## 1 Introduction  
Data collection and transmission operations are often marred because of the finite node-lifetime of a WSN. And since this operation is purely energy-dependent and indeed energy-constrained sensor-nodes with limited battery power cannot smoothly transmit their data to the access point, it has become a fundamental operational bottleneck. Hence, for an efficient data aggregation and transmission, it is desirable for the sensor-nodes to be energy-efficient. However, due to the fact that IoT sensors are energy-constrained as a result of their smallness and mode of deployments across different operating environments as well as the very large costs of battery replacements, batteries are therefore employed in powering the sensors as direct power transfer from the grid is not feasible [1; 2]. Research has also revealed that nodes closely attached to the sinks usually discharge their power so quickly and are mostly energy constrained, because of their frequent data forwarding activity. Sensor-nodes are low-power and low-cost devices characterized with multifunctional capabilities and are sparsely and randomly distributed across a WSN [1]. Sensor-nodes are also characterized with four basic modules: sensing, processing, transceiver, and power units. Due to limited battery capacity of a sensor-node, a WSN does not remain operational in perpetuity. To prolong the lifetime of a sensor network, many techniques have emerged; the most common being the energy harvesting (EH) technique [3; 4; 5; 6; 7]. The EH technique heavily relies on the energy-extraction from the environment. The success of this scheme for sensor networks remains limited in operation because the proper operation of an EH scheme is environmentally-dependent. The natural sources of energy harvesting such as sunlight (for solar energy) have shown several limitations and cannot be employed to charge IoT network sensors due to the stochastic mode of their energy arrivals and their seeming non-availability when needed [3; 4; 5]. Wireless RF power transmitter leverages on this shortcoming and presents as an alternative source of power to the IoT sensors. Compared to the natural energy harvesting model, the wireless RF power transfer presents several advantages including flexibility in sensor placement planning, easily controllable, more reliable, and easily managed [2]. A sensor network in a smart city is characterized by the deployment of several sensor-nodes with various sensing capabilities, communication and computation for application specific analysis [3]. A widely known application classification is the deployment of battery-powered sensor-nodes across even the most difficult areas. A battery-powered sensor-node has the huge limitation of finite node lifetime. Nodes arbitrarily cease to operate when their batteries are depleted thereby leading to low duty-cycle or reduced sensing reliability and low transmission range. Nodes may however choose to possibly use large batteries for longer sensing durations, but this will incur increased size, weight and cost occasioned by higher power requirements [3]. Several solution-techniques are currently trending and relate to how to effectively maximize the network lifetime of battery-powered and energy-hungry sensor-nodes. Some of these are discussed in [3] and are only useful in prolonging the interval of time for battery replacements and by no means guaranteeing perpetual lifetime of the sensor-nodes. Recently, energy harvesting has emerged and has shown some potentials as an alternative technique to address the finite node lifetime challenge. [PERSON] and [PERSON] [3] define energy harvesting as a technique of acquiring/scavenging energy from the environment or different energy sources and further utilizing the harnessed energy (electrically-converted) to power the sensor nodes to increase their operational lifetime and capability. Despite the successes made using energy scavenging techniques, it has not gone without limitations in the context of sensor network applications.