Consumer electronics such as smart home devices, smartphones and smart wearables rely on accurate collection and sharing of data for massive user interaction. These form essential components of next generation internet-of-things (IoT) which require connected support. But owing to the limited battery life, these consumer nodes suffer from power limitations requiring frequent battery charging or replacements. This paper presents digital twin enabled wireless powering scheme for a massive IoT network. Digital twin creates a virtual replica of communication network involving large number of sensor nodes and access points (APs). This network is tested for energy deficient consumer IoT nodes and energy harvesting is enabled for the sensor nodes to harness the RF energy transmitted by the APs. Minimum Interference AP Clustering (MIAC), an energy conservation scheme for wireless powering of cell-free massive IoT is proposed which forms subsets of APs or AP clusters for each active node. In this paper, the performance of a digital twin IoT network is evaluated with energy harvesting and AP clustering for different transmission schemes. The amount of energy harvested and the spectral efficiency achieved is obtained along with the mathematical formulations. It is shown that the coherent transmission with linear energy harvesting achieves maximum average spectral efficiency of 2.659 bits/s/Hz. Further, the impact of incorporating AP clustering in the network transmission model suggests that the spectral efficiency achieved with AP clustering is 12% higher than the transmission scheme with no AP clustering. In the end, the proposed network is compared with two other network models for performance evaluation. A use case scenario of digital twin in consumer health for enabling smart healthcare ecosystem is also provided.

Consumer electronics has witnessed rapid proliferation owing to the tremendous adoption of Internet-of-Things (IoT) technologies. The growing interaction between cyber world and the physical world demands intelligent and connected consumer products for ubiquitous information exchange. But the vulnerability to lack of coordinated connections, energy aware network solutions and regulated technology integrations is the main challenge. Thus it is very important to have new and sustainable support paradigm for next generation (next-gen) IoT based consumer electronics. This paper presents a robust resource control mechanism for consumer electronics based IoT network that offers communication support and reduced energy overhead. An intelligent reflecting surface (IRS) aided network model is proposed in which the consumer nodes or devices connect to the access points using various multiple IRSs. To foster the connected devices network in sustainable manner, resource control algorithm is proposed which enables the selection of optimal IRS against all IRSs. The performance of the network is evaluated for achievable sum rate for different transmit powers, different IRS phase shifts and number of IRS passive elements. It is observed that introduction of resource control scheme improves the achievable sum rate of the system by 9% using IRS with more reflecting elements with optimal phase shifts. Further, the energy efficiency analysis is carried out by proposing a power consumption model taking the power consumed by the circuit components into account. The proposed resource control enables the network model to attain maximum energy efficiency at lower signal-to-noise ratio (SNR) regime.

The emerging consumer electronic products and sensor devices are involved in data driven consumer applications. E-commerce is an important service application based on sensor web technology. It involves networks of intelligent objects or sensor nodes for data collection and data sharing for e-commerce systems. But these massive devices involved in the huge data collection are battery limited. This puts huge energy burden on the internet-of-things (IoT) based wireless network. Thus, energy sustainability is very important in energy constrained IoT networks for next generation sustainable IoT support. This paper presents a wireless powered IoT network aided by self-sustainable intelligent reflecting surfaces (IRSs). The energy is harvested by the user nodes as well as the IRSs by tuning the IRS reflection coefficients. An algorithm is proposed which associates a set of IoT nodes to each IRS such that the energy requirements and sum rate of the network are met. It is observed that the IRS-aided system with energy harvesting capabilities achieve maximum average sum rate of 20 bits/s/Hz with the proposed algorithm at base station (BS) transmit power Pt of 40 dBm. Further, the impact of energy harvesting on the tradeoff between required power and achieved sum rate is evaluated. The IRS-aided wireless powered system is compared with system model with no IRS and no energy harvesting case for performance evaluation. In the end, the use-case scenarios of energy efficient wireless network in e-commerce consumer electronic systems are also discussed.

Internet-of-Things (IoT) networks are witnessing a rapid proliferation of connected devices and mobile terminals each day. The wireless information flow between these massive battery-powered devices has a huge energy burden and will lead to an energy crisis in the near future; thus, there is an urgent search for sustainable energy networks. To offer a sustainable energy solution in order to meet the energy demands of these massive IoT networks, this paper presents a dynamic practical model that enables the efficient management of power resources. Two user-scheduling algorithms, namely, minimum distance scheduling (MDS) and maximum channel gain scheduling (MCS), are proposed; when these algorithms were used alongside a power optimization, they led to improved network efficiency. Further, the network’s performance was measured with parametric variations in the number of access points (APs); the deployment of APs and AP configuration is carried out for different precoding schemes. The impact of spatial correlation and the access to perfect channel state information (CSI) on the spectral efficiency of the system was also evaluated. In the end, the study compares the performance of different power-allocation methods and suggests that the power allocated to a particular user node by an AP can be controlled using the proposed algorithms. It is observed that, as compared to the MDS algorithm, the MCS algorithm results in better spectral efficiency for all the users with fractional power allocation. In addition, each AP assigns a maximum power of 141.7 mW to a user with strong channel conditions with the AP, and a minimum power of 3.1882 mW to the user with the worst channel conditions using centralized PMMSE precoding.