Power Saving Class (PSC) is an essential issue on IEEE 802. . In previous research, many algorithms had been proposed to reduce the consumption of power, but most of them only considered multiple connections in a Mobile Subscriber Station (MSS); in fact, it does not fit in with the situation of real world. On the contrary, others proposed algorithms considering the situation of multiple MSSs with multiple connections; nevertheless, it is difficult to increase the amount of MSSs. In this paper, we propose an efficient algorithm, which refers to both categories and avoids state transitions. When packet size is much smaller or delay bound is more loosening, the result shows that our scheduling algorithm can serve almost double multiple MSSs with multiple connections and still maintain high sleep ratio for energy efficiency.

Summary Because static pricing models (such as flat‐rate or tiered‐rate models) cannot improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service versus the prices that they are willing to pay. Even though PMP cannot guarantee the actual quality of service during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose an adaptive PMP scheme, so‐called APMP, which determines the dynamic access prices of different classes for the next 24 h. The accessible prices should try to increase the revenue while operators can serve more subscribers. Our simulation results show that APMP can significantly improve total revenue and average revenue per user for the operator. Copyright © 2016 John Wiley & Sons, Ltd.

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SummaryAccording to the specifications of the Third Generation Partnership Project (3GPP), any online charging system must determine a granted unit (GU) and make reservations for each session before the serving network delivers the service. Allocating insufficient GUs to user equipment may incur excessive quantities of reservation messages; conversely, providing excessive GUs could result in an unbalanced distribution of resources. Therefore, a prudent investigation of a reservation scheme for multiple Internet of things (IoT) devices is urgently needed. In this paper, we focus on IoT devices with regular traffic, which usually generate numerous routine traffic events and few event‐driven traffic events. We combine a fixed‐order quantity model (sometimes called a Q model) and propose a novel solution to this issue. To the best of our knowledge, this is the first research to propose a solution based on such a concept. Compared with typical schemes, the simulation results show that our proposed scheme can maintain the success rate with a smaller‐than‐usual number of messages.

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