Received November 28, 2016, accepted December 10, 2016, date of publication December 14, 2016, date of current version February 25, 2017. Digital Object Identifier 10.1109/ACCESS.2016.2639517 Optimization of the Overall Success Probability of the Energy Harvesting Cognitive Wireless Sensor Networks MATEEN ASHRAF 1 , ADNAN SHAHID 2 , (Member, IEEE), JU WOOK JANG 3 , AND KYUNG-GEUN LEE 1 1 Sejong University, seoul 143-747, South Korea 2 Ghent University, Ghent 9052, Belgium 3 Sogang University, seoul 04107, South Korea Corresponding author: Kyung-Geun Lee (kglee@sejong.ac.kr) This work was supported by the Institute for Information and Communications Technology Promotion by the Korean Government (MSIP) under Grant B0126-16-1051. ABSTRACT Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings. INDEX TERMS RF energy harvesting, success probability, spectrum sharing, stochastic geometry, wireless sensor networks. I. INTRODUCTION Smart buildings will comprise the major portion of the smart cities of the future. WSNs are widely used for surveillance and control purposes of the smart buildings [1]. The sen- sors usually sense some physical activity in the surrounding environment and report it to the information sink (IS). The frequency band that is used by the sensors is also used simul- taneously by some other applications (e.g. wifi). This results in interference at the receiving end. The other major problem is linked with the batteries of the sensors. The process of reporting information to the IS can exhaust the batteries of the sensors quite quickly. Replacing the batteries may not be feasible due to the physical location of the sensors or due to the dependence of the human activity for performing battery replacement. The problem of battery exhaustion can be solved by using wireless energy harvesting. The study of wireless energy harvesting for communication networks is gaining much interest from the research community [2]–[7]. The main reason for these research efforts is the vast avail- ability of the energy present in the ambient environment. The main advantages of energy harvesting are the decrease in the carbon emission in the environment, improvement in the life time of the network and the ability to provide power to those devices that cannot be charged through fixed power outlets [5]. Wireless energy harvesting in communication networks can be performed from renewable sources [6] e.g. wind, vibrations, solar, thermoelectric or from ambient radio frequency transmissions [2]–[5], [7]. Energy harvesting from renewable sources may not be reliable due to the dependence on the weather conditions, time of day and physical location of the energy harvesting device. In particular, energy harvest- ing from solar energy can only be performed during the day time. The spectrum sharing with energy harvesting nodes is discussed in [8]–[13]. In [8], the cognitive relay selection is discussed, and the exact outage performance of the cognitive VOLUME 5, 2017 2169-3536  2016 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 283