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Energy efficiency as well as fast data transmission is vital to green communications-based applications for Internet of Things (IoT). Wireless sensors, which constitute one of the important parts of IoT, adopt duty cycle operating mode to save energy. Although duty cycle operating mode will decrease the energy consumption of sensor nodes, it leads to a larger communication delay. In this paper, a utility-based adaptive duty cycle (UADC) routing algorithm is proposed to increase energy efficiency, reduce transmission delay, and keep long lifetime at the same time. First, UADC routing algorithm adopts a comprehensive performance evaluation function to evaluate the utility of choosing different relay nodes. Then it selects the node which maximizes the utility of the system to perform data relay. The utility function synthesizes comprehensive indexes like the reliability, energy consumption, and delay of the node. UADC routing algorithm adopts a high-duty cycle operating mode in the areas which have more remaining energy to decrease the delay. And a low-duty cycle operating mode in the energy-strained areas is adopted to ensure a long lifetime. The simulation results also prove the significant performances of our proposed algorithms.

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Summary In service‐oriented cloud computing systems (CCSs), the aim of cloud service organizers (CSOs) is to achieve maximum profit by collecting metadata with low cost from big data reporters (BDRs) and to provide advanced services to customers at a high price. In these systems, BDRs receive payoffs by reporting metadata to CSOs and exchanging metadata with other BDRs, and customers expect to get high‐quality services at a low price. However, because the missing of a critical parameters decision model in such service‐oriented CCSs, it is difficult to measure key parameters in CCSs such as price and quality of services in the competitive market. In this paper, we propose a multiple game (MG) model to formulate the critical parameters decision process. In the MG model, there are multiple games: games among BDRs and games among CSOs under the rule of “survival of the fittest,” games between BDRs and CSOs under the rule of “the highest payoff first,” and games between customers and CSOs under the rule of “the lowest price and the highest quality of service (QoS) first.” With the proposed multiple game (MG) model, the optimal key parameters can be obtained and the Pareto‐optimal equilibrium point can be achieved. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed MG model in dynamically deciding key parameters in CCSs.