Ad Hoc, Vehicular, Sensor and Mobile IoT Networks

Introduction and Context

Mobile and wireless networks increasingly operate without fixed infrastructure, under changing topology, limited energy, intermittent connectivity, and heterogeneous device capabilities. Examples include disaster-response teams forming a temporary network, vehicles exchanging safety messages, battery-powered sensors monitoring an industrial plant, and mobile Internet-of-Things (IoT) devices coordinating through cellular, Wi-Fi, satellite, or low-power wide-area technologies.

These systems differ from conventional infrastructure-based networks because communication, computation, sensing, and mobility are closely coupled. A node may simultaneously act as an end device, router, data collector, and service provider. Network performance therefore depends not only on protocol efficiency but also on node movement, radio propagation, energy availability, traffic patterns, sensing correlation, security exposure, and application deadlines.

This chapter develops an integrated framework for designing and evaluating mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), wireless sensor networks (WSNs), and mobile IoT systems. Particular attention is given to topology control, routing, clustering, medium access control, data aggregation, mobility, resilience, and practical design trade-offs.

Learning Objectives

After completing this chapter, the reader should be able to:

1. Distinguish MANET, VANET, WSN, and mobile IoT architectures and use cases.

2. Explain how mobility, radio propagation, energy constraints, and traffic affect network design.

3. Compare proactive, reactive, geographic, opportunistic, and information-centric routing.

4. Design suitable topology-control and clustering strategies.

5. Evaluate MAC protocols, data aggregation methods, and resilience mechanisms.

6. Select performance metrics and interpret experimental or simulation results.

7. Analyze security, privacy, safety, governance, and deployment constraints.

8. Propose an integrated architecture for a realistic mobile sensing or communication application.

1. Network Families and Their Distinguishing Characteristics

1.1 Mobile ad hoc networks

A MANET is a self-organizing multihop wireless network in which nodes communicate without depending on fixed infrastructure. Each node may forward packets for others. Typical applications include military communication, emergency response, temporary classrooms, field operations, and collaborative robotics.

MANETs usually exhibit:

• rapidly changing links;
• distributed control;
• variable node density;
• limited wireless bandwidth;
• battery or energy constraints;
• asymmetric or unreliable links; and
• absence of a permanently trusted central authority.

The defining design problem is maintaining useful connectivity while routes, neighbors, and available resources change.

1.2 Vehicular ad hoc networks

A VANET is a specialized ad hoc network involving vehicles and roadside units (RSUs). Vehicles may exchange information directly through vehicle-to-vehicle (V2V) communication or with infrastructure through vehicle-to-infrastructure (V2I) communication. The broader term vehicle-to-everything (V2X) includes communication with pedestrians, networks, and traffic infrastructure.

VANET mobility is more structured than general MANET mobility: vehicles follow roads, lanes, traffic rules, and correlated movement patterns. Nevertheless, high relative speed can cause short link lifetimes. Safety applications require low latency and high delivery reliability, whereas infotainment applications can tolerate delay but demand greater throughput.

Typical applications include collision warnings, cooperative awareness, emergency braking alerts, platooning, traffic management, electronic tolling, and road-condition reporting.

1.3 Wireless sensor networks

A WSN consists of sensor nodes that measure physical phenomena and communicate observations to a sink, gateway, or distributed processing system. Nodes commonly have low-power radios, modest processors, limited memory, and battery supplies. Sensor networks may be static, mobile, or mixed; mobile sinks and mobile sensor platforms are increasingly important.

Unlike a conventional data network, a WSN is often evaluated by the quality and timeliness of information delivered rather than by packet delivery alone. Redundant measurements can be exploited through aggregation, compression, and in-network processing.

1.4 Mobile IoT networks

Mobile IoT combines sensing, actuation, computation, and connectivity across devices that may move or interact with mobile users, vehicles, robots, drones, or wearables. Connectivity can be provided by cellular networks, Wi-Fi, Bluetooth Low Energy, IEEE 802.15.4-based systems, LPWAN, satellite systems, or hybrid combinations.

