8. Spatial, Mobility and Context-Aware Analytics

Introduction and Context

Many IoT systems observe not only what is happening, but also where, when, and under what circumstances it is happening. A temperature reading from a stationary sensor, for example, has a different meaning from the same reading obtained by a vehicle moving through an urban heat island. A machine vibration alarm is more actionable when its location, operating state, maintenance history, and nearby equipment are known. Spatial, mobility and context-aware analytics provide the methods required to interpret such observations.

Spatial analytics studies relationships between objects, events and measurements in geographic or geometric space. Mobility analytics examines movement through space and time, including trajectories, trips, flows and activity patterns. Context-aware analytics enriches sensor observations with information about the surrounding situation, such as identity, location, time, weather, device state, user activity or business rules. Together, these approaches convert raw location streams into operational knowledge.

The topic is central to intelligent transport, logistics, public safety, smart cities, healthcare, agriculture, retail, environmental monitoring and industrial IoT. It also introduces distinctive challenges. Location data may be noisy, incomplete or sampled irregularly. Movement produces high-volume streams. Spatial relationships often change over time, and privacy risks are substantial because a sequence of locations can reveal personal behaviour.

This chapter develops a practical and analytical foundation for working with these data. It explains spatial representations, trajectories, spatial–temporal models, geofencing, mobility measures, context modelling and sensor fusion. It also considers implementation choices, governance requirements and emerging developments.

Learning Objectives

After studying this chapter, the learner should be able to:

1. Explain the meaning and significance of spatial, mobility and context-aware analytics in IoT.

2. Distinguish geographic coordinate systems, spatial objects and location representations.

3. Construct and analyse trajectories and spatial–temporal event streams.

4. Apply geofencing, proximity analysis, map matching and mobility measures to practical problems.

5. Design context models that combine location, time, activity and environmental information.

6. Explain sensor fusion approaches for improving location and situational awareness.

7. Evaluate the technical, organizational, privacy and ethical risks of location analytics.

8. Select appropriate methods for real-time and batch-based IoT applications.

1. The Spatial–Temporal Nature of IoT Data

An IoT observation is usually represented as a value associated with an entity and a timestamp. For spatial analytics, this representation is extended to include position and often uncertainty:

\[

o_i=(e_i,t_i,p_i,v_i,q_i)

\]

where \(e_i\) is the entity, \(t_i\) is time, \(p_i\) is position, \(v_i\) is the observed value and \(q_i\) represents quality information such as accuracy, confidence or battery condition. Position may be a point, line, polygon, address, grid cell or three-dimensional coordinate.

The same observation can support different interpretations depending on analytical scale. At a local scale, a delivery vehicle may be approaching a warehouse entrance. At a city scale, it may contribute to a congestion pattern. At a regional scale, its movement may describe freight corridors. Spatial scale, temporal resolution and analytical purpose must therefore be considered together.

Spatial data commonly contain three forms of information:

• Location: where an object or event is situated.
• Geometry: the shape, extent and relationships of the object.
• Attributes: descriptive values associated with the object, such as speed, temperature, category or status.

Spatial relationships include distance, direction, containment, intersection, adjacency, connectivity and proximity. Temporal relationships include before, after, during, overlap and recurrence. A spatial–temporal query combines both, such as identifying vehicles within 500 metres of an accident during the ten minutes preceding it.

Coordinate Systems and Location Quality

Global Positioning System (GPS) receivers generally report latitude, longitude and sometimes altitude using a geographic coordinate reference system. Distance calculations performed directly on latitude and longitude can be inaccurate over large areas because the Earth is curved. Projected coordinate systems represent the Earth on a plane and are often preferred for local distance, area and buffering operations.

Location quality depends on signal conditions, receiver characteristics, atmospheric effects, multipath reflections and the positioning technique used. Indoor environments may require Wi-Fi positioning, Bluetooth beacons, ultra-wideband, inertial measurement units or radio fingerprinting. A location record should ideally include an accuracy estimate and timestamp. Treating every coordinate as exact can produce false geofencing alerts, incorrect road assignments and misleading mobility measures.

2. Representing Movement: From Points to Trajectories

A trajectory is an ordered sequence of spatio-temporal positions associated with a moving object:

\[

T=p_1,t_1,p_2,t_2,\ldots,p_n,t_n

\]

The trajectory may describe a person, animal, vehicle, shipment, drone or piece of equipment. A trajectory is not simply a collection of points; order and timing are essential. Consecutive observations can be used to estimate distance, speed, heading, acceleration and stop duration.

