Delay Tolerant Network Projects examples using omnet++

Delay-Tolerant Networks (DTNs) are particular kinds of networks intended to perform in environments with periodical connectivity, long delays, and high error rates. These networks are especially helpful in scenarios in which the traditional networking techniques fail, like in space communications, remote sensing, disaster recovery, and mobile ad-hoc networks. The given below are the samples of Delay-Tolerant Network projects that can be implemented using OMNeT++:

1. Simulation of Epidemic Routing in DTNs

• Objective: Emulate and measure the performance of the Epidemic Routing protocol in a Delay-Tolerant Network, where messages are spread such as a “virus” across node encounters.
• Implementation: Generate a network in which nodes move randomly and exchange messages when they come into contact. Execute the Epidemic Routing protocol, in which each node duplicates and forwards messages to every other node it encounters.
• Extension: Measure the performance of Epidemic Routing in terms of delivery probability, latency, and overhead. Emulate various node densities, mobility patterns, and buffer sizes to measure the protocol’s scalability and efficiency.

2. Simulation of Spray and Wait Routing in DTNs

• Objective: Mimic the Spray and Wait routing protocol in a DTN to examine its efficiency in minimizing message replication while maintaining high delivery rates.
• Implementation: Execute the Spray and Wait protocol in a network in which the nodes are occasionally connected. In the “Spray” phase, a limited number of message copies are allocated to encountered nodes. In the “Wait” phase, these nodes carry the message until they encounter the destination.
• Extension: Associate the performance of Spray and Wait with Epidemic Routing in terms of delivery delay, resource consumption, and network overhead then emulate scenarios with changing network sizes and node mobility to measure the protocol’s efficiency.

3. Energy-Efficient Routing in DTNs

• Objective: To emulate energy-efficient routing protocols in DTNs to expand the network lifetime, especially in resource-constrained environments such as sensor networks or mobile devices.
• Implementation: Develop a network in which the nodes have limited energy resources. Execute energy-aware routing protocols that diminish energy consumption by choosing energy-efficient paths or limiting message replication.
• Extension: To measure the effect of energy-efficient routing on message delivery rates, network lifetime, and latency. To emulate various energy harvesting scenarios or battery depletion rates to measure the trade-offs among the energy efficiency and network performance.

4. Secure Communication in Delay-Tolerant Networks

• Objective: To emulate security mechanisms in DTNs to secure data integrity, confidentiality, and authenticity despite intermittent connectivity and long delays.
• Implementation: To execute security protocols that make sure secure message forwarding and storage in a DTN and the algorithms like encryption, digital signatures, and key management can be used to secure the messages.
• Extension: To emulate numerous attack scenarios, like message tampering, eavesdropping, or node compromise, and measure the efficiency of the security mechanisms in preventing these threats. Measure the trade-offs among the security and performance in the network.

5. DTN for Space Communications

• Objective: To emulate a DTN designed for space communications in which nodes denotes satellites, space probes, or ground stations with long propagation delays and intermittent connectivity.
• Implementation: Generate a network topology with nodes that denotes space entities that have predictable orbits and communication windows and then apply routing protocols such as Contact Graph Routing (CGR) or Proximity-Based Routing, which are personalized for space DTNs.
• Extension: Assess the effect of numerous orbital scenarios, communication windows, and data volumes on network performance. Measure the parameters like message delivery success rate, latency, and buffer management efficiency in space communication scenarios.

6. Disaster Recovery Using DTNs

• Objective: To mimic a DTN intended for disaster recovery scenarios in which traditional communication infrastructure is unavailable or compromised.
• Implementation: To model a network with mobile nodes like rescue teams, drones that can interact using DTN protocols. Execute routing protocols such as PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity) or MaxProp to provide critical information in the absence of continuous connectivity.
• Extension: To emulate disaster scenarios with changing levels of infrastructure damage, node mobility, and data priorities. Measure the performance of the DTN in terms of message delivery, latency, and network resilience under challenging conditions.

7. Buffer Management in DTNs

• Objective: To emulate and measure buffer management strategies in DTNs to enhance message storage and forwarding under limited buffer space conditions.
• Implementation: Execute the various buffer management strategies, like FIFO (First In, First Out), drop-oldest, drop-least-likely-to-be-delivered, or priority-based dropping, in a DTN in which the nodes have limited storage capacity.
• Extension: To measure the effect of these approaches on message delivery success, latency, and network performance. Emulate scenarios with high message load, changing node densities, and various message priorities to measure the efficiency of each strategy.

