Abstract:
In this paper, a new approach to energy harvesting and data transmission optimization in a heterogeneous-based
multi-class and multiple resource wireless transmission wireless sensor network system that focus on monitoring
water and its quality is presented. Currently, energy is a scarce resource in wireless sensor networks due to the
limited energy budget of batteries, which are typically employed for powering sensors. Once the available energy
of a particular sensor node battery is depleted, such sensor node becomes inactive in a network. As a consequence,
such node may not be able to participate in the transmission of the application signal in the uplink stage of the
network, resulting in a lack of ability to communicate vital signals in a timely manner. Energy scarcity has been a
long standing problem in wireless sensor network applications. To address this problem, energy harvesting from
intended radiofrequency power source is considered in this work. However, wirelessly powered wireless sensor
network systems are confronted by unfairness in resource allocation problem, as well as interference problem in
multiple energy resource transmissions. These problems adversely impact the performance of the system in the
context of the harvested energy by the sensor nodes, sensors information transmission rates, and the overall system
throughput rate. These problems are tackled in this paper by formulating a sum-throughput maximization problem
to reduce system energy consumption and enhance the system overall throughput rate. The throughput optimization
problem is formulated as a non-convex function. Through the exploitation of the problem structure, it is converted to
a convex function. The mathematical models of the optimization problem are validated through numerical simulations.
The simulation results reveal that the proposed wireless powered sensor network system outperforms an existing
wireless powered sensor network system, by comparison of the numerical simulations of this work to the numerical
simulations of the existing WPSN system, regardless of the distances of the sensor nodes to the IPS and the base
station. Also, the newly proposed method performs efficiently using parameters that include path-loss exponent
impact, performance comparison of systems, convergence based on iteration, comparison based on unequal network
distances to the BS, transmission power impact on the attainable throughput and on fraction of energy consumed on
information transmissions, and influence of different number of nodes in the network classes.
