Reduction of Energy consumption in Wireless Sensor Networks and IEEE 802.11 DCF
Hajibabaei Najafabadi, Zahra
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We consider the multiple hypothesis testing problem in wireless sensor networks (WSN), where multiple sensors make M-ary decisions. The sensors transmit their digitally modulateddecisions over orthogonal channels, subject to Rayleighfading and noise, to a fusion center (FC) that makes the final decision. Adopting a Bayesian optimalitycriterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel state information (CSI) on the optimal maximum a posteriori probability (MAP) fusion rules and optimal power allocation among sensors, when there is a constraint on the total transmissionpower of training and data symbols. We derive the J-divergence for different receiver architectures at the FC as a performance metric to allocate transmission power among the sensors. The theoretical results show a 3 dB power reduction for a coherent receiver and 7 dB power reduction for a noncoherent receiver with amplitude estimation or a noncoherent statistic receiver based on our proposed scheme. The results also show that a noncoherent receiver performs better than a coherent receiver in low SNR regime if the training power allocation is zero. For a coherent receiver, we have to allocate half of the power for training to maximize the detection performance.\par In the next work, we propose a novel adaptive-rate buffer-aware IEEE 802.11 Distributed Coordination Function (DCF) protocol, in which each station adapts its transmission rate based on its buffer backlog. This is in contrast to traditional IEEE 802.11 wireless local area networks (WLANs), which adapt their transmission rates based on the channel conditions.We model the protocol using a three-dimensional Markov chain with state space spanning a station's back-off counter, back-off stage, and buffer backlog. Using this model, we analyze the network's steady-state performance under both homogeneous and heterogeneous arrival rates.We then formulate an optimization with the goal of finding each station's optimal transmission policy (i.e., mapping from buffer states to transmission rates), which minimizes the total energy consumption across all stations subject to a common queuing delay constraint at each station.Our results suggest that the adaptive-rate buffer-aware IEEE 802.11 DCF can significantly reduce the energy consumption of stations with lower traffic arrival rates compared to the conventional IEEE 802.11 DCF.However, due to the three-dimensional Markov chain's complexity, we are not able to calculate the globally optimal transmission policies offline, let alone online. To overcome this challenge, we propose to use the well-known drift-plus-penalty method so that the stations can determine their (near) optimal transmission policies online without a prior knowledge of their traffic demands or the multi-user network dynamics. The results obtained using this method further confirm that stations with smaller arrival rates can save significant energy.