SkiScape Sensing Shane B. Eisenman† and Andrew T. Campbell‡

†Electrical Engineering, Columbia University ‡Computer Science, Dartmouth College

With support from the Institute for Security Technology Studies (ISTS) and Intel Corp.More information on the MetroSense Project, including publications, technical reports, and source code from http://metrosense.cs.dartmouth.edu/.

SkiScape Simulation

Initial Results: Skier Node / Pole Node Connectivity

Motivation Physical Components Description

Opportunistic Sensing and Collection Data Usage and Presentation

Physical drawing of the Dartmouth Skiway is translated to a reachability graph, where trails, lodges and lifts compose the set of vertices

Skier Node- Mote-class devices embedded in ski equipment (e.g., ski boot heels)- Powered with rechargeable batteries- Sensors: temperature, accelerometer, microphone

Pole Node- Line powered, mounted on trail-side light poles- Gateway between Skiers and backhaul network- Sensors: webcam, microphone, radarLodge Node- Line powered, mounted inside lodge structures- Gateway between Skiers and backhaul network- Sensors: temperature, photogate, webcam

Lift Node- Line-powered, mounted on lift structures- Gateway between Skiers and backhaul network- Sensors: temperature, accelerometer

Attributes for each of the vertices are assigned. Trail: dimensions, difficulty, pole location. Lifts: speed, length. Lodge: dimensions, locations of Lodge Nodes. Attributes for each skier are assigned: skill level.Set tx power for Node radios, set radio duty cycles, beacon rates.

Compared to traditional applications for sensor networks (environmental and industrial monitoring), recreational sports is a domain that will spark more general interest in wireless sensor network technology by bringing people into the loop. The SkiScape is part of our broader goal to push People-Centric Sensing.

Provide sensed data feedbackto skiers about trail conditions.

Time-stamped data allows forautomatic speed policing of skiers (AutoPatrol feature).

Location-stamped data allowsfor FriendFinder feature.

Data archives allow for skiersto analyze long-term traces on their skiing habits.

Safety/emergency workers track skiers’ speed and location in case of accident

Management monitors skier flow statistics to estimate wear and enact preventative trail maintenance (snow making)

- 300 Skiers- 8 hour simulated time - Random placement of Pole Nodes along trails’ edge at average 300 ft spacing.

Sparse deployment of mobile and static sensors give a more complete picture over time of the trail conditions. The mobility of Skier Nodes reduces the cost of data transport to the ultimate data sinks.

Nominal values suggest10 hours between battery recharge is possible, if operating all Skier node components at full duty cycle.

Skier nodes physically carry locally sensed data and may also mule the data of other Skiers for robustness or timeliness.

Data transfer between Skiers and gateway Nodes depends on radio duty cycle and beacon rate.