Machine‐to‐machine (M2M) communications is one of the major enabling technologies for the realization of the Internet of Things (IoT). Most machine‐type communication devices (MTCDs) are battery powered, and the battery lifetime of these devices significantly affects the overall performance of the network and the quality of service (QoS) of the M2M applications. This paper proposes a lifetime‐aware resource allocation algorithm as a convex optimization problem for M2M communications in the uplink of a single carrier frequency division multiple access (SC‐FDMA)‐based heterogeneous network. A K‐means clustering is introduced to reduce energy consumption in the network and mitigate interference from MTCDs in neighbouring clusters. The maximum number of clusters is determined using the elbow method. The lifetime maximization problem is formulated as a joint power and resource block maximization problem, which is then solved using Lagrangian dual method. Finally, numerical simulations in MATLAB are performed to evaluate the performance of the proposed algorithm, and the results are compared to existing heuristic algorithm and inbuilt MATLAB optimal algorithm. The simulation results show that the proposed algorithm outperforms the heuristic algorithm and closely model the optimal algorithm with an acceptable level of complexity. The proposed algorithm offers significant improvements in the energy efficiency and network lifetime, as well as a faster convergence and lower computational complexity.

中文翻译：

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高效的资源分配，以实现M2M网络中的生命周期最大化

机器对机器（M2M）通信是实现物联网（IoT）的主要支持技术之一。大多数机器类型的通信设备（MTCD）都是电池供电的，这些设备的电池寿命会严重影响网络的整体性能以及M2M应用程序的服务质量（QoS）。本文提出了一种基于生命周期的资源分配算法，作为基于单载波频分多址（SC-FDMA）异构网络的上行链路中M2M通信的凸优化问题。一个ķ引入了均值群集，以减少网络中的能耗并减轻来自相邻群集中MTCD的干扰。使用弯头法确定最大簇数。将寿命最大化问题公式化为功率和资源块的联合最大化问题，然后使用拉格朗日对偶方法进行求解。最后，在MATLAB中进行了数值仿真，以评估该算法的性能，并将结果与​​现有的启发式算法和内置的MATLAB最佳算法进行比较。仿真结果表明，所提出的算法优于启发式算法，并以可接受的复杂度对最优算法进行了精确建模。所提出的算法在能效和网络寿命方面提供了显着的改进，