Mobile IoT is not a single protocol or network type. It is an architectural and application domain in which devices, gateways, edge platforms, cloud services, and digital identities cooperate. Its principal challenge is interoperability across devices with different energy budgets, data models, security capabilities, and mobility patterns.

2. System Models and Design Constraints

2.1 Network graph model

A wireless network can be represented by a time-varying graph G(t) = (V, E(t)), where V is the set of nodes and E(t) is the set of communication links at time t. A link exists when the received signal satisfies the required radio sensitivity and the protocol conditions permit communication. Because nodes move, radio conditions vary, or devices sleep, E(t) changes over time.

A route is useful only while its constituent links remain available. If a path contains many links, its reliability can be lower than that of a shorter path, although a short path is not always optimal: it may pass through congested, energy-poor, or unstable nodes.

2.2 Radio and propagation considerations

A received signal is affected by distance, antenna gains, transmit power, path loss, fading, shadowing, interference, and obstacles. A simplified link-budget relationship is

Pr(dBm) = Pt(dBm) + Gt(dBi) + Gr(dBi) - PL(dB) - Lmisc(dB),

where Pr is received power, Pt is transmit power, Gt and Gr are antenna gains, PL is path loss, and Lmisc represents other losses. A link is feasible only if received power exceeds receiver sensitivity by an adequate margin. This relationship is a planning approximation, not a complete model of packet reliability.

For a log-distance path-loss model,

PL(d) = PL(d0) + 10n log10(d/d0) + Xσ,

where d0 is a reference distance, n is the path-loss exponent, and Xσ models shadowing. The model assumes a statistically appropriate propagation environment and should be calibrated using measurements. It is not sufficient by itself for predicting performance in obstructed urban roads or indoor industrial environments.

2.3 Mobility and link lifetime

Mobility affects route validity, neighbor discovery, channel access, and application semantics. A mobility model should reflect the intended setting. Random waypoint mobility may be useful for controlled comparative studies but does not accurately represent roads, pedestrian flows, drone flight paths, or robot formations.

A link-lifetime estimate can be derived from relative node motion, but its accuracy depends on position, velocity, radio range, direction, and obstacles. In practice, protocols often use measured link stability, historical contact duration, position prediction, or received-signal trends rather than relying on a single geometric estimate.

2.4 Energy and resource constraints

For a battery-powered device, energy consumption can be approximated by

Etotal = Etx + Erx + Eidle + Esleep + Eprocessing,

where the terms represent transmission, reception, idle listening, sleep, and processing energy over the observation interval. The equation is an accounting model; each component must be estimated using the actual hardware duty cycle and workload.

In many low-power devices, idle listening and repeated retransmissions consume more energy than successful payload transmission. Consequently, sleep scheduling, efficient neighbor discovery, adaptive retransmission, and reduction of control overhead are central design mechanisms.

3. Topology Control and Clustering

Topology control seeks to maintain a communication structure that is connected and sufficiently capable while reducing interference, energy consumption, or routing complexity. It may adjust transmit power, select forwarding nodes, schedule active radios, or construct a logical backbone.

3.1 Power-based topology control

Reducing transmit power can lower interference and energy expenditure, but excessive reduction may partition the network. Increasing power improves neighborhood size but may increase contention and expose transmissions to interference. A robust design therefore considers connectivity, capacity, energy, and link margin together.

Topology control is particularly useful in dense WSNs and MANETs. Nodes can form a connected dominating set or a virtual backbone, allowing only selected nodes to forward traffic. The practical difficulty is maintaining the structure as nodes move or sleep.

3.2 Cluster-based organization

Clustering divides nodes into groups managed by cluster heads. Ordinary nodes communicate with a cluster head, which may aggregate data or relay traffic to a gateway. Cluster heads can be selected according to residual energy, degree, mobility, link quality, geographic position, or application responsibility.

