Distribution of Updates to IoT Nodes in a Resource-Challenged Environment

Tollefsen, Roberth; Raïs, Issam; Bjørndalen, John Markus; Ha, Phuong Hoai; Anshus, Otto J.

doi:10.1109/ccgrid51090.2021.00082

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…However, most devices need to be shut down in order to perform a software or firmware update in order to allow a bootloader to load a new full FW image [11], [43]. Many devices are often in sleep mode, where memory and CPU intensive tasks, i.e., FW updates, are hardly possible [38].…”

Section: Software Engineering For Internet Of Things' Patches and Upd...mentioning

confidence: 99%

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

Ebbers

2023

IIEEE Trans. Software Eng.

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This paper examines the up-to-dateness of installed firmware versions of Internet of Things devices accessible via public Internet. It takes a novel approach to identify versions based on the source code of their web interfaces. It analyzes data sets of 1.06m devices collected using the IoT search engine Censys and then maps the results against the latest version each manufacturer offers. A fully scalable and adaptive approach is developed by applying the SEMMA data mining process. This approach relies on three data artifacts: raw data from Censys, a mapping table with firmware versions, and a keyword search list. The results confirm the heterogeneity of connected IoT devices and show that only 2.45 percent of the IoT devices "in the wild" run the latest available firmware. Installed versions are 19.2 months old on average. This real-world evidence suggests that the updating processes and methods used by engineers so far are not sufficient to keep IoT devices up-to-date. This paper identifies and quantifies influencing factors and captures the global and diverse distribution of IoT devices. It finds manufacturer and device type influence the up-todateness of firmware, whereas the country in which the device is deployed is less significant.

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Section: Software Engineering For Internet Of Things' Patches and Upd...mentioning

confidence: 99%

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

Ebbers

2023

IIEEE Trans. Software Eng.

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“…For coordinations, two types of communication are considered: either direct (i.e., ONto-ON) or using an available RN, as an intermediary. In the DAO-CPS, ONs neighborhood sizes can vary up to 29 ONs [5]. To cover most cases, simulations are run using 1 aggregation ON and 5, 15, or 30 measurement ONs.…”

Section: B Simulation Parametersmentioning

confidence: 99%

“…Finally, two suitable radio technologies for the DAO-CPS [1] are simulated: LoRa and NB-IoT. The energy calibration is done from the energy consumption of a Raspberry Pi based ON, previously used in papers dealing with CPS deployment in the AT [5], [7]. The extreme values measured in [6] are used (1.339W for P idle , and 2.697W for P stress ).…”

Section: B Simulation Parametersmentioning

confidence: 99%

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Evaluating the energy consumption of adaptation tasks for a CPS in the Arctic Tundra

Omond,

Rais,

Coullon

2023

2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &Amp;amp; Communications (GreenCom

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Cyber-Physical Systems deployed in scarce resource environments like the Arctic Tundra (AT) face extreme conditions. Nodes deployed in such environments have to carefully manage a limited energy budget, forcing them to alternate long sleeping and brief uptime periods. During uptimes, nodes can collaborate for data exchanges or computations by providing services to other nodes. Deploying or updating such services on nodes requires coordination to prevent failures (e.g., sending new/updated API, waiting for service activation/deactivation, etc.). In a CPS with short uptime periods, such coordination can be energy-consuming due to low opportunities for communications.This paper evaluates and studies nodes' energy consumption during deploy or update tasks coordination according to different CPS configurations (i.e., number of nodes, uptime duration, radio technology, or relay node availability). Results show high energy consumption in scenarios where nodes wake up specifically to deploy/update. It is shown that it is beneficial to do adaptation tasks while overlapping with existing uptimes (i.e., reserved for observation activities). This paper also evaluates and studies how nodes' uptime duration and relay node availability influence energy consumption. Increasing uptime duration can reduce energy consumption, up to 12%. Using an available relay node for communication reduces the energy consumption by 47% to 99%.

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