A Reliable Energy-Efficient Multi-Level Routing Algorithm for Wireless Sensor Networks Using Fuzzy Petri Nets

A reliable energy-efficient multi-level routing algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy, number of the neighbors and centrality of each node for cluster formation, which is critical for well-balanced energy dissipation of the network. In the algorithm, a knowledge-based inference approach using fuzzy Petri nets is employed to select cluster heads, and then the fuzzy reasoning mechanism is used to compute the degree of reliability in the route sprouting tree from cluster heads to the base station. Finally, the most reliable route among the cluster heads can be constructed. The algorithm not only balances the energy load of each node but also provides global reliability for the whole network. Simulation results demonstrate that the proposed algorithm effectively prolongs the network lifetime and reduces the energy consumption

Nutrient co‐limitation in the subtropical Northwest Pacific

Nutrients limiting phytoplankton growth in the ocean are a critical control on ocean productivity and can underpin predicted responses to climate change. The extensive western subtropical North Pacific is assumed to be under strong nitrogen limitation, but this is not well supported by experimental evidence. Here, we report the results of 14 factorial nitrogen–phosphorus–iron addition experiments through the Philippine Sea, which demonstrate a gradient from nitrogen limitation in the north to nitrogen–iron co-limitation in the south. While nitrogen limited sites responded weakly to nutrient supply, co-limited sites bloomed with up to ~60-fold increases in chlorophyll a biomass that was dominated by initially undetectable diatoms. The transition in limiting nutrients and phytoplankton growth capacity was driven by a gradient in deep water nutrient supply, which was undetectable in surface concentration fields. We hypothesize that this large-scale phytoplankton response gradient is both climate sensitive and potentially important for regulating the distribution of predatory fish

Locating a two-wheeled robot using extended Kalman filter

Kalman filter je vrlo općenita metoda filtriranja kojom se mogu riješiti problemi kao što su optimalna procjena, predviđanje, filtriranje buke i optimalna kontrola. Problem koji se javlja kod detekcije ispravne putanje gibajućih predmeta je prijem bučnih podataka. Stoga je moguće da se informacije neispravno pronađu. Postoje razne metode za dobivanje ispravnih podataka iz primljene obavijesti. U ovom radu cilj je otkriti putanju robota s dva kotača primjenom proširenog Kalman filtera. U tu su se svrhu rabile trokutaste, kružne, eliptične i sinusoidne putanje u istraživanju raznih scenarija. Rezultati pokazuju da se Kalmanovim filterom optimalno pronalazi ispravna putanja s manje od 3 % učestalosti pogreške. Ti rezultati također pokazuju da učestalost pogreške kod pronalaženja kružnih i trokutastih putanja ima najvišu i najnižu vrijednost primjenom Kalman filtera; uz to, rezultati su pokazali da učestalost pogreške uvelike ovisi o promjenama putanje.The Kalman filter is a very general method of filtering which can solve problems such as optimal estimation, prediction, noise filtering, and optimal control. A problem with detection of correct path of moving objects is the received noisy data. Therefore, it is possible that the information is incorrectly detected. There are Different methods to extract the correct data from the received information. This paper aims to detect the path of a two-wheeled robot using extended Kalman filter. For this purpose, triangular, circular, elliptical, and Sinusoidal paths were used to explore various scenarios. The results showed that the Kalman filter optimally detects the correct path with less than 3 % error rate. These results also show that error rate related to detect circular and triangular paths has the highest and lowest value, respectively, using the extended Kalman filter; in addition, the results showed that the error rate strongly depends on path changes

Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF

A Nonlinear Finite-Time Robust Differential Game Guidance Law

In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving the nonlinear Hamilton–Jacobi–Isaacs equation, a time-varying cost function is developed to reflect the fixed terminal time, and the robust guidance law is developed to compensate for the external disturbance. Then, a novel neural network identifier is designed to approximate the unknown nonlinear dynamics with online weight tuning. Subsequently, an online critic neural network approximator is presented to estimate the cost function, and time-varying activation functions are considered to deal with the fixed final time problem. An adaptive weight tuning law is given, where two additional terms are added to ensure the stability of the closed-loop nonlinear system and so as to meet the terminal cost at a fixed final time. Furthermore, the uniform ultimate boundedness of the closed-loop system and the critic neural network weights estimation error are proven based upon the Lyapunov approach. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed robust differential game guidance law for nonlinear interception

Object-Oriented Petri nets Based Architecture Description Language for Multi-agent Systems Manuscript revised January 29, 2006.

To narrow the gap between multi-agent formal modeling and multi-agent practical systems, multi-agent systems (MAS) are studied from the point of view of software architecture. As the existing architecture description languages (ADLs) are not suitable for describing the semantics of MAS, a novel architecture description language for MAS (ADLMAS) rooted in BDI model is proposed, which adopts Object-Oriented Petri nets presented in this paper as a formal theory basis. ADLMAS is suitable for representing concurrent, distributed and synchronous MAS, and it is brought directly into the design phase and served as the high-level design for MAS implementation. ADLMAS can visually and intuitively depict a formal framework for MAS from the agent level and society level, describe the static and dynamic semantics, and analyze, simulate and validate MAS and interactions among agents with formal methods. To illustrate the favorable representation capability of ADLMAS, an example of multi-agent systems in electronic commerce is provided. Finally, the MAS model and its key behaviors properties are analyzed and verified. Key words: Multi-agent systems, software architecture, architectur