Logistics
15 columns
125 rows
8 formats

Cold Chain IoT Sensor Logs

This dataset provides granular IoT sensor logs for cold chain shipments, recording temperature, humidity, shock events, and device status with precise timestamps and geolocation. It enables logistics managers to monitor perishable goods in real-time, detect supply chain disruptions, ensure regulatory compliance, and minimize spoilage risk through actionable insights.

last updated 20 Jan 2026
by GoMask

The brief that made it

Real-time monitoring and alerting for cold chain shipments

Sample rows

preview · 8 of 125 rows · all 15 columns

log_idstring	sensor_statusstring	battery_level_percentfloat	shock_detectedboolean	location_descriptionstring	device_idstring	shipment_idstring	timestampdatetime	temperature_celsiusfloat	humidity_percentfloat	shock_g_forcefloat	location_latitudefloat	location_longitudefloat	regulatory_compliance_flagboolean	notesstring
LOG-00001	active	98.7	false	warehouse	DEV-123A	SHIP-5010	2024-01-03T07:34:21Z	17.2	54.8	blank	34.0522	-118.2437	true	blank
LOG-00002	active	89.2	false	urban depot	DEV-456B	SHIP-5011	2024-01-03T09:12:44Z	19.6	57.1	blank	40.7128	-74.006	true	blank
LOG-00003	active	96.1	false	warehouse	DEV-123A	SHIP-5010	2024-01-03T11:45:02Z	16.8	41.3	blank	34.0522	-118.2437	true	blank
LOG-00004	active	82.5	false	dock	DEV-789C	SHIP-5022	2024-01-04T02:25:17Z	22	59.8	blank	41.8781	-87.6298	true	blank
LOG-00005	active	87	false	urban depot	DEV-456B	SHIP-5011	2024-01-04T08:18:56Z	15.3	48.4	blank	40.7128	-74.006	true	blank
LOG-00006	active	71.6	false	freezer	DEV-X999	SHIP-5023	2024-01-05T03:44:10Z	-19.1	21.2	blank	61.2181	-149.9003	true	Deep freeze shipment
LOG-00007	active	67.3	true	truck	DEV-888F	SHIP-5030	2024-01-05T06:12:37Z	36.7	63.2	2.2	29.7604	-95.3698	false	High temp/humidity
LOG-00008	active	88.6	false	port	DEV-456B	SHIP-5015	2024-01-05T09:20:11Z	24.7	51	blank	38.9072	-77.0369	true	blank
LOG-00009	active	92.1	false	warehouse	DEV-101D	SHIP-5042	2024-01-06T01:16:42Z	23.5	56.2	blank	33.4484	-112.074	true	blank
LOG-00010	active	76.2	false	airport	DEV-Y777	SHIP-5043	2024-01-06T04:57:21Z	26.4	52.7	blank	25.7617	-80.1918	true	blank
LOG-00011	active	81.9	true	dock	DEV-789C	SHIP-5022	2024-01-06T08:34:53Z	27.8	63.9	1.3	41.8781	-87.6298	false	Minimal shock event
LOG-00012	active	94.8	false	urban depot	DEV-W002	SHIP-5045	2024-01-06T12:13:42Z	18.9	44.6	blank	32.7767	-96.797	true	blank
LOG-00013	error	2	true	Arctic boundary	DEV-999Z	SHIP-5047	2024-01-07T05:39:08Z	-89.5	2.2	19.4	69.6496	18.9553	false	Extreme cold edge case
LOG-00014	active	93.5	false	warehouse	DEV-123A	SHIP-5014	2024-01-07T07:14:31Z	21.1	57.3	blank	34.0522	-118.2437	true	blank
LOG-00015	active	59.4	true	customs	DEV-555E	SHIP-5049	2024-01-07T13:26:45Z	32	81.4	3.1	42.3601	-71.0589	false	Minor shock detected
LOG-00016	active	97.9	false	warehouse	DEV-123A	SHIP-5012	2024-01-08T04:41:59Z	15.7	46.8	blank	34.0522	-118.2437	true	blank
LOG-00017	active	85.2	false	urban depot	DEV-456B	SHIP-5016	2024-01-08T06:19:51Z	20.9	50.5	blank	40.7128	-74.006	true	blank
LOG-00018	active	72.1	true	dock	DEV-789C	SHIP-5025	2024-01-08T09:30:07Z	38.6	88.7	4.7	41.8781	-87.6298	false	High temp/humidity
LOG-00019	active	84.6	false	urban depot	DEV-456B	SHIP-5017	2024-01-09T01:55:03Z	21.4	53.4	blank	40.7128	-74.006	true	blank
LOG-00020	active	61.5	true	truck	DEV-888F	SHIP-5031	2024-01-09T07:42:22Z	37.2	74.3	2.9	29.7604	-95.3698	false	High temp/humidity