For two positions separated by distance \(d_i\) and time interval \(\Delta t_i\), average speed is:

\[

s_i=\frac{d_i}{\Delta t_i}

\]

Distance on a spherical Earth is often estimated using the Haversine formula. In operational systems, however, road-network distance and travel time may be more meaningful than straight-line distance. A vehicle can be geographically close to a destination but separated by a river, restricted road or one-way network.

Trajectory Preparation

Raw mobility data require careful preparation before analysis. Typical steps include:

1. Validation: remove impossible coordinates, invalid timestamps and duplicate records.

2. Ordering: sort observations by device and event time.

3. Noise reduction: identify jumps caused by positioning errors or signal loss.

4. Resampling: convert irregular observations into a useful temporal interval where appropriate.

5. Segmentation: divide a continuous record into trips, stops, visits or activity episodes.

6. Map matching: associate positions with likely roads, paths or indoor zones.

7. Enrichment: join weather, land use, traffic, facility and administrative data.

Resampling must be used cautiously. Interpolating a missing point may create an apparently smooth movement that never occurred. For safety applications, preserving uncertainty and identifying missing intervals may be more appropriate than fabricating a precise path.

Stops, Trips and Visits

A stop is a period during which movement remains below a chosen speed or displacement threshold. A trip is a movement episode between meaningful stops. A visit is a stay within a place or region that meets duration criteria. These definitions are application-dependent. A bus at a traffic signal should not be interpreted as a destination visit, while a delivery vehicle stopped for several minutes near a customer address may be.

Useful mobility measures include trip duration, travelled distance, average and maximum speed, stop frequency, dwell time, route deviation, destination frequency and temporal regularity. At an aggregate level, origin–destination matrices describe flows between zones. A flow matrix can support transport planning, fleet allocation and emergency evacuation modelling.

3. Spatial Analytics Methods for IoT Applications

Proximity and Buffer Analysis

Proximity analysis identifies objects within a distance or travel-time threshold. A buffer creates a region around a point, line or polygon. Examples include detecting assets within a maintenance radius, identifying homes near an air-quality sensor, or alerting when a vehicle approaches a loading bay.

Distance thresholds should reflect the physical process and data quality. A 20-metre buffer may be suitable for a gate sensor but inappropriate for a low-accuracy urban positioning service. Where network accessibility matters, network distance or estimated travel time should replace Euclidean distance.

Point-in-Polygon and Spatial Join

Point-in-polygon analysis determines whether a point lies within a region. It is used to assign vehicles to service zones, determine whether a device is inside a building, or classify pollution readings by administrative area. A spatial join combines records based on a spatial relationship, such as assigning each incident to its nearest hospital or each farm sensor to a field boundary.

Boundary effects require attention. A coordinate near a border may fall into the wrong region because of positional error, outdated boundaries or differing definitions of the boundary itself. Systems should preserve the original coordinate and confidence information rather than storing only the assigned category.

Density and Hotspot Analysis

Spatial density methods identify concentrations of events. Kernel density estimation produces a smoothed surface, while grid aggregation counts events in cells. Hotspot analysis can reveal recurring accident locations, crowd concentrations, equipment failures or disease clusters. Results depend strongly on cell size, bandwidth, observation bias and time window. A visually attractive hotspot map is not automatically evidence of causation.

Map Matching

Map matching assigns noisy location points to a road or pathway network. Candidate road segments are evaluated using position, heading, speed, connectivity and historical movement. A simple nearest-road method can fail at parallel roads, flyovers, complex intersections and urban canyons. More advanced approaches use probabilistic models or hidden Markov models to estimate the most likely route sequence.

4. Geofencing as a Decision Mechanism

A geofence is a virtual boundary associated with a rule or action. It may be circular, polygonal, corridor-based, route-based or three-dimensional. When an object enters, exits, remains within, or crosses a boundary, the system generates an event.

A practical geofencing workflow contains five elements:

1. Define the spatial region: specify geometry, coordinate system and version.

2. Define the event condition: entry, exit, dwell, crossing, approach or restricted presence.

3. Apply temporal and quality rules: require persistence, confidence or multiple observations.

4. Trigger an action: notification, access decision, workflow, logging or actuation.

5. Audit the result: record the input, rule version, decision and response.

For example, a cold-chain platform may create a geofence around a distribution centre. A truck entering the region can initiate unloading procedures, while a refrigerated container leaving the approved route may generate an exception. A single noisy coordinate should not necessarily trigger a high-cost intervention. Hysteresis, such as requiring entry and exit to cross slightly different boundaries, can reduce alert oscillation near an edge.

