Abstract
Smartphones manufactured at present are equipped with the new Wireless Local Area Network (WLAN) calibrated to IEEE standards on its interface, which supports the Multiple Input Multiple Output (MIMO) feature. This technological advancement has enhanced smartphones to satisfy the present IEEE standard requirements for WLANs. However, the merits of smartphones are restricted by battery lifecycle. In this paper, the energy consumption of a smartphone equipped with the MIMO system throughout the transmission and reception of data is studied. The impact of the various factors of recently developed WLANs (such as 802.11n, which includes modulation and coding scheme and MIMO) are considered in conditions of throughput and power/energy consumption by the use of well-known simulator, which is called Network Simulator 3 (NS-3). In addition, the energy consumed in the course of transmitting and receiving data is differentiated through per-bit energy consumption with various MIMO compositions and physical data rates. The consequences reveal that growing the system configuration farther 2 x 2 MIMO enhances the throughput and reduces the per-bit energy consumption. Furthermore, the capability to predict the energy consumed for data transmission is considered essential for energy-aware applications. For example, task offloading from modern mobile appliances to cloud computing is a highly potential approach for saving energy in mobile devices. The decision to offload is essential to make offloading more effective. Energy models are required to precisely predict the energy consumption of networking and computing processes. Thus, this precision enables the offloading technology to precisely assess whether offloading a specified task will save energy. For this purpose, an energy consumption model for transmitting and receiving is developed based on a MIMO parameter with high accuracy. The simulation demonstrates that our energy models are practical and effective in real-world scenarios. In addition, these models estimate the energy consumption per bit, and offer system designers and application developers effective tools for designing energy-efficient systems.