A good clustering strategy should avoid:

• repeatedly selecting low-energy nodes;
• creating clusters with excessive size;
• assigning highly mobile nodes as stable coordinators;
• producing long, weak links to cluster heads; and
• imposing greater control overhead than the clustering saves.

In VANETs, clusters may be formed from vehicles with similar speed, direction, and route. In WSNs, cluster-head selection often emphasizes energy and sensing coverage. In drone or robot networks, geographic stability and three-dimensional connectivity may dominate.

3.3 Adaptive and hierarchical organization

Hierarchical architectures reduce the burden on individual nodes by separating local access, aggregation, and backhaul functions. A sensor node may communicate with a cluster head, which communicates with an edge gateway, which then uses cellular or wired connectivity to reach an application platform.

However, hierarchy introduces failure concentration. A cluster-head failure can disconnect many nodes, and frequent re-clustering can consume substantial energy. Redundant heads, rapid re-election, mobile sinks, and distributed backup paths improve resilience.

4. Routing Approaches

4.1 Proactive routing

Proactive protocols maintain routes to destinations continuously. They can provide low data-plane delay when routes are current, but they generate periodic control traffic even when routes are unused. This approach is suitable when traffic is frequent, topology changes are moderate, and memory and bandwidth are available.

4.2 Reactive routing

Reactive protocols discover a route when communication is needed. Route-request flooding may reduce unnecessary maintenance but can cause discovery delay and broadcast overhead. Route errors are required when links fail. Reactive routing is useful for intermittent traffic and networks where maintaining all routes would be expensive.

4.3 Hybrid routing

Hybrid protocols use local proactive knowledge and reactive discovery for distant or less frequently used destinations. They attempt to balance route availability against control overhead, but their configuration is sensitive to node density, mobility, and traffic distribution.

4.4 Geographic and position-based routing

Geographic routing forwards packets using destination position and neighbor location, often selecting a neighbor that makes progress toward the destination. It avoids maintaining complete end-to-end routes, but it requires location information and must address local minima, inaccurate coordinates, obstacles, and disconnected neighborhoods.

Recovery mechanisms include perimeter-style forwarding, face traversal in suitable planar graphs, buffer-and-forward operation, and selection of a temporary relay region. Geographic routing is attractive in VANETs because vehicles can obtain position and motion information through positioning systems and periodic beacons.

4.5 Opportunistic and delay-tolerant routing

Opportunistic routing exploits multiple potential receivers or future contacts. Instead of committing to one next hop, a transmission may be accepted by several candidates, after which coordination determines the preferred forwarder. This can improve delivery in lossy environments but requires duplicate suppression and priority coordination.

Delay-tolerant networking is appropriate when continuous paths do not exist. Nodes store, carry, and forward messages when contacts become available. It is useful for sparse vehicular networks, remote sensing, wildlife monitoring, and disconnected disaster zones. Delivery delay becomes an application parameter rather than merely a protocol defect.

4.6 Information-centric and publish-subscribe routing

Information-centric designs route requests for named data rather than to a particular host address. Caching and replication can reduce dependence on a single source. Publish-subscribe systems decouple producers and consumers, which is valuable for mobile IoT applications in which devices appear and disappear.

The major challenges are naming, cache consistency, access control, data provenance, and efficient operation on constrained devices.

5. Medium Access Control

The MAC layer must share a time-varying wireless medium among nodes that may not hear one another. Hidden terminals, exposed terminals, fading, capture effects, and broadcast reliability complicate protocol design.

5.1 Contention-based MAC

Carrier-sense protocols attempt to avoid simultaneous transmissions. Random backoff reduces repeated collisions, but it does not eliminate hidden-terminal problems. Request-to-send/clear-to-send exchanges can help in some unicast situations, although they add overhead and are less effective for broadcast safety messages.

Contention-based MAC is flexible and suitable for bursty traffic, but delay and collision probability are variable. This is problematic for strict safety deadlines.

5.2 Schedule-based MAC

Time-division approaches assign transmission opportunities, reducing collision uncertainty. They require synchronization, scheduling, and adaptation when nodes move. They are useful for industrial sensing, cooperative robotics, and high-priority traffic where bounded access delay is important.

