In multi-radio multi-channel wireless mesh networks, energy saving mechanisms try to save energy by putting radios into sleep mode. The decision to switch energy states of radios is taken based on parameters like remaining energy or traffic requests at nodes. In IEEE 802.11 power saving mode (PSM), nodes turn off the radios whenever there is no traffic to receive, send or forward. Nodes wake up radios periodically to check if there is any new traffic demand. Due to waking up radios redundantly and a requirement of tight synchronization PSM misses opportunities to save energy in the multi radio scenario. We propose an advanced energy saving method (AESM), where each node makes an independent decision on switching radios states while satisfying QoS requirements for different types of traffic flows. Experimental evaluation shows that AESM reduces energy consumption by 20% over PSM, while also reducing delay and packet loss to maintain QoS for network performance.

Multi-radio multi-channel (MRMC) mesh networks improve latency and spectrum utilisation, but at the cost of increased energy consumption. The standard mesh networking power saving mechanism (802.11 PSM) does not apply to MRMC. We propose an enhanced energy saving mechanism, EESM, in which each node switches its radios between different energy states based on observed traffic. In an empirical evaluation, we investigate the tradeoff between energy savings and decreased goodput. We show that energy savings are possible without impacting goodput, and that more aggressive control can generate more significant energy savings.

Microbial Genetic Algorithm (MGA) is a simple variant of genetic algorithm and is inspired by bacterial conjugation for evolution. In this paper we have discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator. We also proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity in population in a cost effective way. Our investigation suggests that a clever choice of mutation operator can enhance the performance of basic MGA significantly.