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This paper introduces cognitive MAC-layer techniques to wireless sensor networks (WSN) to optimize Network survivability. We compare Adaptive Modulation (AM) over flat-fading channels, with data rate and transmit power being varied according to channel conditions with two variants: Adaptive Modulation with Idle mode (AMI) and a new Adaptive Sleep with Adaptive Modulation (ASAM) which dynamically adjusts the transmission and sleep modes based shared global information on channel conditions. These introduced cognitive methods assume power allocation schemes that improve energy efficiency and this node life assuming multi-hop relay networks.Simulation results indicate that a notable reduction in energy consumption can be achieved by jointly adapting the data rate and the transmit power in WSNs. The proposed ASAM algorithm can considerably improve node lifetime compared to AM and AMI. The optimal power control values and optimal power allocation factors are further considered for multi-hop relay networks, respectively, thus reducing the need for higher layer network protocols in local switching.

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This paper presents a methodology, a theoretical framework, and some novel ideas on performance modeling and evaluation of application-specific cognitive wireless sensor networks applied to environmental protection. Cross-layer optimizations integrating the use of adaptive sleep, adaptive modulation and energy-aware higher layer processing and protocols are assumed. Routing and application layer processing are assumed to be dependent on lower-layer protocols, requirements, and constraints. Applications relevant to this study are forest monitoring, where the probability of network failure is the main parameter to be minimized, and endangeredspecies monitoring, where the probability of node failure is reduced by increasing the expected node life. Results are shown comparing expected node life of a cross-layered design to that of a traditional adaptive modulation system.