The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around 1.4 million smartphones. By 2023, this number reached over 231 million units.

The newest models and iPhone’s lasting popularity

Apple has ventured into its 17th smartphone generation with its Phone 15 lineup, which, released in September 2023, includes the 15, 15 Plus, 15 Pro and Pro Max. Powered by the A16 bionic chip and running on iOS 17, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of 1,000 U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over 60 million smartphones, while Apple recorded shipments of roughly 50 million units.

Success of Apple’s other products

Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around 50 percent of the market share as of the latest quarter.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.

This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.

It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Related dataset

Same authors also produced the Labeled dataset of IEEE 802.11 probe requests with same data layout and recording equipment.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device).
Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.

The following information about each received PR is collected:
- MAC address
- Supported data rates
- extended supported rates
- HT capabilities
- extended capabilities
- data under extended tag and vendor specific tag
- interworking
- VHT capabilities
- RSSI
- SSID
- timestamp when PR was received.

The collected data was forwarded to a remote database via a secure VPN connection.
A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.

Data preprocessing

The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database.
For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:

PR_IE_data =
{
  'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
  'HT_CAP': DATA_htcap,
  'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
  'VHT_CAP': DATA_vhtcap,
  'INTERWORKING': DATA_inter,
  'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
  'VENDOR_SPEC': {VENDOR_1:{
                'ID_1': DATA_1_vendor1,
                'ID_2': DATA_2_vendor1
                ...},
          VENDOR_2:{
                'ID_1': DATA_1_vendor2,
                'ID_2': DATA_2_vendor2
                ...}
          ...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:

{
  'TIME': [ DATA_time ],
  'RSSI': [ DATA_rssi ],
  'DATA': PR_IE_data
}.

This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended.
If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png

At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.

Folder structure

For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period.
Each folder contains four files, each containing samples from that device.

The folders are named after the start and end time (in UTC).
For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time.

Files representing their location via mapping:
- 1.json -> location 1
- 2.json -> location 2
- 3.json -> location 3
- 4.json -> location 4

Environments description

The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo
The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset.
As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system.
Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.

Four Raspbery Pi-s were used:
- location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano)
- location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo
- location 3 -> nothernmost window in the building of Via Etnea near Piazza Università
- location 4 -> first window top the right of the entrance of the University of Catania

Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access)
Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.

Known dataset shortcomings

Due to technical and physical limitations, the dataset contains some identified deficiencies.

PRs are collected and transmitted in 10-second chunks.
Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.

Every 20 minutes the service is restarted on the recording device.
This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond.
For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.

The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.

Location 1 - Piazza del Duomo - Chierici

The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.
Its location is constant and is not disturbed, dataset seems to have complete coverage.

Location 2 - Via Etnea - Piazza del Duomo

The device is located inside the building.
During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.
As the device was moved back and forth, power outages and internet connection issues occurred.
The last three days in the record contain no PRs from this location.

Location 3 - Via Etnea - Piazza Università

Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.
Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present.
This device appears to have been collecting data throughout the whole dataset period.

Location 4 - Piazza Università

This location is wirelessly connected to the access point.
The device was placed statically on a windowsill overlooking the square.
Due to physical limitations, the device had lost power several times during the deployment.
The internet connection was also interrupted sporadically.

Recognitions

The data was collected within the scope of Resiloc project with the help of City of Catania and project partners.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.
The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received.
The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database.
For each detected PR in the scan interval, IEs fields are saved in the following JSON structure:
PR_IE_data =
{
'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
'HT_CAP': DATA_htcap,
'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
'VHT_CAP': DATA_vhtcap,
'INTERWORKING': DATA_inter,
'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
'VENDOR_SPEC': {VENDOR_1:{
'ID_1': DATA_1_vendor1,
'ID_2': DATA_2_vendor1
...},
VENDOR_2:{
'ID_1': DATA_1_vendor2,
'ID_2': DATA_2_vendor2
...}
...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:
{
'TIME': [ DATA_time ],
'RSSI': [ DATA_rssi ],
'DATA': PR_IE_data
}.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended.
If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute.
See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled.
All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet.
Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods: Each project map is developed using the following steps: 1. The project map was drawn based on the information provided in the research project proposals. 2. The map was refined based on feedback during the first data discussions with the project leader. 3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.

Limitations of the data: The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data: The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.

Data dictionary: NAME - Title of the layer PROJ - Project code of the project relating to the layer NODE - Whether the project is part of the Northern or Southern Nodes TITLE - Title of the project P_LEADER - Name of the Project leader and institution managing the project PROJ_LINK - Link to the project metadata MAP_DESC - Brief text description of the map area MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities MOD_DATE - Last modification date to the individual map layer (prior to merging)

Updates & Processing: These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated: 20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)

Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

Ecosystems provide numerous services and benefits to society. While historically overlooked, these services are increasingly recognized and are now being mapped and accounted for. There are several approaches to mapping and evaluating these ecosystem services. In this report, we use two increasingly common approaches, Ocean Accounting and Welfare Economics, to evaluate ecosystem services for the Great Southern Reef.

The Great Southern Reef is a network of rocky reefs dominated by temperate algal forests known as kelp. It spans over 8,000 Km of coastline and supports two thirds of the Australian population. Despite its presumed importance, there has been little work quantifying the extent and value of the ecosystem services provided by the Great Southern Reef.

Through a systematic review we assessed the current state of knowledge of the ecosystem services provided by the Great Southern Reef. Using the Common International Classification of Ecosystem Services (CICES) framework, we created an overview of the ecosystem services (provisioning, regulating, and cultural) provided by the Great Southern Reef in New South Wales, Victoria, Tasmania, South Australia, and Western Australia. We then created metrics to quantify how these services benefit coastal societies in these five states.

Highlight summaries include over 17 million Australians who live within 50 Km of the reef, 26 wild seaweed harvest companies, 115 tourism SCUBA operators, 1436 mapped dive sites, 18 million tourist visits each year, 16 temperate marine biology university programs, 43 books and films, key medical products, 23 tons of harvested seaweed, 1116 grams of carbon per m2 used for growth each year, 2,361 peer-reviewed scientific publications from 1976 to 2022, 186 marine protected areas, 2.16 million recreational fishers, and over 28 commercial fisheries with 20,000 tons of biomass taken each year.

We then conducted economic evaluations using these biophysical values and the available information. Using a variety of approaches, we found that the total economic value of the Great Southern Reef was $11.56 billion each year. Individually the values were as follows, commercial fishing (producer surplus - $33.2 million), carbon sequestration (avoided damages - $37.8 million), nutrient cycling (avoided damages - $6,484 million), recreational fishing (consumer surplus - $1,668 million), diving and snorkelling (consumer surplus - $403 million), other recreational activities (consumer surplus $1,836 million), and the existence value (consumer surplus - $1,096 million).

This dataset provides values for BIG MAC INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.