5.3 Hybrid and priority-aware MAC

Hybrid MAC combines contention for ordinary traffic with reserved or prioritized access for safety-critical data. Priority mechanisms should be designed carefully: giving all traffic high priority defeats the purpose, while starvation of low-priority sensing traffic may degrade situational awareness.

MAC evaluation should include access delay, collision rate, packet delivery ratio, channel utilization, fairness, energy consumption, and deadline-miss probability—not throughput alone.

6. Data Aggregation, Compression and Edge Intelligence

Sensor measurements are often correlated. Sending every raw measurement wastes energy and bandwidth. Data aggregation combines values, such as computing an average temperature, detecting an event, suppressing duplicates, or transmitting only changes beyond a threshold.

Aggregation may be:

• lossless, preserving the original data;
• lossy but controlled by an application error bound;
• semantic, transmitting events or inferred states; or
• spatial or temporal, combining nearby or successive measurements.

Aggregation must preserve information needed for decisions. An average may conceal a dangerous local maximum, and duplicate suppression may remove evidence of a rapidly changing event. Thus, aggregation policies should be linked to sensing objectives and uncertainty models.

Edge computing places processing near data sources. It can reduce latency, backhaul traffic, and cloud dependence. Edge functions may include anomaly detection, local control, feature extraction, digital-twin updates, and privacy filtering. AI models can classify sensor events or predict link quality, but model size, training data, explainability, adversarial robustness, and energy use must be considered.

7. Mobility Management and Resilience

Mobility management includes neighbor discovery, location estimation, handover, route adaptation, service continuity, and contact prediction. A mobile IoT device may change not only its radio link but also its access network, gateway, identity context, and application session.

7.1 Resilience dimensions

Resilience is the ability to resist, absorb, adapt to, and recover from failures or attacks. Relevant failures include node depletion, link blockage, gateway loss, interference, software faults, jamming, congestion, and inaccurate sensing.

Useful mechanisms include:

• multipath routing;
• redundant sensing and gateways;
• adaptive coding and retransmission;
• local autonomous control;
• store-and-forward operation;
• rapid service discovery;
• trust and reputation assessment;
• secure boot and authenticated updates; and
• graceful degradation of application quality.

Redundancy must be engineered rather than assumed. Multiple sensors using the same faulty calibration or multiple gateways sharing one power source do not provide independent protection.

7.2 Performance metrics

Important metrics include:

• packet delivery ratio: successfully delivered packets divided by generated packets;
• end-to-end delay and delay percentile;
• jitter, or variation in packet delay;
• throughput and goodput;
• route discovery and repair overhead;
• energy per delivered bit or message;
• network lifetime, defined according to an explicit failure criterion;
• control overhead;
• coverage, connectivity, and localization error;
• age of information, measuring the freshness of received status; and
• resilience measures such as recovery time and service availability.

Metrics must be tied to application requirements. A safety system may prioritize deadline reliability and false-alarm behavior, whereas environmental monitoring may prioritize lifetime and coverage.

8. Security, Privacy and Governance

Wireless ad hoc and IoT networks are exposed because transmissions are broadcast, devices may be physically accessible, and infrastructure may be absent. Threats include eavesdropping, spoofing, replay, Sybil identities, routing attacks, wormholes, selective forwarding, jamming, malware, and denial of service.

Security should be designed across layers. Device identity, authenticated communication, key establishment, secure routing, access control, secure firmware updates, logging, and incident response are complementary controls. Encryption alone does not prevent traffic analysis, compromised endpoints, or malicious sensor data.

Privacy is especially important for location-aware vehicles, wearables, and smart environments. Data minimization, pseudonymous identifiers, retention limits, access auditing, and purpose limitation should be applied. Safety-critical vehicular systems also require trustworthy certificate management and mechanisms for revocation without disabling legitimate emergency communication.

