Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Directional sensor networks are ad hoc networks which are utilized in different applications to monitor and coverage all of the specific targets in the observing fields permanently. These kinds of networks include several configurable directional sensors in which they can be adjusted in one the possible directions along with one of its adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization is still a challenge. This paper formulates the expansion of directional sensor network lifespan to a discrete optimization problem. Several proposals were presented in literature to solve the stated problem, but majority of them are threatened to get stuck in local optimum and led low efficiency. To solve this combinatorial problem, an advanced discrete cuckoo search algorithm is designed and is called several times until the remaining battery associated to alive sensors do not let observe all targets. In each time, algorithm returns an efficient cover along with its activation time. A cover is a sub set of available sensors capable of monitoring all targets in the observing field. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Designing miscellaneous discrete walking around procedures makes to reach a good balance between exploration and exploitation in search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in variety circumstances proves the superiority of the proposed algorithm is about 19.33%, 14.83%, 13.50%, and 5.33% in term of average lifespan improvement against H-MNLAR, ACOSC, GA, and HDPSO algorithms respectively.

Solving target coverage problem in directional sensor networks with ability to adjust sensing range using continuous learning automata

Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime.

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Measurement-While-Drilling MWD Error Model Validation – Does the Model Reflect Reality?

It has been well documented that magnetic models and Measurement-while-Drilling (MWD) directional sensors are not free from error. It is for this reason that directional surveys are accompanied by an error model that is used to generate an ellipse of uncertainty (EOU). The directional surveys represent the highest probable position of the wellbore and the EOU is meant to encompass all of the possible wellbore positions to a defined uncertainty level. The wellbore position along with the individual errors are typically presumed to follow a Normal (Gaussian) Distribution. In order for this assumption to be accurate, 68.3% of magnetic model and directional sensor error should fall within plus or minus one standard deviation (1σ), 95.5% within two standard deviations (2σ), and 99.7% within three standard deviations (3σ) of the limits defined in the error model. It is the purpose of this study to evaluate the validity of these assumptions. The Industry Steering Committee on Wellbore Survey Accuracy (ISCWSA) provides a set of MWD error models that are widely accepted as the industry standard for use in wellbore surveying. The error models are comprised of the known magnetic model and MWD directional sensor error sources and associated limits. It is the purpose of this paper to determine whether the limits defined in the ISCWSA MWD error models are representative of the magnitude of errors observed in practice. In addition to the ISCWSA defined error model terms, this research also includes an analysis of the sensor twist error term and the associated limits defined in the Fault Detection, Isolation, and Recovery (FDIR) error model. This study is comprised of 138 MWD runs that were selected based on the criteria that they were processed using FDIR with overlapping gyro surveying to ensure highly accurate and consistent estimated values. The error magnitudes and uncertainties estimated by FDIR were compiled and analyzed in comparison to the expected limits outlined in the error models. The results conclude that the limits defined in the ISCWSA error models are not always representative of what is observed in practice. For instance, in U.S. land the assumed magnitudes of several of the error sources are overly optimistic compared to the values observed in this study. This means that EOUs with which wells are planned may not be large enough in some scenarios which could cause the operator to assume unanticipated additional risk. The final portion of this analysis was undertaken to test the hypothesis that preventative measures such as additional non-magnetic spacing are generally being taken by operators and directional service providers to minimize additional injected error when survey corrections are not being implemented while drilling the well. This hypothesis was tested by dividing the 138 MWD runs into Historical (survey corrections were not utilized in real-time) and Real-Time (survey corrections were utilized in real-time) categories. The results indicate that there are no significant differences in the error estimates between the Historical and Real-Time categories. This result in combination with the determination that the majority of the error model error terms should be categorized as fat-tail distributed indicate that proper well spacing and economics calculated using separation factor alone are insufficient without the use of survey corrections in Real-Time.