This document discusses research challenges in real-time multimedia monitoring in large-scale wireless multimedia sensor networks. It describes how these networks enable new monitoring applications but face challenges due to limited computational and energy resources. The document outlines solutions needed at the MAC, network, and transport layers to efficiently deliver visual data while meeting requirements for steady data flows and delay-bounded delivery. It also discusses challenges in areas like multimedia coding, in-network processing, energy harvesting, and cross-layer optimization across the protocol stack.

1. Real-time Multimedia Monitoring in
Large-Scale Wireless Multimedia Sensor
Networks: Research Challenges
Joint work by:
M.Cesana, A.Redondi – Politecnico di Milano
N. Tiglao, A. Grilo – INESC-ID/INOV/IST
J. M. Barcelo-Ordinas, M. Alaei – Universitat Politecnica de Catalunya
P. Todorova – Fraunhofer FOKUS

2. MWMSN Project
 Multi-tier Wireless Multimedia Sensor
Networks
 Goal: To enable support for enhanced
monitoring and tracking applications through
multimedia visual/audio wireless sensor nodes
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3. Outline
 Introduction
 Real-time multimedia monitoring applications
 Efficient delivery of visual data in WMSNs
 MAC Layer
 Network Layer
 Transport Layer
 Research challenges and final discussion
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4. Introduction
 WSN equipped with multimedia sensors give
birth to WMSN.
 They enable a new class of monitoring
applications, but demanding in terms of:
 computational resources
 energy resources
 Need for innovative solutions:
 combination/optimization techniques at
the different layers of the protocol stack
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5. Real-Time Multimedia Monitoring
 Supervised monitoring (Image/Video-based)
 Delivery of compressed image/video flows,
analyzed by a human operator
 Low bitrate achievable with complex encoders
(e.g. H.264/AVC), not supported by WMSN
 Suitable solutions: Object-based approaches,
Distributed Video Coding
 Challenge: Successful implementation of these
techniques
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6. Real-Time Multimedia Monitoring
 Unsupervised monitoring (Feature-based)
 Use visual features to describe the underlying pixel
content
 Suitable for a broad range of monitoring tasks
(e.g., object recognition, face detection…)
 Main challenges:
 Coding of visual features
 Low-complexity feature extraction algorithms
 Rate-accuracy models for resources allocation
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7. MAC Layer
 Main requirements for video streaming
over WMSN:
 Steady-flow of information
 Delay-bounded delivery of packets
 As a consequence, the MAC layer has to:
 support reliable communication
 be QoS-aware
 save as much energy as possible
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8. MAC Layer – available solutions
 Available solutions to tune QoS metrics:
 Power control
 Traffic class differentiation (Q-MAC)
 Contention-free vs. contention-based approaches
 Duty-cycling control
 Queuing and scheduling mechanisms
 Error control mechanisms
 For WMSN, most important features are:
 Intra/Inter-node traffic class differentiation
 Node synchronization (duty-cycling control)
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9. MAC Layer – available solutions
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10. Network Layer: Routing
 Traditional solutions for WSN focused on energy
consumption
 In the WMSN case, need also for real-time
delivery
 Desirable features
 Traffic differentiation and joint-optimization of
multiple QoS goals
 Resource balancing
 Fast adaptation to change in monitoring conditions
 Support to in-network processing / cross-layer opt.
 Scalability
 Energy-harvesting awareness
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11. Network Layer – available solutions
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12. Transport Layer
 Similarly to the network layer case, available
solutions are not suitable for WMSN.
 Design guidelines
 Differentiated reliability
 Trade-off between reliability/timeliness
 Media-centric collaborative reliability
 Congestion control
 Cross-layer optimization
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13. Transport Layer – available solutions
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14. Collaborative Sensing in WMSN
 Multimedia nodes are characterized by a
directional sensing model (FoV)
 They can be grouped basing on their common
sensing coverage
 Several challenges
 Directional coverage
 Clustering / Scheduling
 Collaboration protocols
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15. Conclusions and Research Challenges
 Application Layer (feature-based):
 Novel coding techniques
 Practical implementations
 MAC Layer
 Service differentiation
 Dynamic duty-cycling control
 Network Layer:
 In-network processing
 Energy harvesting
 Transport Layer
 Media-centric reliability
 Cross-layer optimization with routing
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16. Thank your for your attention!
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17. Project Members
 M. Cesana, A. Redondi – {cesana,
redondi}@elet.polimi.it
 Multimedia coding, Application
 N. Tiglao, A. Grilo – {nestor.tiglao,
antonio.grilo}@inesc-id.pt
 Routing and Transport
 J. M. Barcelo-Ordinas, M. Alaei –
{joseb,malaei}@ac.upc.edu
 MAC Layer
 P. Todorova –
petia.todorova@fokus.fraunhofer.de
 Collaborative Sensing
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