{'fraction_non_alphanumeric': 0.01569506726457399, 'fraction_numerical': 0.0, 'mean_word_length': 5.661691542288557, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Localization in wireless sensor network using merged population based particle swarm optimization: LMPPSO

Estimation of position in the wireless sensor networks is presently taken as prime focus in the field of research and applications. Various innovative location-aware technologies and services are being investigated. Applications area are at present having expansion in both two and three dimensions. In Practical applications, generally only approximate distances are available from sensor to anchors; hence there is more error in position accuracy, if linear method is applied. The nonlinear numeric iterative methods (like Newton) are suffered from problem of convergence where success of convergence completely depends upon the initialization of process. Nature inspired nonlinear iterative methods having their own problem of consistency. A noble concept based on PSO, namely merged population based particle swarm optimization (MPPSO), presented in this paper to overcome the problem of inconsistency available with PSO. In fact present concept can apply to solve the consistency problem with almost all nature inspired iterative algorithms. MPPSO required less population size to generate more precise value of the position within less number of iterations. Proposed method is also having very good generalized applicability.