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... However, digital twins are an interesting research topic, especially in terms of automated discovery and digital representation of physical industrial settings, and the subsequent optimization. Finally, human interaction with Smart applications can be used to augment AI, creating Social Edge Intelligence (SEI) [175]. SEI can drastically improve applications in which AI is used to analyze gathered data, but in which some steps benefit from higher cognitive abilities than the state of the art currently offers. ...
... Since CovidTrak relies on data contributed by unvetted online users dispersed around the world [95], [117], one arduous task is to distill trustworthy COVID-19 contact tracing data from the noisy incoming social signals. We deem this the data reliability challenge in CovidTrak. ...
With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.
... However, digital twins are an interesting research topic, especially in terms of automated discovery and digital representation of physical industrial settings, and the subsequent optimization. Finally, human interaction with Smart applications can be used to augment AI, creating Social Edge Intelligence (SEI) [163]. SEI can drastically improve applications in which AI is used to analyze gathered data, but in which some steps benefit from higher cognitive abilities than the state of the art currently offers. ...
The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with “Enabling Technology” for Edge Intelligence“, Organization” of the edge using AI, and AI “Applications” in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The “Organization” aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.
... A third probable future avenue of research can focus on designing decentralized model training algorithms for collaboratively acquiring local model updates from privately-owned devices. With the intent for preserving privacy and reducing network bandwidth requirements, federated learning (FL) is gaining traction as a decentralized AI training paradigm [271], [272], where a shared global AI model is trained from a collection of edge devices owned by end users [273]. Future research can focus on constructing FL solutions that can consider the data and device heterogeneity originating from the social and physical sensors in SPS. ...
Propelled by versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing dependable SPS systems, which require careful study to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and road-map for future work. This paper intends to bridge the knowledge gap in current literature by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.
... At the level of sensing, [22] develops the concept of Social Edge Intelligence, that proposes the integration of artificial intelligence with human intelligence to address critical research challenges of Edge computing. In this context, it proposes the challenge of efficient resource management that exploit the heterogeneity present in the Edge devices and diagnoses the need of additional research to enable seamless device collaboration for timely task execution. ...
... At the level of sensing, [22] develops the concept of Social Edge Intelligence, that proposes the integration of artificial intelligence with human intelligence to address critical research challenges of Edge computing. In this context, it proposes the challenge of efficient resource management that exploit the heterogeneity present in the Edge devices and diagnoses the need of additional research to enable seamless device collaboration for timely task execution. ...
In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional data center boundaries. The focal point of this continuum are cognitive devices, which have to make decisions autonomously using their on-board computation and storage capacity based on information sensed from their environment. Such devices are moving and cannot rely on fixed infrastructure elements, but instead realise on-the-fly networking and thus frequently join and leave temporal swarms. All this creates novel demands for the underlying architecture and resource management, which must bridge the gap from edge to cloud environments, while keeping the QoS parameters within required boundaries. The paper presents an initial architecture and a resource management framework for the implementation of this type of IT service provisioning.
... Social sensing has emerged as a new networked sensing paradigm that uses humans as sensors to report the states of the physical world [1,2,3]. ...
This paper focuses on the migratable traffic risk estimation problem in intelligent transportation systems using the social sensing. The goal is to accurately estimate the traffic risk of a target area where the ground truth traffic accident reports are not available by leveraging an estimation model from a source area where such data is available. Two important challenges exist. The first challenge lies in the discrepancy between source and target areas and such discrepancy would prevent a direct application of a model from the source area to the target area. The second challenge lies in the difficulty of identifying all potential features in the migratable traffic risk estimation problem and decide the importance of identified features due to the lack of ground truth labels in the target area. To address these challenges, we develop DeepRisk, a social sensing based migratable traffic risk estimation scheme using deep transfer learning techniques. The evaluation results on a real world dataset in New York City show the DeepRisk significantly outperforms the state-of-the-art baselines in accurately estimating the traffic risk of locations in a city.
... Due to the multi-lingual, multi-cultural, and varied educational and financial background, the presentation of data to the general population in an effective manner remains a significant challenge. Natural language mode and graphical displays have been widely used for data visualization and presentation but still is far from adequate and reachable to all sections of the population [25]. ...
According to the World Health Organization (WHO), a pandemic is “the worldwide spread of a new disease.” Another descriptive definition of a pandemic says: “an epidemic occurring worldwide, or over a vast area, crossing international boundaries and usually affecting a large number of people.” The WHO, on March 11, 2020, has announced the outbreak of novel coronavirus disease (nCoV or COVID-19 or SARS-CoV-2) as a pandemic. Since then, COVID-19 has come as a shock to society and health systems. It has surpassed provincial, radical, conceptual, spiritual, social, and pedagogical boundaries. In the present pandemic situation, all countries are fighting their battle with COVID-19 and looking for a practical and cost-effective solution to face the problems. This chapter highlights the COVID-19 pandemic challenges faced by individuals and healthcare systems and how society is trying to utilize the benefits of the latest technologies, such as the sensor network and the Internet of things.
... Due to the multi-lingual, multi-cultural, and varied educational and financial background, the presentation of data to the general population in an effective manner remains a significant challenge. Natural language mode and graphical displays have been widely used for data visualization and presentation but still is far from adequate and reachable to all sections of the population [25]. ...
The Internet of things (IoT) subject speaks to a thought for the electronic-mechanical arrangement of devices to detect and gather data from the encompassing environmental factors and a short time later share that data over the Internet where it will, in general, be arranged and utilized for various purposes. This chapter describes the IoT architecture, components, and protocols used for various applications.
... The first challenge is leveraging the sparse and unreliable social media data to guide cars to desired locations. A key challenging task in social sensing applications is the accurate identification of reliable sources and truthful claims from the sparse and uncertain social sensing data, otherwise known as truth discovery [10]. To discover truthful information from unvetted social media users, existing truth discovery solutions primarily rely on the posts presented on social media. ...
While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more pervasive, but one of its key limitations lies in its inconsistent reliability stemming from the data contributed by unreliable human sensors. In this paper, we present DASC, a road Damage-Aware Social-media-driven Car sensing framework that exploits the collective power of social sensing and VSNs for reliable disaster response applications. However, integrating VSNs with social sensing introduces a new set of challenges: i) How to leverage noisy and unreliable social signals to route the vehicles to accurate regions of interest? ii) How to tackle the inconsistent availability (e.g., churns) caused by car drivers being rational actors? iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media? The DASC framework addresses the above challenges by establishing a novel hybrid social-car sensing system that employs techniques from game theory, feedback control, and Markov Decision Process (MDP). In particular, DASC distills signals emitted from social media and discovers the road damages to effectively drive cars to target areas for verifying emergency events. We implement and evaluate DASC in a reputed vehicle simulator that can emulate real-world disaster response scenarios. The results of a real-world application demonstrate the superiority of DASC over current VSNs-based solutions in detection accuracy and efficiency.