8. Routing in Heterogeneous DTNs

• Objective: To mimic routing protocols in a heterogeneous DTN environment where nodes have changing the capabilities, like numerous communication ranges, mobility patterns, and energy resources.
• Implementation: To develop a network with heterogeneous nodes and execute the routing protocols that can adjust to the various features of the nodes. For instance, use adaptive routing protocols that deliberate node capabilities when making forwarding decisions.
• Extension: Measure the performance of routing protocols in terms of delivery probability, latency, and network overhead in a heterogeneous environment. To mimic the scenarios in which the node heterogeneity is significant, like in mixed civilian and military networks or combined terrestrial and aerial networks.

9. Socially-Aware Routing in DTNs

• Objective: To mimic socially-aware routing protocols in DTNs that leverage social relationships or contact patterns among the nodes to enhance message delivery.
• Implementation: Execute the routing protocols that think through social metrics like node centrality, community structures, or contact frequency. These parameters can be used to generate forwarding decisions that upsurge the likelihood of successful message delivery.
• Extension: To implement various social network scenarios, like urban environments, campus networks, or disaster response teams, and measure the performance of socially-aware routing protocols. Measure the effect of social structures on network performance metrics such as delivery rate, latency, and overhead.

10. Mobility Models for DTNs

• Objective: To mimic various mobility models in DTNs and study their effect on the performance of routing protocols and network metrics.
• Implementation: Execute the numerous mobility models like Random Waypoint, Gauss-Markov, or real-world mobility traces, in a DTN and these models will command how nodes move and communicate within the network.
• Extension: To measure the effect of various mobility models on routing protocols’ performance in terms of delivery ratio, latency, and network overhead. To emulate the scenarios with changing the node speeds, mobility patterns, and network densities to measure the impact of mobility on DTN performance.

11. Application of DTNs in Remote Sensing

• Objective: To mimic a DTN for remote sensing applications in which sensor nodes are implemented in harsh environments with intermittent connectivity.
• Implementation: Develop a DTN with sensor nodes that gather and store data until they can transmit it to a central base station or a passing data mule. Execute routing protocols that make sure data delivery despite long delays and sparse connectivity.
• Extension: Measure the DTN’s performance in various remote sensing scenarios, like wildlife monitoring, environmental data collection, or glacier movement tracking. Evaluate the parameter like data delivery success, latency, and energy consumption.

12. QoS-Aware Routing in DTNs

• Objective: To emulate QoS-aware routing protocols in DTNs that select particular kinds of traffic or data based on quality of service requirements.
• Implementation: Execute QoS-aware routing protocols that identify messages based on their priority, delay tolerance, or required delivery probability. The routing decisions should deliberate these QoS requirements to make sure that high-priority messages are delivered with higher reliability.
• Extension: Assess the effect of QoS-aware routing on various classes of traffic in the DTN, like emergency alerts, routine data, and delay-tolerant bulk transfers. Measure the trade-offs among meeting QoS necessities and overall network performance.

13. Data Aggregation in DTNs

• Objective: Mimic data aggregation techniques in DTNs to minimize the amount of data transmitted, conserve energy, and enhance network efficiency.
• Implementation: Execute data aggregation techniques in which intermediate nodes integrate multiple data packets into a single packet before forwarding it. This can be particular helpful in sensor networks or scenarios where bandwidth is limited.
• Extension: Assess the effect of data aggregation on network parameters like delivery delay, energy consumption, and data accuracy. Mimic various data generation rates, network densities, and aggregation strategies to measure their efficiency.

14. Performance Evaluation of Hybrid DTN Architectures

• Objective: To emulate hybrid DTN architectures that integrates the features of both DTNs and traditional networks to enhance the performance in environments with variable connectivity.
• Implementation: Generate a hybrid network in which the particular nodes have continuous connectivity, since others perform in a delay-tolerant mode. Execute protocols that can switch among the traditional and DTN modes based on connectivity status.
• Extension: Assess the performance of hybrid DTN architectures in scenarios with changing connectivity patterns, like an urban-rural transitions or mixed terrestrial-satellite networks. Measure the trade-offs among the network performance, resource usage, and complexity.

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