Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks
ABSTARCT :
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.
EXISTING SYSTEM :
? 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.
? In a WPBC network, a wireless-powered device (WPD) can perform either backscatter communications (i.e., passive transmissions) or transmissions using its radio frequency (RF) circuit (i.e., active transmissions) and the energy harvested from a power beacon (PB).
DISADVANTAGE :
? 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 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.
PROPOSED SYSTEM :
• In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network.
• In general, the utilities of the leader obtained by the proposed scheme, BBCM, and TDMA increase as the backscatter rate increases.
• When these numbers increase, then the proposed scheme can outperform the TDMA mechanism.
• The increase in the number of PWPDs causes no impact on the profit of the proposed scheme due to the low level of backscatter rate.
• The fast convergence and computing efficiency of the proposed iterative algorithm.
ADVANTAGE :
? 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).
? It has also revealed that the JA scheme is superior to the PA scheme in all ISP’s performance evaluations.
? We further study the efficiency of the SE through the concept of price of anarchy (PoA).
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