IEEE Transactions on Mobile Computing · 2020 · 219 citations · 35 references
ComputingComputer SystemsHeterogeneous ComputingInternet Of ThingsMobile ComputingIot Energy ManagementMobile NetworksEnergy ConsumptionEnergy-efficient CommunicationHeterogeneous NetworksNetwork Energy OptimizationOutage ProbabilityOutage Probability AnalysisSmall Cell NetworksTraffic Loads
LTE‑A heterogeneous networks provide reliable, service‑differentiated communication that supports mobile applications such as smart meters, remote sensors, and vehicular systems, thereby driving the trend of IoT underlaying small‑cell networks. This paper proposes an energy‑efficient framework that enables multitier heterogeneous small‑cell networks to deliver seamless wireless connectivity for both mobile users and IoT nodes. The framework employs an elastic cell‑zooming algorithm that adaptively adjusts small‑cell transmission power based on quality‑of‑service and traffic loads, and a clustering‑based IoT structure with a SWIPT‑CH selection algorithm to maximize residual energy and reduce resource contention. Simulations demonstrate that the framework markedly improves energy efficiency for IoT underlaying small‑cell networks while maintaining a guaranteed outage probability.
Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.
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