Energy-Efficient Sensor Grouping for IEEE 802.11ah Networks With Max-Min Fairness Guarantees

For the large scale wireless networks, restricted access window (RAW) mechanism is a promising technique for realizing large-scale sensor access with the limited collision probability. In this paper, we are committed to designing the traffic distribution based sensor grouping scheme to balance the energy efficiency (EE) of different groups in the large scale access networks. Specifically, by adopting the Markov chain model, we formulate the optimization problem of max-min EE by taking into account traffic demands with even distribution of different all groups, but the formulated problem is an integer nonlinear programming (INLP) problem. In order to solve the INLP problem, we propose an optimal traffic grouping algorithm (OTGA) by utilizing the branch-and-bound method (BBM) to accommodate for the congestion level among groups. Though the traffic demands of each group can be obtained from the traffic grouping scheme, different combination of heterogeneous sensors can generate the same traffic demands, which make it difficult to find the optimal solution of sensor grouping from the proposed traffic grouping scheme. Furthermore, a heuristic traffic-sensor mapping algorithm (HTMA) is presented to make the traffic demands of each group appropriate. Thus, the proposed scheme can achieve a sub-optimal performance with the individual EE. The numerical results are provided to verify the effectiveness of the proposed schemes.