Geofencing can be implemented on devices, at the network edge or in a central cloud platform. Edge geofencing reduces latency and permits operation during connectivity loss, but synchronizing boundary updates and maintaining consistent rule versions become important engineering tasks.

5. Context-Aware Analytics

Location alone rarely explains an event. Context is the set of relevant conditions that helps interpret an observation and select an appropriate response. It may include:

• identity and role of a person or device;
• location, zone and nearby objects;
• time, schedule, season and duration;
• activity or operational state;
• environmental conditions such as weather, noise or occupancy;
• social, organizational and business rules;
• confidence, data quality and historical state.

A context model represents these elements and their relationships. A simple rule might state: “If a maintenance worker is inside a high-voltage zone, the gate is open, and the worker’s permit is valid, record authorized access.” A more complex model may infer that an elderly resident has fallen by combining posture, acceleration, room location and lack of subsequent movement.

Context Acquisition, Interpretation and Action

Context-aware systems generally perform four functions. They acquire data from sensors and information systems, interpret raw observations into meaningful states, reason over those states, and adapt an application response. Context interpretation can use thresholds, finite-state machines, ontologies, probabilistic models, machine learning or combinations of these.

A finite-state model is useful when behaviour follows recognizable stages, such as a vehicle being available, dispatched, en route, arrived or completed. Probabilistic models are preferable when observations are uncertain. A context engine should distinguish observed facts from inferred states and should retain confidence values and evidence paths.

Context is also hierarchical. A sensor can report “motion detected”; a room-level model may infer “person present”; a building-level model may infer “meeting in progress.” Errors at lower levels can propagate upward, so validation and uncertainty management are essential.

6. Sensor Fusion for Position and Situation Awareness

Sensor fusion combines observations from multiple sources to produce a more reliable estimate than any individual source can provide. In mobility systems, GPS may be combined with accelerometers, gyroscopes, wheel encoders, cameras, LiDAR, cellular signals, Wi-Fi and maps. In context-aware systems, location may be combined with access-control logs, wearable data and environmental sensors.

Fusion methods differ in their assumptions and outputs:

• Rule-based fusion is transparent and suitable for well-defined operational logic.
• Weighted averaging combines estimates according to reliability, but requires meaningful uncertainty estimates.
• Bayesian fusion updates belief as evidence arrives and explicitly represents uncertainty.
• Kalman filtering is effective for approximately linear, continuous systems with Gaussian noise.
• Particle filtering represents multiple possible states and handles nonlinear or multimodal movement.
• Machine-learning fusion learns relationships from labelled or historical data but may be difficult to explain and maintain.

A sensor fusion pipeline should address time synchronization, coordinate alignment, calibration, missing values and contradictory observations. For example, a wearable may report movement while a room sensor reports no occupancy because the person is outside its range. The system should not silently choose one reading; it should reason about coverage and confidence.

7. Analytical Models and Evaluation

Spatial and mobility analytics can be descriptive, predictive or prescriptive. Descriptive analysis summarizes where events occur and how movement is distributed. Predictive analysis estimates future location, arrival time, congestion or failure risk. Prescriptive analysis recommends routes, patrol assignments, charging schedules or interventions.

Common modelling approaches include clustering of destinations, trajectory similarity analysis, origin–destination modelling, time-series forecasting, anomaly detection and graph-based analysis. A road network can be represented as a graph in which intersections are nodes and road segments are edges. Routing then becomes a shortest-path or constrained optimization problem. Constraints may include vehicle capacity, time windows, energy availability and restricted zones.

Evaluation must match the decision objective. Location prediction can be assessed using distance error and percentile error. Geofence classification can use precision, recall and false-alert rate. Arrival-time prediction should consider calibration and errors across different traffic conditions. A model with high average accuracy may still be unsafe if it performs poorly in rare but consequential situations.

8. Applications Across IoT Domains

In smart transportation, trajectory analytics supports congestion monitoring, public-transit reliability, dynamic routing, parking management and fleet utilization. In logistics, location streams enable shipment visibility, route compliance, estimated arrival times and cold-chain monitoring. In agriculture, spatial sensor data support variable-rate irrigation, disease detection and field-level yield analysis.