Governance must address responsibility when autonomous decisions cause harm. Operators should define who may change models, firmware, routing policies, and emergency overrides. Conformance with applicable spectrum rules, road-transport regulations, cybersecurity requirements, data-protection law, and sector-specific standards is part of engineering design rather than an afterthought.

9. Design Methodology

A disciplined design process proceeds as follows:

1. Define the application objective, traffic classes, deadlines, reliability targets, coverage area, and operating duration.

2. Characterize mobility, density, radio environment, energy supply, and likely failure modes.

3. Select the architecture: flat, clustered, hierarchical, infrastructure-assisted, edge-based, or delay-tolerant.

4. Choose MAC, routing, topology-control, and aggregation strategies jointly rather than independently.

5. Specify security, identity, update, and governance requirements.

6. Model realistic workloads and mobility patterns.

7. Evaluate using field measurements, emulation, and simulation where appropriate.

8. Conduct sensitivity analysis for density, interference, mobility, energy, and failures.

9. Prototype critical functions and test interoperability.

10. Define monitoring, maintenance, and recovery procedures for deployment.

Simulation is valuable for controlled comparison but can be misleading if propagation, mobility, traffic, or radio interference are unrealistic. Reproducibility requires documenting random seeds, topology, channel model, protocol parameters, hardware assumptions, and statistical treatment. Results should include confidence intervals or repeated trials where variability is significant.

10. Illustrative Case: Emergency Flood-Response Network

Background

Consider an illustrative deployment after severe flooding has disrupted cellular towers and electrical power. Rescue teams use handheld devices, unmanned aerial vehicles (UAVs), portable gateways, and water-level sensors. Some teams move through streets while UAVs act as temporary elevated relays.

Problem

The system must deliver high-priority distress and location messages within a short deadline, collect water-level measurements, and support lower-priority text and image traffic. Connectivity is intermittent, devices have unequal battery capacities, and some streets are obstructed.

Analysis

A flat MANET would be simple but could create excessive route maintenance as rescuers move. A purely infrastructure-based design is impossible because fixed infrastructure is damaged. Raw sensor reporting would consume scarce bandwidth, while a single gateway would create a critical failure point.

Approach and solution

The proposed design uses three cooperating layers:

• handheld devices form local clusters based on proximity and mobility similarity;
• UAVs and portable gateways provide inter-cluster relaying and edge processing; and
• delay-tolerant store-and-forward operation preserves messages during temporary partitioning.

Distress messages use authenticated broadcast with repetition and multiple relay candidates. Ordinary messages use reactive routing. Water-level sensors transmit threshold-crossing events immediately and periodic summaries otherwise. Edge nodes aggregate duplicate observations but preserve maximum water level, timestamp, location uncertainty, and sensor identity.

Topology control limits unnecessary transmission power in dense groups, while selected UAVs maintain longer-range links. Gateway redundancy is achieved by allowing a handheld device or UAV to assume relay responsibility if a gateway fails. Secure boot, signed software updates, and role-based access control reduce the risk of compromised equipment.

Outcome

The architecture is expected to improve emergency-message reachability and reduce sensor traffic compared with continuous raw reporting. Its principal costs are UAV energy consumption, synchronization and coordination overhead, cryptographic processing, and the operational complexity of assigning relay roles. Field validation would be required because urban flooding changes propagation and mobility in ways that simplified simulations may not capture.

Learning points

The case demonstrates that no single protocol solves the complete problem. Routing, clustering, MAC priority, aggregation, mobility management, security, and energy planning must be co-designed. It also shows why resilience requires alternatives for power, gateways, routes, and operational control.

11. Contemporary Developments and Research Directions

Current research explores 5G and 6G-assisted sidelink communication, integrated sensing and communication, UAV and satellite support, reconfigurable intelligent surfaces, digital twins, federated learning, semantic communication, and energy harvesting. These technologies may improve coverage or intelligence, but they introduce new dependencies and attack surfaces.

