Energy, latency and staleness tradeoffs in AI-driven IoT

Babu, Naveen T. R.; Stewart, Christopher

doi:10.1145/3318216.3363381

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the integration of edge AI into IoT systems has a great potential to increase autonomy and intelligence in a variety of domains. However, challenges such as accurate data, dynamic repartition of control, and model training and updates need to be addressed through continuous research and development [40,111].…”

Section: Edge Ai For Increasing Autonomy Of Iot Systemsmentioning

confidence: 99%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

View full text Add to dashboard Cite

A Multiagent System (MAS) refers to a network of agents collaborating to achieve same objective. This system comprises numerous individual programs or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems. The Multiagent System (MAS) is not a substitute or rival for Artificial Intelligence (AI) methods. Instead, AI techniques can be integrated within the agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous. The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologies that have long been applied to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Intelligence, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of artificial intelligence (AI) and IoT, known as AIOT, Allows those electronic devices to make intelligent, autonomous and more automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze this data to derive valuable insights and make informed decisions. The authors devoted efforts on the critical analysis of AIOT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, the authors contribution is in the formulation of innovative research directions, outlining a clear guide for researchers and professionals in the expansion of knowledge in AIOT integration. Research results in a significant contribution to the continuous advance of the area, enriching the understanding of challenges and boosting the development of solutions and strategies in this technological convergence. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results the research directions are: (i) Development of a methodology that shows how to integrate the different applications independent of the scenarios that are deployed in. Additionally, elaboration of the tools used in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology and Internet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

Section: Edge Ai For Increasing Autonomy Of Iot Systemsmentioning

confidence: 99%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: Edge Ai For Increasing Autonomy Of Iot Systemsmentioning

confidence: 99%

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Luzolo

Galland

Elrawashdeh

et al. 2023

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologiesthat have long been applied to the development of smart systems. These systemscover various areas, such as smart cities, energy management, autonomous cars,etc. Intelligence, autonomy, and real-time monitoring are the fundamental ele-ments that characterize these application areas. The convergence of multi-agentsystems (MAS), IoT, and AI has opened up new possibilities for creating smarterbuildings and cities. This integration leverages the power of MAS to enable intelli-gent communication and collaboration among various entities, while IoT providesa vast network of interconnected sensors and devices that collect and transmitreal-time data. On the other hand, AI algorithms process and analyze this data toderive valuable insights and make informed decisions. Multi-agent systems tech-nology is one of the most used for the implementation of such intelligent systems.This article aims to focus on multi-agent systems, although it also covers otherrelated topics by carrying out a Systematic Literature Review (SLR) on relevantresearch works in the previous ten years. Eleven research questions are consideredat the beginning of the review, including typical research topics and applicationdomains. From the SLR results the research directions are: (i) Development ofa methodology that shows how to integrate the different applications indepen-dent of the scenarios that are deployed in. Additionally, elaboration of the toolsused in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology andInternet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

“…In addition, when building an advanced metering infrastructure (AMI) and data collector unit (DCU), backhaul networks for long-distance distribution facility monitoring, factory facility detection, and diagnostic systems, due to their large number of nodes, should transmit and receive real-time data in broadband; the data rate and service coverage of existing sensor network technologies are insufficient [8,10]. Furthermore, recently emergent security cameras and Artificial Intelligence (AI)-based applications require high resolution and high computing power, and data requirements are increasing due to IoT convergence that supports various functions [11,12]. In medical healthcare smart devices, intelligence and security embedded solutions are needed for ambient assisted living [13].…”

Section: Introductionmentioning

confidence: 99%

Battery Draining Attack and Defense against Power Saving Wireless LAN Devices

Lee

2020

Sensors

View full text Add to dashboard Cite

Wi-Fi technology connects sensor-based things that operate with small batteries, and allows them to access the Internet from anywhere at any time and perform networking. It has become a critical element in many areas of daily life and industry, including smart homes, smart factories, smart grids, and smart cities. The Wi-Fi-based Internet of things is gradually expanding its range of uses from new industries to areas that are intimately connected to people’s lives, safety, and property. Wi-Fi technology has undergone a 20-year standardization process and continues to evolve to improve transmission speeds and service quality. Simultaneously, it has also been strengthening power-saving technology and security technology to improve energy efficiency and security while maintaining backward compatibility with past standards. This study analyzed the security vulnerabilities of the Wi-Fi power-saving mechanism used in smart devices and experimentally proved the feasibility of a battery draining attack (BDA) on commercial smartphones. The results of the experiment showed that when a battery draining attack was performed on power-saving Wi-Fi, 14 times the amount of energy was consumed compared with when a battery draining attack was not performed. This study analyzed the security vulnerabilities of the power-saving mechanism and discusses countermeasures.

show abstract

Energy, latency and staleness tradeoffs in AI-driven IoT

Cited by 6 publications

References 9 publications

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Battery Draining Attack and Defense against Power Saving Wireless LAN Devices

Contact Info

Product

Resources

About