Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.

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Conflict-Resilient Incremental Offloading of Deep Neural Networks to the Edge of Smart Environment

Mathematical Problems in Engineering ◽

10.1155/2021/9985006 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Zhongmin Chen ◽

Zhiwei Xu ◽

Jianxiong Wan ◽

Jie Tian ◽

Limin Liu ◽

...

Keyword(s):

Neural Networks ◽

Smart Home ◽

Deep Neural Networks ◽

State Of The Art ◽

Smart Environments ◽

Smart Environment ◽

Workload Scheduling ◽

Computationally Intensive ◽

Local Edge ◽

Huge Challenge

Novel smart environments, such as smart home, smart city, and intelligent transportation, are driving increasing interest in deploying deep neural networks (DNN) in edge devices. Unfortunately, deploying DNN at resource-constrained edge devices poses a huge challenge. These workloads are computationally intensive. Moreover, the edge server-based approach may be affected by incidental factors, such as network jitters and conflicts, when multiple tasks are offloaded to the same device. A rational workload scheduling for smart environments is highly desired. In this work, we propose a Conflict-resilient Incremental Offloading of Deep Neural Networks at Edge (CIODE) for improving the efficiency of DNN inference in the edge smart environment. CIODE divides the DNN model into several partitions by layer and incrementally uploads them to local edge nodes. We design a waiting lock-based scheduling paradigm to choose edge devices for DNN layers to be offloaded. In detail, an advanced lock mechanism is proposed to handle concurrency conflicts. Real-world testbed-based experiments demonstrate that, compared with other state-of-the-art baselines, CIODE outperforms the DNN inference performance of these popular baselines by 20 % to 70 % and significantly improves the robustness under the insight of neighboring collaboration.

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IoT Traffic: Modeling and Measurement Experiments

IoT ◽

10.3390/iot2010008 ◽

2021 ◽

Vol 2 (1) ◽

pp. 140-162

Author(s):

Hung Nguyen-An ◽

Thomas Silverston ◽

Taku Yamazaki ◽

Takumi Miyoshi

Keyword(s):

Smart Home ◽

Large Scale ◽

Traffic Modeling ◽

The Novel ◽

Network Behavior ◽

Performance Accuracy ◽

Traffic Generator ◽

Multiple Devices ◽

Iot Devices ◽

The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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A Concept for Context Awareness in Smart Environments

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3098 ◽

2020 ◽

Vol 6 (3) ◽

pp. 380-383

Author(s):

Jochen Bauer ◽

Michael Hechtel ◽

Martin Holzwarth ◽

Julian Sessner ◽

Jörg Franke ◽

...

Keyword(s):

Context Awareness ◽

Smart Home ◽

Daily Life ◽

Smart Environments ◽

Emotion Detection ◽

Post Intervention ◽

Detection Software

AbstractAll aspects of daily life increasingly include digitization. So-called „smart home“ technologies, as well as „wearables“, are gaining attention from more and more dwellers. Therefore, sensor-based, individualized, AI-based services for improved post-intervention monitoring and therapy accompaniment will become feasible and possible if these systems offer a related context-awareness. This paper provides an approach on how to sense and interpret specific contexts with the help of wearables, smartwatches, smart home sensors, and emotion detection software.

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A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation

Sensors ◽

10.3390/s21113587 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3587

Author(s):

Ezequiel Simeoni ◽

Eugenio Gaeta ◽

Rebeca I. García-Betances ◽

Dave Raggett ◽

Alejandro M. Medrano-Gil ◽

...

Keyword(s):

Smart Home ◽

Home Automation ◽

Dynamic Configuration ◽

Home Gateway ◽

Security Authentication ◽

Living Lab ◽

Automation Systems ◽

Authentication And Authorization ◽

Iot Devices ◽

Bridge Technology

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

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HomeTec Software for Security Aspects of Smart Home Devices Based on IoT

10.54216/jcim.050101 ◽

2021 ◽

pp. 5-16

Author(s):

Parth Rustagi ◽

◽

...

Keyword(s):

Smart Home ◽

Denial Of Service ◽

Security Protocols ◽

The Internet ◽

Security Threats ◽

Deep Dive ◽

Cost Cutting ◽

Full Control ◽

Iot Devices ◽

Service Data

As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.

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Preserving Contextual-Privacy for Smart home IoT devices with Dynamic Traffic Shaping

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3126453 ◽

2021 ◽

pp. 1-1

Author(s):

Joy Brahma ◽

Debanjan Sadhya

Keyword(s):

Smart Home ◽

Traffic Shaping ◽

Dynamic Traffic ◽

Iot Devices

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Manipulation and Task Execution by Humanoids

Humanoid Robotics: A Reference ◽

10.1007/978-94-007-6046-2_31 ◽

2018 ◽

pp. 1633-1655

Author(s):

Kensuke Harada ◽

Máximo A. Roa

Keyword(s):

Task Execution ◽

And Task

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An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Scientific Programming ◽

10.1155/2021/5586521 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

C. Saravanakumar ◽

M. Geetha ◽

S. Manoj Kumar ◽

S. Manikandan ◽

C. Arun ◽

...

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

High Availability ◽

Performance Parameters ◽

Task Execution ◽

User Tasks ◽

Cloud User ◽

Computing Models ◽

Bucket Size ◽

And Task

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

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In-Lecture Media Use and Academic Performance: Investigating Demographic and Intentional Moderators

South African Computer Journal ◽

10.18489/sacj.v30i1.434 ◽

2018 ◽

Vol 30 (1) ◽

Cited By ~ 4

Author(s):

Douglas A. Parry ◽

Daniel B. Le Roux

Keyword(s):

Academic Performance ◽

University Students ◽

Digital Media ◽

Empirical Investigation ◽

Media Use ◽

Significant Negative Correlation ◽

Task Execution ◽

Communication Processes ◽

Undergraduate University Students ◽

And Task

The growing prevalence of continuous media use among university students in lecture environments has potential for detrimental effects. In this study we investigate the relationships between in-lecture media use and academic performance. Previous studies have shown that students frequently engage with digital media whilst in university lectures. Moreover, multitasking imposes cognitive costs detrimental to learning and task execution. We propose, accordingly, that the constant distractions created by digital media, interrupt the thought and communication processes of students during lectures and, subsequently, obstruct their ability to learn. To test this proposition we conducted a survey-based empirical investigation of digital media use and academic performance among undergraduate university students. A significant negative correlation was found between the number of in-lecture media use instances and academic performance. Furthermore, this effect was found to be pervasive independent of individual demographic factors and the intention with which a medium was used.

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