Federated learning can reduce transfer of raw data by training models locally, yet model updates may leak information and can be poisoned. Reinforcement learning may adapt routing or spectrum access, but exploration is unsafe in critical systems and learned policies can behave unpredictably outside their training distribution. Semantic communication may reduce bandwidth by transmitting task-relevant meaning, but it requires explicit definitions of utility, uncertainty, and acceptable error.

Research evaluation should therefore move beyond average throughput. It should investigate worst-case delay, rare failures, correlated attacks, distribution shifts, energy fairness, explainability, and the effect of protocol decisions on real application outcomes.

Best Practices

• Match mobility models to the actual deployment environment.
• Treat link quality as time-varying and probabilistic rather than binary.
• Co-design topology, routing, MAC, aggregation, and energy policies.
• Use application-specific metrics and deadline-based evaluation.
• Avoid single points of failure in gateways, keys, power, and control.
• Validate simulation assumptions with measurements.
• Protect device identity, firmware, data, and privacy throughout the lifecycle.
• Use AI only where its data, safety, energy, and governance requirements can be justified.
• Design for graceful degradation and operational recovery.

Chapter Summary

MANETs, VANETs, WSNs, and mobile IoT networks provide communication and sensing without relying exclusively on fixed infrastructure. Their defining difficulties are changing topology, unreliable wireless links, mobility, limited energy, heterogeneous devices, and increased security exposure. Topology control and clustering can reduce interference and energy use, but they introduce maintenance and failure-concentration risks. Routing may be proactive, reactive, geographic, opportunistic, delay tolerant, or information centric, with selection determined by mobility, traffic, density, and application requirements. MAC protocols must balance contention, bounded delay, fairness, and energy. Data aggregation and edge intelligence reduce traffic but must preserve application-critical information. Resilience requires redundancy, adaptation, secure operation, and recovery planning. Effective evaluation combines delivery, delay, freshness, energy, coverage, overhead, and failure-recovery metrics. Successful deployments depend on co-design, realistic validation, privacy protection, governance, and lifecycle management.

Key Terms

MANET: A self-organizing multihop wireless network without fixed infrastructure.

VANET: A vehicular ad hoc network involving vehicles and roadside or network infrastructure.

Wireless sensor network: A network of sensor nodes that measure and communicate physical phenomena.

Mobile IoT: An IoT system involving mobile devices, users, vehicles, robots, or changing access networks.

Topology control: Adjustment of links, transmit power, or forwarding structure to achieve desired connectivity and efficiency.

Cluster head: A node that coordinates communication or aggregation within a cluster.

Reactive routing: Routing that discovers paths when communication is required.

Proactive routing: Routing that maintains paths before they are needed.

Geographic routing: Forwarding based primarily on node and destination positions.

Opportunistic routing: Routing that exploits multiple possible receivers or future contacts.

Delay-tolerant networking: Networking that stores, carries, and forwards data across intermittent contacts.

Medium access control: Procedures that coordinate access to a shared wireless channel.

Data aggregation: Combining measurements or messages to reduce communication or extract information.

Edge computing: Processing data near its source rather than exclusively in a remote cloud.

Link lifetime: The duration for which a wireless link remains usable under specified conditions.

Age of information: A measure of how fresh a received status or measurement is.

Resilience: The ability to withstand, adapt to, and recover from failures or attacks.

V2X: Vehicle-to-everything communication, including V2V, V2I, and related interactions.

LPWAN: Low-power wide-area networking designed for long-range, low-data-rate devices.

Goodput: Successfully delivered useful application data excluding overhead and discarded data.

Review Questions

Short Answer Questions

1. What characteristics distinguish a MANET from an infrastructure-based wireless network?

2. Why can a VANET have structured mobility but still experience unstable links?

3. State two advantages and two risks of clustering in a sensor network.

4. What is the purpose of data aggregation in a WSN?

5. Why is packet delivery ratio alone insufficient for evaluating a safety-critical network?

Long Answer Questions

1. Compare proactive, reactive, geographic, opportunistic, and delay-tolerant routing in terms of assumptions, overhead, and suitable applications.

