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The Igliniit Project brought together Inuit hunters and geomatics engineering students during the International Polar Year (IPY) to collaborate on the development and testing of a new integrated GPS/PDA/mobile weather station technology for observing and monitoring the environment. Part of the larger Inuit Sea Ice Use and Occupancy Project (ISIUOP), the Igliniit Project culminated in a tangible product that is the direct result of combined scientific and Inuit knowledge, ingenuity, and engineering. This paper describes the Igliniit Project and examines the resulting technology as (i) an artifact of Inuit knowledge, science and engineering collaboration; (ii) a tool for meaningful engagement of Inuit in environmental science and community‐based monitoring; (iii) a new approach and tool in the field of indigenous mapping; and (iv) an example of one technology in the expanding ecology of technologies in everyday Inuit life. The technology requires improvements in hardware and further development of supporting systems such as data management and mapping capability, but there is potential for the Igliniit Project approach and system to have wide appeal across the North for a variety of applications including environmental monitoring, wildlife studies, land use mapping, hazards research, place names research, archaeological and cultural inventories, and search and rescue operations.

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.