422  MWSN Components: Nodes, Sinks, and MULEs

422.1 Learning Objectives

By the end of this chapter, you will be able to:

• Design Mobile Sensor Nodes: Understand operational models for self-propelled and attached sensors
• Plan Mobile Sink Paths: Select appropriate path planning strategies (random, predefined, adaptive)
• Implement Data MULEs: Design store-carry-forward data collection systems
• Compare Collection Strategies: Evaluate trade-offs between mobile sinks and data MULEs
• Apply DTN Routing: Understand Spray and Wait and other delay-tolerant protocols

422.2 Prerequisites

Before diving into this chapter, you should be familiar with:

• Mobile WSN Fundamentals: Understanding MWSN architecture and mobility benefits
• Stationary WSN Fundamentals: Knowledge of the energy hole problem that mobile components solve
• Sensor Network Routing: Basic routing concepts for data aggregation

TipFor Beginners: Three Ways to Add Mobility

Mobile WSNs have three main mobile components:

1. Mobile Sensor Nodes - The sensors themselves move

• Animal collars tracking wildlife
• Smartphones carried by people
• Sensors on autonomous robots

2. Mobile Sinks - The base station moves

• Tractor with receiver driving through farm
• Bus collecting data from roadside sensors
• Drone flying over remote sensors

3. Data MULEs - Dedicated data ferries

• Like a mail carrier picking up letters from mailboxes
• Zebras passing near sensors and carrying data to watering holes
• City buses collecting parking sensor data along routes

Key Difference: Mobile sinks are designed specifically to collect data. Data MULEs use existing mobility (they were going that way anyway!).

422.3 Overview of MWSN Components

Figure 422.1: Components of Mobile WSNs - Overview of mobile sensor nodes, mobile sinks, and data MULEs in MWSN architecture

422.4 Mobile Sensor Nodes

Figure 422.2: Mobile Sensor Nodes - Self-propelled sensors with mobility capability for dynamic network topology

Mobile sensor nodes are the fundamental sensing units equipped with mobility mechanisms.

Mobility Mechanisms:

• Wheels (ground robots)
• Propellers (aerial drones)
• Thrusters (underwater vehicles)
• Animal attachment (wildlife tracking)
• Human-carried (smartphones)

422.4.1 Operational Model

1. Sensing Phase: Nodes sense environmental parameters while moving
2. Data Buffering: Store sensed data locally with timestamps and location
3. Proximity Detection: Identify when sink is within communication range
4. Data Transfer: Opportunistically transmit buffered data to sink
5. Continuation: Resume sensing and movement

%% fig-alt: "State diagram showing mobile sensor node operation: Sensing to Buffering to ProximityCheck, then either back to Buffering (no sink) or to DataTransfer (sink in range), then back to Sensing."
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stateDiagram-v2
    [*] --> Sensing
    Sensing --> Buffering: Data Collected
    Buffering --> ProximityCheck: Continuous Monitoring
    ProximityCheck --> Buffering: No Sink
    ProximityCheck --> DataTransfer: Sink in Range
    DataTransfer --> Sensing: Transfer Complete
    Sensing --> [*]: Mission Complete

    note right of Sensing
        Sense environmental<br/>parameters while mobile
    end note
    note right of DataTransfer
        Opportunistic<br/>communication
    end note

Figure 422.3: Mobile sensor node state machine showing sensing, buffering, and opportunistic data transfer

422.5 Mobile Sink (Mobile Base Station)

Original

AI Modern Style

Mobile Sink Path

AI Geometric Style

Mobile Sink Routing

Figure 422.4: Mobile Sink architecture - Base station moves through network to balance energy consumption and extend lifetime

A mobile sink actively moves through the sensor field to collect data from stationary or mobile sensor nodes. This approach addresses the hotspot problem in stationary sink deployments.

Benefits:

• Distributes energy consumption evenly
• Increases network lifetime significantly (up to 5-10x)
• Reduces multi-hop communication overhead
• Balances traffic load

422.5.1 Path Planning Strategies

%% fig-alt: "Decision tree for mobile sink path planning showing three strategies: Random Walk (simple, unpredictable latency), Predefined Path (predictable, not adaptive), and Adaptive Path (optimized but complex computation)."
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graph TD
    Strategy{Mobile Sink<br/>Path Planning}

    Strategy --> Random[Random Walk<br/>Unpredictable path]
    Strategy --> Predefined[Predefined Path<br/>Fixed route]
    Strategy --> Adaptive[Adaptive Path<br/>Based on data demand]

    Random --> R_Pro[Pro: Simple,<br/>Low overhead]
    Random --> R_Con[Con: Unpredictable<br/>latency]

    Predefined --> P_Pro[Pro: Predictable,<br/>Easy planning]
    Predefined --> P_Con[Con: Not adaptive]

    Adaptive --> A_Pro[Pro: Optimized<br/>collection]
    Adaptive --> A_Con[Con: Complex<br/>computation]

    style Strategy fill:#16A085,stroke:#2C3E50,color:#fff
    style Random fill:#E67E22,stroke:#16A085,color:#fff
    style Predefined fill:#E67E22,stroke:#16A085,color:#fff
    style Adaptive fill:#2C3E50,stroke:#16A085,color:#fff
    style A_Pro fill:#16A085,stroke:#2C3E50,color:#fff

Figure 422.5: Mobile sink path planning strategies with trade-offs

Strategy Comparison:

Strategy	Latency	Energy Balance	Complexity	Best For
Random Walk	Variable	Good (long-term)	Low	Simple deployments
Predefined	Predictable	Moderate	Low	Regular routes (buses)
Adaptive	Optimized	Excellent	High	Critical data collection

422.6 Data MULEs (Mobile Ubiquitous LAN Extensions)

Figure 422.6: Data MULEs concept - Mobile entities collecting data from stationary sensors by physically moving through network

Data MULEs are mobile entities that physically transport data from sensors to the sink. Unlike mobile sinks that follow planned paths, MULEs often move based on existing mobility patterns (e.g., buses, animals, humans).

