Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks
Abstract : This article studies the strategic interactions between an IoT service provider (IoTSP) which consists of heterogeneous IoT devices and its energy service provider (ESP). To that end, we propose an economic framework using the Stackelberg game to maximize the network throughput and energy efficiency of both the IoTSP and ESP. To obtain the Stackelberg equilibrium (SE), we apply a backward induction technique which first derives a closed-form solution for the ESP (follower). Then, to tackle the non-convex optimization problem for the IoTSP (leader), we leverage the block coordinate descent and convex-concave procedure techniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and time allocation for IoT devices, one can achieve significant improvements in terms of the IoTSP’s profit compared with those of conventional transmission methods (up to 38.7 folds). Different tradeoffs between the ESP’s and IoTSP’s profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the optimal social welfare when the benefit per transmitted bit exceeds a given threshold.
? Most existing work on the WPBC optimizes time allocation for devices to perform energy harvesting, active and passive transmissions under the time-division multiplexing (TDM) framework with the assumption of homogeneous IoT devices.
? In practice, various types of WPDs with different hardware capabilities and configurations, e.g., performing backscattering or HTT or both can co-exist.
? Moreover, a large number of IoT devices can belong to an IoT service provider (ISP) who is required to pay for energy to operate its service (e.g., a contractor that provides data collecting/monitoring services for smart cities).
? In particular, two schemes (i.e., the PA and JA schemes) performing iterative algorithms based on the BCD and CCCP techniques are proposed to address the non-concave optimization problem of the ISP.
? To tackle the profit maximization problem of the ISP, we propose two partitioning schemes, called partial adjustment (PA) and joint adjustment (JA) schemes.
? However, the profit maximization of the ISP is a nonconcave problem with respect to the requested energy price and operation times of the PB and IoT devices.
? Moreover, these variables are strongly coupled, making the non-concave optimization problem of the ISP more challenging.
• In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network.
• To study the Stackelberg equilibrium, we first obtain a closed-form solution for the ESP and propose a low-complexity iterative method based on block coordinate descent (BCD) to address the non-convex optimization problem for the ISP.
• we propose an iterative algorithm developed based on the block coordinate descent (BCD) method.
• In general, the utilities of the leader obtained by the proposed scheme, BBCM, and TDMA increase as the backscatter rate increases.
? The performance of the WPBC system not only depends on the scheduling or time allocated for energy harvesting, passive, and active transmission operations of IoT devices but also the energy contract with the ESP.
? We conduct intensive simulations to numerically study the performance and complexity tradeoff for various practical setups.
? These strategies hence may lead to the performance loss in terms of the total profit achieved by both the ISP and ESP (often referred to as the social welfare.
? We propose an economic framework based on the Stackelberg game to jointly maximize the network throughput and energy efficiency of both the ISP and the ESP.
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