2. Explain how mobility, propagation, interference, and energy constraints jointly influence topology control.

3. Discuss contention-based, schedule-based, and hybrid MAC approaches for mobile IoT systems.

4. Develop a resilience strategy for a multihop disaster-response network, including technical and operational controls.

5. Explain the security, privacy, and governance requirements of a location-aware vehicular IoT system.

Analytical / Application Questions

1. Design an architecture for a fleet of autonomous agricultural vehicles and soil sensors. Specify clustering, routing, MAC, aggregation, edge processing, and resilience mechanisms, and justify each choice.

2. A sensor deployment provides high average packet delivery but frequently misses urgent threshold events. Analyze possible causes and propose a metric-driven improvement plan.

3. Compare a single powerful gateway with several lower-power redundant gateways for a mobile emergency network. Evaluate the alternatives using energy, coverage, latency, reliability, security, and maintenance criteria.

Multiple Choice Questions with Answers

Question 1

Which characteristic most directly distinguishes a MANET from a conventional infrastructure-based wireless network?

A. Every node permanently uses a wired backhaul

B. Nodes can forward packets for one another without fixed infrastructure

C. Only one device is allowed to transmit

D. All nodes remain stationary

Correct Answer: B. Nodes can forward packets for one another without fixed infrastructure

Question 2

What is a principal reason that high vehicle speed can reduce VANET route stability?

A. It eliminates radio propagation

B. It increases the rate at which relative positions and link conditions change

C. It prevents vehicles from using positioning information

D. It guarantees permanent line of sight

Correct Answer: B. It increases the rate at which relative positions and link conditions change

Question 3

Which routing approach is most suitable when a network commonly lacks an end-to-end path?

A. Delay-tolerant store-carry-forward routing

B. Permanent circuit routing

C. Static shortest-path routing only

D. Proactive routing with no route repair

Correct Answer: A. Delay-tolerant store-carry-forward routing

Question 4

What is a major purpose of topology control?

A. To maximize control traffic

B. To adjust links or transmit power while preserving useful connectivity and efficiency

C. To remove all intermediate nodes

D. To prevent nodes from sleeping

Correct Answer: B. To adjust links or transmit power while preserving useful connectivity and efficiency

Question 5

Which metric measures the freshness of received status information?

A. Jitter

B. Age of information

C. Path loss exponent

D. Cluster degree

Correct Answer: B. Age of information

Question 6

Why can raw periodic sensor reporting be undesirable in a constrained WSN?

A. It always improves battery lifetime

B. It can waste energy and bandwidth by transmitting correlated or unnecessary data

C. It prevents any form of aggregation

D. It guarantees lower latency for all traffic

Correct Answer: B. It can waste energy and bandwidth by transmitting correlated or unnecessary data

Question 7

Which MAC strategy generally provides more predictable access delay when synchronization and scheduling are available?

A. Schedule-based MAC

B. Uncontrolled random transmission

C. Pure flooding

D. Passive eavesdropping

Correct Answer: A. Schedule-based MAC

Question 8

In the link-budget expression, what does receiver sensitivity represent?

A. The minimum usable received signal level under specified receiver conditions

B. The number of nodes in a cluster

C. The application payload size

D. The route repair frequency

Correct Answer: A. The minimum usable received signal level under specified receiver conditions

Question 9

Which mechanism best reduces the risk that one failed gateway disconnects an entire network?

A. Removing all alternate routes

B. Using redundant gateways or relay paths

C. Increasing packet size indefinitely

D. Disabling authentication

Correct Answer: B. Using redundant gateways or relay paths

Question 10

Why should AI-based routing or sensing policies be evaluated beyond average throughput?

A. Average throughput is always unrelated to networking

B. Safety, rare failures, energy use, explainability, and behavior under distribution shifts may also determine suitability

C. AI systems never require data

D. Throughput cannot be measured in wireless networks

Correct Answer: B. Safety, rare failures, energy use, explainability, and behavior under distribution shifts may also determine suitability