Comparing Response Time of Home IoT Devices with or without Cloud

Lee, Hyungjin; Mun, Hyunsu; Lee, Youngseok

doi:10.1109/icce46568.2020.9043102

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Besides, Herrera et al [6] proposed DADO framework, which provides scalable deployment plans that trade-off execution time and reduce response time. Another measurement study, conducted by Lee et al [7], examines common IoT home devices response times, such as smart lights and smart plug. This study includes a performance comparison with and without cloud concept.…”

Section: Related Workmentioning

confidence: 99%

Towards Self-Optimization of Publish/Subscribe IoT Systems using Continuous Performance Monitoring

Mohammed,

Nabila

2024

Networks, Blockchain and Internet of Things

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Today, more and more embedded devices are being connected through a network, generally Internet, offering users different services. This concept refers to Internet of Things (IoT), bringing information and control capabilities in many fields like medicine, smart homes, home security, etc. Main drawbacks of IoTenvironment are its dependency on Internet connectivity and need continuous devices power. These dependencies may affect system performances, namely request processing response times. In this context,we propose in this paper a continuous performance monitoring methodology, applied on IoT systems based on Publish/subscribe communication model. Our approach assesses performances using Stochastic Petri net modeling, and self-optimizes whenever poor performances are detected. Our approach relies on a Stochastic Petri nets modelling and analysis to assess performances. We target improving performances, in particular response times, by online modification of influencing factors.

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Section: Related Workmentioning

confidence: 99%

Towards Self-Optimization of Publish/Subscribe IoT Systems using Continuous Performance Monitoring

Mohammed,

Nabila

2024

Networks, Blockchain and Internet of Things

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“…Delays among IoT Components. We use delays recorded among real IoT components from previous works [33], [34]. These works study the response time of typical home IoT devices when multiple clouds interact with each other, such as user IoT and IFTTT clouds.…”

Section: Smart Home Deploymentmentioning

confidence: 99%

“…Table III shows the mean and std of delays in seconds. The high and unreliable delays stem from (1) remote clouds that incur high round-trip time delays, (2) events traversing across multiple proprietary vendor clouds, (3) authentication overhead from TLS connections, and (4) the processing time overhead at the clouds due to the app and device identification [33]. In our experiments, we randomly sample the delays from a Gaussian distribution using the mean and standard deviation of delays between each component.…”

Section: Smart Home Deploymentmentioning

confidence: 99%

On the Safety Implications of Misordered Events and Commands in IoT Systems

Goksel¹,

Ozmen²,

Reeves³

et al. 2021

Preprint

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IoT devices, equipped with embedded actuators and sensors, provide custom automation in the form of IoT apps. IoT apps subscribe to events and upon receipt, transmit actuation commands which trigger a set of actuators. Events and actuation commands follow paths in the IoT ecosystem such as sensor-toedge, edge-to-cloud, and cloud-to-actuator, with different network and processing delays between these connections. Significant delays may occur especially when an IoT system cloud interacts with other clouds. Due to this variation in delays, the cloud may receive events in an incorrect order, and in turn, devices may receive and actuate misordered commands. In this paper, we first study eight major IoT platforms and show that they do not make strong guarantees on event orderings to address these issues. We then analyze the end-to-end interactions among IoT components, from the creation of an event to the invocation of a command. From this, we identify and formalize the root causes of misorderings in events and commands leading to undesired states. We deploy 23 apps in a simulated smart home containing 35 IoT devices to evaluate the misordering problem. Our experiments demonstrate a high number of misordered events and commands that occur through different interaction paths. Through this effort, we reveal the root and extent of the misordering problem and guide future work to ensure correct ordering in IoT systems.

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“…Similarly, the server-side reproduces the processing of incoming requests from IoT devices, updating the parking status and communicating the information back to the client. As deduced from [104,105,106], typical processing and response times for IoT devices in an Internet-connected infrastructure system are likely to range anywhere between milliseconds to several seconds or even minutes, depending on the network congestion and the computational load exerted on the target system. Taking into account that in the given scenario, up to 500 IoT devices can be required to continuously transmit status information for parking availability, as well as execute heavy-duty tasks of detecting the presence of each arriving vehicle, several adjustments were made on the server end to reflect realistic conditions.…”

Section: Server-side Emulationmentioning

confidence: 99%

A Scalable Approach to Improve Security and Resilience of Smart City IoT Architectures

Zakurdaev

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The swift emergence of smart technologies, notably the Internet of Things (IoT), has revolutionized numerous industry sectors. However, outdated IoT architectures constrain innovation in smart cities due to scalability issues, compromised resilience, and security vulnerabilities. This thesis scrutinizes these challenges, advocating a contemporary approach to IoT system design. Prioritizing performance, scalability, security, and resilience, the research delves into the applicability of Platform-as-a-Service (PaaS) and Infrastructure-as-Code (IaC) methodologies, endorsing containerization and cloud-centric patterns for smart city IoT. The introduced model is contrasted with prevailing architectures, highlighting a trajectory for IoT progression. Merging empirical and theoretical insights, this study furnishes guidelines for the future of IoT in smart industries, underscoring the benefits of service-based architectures for efficiency, resilience, and security. iii This MASc thesis owes its existence to the guidance and encouragement of many. My appreciation goes to Dr. Chung-Horng Lung, whose keen insights sparked my interest in cloud technologies, one of the most fascinating focus areas of this research. I am also indebted to Mohammed Ismail, a long-time friend and academic cohort. His eagerness to explore the complexities of containerization technologies along with me, combined with his invaluable advice during the implementation and testing phases of my experiments, contributed significantly to the success of this work. My family, a constant source of strength, and my patient fiancé, who has endured my long working hours and has always stood by me, deserve heartfelt recognition. I would be remiss not to mention my two feline companions, whose incessant meows formed a harmonic and cheering soundtrack outside my office door. Finally, I attribute the greatest part of this journey's success to the unwavering guidance of my supervisor, Dr. Jason Jaskolka. His mentorship, from igniting my interest in cybersecurity to the detailed critiques of my work and consistent support, has been profoundly inspirational. His exemplary efficiency and dedication to his students' success were guiding lights during challenging times, for which I am endlessly grateful.

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Comparing Response Time of Home IoT Devices with or without Cloud

Cited by 7 publications

References 7 publications

Towards Self-Optimization of Publish/Subscribe IoT Systems using Continuous Performance Monitoring

Towards Self-Optimization of Publish/Subscribe IoT Systems using Continuous Performance Monitoring

On the Safety Implications of Misordered Events and Commands in IoT Systems

A Scalable Approach to Improve Security and Resilience of Smart City IoT Architectures

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