Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols

Abstract

Delay Tolerant Networking (DTN) is well suited to challenging environments, defined by the lack of reliable end-to-end communication paths to the destination. However, the available energy is not considered in the majority of existing DTN routing protocols when they make forwarding decisions. This limits both delivery probabilities and the network lifetimes in energy-constrained applications. This paper investigates energy-aware routing protocols for wildlife tracking application to transmit data from attached sensors on the animal's back to data collection base stations. We propose three new network protocol strategies to extend common DTN routing protocols, and consider the available energy to achieve efficient utilization of the node's energy in transmission and sensing. These strategies enhance packet delivery rates up to 13% by carefully using the limited energy resources. We simulate two different animal tracking scenarios and show that the new strategies provide significant performance improvement under different scenarios.

Performance Evaluation of Routing Protocols for Opportunistic Networks: An Energy Efficiency Perspective

“…Another reason was that the the algorithms that were used in developing AEHBR routing protocol are easier to implement compared to the other algorithms discussed in this section. Kavian et al [9] proposed energy-aware algorithms for Delay Tolerant Networks (DTN) routing protocols namely: Threshold, RRE and ORRE algorithms. These strategies can be combined with PRoPHET, Epidemic and Spray and Wait routing protocol, making them energy-efficient.…”

“…The user specifies an energy threshold as a percentage of the node's energy. If a node's energy becomes lower than the userdefined energy threshold, the node stops forwarding messages to other nodes and will only transmit messages to a base station [9]. This enables nodes to function for a longer duration but finding the optimal value for the energy threshold can be a difficult task [9].…”

“…• Remaining Required Energy (RRE) Algorithm: The RRE builds on top of the Threshold algorithm. However, instead of a user specifying the energy threshold, the algorithm determines it automatically [9]. The algorithm uses the energy needed by the node to transmit messages obtained from the last encounter with a base station and energy needed to run by the node to run its basic functions [9].…”

“…• Optimized Remaining Required Energy (ORRE) Algorithm: According to Kavian et al [9], the RRE algorithm can overestimate or underestimate the energy resources available if the node movement is not uniform. The ORRE algorithm does not have this limitation.…”

“…The ORRE algorithm does not have this limitation. Unlike the RRE algorithm, the ORRE algorithm also depends on the number of messages stored in the buffer [9].…”