研究目的
To explore energy efficiency (EE) optimization as measured in bits per Joule for mobile ad hoc networks (MANETs) based on the cross-layer design paradigm.
研究成果
The proposed BB algorithm significantly outperforms the reference algorithm in terms of computational complexity, decreasing the optimality gap by 81.98% and increasing the best feasible solution by 32.79%. The study provides insights into the design of EE maximization algorithms for MANETs by employing cooperations between different layers and serves as performance benchmarks for distributed protocols developed for real-world applications.
研究不足
The nonconvex MINLP problem is NP-hard in general, making it exceedingly difficult to globally optimize. The computational time increases with the number of partitions K, indicating a tradeoff between computational time and the number of nodes requiring investigation during the BB process.
1：Experimental Design and Method Selection:
The study models the EE optimization problem as a nonconvex mixed integer nonlinear programming (MINLP) formulation by jointly considering routing, traf?c scheduling, and power control. A customized branch and bound (BB) algorithm is devised to solve this globally optimal problem.
2：Sample Selection and Data Sources:
A MANET with twenty nodes randomly located in a 250 × 250 m2 region is considered. There are four sessions among the nodes with specific source node, destination node, minimum sustained rate, and peak rate demands.
3：List of Experimental Equipment and Materials:
Maximum transmission power is set to pmax = 100 mW and channel bandwidth W = 5 MHZ. Propagation gain hl = ?d ?4, where dl is the distance between transmitter and receiver of link l, and ? = 0.002 is a constant characterizing the antenna gain and average channel attenuation.
4：002 is a constant characterizing the antenna gain and average channel attenuation.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The study involves optimizing the EE of the MANET by jointly computing routing path, transmission schedule, and power control corresponding to the network, link, and PHY layers, respectively.
5：Data Analysis Methods:
The performance of the proposed BB algorithm is compared with a reference BB algorithm using the relaxation manners suggested in [1]–[3]. Numerical results are analyzed to evaluate computational efficiency and the impact of power control, traf?c scheduling, and routing on the design of EE optimization protocols and algorithms for MANETs.
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