What the 125 rows show

from the 125-row sample

Active (sensor status) stands out: mean battery_level_percent is 78.4, against 22.1 for the rest.

28%shock_detected = true
76.4median battery_level_percent
10devices
21.4median temperature_celsius
53.4median humidity_percent
2.7median shock_g_force

Mean battery_level_percent by sensor_status125 rows

battery_level_percent125 rows, in bands of 10

Median 76.4, from 0.0 to 98.7.

location_description125 rows · top 10 of 14 values

warehouse29
urban depot21
dock18
truck13
cold storage11
port7
airport7
Arctic boundary6
customs5
freezer3

15 columns by typefrom the column list below

string 6
float 6
datetime 1
boolean 2

Columns

15 columns in four groups

blueprint · 15 columns
column	type	description	example
Text 6 columns
`log_id`	string	Unique identifier for each IoT sensor log entryunique	LOG-00001
`device_id`	string	Unique identifier for the IoT sensor device10 devices	DEV-123A
`shipment_id`	string	Identifier for the shipment or consignment being monitored	SHIP-5010
`location_description`	string	Optional textual description of the sensor's location (e.g., warehouse, truck, port)optional	warehouse
`sensor_status`	string	Operational status of the sensor at the time of loggingactive · inactive · error · maintenance	active
`notes`	string	Optional free-text notes or comments about the log entryoptional	Deep freeze shipment
Numbers 6 columns
`temperature_celsius`	float	Measured temperature in degrees Celsius-100 to 100	17.2
`humidity_percent`	float	Measured relative humidity as a percentage (0-100%)0 to 100	54.8
`shock_g_force`	float	Peak G-force recorded during a shock event (null if no shock detected)0 or more · optional	2.2
`location_latitude`	float	Latitude coordinate where the sensor reading was recorded-90 to 90 · optional	34.0522
`location_longitude`	float	Longitude coordinate where the sensor reading was recorded-180 to 180 · optional	-118.2437
`battery_level_percent`	float	Battery level of the sensor device as a percentage (0-100%)0 to 100 · optional	98.7
Dates and times 1 column
`timestamp`	datetime	Date and time when the sensor reading was recorded (in UTC)	2024-01-03T07:34:21Z
True or false 2 columns
`shock_detected`	boolean	Indicates if a shock event was detected at the time of logging	false
`regulatory_compliance_flag`	boolean	Indicates if the reading is within regulatory compliance thresholdsoptional	true

Use it for

A logistics dashboard
The shock_detected rate, battery_level_percent by sensor_status and a breakdown of location_description. Excel, Power BI or Tableau.
Why do the 109 active rows have a mean battery_level_percent of 78.4?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs125
LOG-0000198.7active
LOG-0000767.3active
LOG-0001181.9active
A software demo
Believable logs with device_id, shipment_id and timestamp to fill a screen in front of a buyer.

This dataset125 rows15 columns

Yours1,000 rows15 columnslocation_latitude: UK only

blueprint · cold-chain-iot-sensor-logs

Behind this dataset

Same schema. As many rows as you need.

These 125 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each row represents a unique sensor event at a specific timestamp.
Temperature readings must be between -30°C and 30°C.
Humidity values range from 20% to 90%.
Shock events are recorded as binary (yes/no) and must be linked to a location.
Shipments are uniquely identified and mapped to route segments.
Compliance flags are set if thresholds are breached.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: cold-chain-iot-sensor-logs

What should your data show?

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Sample rows

What the 125 rows show

Columns

Use it for

A logistics dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Logistics

What should your data show?