Industrial facilities use indoor positioning and geofencing to manage hazardous zones, track tools and improve maintenance response. Healthcare organizations may use mobility patterns to monitor rehabilitation, support wandering-risk alerts and optimize home-care visits, subject to strong consent and privacy controls. Public-safety agencies combine incident locations, responder trajectories and environmental data to allocate resources.

Retail and marketing applications can analyse footfall and dwell time, but these uses require particular care because inferred behaviour may be sensitive. Data minimization, aggregation and explicit purpose limitation are essential.

Illustrative Case Study: Geofenced Cold-Chain Distribution

Background

A pharmaceutical distributor transports temperature-sensitive products between a central warehouse, regional depots and hospitals. Each vehicle reports GPS position, compartment temperature, door state and timestamp. The organization wants to reduce delivery delays and identify risks before products become unusable.

Situation and Problem

The existing system sends a temperature alarm whenever a reading exceeds a threshold. It does not distinguish between a vehicle parked at an approved depot, a vehicle delayed in traffic, or a container whose door has been opened during an unauthorized stop. Location records are also irregular, and urban GPS errors occasionally create false route-deviation alerts.

Analysis

The distributor models the supply network as a set of geofenced facilities connected by approved route corridors. Each shipment has a state: loaded, in transit, arrived, unloading, completed or exception. The analytical team examines temperature, door events, speed, dwell time, route position and facility schedules together rather than treating each sensor independently.

A spatial–temporal rule is defined: an open-door event is routine when the vehicle is inside an approved facility during a scheduled delivery window, but suspicious when it occurs outside an approved zone or for longer than the permitted duration. Route deviation is calculated using corridor distance and map-matched position, with a tolerance based on location accuracy. Temperature alerts are weighted by duration and product requirements.

Approach and Solution

A lightweight edge component performs immediate checks for door opening, temperature persistence and entry into facility geofences. The cloud platform maintains trajectory history, performs map matching and calculates estimated arrival time. Sensor fusion combines GPS, accelerometer and vehicle ignition data to distinguish genuine stops from signal artefacts. A dashboard shows current shipment state, confidence and reason for each alert.

The organization also establishes governance rules: access is restricted by role, location data are retained only for operational and regulatory needs, and every alert includes an audit record. Operators can correct a wrongly inferred state, and corrections are used to improve the rule set.

Outcome

The redesigned approach reduces unnecessary alarms by incorporating location, time and operational context. More importantly, it prioritizes alerts that indicate a plausible product risk, such as prolonged temperature exposure during an unauthorized stop. The system does not eliminate uncertainty; instead, it makes uncertainty visible and directs human attention to the most consequential cases.

Learning Points

The case illustrates that geofencing is not merely drawing a boundary. Effective spatial IoT analytics requires quality-aware positioning, temporal rules, state modelling, sensor fusion, human review and governance. Combining multiple signals can improve decisions, but only when the organization understands the limitations of each source.

Technical and Organizational Challenges

Spatial data are often incomplete, delayed and inconsistent. Devices may use different coordinate systems, clocks or sampling intervals. Indoor and outdoor positioning may switch abruptly. Data volume can be substantial when thousands of devices report frequently. Stream processing, spatial indexes, tile-based storage and edge computation can help, but architecture should be driven by latency and accuracy requirements rather than by technology fashion.

Common analytical mistakes include using straight-line distance when network distance matters, ignoring positional uncertainty, treating correlation as causation, aggregating data over unsuitable zones, and interpreting missing observations as absence of activity. Another error is applying one threshold to all devices or environments. A GPS error of ten metres may be acceptable for regional fleet monitoring but unacceptable for access control at a hazardous machine.

Models can also reproduce social and geographic bias. Areas with better connectivity generate more observations, while marginalized communities may be underrepresented. Mobility patterns can be distorted by device ownership, sampling design and differential access to transport. Analysts should document coverage, missingness and uncertainty.

Privacy, Ethics and Governance

Location histories are highly revealing. Even without names, repeated movements may identify a home, workplace, medical facility, religious institution or personal relationship. Responsible governance begins with a clear purpose, lawful basis or valid consent where required, and collection limited to what is necessary.

Important controls include pseudonymization, aggregation, encryption, role-based access, retention limits, audit trails and secure deletion. Individuals should receive understandable information about collection and use. Sensitive inferences should not be made solely from mobility data without appropriate justification and safeguards. Automated geofence decisions affecting employment, access, insurance or public services require transparency, contestability and human oversight.

