each layer of IoT architecture. We survey a vast range of relevant IoT security works that use classical solutions and new emerging security solutions. The advantages of classical solutions are effective in optimizing resources such as memory, bandwidth and computing, but do not face the challenges of scalability and heterogeneity, While the emerging approaches like Software Based Networking and blockchain can offer to IoT through to their efficiency and scalability
Clayodor (\klei-o-dor\) is a clay-like malleable material that changes smell based on user manipulation of its shape. This work explores the tangibility of shape changing materials to capture smell, an ephemeral and intangible sensory input. We present the design of a proof-of-concept prototype, and discussions on the challenges of navigating smell though form.
Technologies for capturing and generating smell are emerging, and our ability to engineer such technologies and use them in HCI is rapidly developing. Our understanding of how these technologies match the experiences with smell that people have or want to have is surprisingly limited. We therefore investigated the experience of smell and the emotions that accompany it. We collected stories from 439 participants who described personally memorable smell experiences in an online questionnaire. Based on the stories we developed 10 categories of smell experience. We explored the implications of the categories for smell-enhanced technology design by (a) probing participants to envision technologies that match their smell story and (b) having HCI researchers brainstorm technologies using the categories as design stimuli. We discuss how our findings can benefit research on personal memories, momentary and first time experiences, and wellbeing.
We present a study into the use of smell for searching digi-tal photo collections. Many people now have large photo libraries on their computers and effective search tools are needed. Smell has a strong link to memory and emotion so may be a good way to cue recall when searching. Our study compared text and smell based tagging. For the first stage we generated a set of smell and tag names from user de-scriptions of photos, participants then used these to tag pho-tos, returning two weeks later to answer questions on their photos. Results showed that participants could tag effec-tively with text labels, as this is a common and familiar task. Performance with smells was lower but participants performed significantly above chance, with some partici-pants using smells well. This suggests that smell has poten-tial. Results also showed that some smells were consistently identified and useful, but some were not and highlighted issues with smell delivery devices. We also discuss some practical issues of using smell for interaction.
Attempts to extract marketable paper fibers from mixed municipal solid waste by mechanical means have not been successful on a large scale in the United States. Hand-picking of corrugated board and newspapers from municipal solid waste has been successful, and is often practiced by waste collectors when waste paper prices are high. The recent development of new processing machinery to separate the incoming wastes aerodynamically as the first processing step, and a second device that reliably separates large plastics and textiles from paper fibers, now makes it technically feasible to recover a corrugated-rich pulp and a news-rich pulp from unsegregated municipal solid waste in high tonnage plants.
In this paper, we introduce a new technology, which allows people to share taste and smell sensations digitally with a remote person through existing networking technologies such as the Internet. By introducing this technology, we expect people to share their smell and taste experiences with their family and friends remotely. Sharing these senses are immensely beneficial since those are strongly associated with individual memories, emotions, and everyday experiences. As the initial step, we developed a control system, an actuator, which could digitally stimulate the sense of taste remotely. The system uses two approaches to stimulate taste sensations digitally: the electrical and thermal stimulations on tongue. Primary results suggested that sourness and saltiness are the main sensations that could be evoked through this device. Furthermore, this paper focuses on future aspects of such technology for remote smell actuation followed by applications and possibilities for further developments.
We propose a new olfactory display system that can generate an odor distribution on a two-dimensional display screen. The proposed system has four fans on the four corners of the screen. The airflows that are generated by these fans collide multiple times to create an airflow that is directed towards the user from a certain position on the screen. By introducing odor vapor into the airflows, the odor distribution is as if an odor source had been placed onto the screen. The generated odor distribution leads the user to perceive the odor as emanating from a specific region of the screen. The position of this virtual odor source can be shifted to an arbitrary position on the screen by adjusting the balance of the airflows from the four fans. Most users do not immediately notice the odor presentation mechanism of the proposed olfactory display system because the airflow and perceived odor come from the display screen rather than the fans. The airflow velocity can even be set below the threshold for airflow sensation, such that the odor alone is perceived by the user. We present experimental results that show the airflow field and odor distribution that are generated by the proposed system. We also report sensory test results to show how the generated odor distribution is perceived by the user and the issues that must be considered in odor presentation.
Ponnampalam Gopalakrishnakone, Digital taste and smell communication
- Kasun Nimesha Ranasinghe
- Adrian David Karunanayaka
- Owen Noel Cheok
- Newton Fernando
- Hideaki Nii