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In a wireless sensor network, energy is almost always the greatest limitation. Energy sources are restricted in many of the environments where nodes are deployed, limiting them to the use of batteries for power. Therefore, conserving energy is supremely important, however, such a task poses many challenges to hardware and protocol design. One of the greatest problems faced is reducing the energy consumption of the communications systems, which represents a substantial amount of the total consumption. This paper surveys the most recent schemes designed to reduce the communications module energy consumption with a focus on novel MAC protocols for ad-hoc wireless sensor networks. It initially describes the many challenges involved, then it analyses each protocol individually. Finally, the presented protocols are compared and the issues that remain open are raised for further research.

The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.