Organizations should maintain data dictionaries, quality indicators, model documentation, boundary versioning and incident procedures. Privacy-preserving techniques such as spatial aggregation, differential privacy and on-device processing may reduce exposure, although each can reduce analytical utility. Governance is therefore an engineering and managerial design problem, not an afterthought.

Current Developments and Emerging Directions

Edge intelligence is increasingly used for low-latency geofencing, indoor positioning and local anomaly detection. Digital twins combine spatial models with live sensor streams to represent buildings, factories, transport networks and cities. Advances in satellite positioning, ultra-wideband, computer vision and cooperative sensing are improving positioning in challenging environments.

Spatio-temporal machine learning is being applied to traffic prediction, demand forecasting and environmental modelling. Graph neural networks can represent road, infrastructure or social connectivity, although their operational value depends on data quality and interpretability. Privacy-enhancing computation, federated learning and synthetic mobility data are receiving attention where organizations need collaborative analytics without sharing raw trajectories.

Emerging systems will increasingly combine multimodal data: coordinates, images, language, maps, device telemetry and human feedback. This creates opportunities for richer context but also increases risks of opaque inference, surveillance and security compromise. Professional practice must therefore combine technical performance with accountability.

Best-Practice Guidance

A robust spatial and context-aware IoT project should:

• define the operational decision before selecting a spatial method;
• record coordinate systems, timestamps, accuracy and provenance;
• distinguish observed, inferred and predicted states;
• select distance, time and aggregation scales appropriate to the process;
• test algorithms under signal loss, boundary conditions and unusual events;
• use confidence-aware rules rather than absolute trust in one sensor;
• evaluate false positives and false negatives in business terms;
• process sensitive data at the edge or in aggregated form where feasible;
• version geofences, models, maps and business rules;
• retain human review for high-impact decisions; and
• monitor performance after deployment, because mobility behaviour and environments change.

Chapter Summary

Spatial, mobility and context-aware analytics extend IoT analysis by incorporating location, movement, time and surrounding circumstances. Spatial data may be represented as points, lines, polygons, grids or network objects, but meaningful analysis requires attention to coordinate systems, scale and positional uncertainty. Trajectories transform ordered observations into measures of speed, distance, stops, trips, visits and flows.

Core techniques include proximity analysis, buffers, spatial joins, density estimation, map matching and geofencing. Context-aware systems interpret sensor observations through identity, activity, time, environment, operational state and business rules. Sensor fusion combines complementary sources using rules, probabilistic methods, filters or machine learning. Applications include transport, logistics, healthcare, agriculture, industry and public safety.

Effective implementation depends on quality management, suitable spatial–temporal models, evaluation aligned with decisions, and reliable stream processing. Location analytics also creates serious privacy and ethical concerns. Purpose limitation, transparency, security, uncertainty reporting and human oversight are essential. The central professional lesson is that spatial intelligence is not produced by coordinates alone; it emerges from carefully governed relationships among place, time, movement, context and action.

Key Terms

Spatial analytics: Analysis of data according to location, geometry and spatial relationships.

Mobility analytics: Study of movement patterns, trajectories, trips, stops and flows.

Context-aware analytics: Interpretation of observations using surrounding situational information.

Trajectory: An ordered sequence of positions associated with an object over time.

Spatial–temporal data: Data whose meaning depends on both geographic position and time.

Coordinate reference system: A framework defining how positions are represented on or relative to the Earth.

Map matching: Assignment of location observations to the most likely road, path or network segment.

Geofence: A virtual spatial boundary linked to an event rule or automated action.

Buffer: A region created at a specified distance around a spatial object.

Spatial join: Combination of records based on a spatial relationship.

Point-in-polygon analysis: Determination of whether a point lies within a defined area.

Origin–destination matrix: A table summarizing movement between starting and ending zones.

Context model: A representation of entities, conditions and relationships used to interpret events.

Sensor fusion: Combination of multiple sensor or information sources to estimate a state.

Hysteresis: Use of different thresholds for entering and leaving a condition to reduce oscillation.

Spatial uncertainty: Imperfection or lack of precision in the reported location.

Dwell time: Duration for which an object remains within a place or state.

Edge analytics: Processing performed near the data source rather than only in a central cloud.

Digital twin: A dynamic digital representation of a physical object, system or environment.