422.6.1 Operational Model

1. Mobile entity (bus, animal, robot) moves through the sensor field
2. Sensors detect MULE proximity and transmit buffered data
3. MULE buffers collected data from multiple sensors
4. MULE delivers aggregated data to sink when in range
5. Process repeats with each MULE pass

%% fig-alt: "Sequence diagram showing Data MULE operation: Sensors buffer data, MULE moves into range of Sensor 1 and collects data, then Sensor 2, then delivers all collected data to Base Station."
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sequenceDiagram
    participant S1 as Sensor 1
    participant S2 as Sensor 2
    participant MULE as Data MULE<br/>(Bus/Animal)
    participant Sink as Base Station

    Note over S1,S2: Sensors buffer data
    S1->>S1: Store measurements
    S2->>S2: Store measurements

    MULE->>S1: Move into range
    S1->>MULE: Upload buffered data
    Note over MULE: Store S1 data

    MULE->>S2: Move into range
    S2->>MULE: Upload buffered data
    Note over MULE: Store S2 data

    MULE->>Sink: Move into sink range
    MULE->>Sink: Upload all collected data
    Note over Sink: Process aggregated data

Figure 422.7: Data MULE store-carry-forward sequence showing opportunistic data collection

422.6.2 Real-World MULE Examples

ZebraNet (Kenya, 2004):

• 200 soil/vegetation sensors scattered across 50 km² savanna
• 30 zebras with GPS collars act as data MULEs
• Sensors buffer readings for days
• When zebra wanders within 50m, sensor wakes and uploads data
• Zebra stores data until visiting watering hole with base station
• Result: Zero mobility energy cost - zebra movement is free!

DakNet (India, 2003):

• Rural villages without internet connectivity
• Buses carry Wi-Fi access points as data MULEs
• Villagers upload/download data when bus passes
• Store-carry-forward email and web content
• Result: Internet access for remote communities at minimal cost

422.7 DTN Routing: Spray and Wait

For networks with intermittent connectivity, Delay-Tolerant Networking (DTN) protocols handle message delivery when end-to-end paths rarely exist.

Spray and Wait limits message replication to control overhead while maintaining good delivery probability.

422.7.1 Operation with L=6 Copies

Spray Phase:

• Source has L=6 message copies
• When encountering other nodes, transfer copies
• Binary spray: Split remaining copies (keep 3, give 3)
• Continue until all L copies distributed to different relay nodes

Wait Phase:

• Nodes with copies only forward to destination (direct delivery)
• No forwarding to other relay nodes
• Each holder “waits” to encounter destination directly

Performance Comparison:

Protocol	Copies	Delivery Rate	Overhead
Epidemic	Unlimited	~98%	150x
Spray and Wait (L=6)	6	~85%	6x
Direct Delivery	1	~40%	1x

422.8 Knowledge Check

NoteQuiz: MWSN Components

Question 1: Data MULEs (Mobile Ubiquitous LAN Extensions) opportunistically collect data from stationary sensors. Which characteristic makes Data MULEs particularly suitable for wildlife habitat monitoring in remote areas?

Data MULE advantage - Zero mobility energy cost: The key insight is exploiting EXISTING mobility rather than deploying dedicated mobile platforms. In ZebraNet, zebra movement costs ZERO electrical energy - animal mobility is free. Collar radio uses 0.1 Wh/day for opportunistic transmission. Collar lasts 2+ years. A dedicated robot would deplete batteries in days.

Question 2: Spray and Wait routing limits message replication to L copies to reduce overhead. If L=6, what happens during the spray phase and wait phase?

Spray and Wait operation: Spray phase distributes exactly L copies to different relay nodes using binary spray (recursive splitting). Wait phase: all holders only forward directly to destination - no further spreading. This controls overhead (exactly L copies) while maintaining good delivery probability through multiple searchers.

Question 3: What distinguishes Delay-Tolerant Networks (DTN) from traditional mobile ad hoc networks (MANETs)?

DTN vs MANET: MANETs assume network is mostly connected - end-to-end paths exist most of the time. DTNs assume network is mostly disconnected - intermittent connectivity is the norm. DTNs use store-carry-forward as primary mechanism, accepting hours/days of delay. MANETs use path-centric routing expecting quick delivery.

Question 4: A logistics company deploys GPS sensors on 500 delivery trucks. Compared to 500 stationary sensors, what is the PRIMARY advantage?

Mobile sensor advantage - Dynamic coverage: Mobile sensors on vehicles provide time-varying spatial coverage that adapts to where vehicles travel. 500 trucks traversing 50-100 km daily collectively cover 70-80% of city streets within 24 hours, compared to 20% coverage with fixed sensors at the same positions.

422.9 Summary

This chapter covered the three key components of mobile wireless sensor networks:

• Mobile Sensor Nodes: Self-propelled or attached sensors using wheels, propellers, thrusters, or animal/human carriers, operating through sense-buffer-transfer cycles
• Mobile Sinks: Moving base stations that actively collect data using random walk, predefined, or adaptive path planning strategies
• Data MULEs: Mobile entities that leverage existing mobility (buses, animals, vehicles) for zero-cost store-carry-forward data collection
• DTN Routing: Spray and Wait and other protocols for intermittent connectivity, trading delivery guarantees for bounded overhead

422.10 What’s Next

The next chapter explores MWSN Types and Mobile Entities, examining underwater, terrestrial, and aerial mobile sensor networks, plus how humans, vehicles, and robots serve as mobile sensing platforms in daily life.