A Self Adaptive Bird Flock Algorithm for Efficient Coverage in MWSNs

In this article, an Optimized Bird Flocking Algorithm (OBFA) has been proposed to reduce the overlapping sensing areas and to improve the optimal coverage rate of a given Area of Interest (AoI) with less number of Sensor Nodes (SNs) in MWSNs. The methods found in the literature still exhibit overlapping sensing areas , resulting in the sensing of redundant information. Further, one can also observe the low coverage rate of existing methods in MWSNs resulting in uncovered positions in AoI. The proposed OBFA addresses these issues by reducing the coverage holes and increasing the coverage rate of an AoI. For the defined objectives the OBFA exploits the distinguishing features of birds in a flock, namely cohesion, separation, and alignment. The cohesion part of the OBFA makes all SNs to be close enough in the given AoI. While the separation behavior of the OBFA avoids congestion among SNs which is caused by cohesion. The alignment feature of OBFA aligns all the SNs to optimally cover the given AoI. Apart from these three features of the basic Bird Flocking Algorithm, the OBFA also includes a repulsive pairwise force to reduce the overlapping sensing areas in AoI and a position optimization function that uses a distributed information map concept to achieve self-organization among SNs for the maximum coverage of AoI. Further, a selfishness goal introduced in OBFA steers each SN towards 1 the less visited positions of AoI. The proposed OBFA is simulated in MATLAB and the results are compared with other state-of-the-art BFA-based algorithms. It has been found from the results that the proposed OBFA algorithm efficiently covers the given AoI with less time and with less number of SNs with maximum coverage rate of 0.99 as compared to the existing algorithms such as BFA and CSSA with coverage rates of 0.98 and 0.96 respectively.