Privacy-preserving analytics: Methods that reduce exposure of identifiable information while retaining analytical value.

Review Questions

Short Answer Questions

1. Why must spatial IoT observations include time and, where possible, an accuracy estimate?

2. Distinguish a trajectory from an unordered collection of location points.

3. What is the purpose of map matching?

4. Define a geofence and identify two types of geofence events.

5. Why is sensor fusion useful in context-aware IoT systems?

Long Answer Questions

1. Explain the preparation steps required before analysing raw vehicle trajectory data.

2. Discuss proximity analysis, spatial joins, density analysis and map matching as spatial IoT methods.

3. Explain how a context-aware system acquires, interprets and acts on contextual information.

4. Compare rule-based fusion, Bayesian fusion, Kalman filtering and machine-learning fusion.

5. Analyse the privacy, ethical and governance issues associated with mobility analytics.

Analytical / Application Questions

1. Design a geofencing and context model for a mining site where worker location, vehicle movement and hazardous zones must be monitored. Specify events, uncertainty controls and responses.

2. A public-transit operator receives irregular GPS data with frequent urban signal errors. Propose a workflow for cleaning, map matching, trajectory analysis and service-performance evaluation.

3. A healthcare provider wants to infer whether a patient has fallen using wearable motion, room occupancy and location data. Develop a sensor-fusion approach and discuss false alarms, consent and human oversight.

22. Multiple Choice Questions with Answers

Question 1

Which representation best captures the essential structure of a moving IoT object?

A. An unordered list of coordinates

B. An ordered sequence of positions with timestamps

C. A single polygon without time information

D. A table containing only sensor values

Correct Answer: B. An ordered sequence of positions with timestamps

Question 2

Why can straight-line distance be inappropriate for vehicle route analysis?

A. It always overestimates satellite accuracy

B. It ignores road connectivity, barriers and travel restrictions

C. It cannot be calculated from coordinates

D. It requires temperature data

Correct Answer: B. It ignores road connectivity, barriers and travel restrictions

Question 3

A geofence system repeatedly alternates between inside and outside near a boundary because of GPS noise. Which technique is most directly useful?

A. Increasing the number of attributes

B. Applying hysteresis or persistence rules

C. Removing all timestamps

D. Replacing polygons with text labels

Correct Answer: B. Applying hysteresis or persistence rules

Question 4

Which analysis determines whether a vehicle is located within an approved delivery region?

A. Point-in-polygon analysis

B. Kernel density estimation

C. Origin–destination analysis

D. Trajectory clustering only

Correct Answer: A. Point-in-polygon analysis

Question 5

What is the main purpose of map matching?

A. To encrypt coordinates

B. To assign noisy positions to likely network segments

C. To increase sensor sampling frequency

D. To remove all movement stops

Correct Answer: B. To assign noisy positions to likely network segments

Question 6

Which situation best illustrates context-aware analytics?

A. Reporting temperature without its timestamp

B. Triggering an access alert only when a worker is in a restricted zone without a valid permit

C. Storing coordinates in alphabetical order

D. Calculating the average of unrelated sensor readings

Correct Answer: B. Triggering an access alert only when a worker is in a restricted zone without a valid permit

Question 7

In the cold-chain case, why should an open-door event be interpreted together with location and schedule?

A. Door sensors cannot produce numerical data

B. The operational meaning differs between an approved delivery and an unauthorized stop

C. Location data automatically correct temperature readings

D. Schedules eliminate the need for audit records

Correct Answer: B. The operational meaning differs between an approved delivery and an unauthorized stop

Question 8

Which measure is most appropriate for evaluating a geofence alerting system?

A. Font size of the dashboard

B. Precision, recall and false-alert rate

C. Number of coordinate decimals only

D. Total number of stored columns

Correct Answer: B. Precision, recall and false-alert rate

Question 9

What is a major privacy risk of repeated mobility data?

A. It prevents all spatial aggregation

B. It can reveal sensitive places, routines and relationships

C. It cannot be transmitted over networks

D. It contains no information about individuals

Correct Answer: B. It can reveal sensitive places, routines and relationships

Question 10

Which practice most directly improves trustworthiness in a context-aware IoT system?

A. Treating every inferred state as an observed fact

B. Recording confidence, evidence and rule or model versions

C. Discarding all missing observations without documentation

D. Using one threshold for every environment

Correct Answer: B. Recording confidence, evidence and rule or model versions