Energy Efficient Routing Protocol for Delay Tolerant Network Based on Fuzzy Logic and Ant Colony

Abstract

Abstract-The messages routing in a DTN network is a complicated challenge, due on the one hand of intermittent connection between the nodes, the lack of the end-to-end path between source / destination and on the other hand, the constraints related to the capacity of the buffer and the battery. To ensure messages delivery in such an environment, the proposed routing protocols use multiple copies of each message in order to increase the delivery ratio. Most of these routing protocols do not take into account the remaining energy of nodes and the history on the relays that have already received a copy of the message in order to select the nodes that will participate in the message routing. This paper proposes a new approach named EERPFAnt inspired by the ant colony intelligence and improved by the fuzzy logic technique to select the best relay by combining the energy level of the nodes, as well as the information on the relay that have already received a copy of the message to estimate intelligently, the energy level of the nodes at the time of encounter with the desired destination. Simulation results will show that the proposed approach performances are better than those of Epidemic routing protocols, Spray and Wait and ProPHET.

Current State of Multicast Routing Protocols for Disruption Tolerant Networks: Survey and Open Issues

“…Eshghi et al [62] studied epidemic routing in an energy-constrained DTN and implemented an optimal energy-aware relay selection based on the node's remaining energy and message's age. Meanwhile, the nodes in the Energy Efficient Routing Protocol based on Fuzzy Ant colonies (EERPFAnt) [63] are able to estimate the remaining energy levels of each potential relay at the time when they meet with their destination. In Reference [64], the Distance-based Energy-Efficient Opportunistic Forwarding (DEEOF) framework estimates the node distance, tolerant delay, and the number of forwarders to broadcast messages in of 28 DTN.…”

“…Besides the discussed open issues, we also should pay attention to the incorporation of artificial intelligence (AI) in DTN to improve routing efficiency and network performance. For example, AI can help to minimize the number of replications using fuzzy logic and ant colony optimization [63], or can compute and learn the trust level to select the best candidate to relay messages [88]. Furthermore, there are potential new applications for DTN in future internet architectures, such as the Information Centric Network (ICN) [89], or for use in new technologies, such as fog computing [90].…”

A New Approach for Dynamic Parametrization of Ant System Algorithms

“…, * ) ∈ verifying ( * ) = min ∈ ( ). There are in general, many fields of swarm approach application in resolving combinatorial optimization problems [7][8][9][10][11], and variants of ant colony algorithms, in neural network [12], telecommunication network [13], computer science engineering [14][15], robotic [16], energetic efficiency [17], and other general fields [18][19].…”

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

“…has been assumed to be 1 [4]. By adopting this simplification in SRNF, the number of network calculations and the parameter tuning equations can be reduced.…”

“…Fuzzy logic is based on inference rules and allows the systems to use human-like "fuzziness." In particular, fuzzy inference systems based on if-then rules provide high robustness and human-like inference [4,5]. However, it is usually hard for human being to design proper fuzzy rules resulting the consumption of a considerable time to tune fuzzy rules.…”