International Conference on Computing, Measurement, Control and Sensor Network 

About: International Conference on Computing, Measurement, Control and Sensor Network is an academic conference. The conference publishes majorly in the area(s): Wireless sensor network & The Internet. Over the lifetime, 153 publications have been published by the conference receiving 477 citations.

An Improved LEACH Algorithm Based on Heterogeneous Energy of Nodes in Wireless Sensor Networks

Abstract: The need of wireless sensor networks for monitoring and control has increased tremendously in these years with new applications in the areas of environmental and medical monitoring, detection of natural calamities, industrial control etc. The finite and irreplaceable energy supply of sensor nodes, however, has been the main constraints. In this paper, an improved LEACH algorithm based on heterogeneous energy of nodes for same initial energy and multiple hop data transmission among cluster heads is proposed. It establishes a new threshold, which introduces current energy and average energy of the node to cluster head election probability so as to ensure these nodes with higher residual energy have greater probability to become cluster heads than that with the low residual energy. Simulation results show that this algorithm extends the network lifetime, while guarantees a well-distributed energy consumption.

A Novel Atmosphere Clouds Model Optimization Algorithm

Abstract: This paper introduces a novel numerical stochastic optimization algorithm inspired from the behavior of cloud in the natural world, which is designated as Atmosphere Clouds Model Optimization Algorithm (ACMO). And the ACMO algorithm has been tested on a set of benchmark functions in comparison with Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA). The results demonstrate that the proposed algorithm has a certain advantage in solving multimodal functions.

A General Video Surveillance Framework for Animal Behavior Analysis

Abstract: This paper proposes a general intelligent video surveillance monitoring system to explore and examine some problems in animal behavior analysis particularly in cow behaviors. In this concern, farmers, animal health professionals and researchers have well recognized that analysis of changes in the behavioral patterns of cattle is an important factor for an animal health and welfare management system. Also, in today dairy world, farm sizes are growing larger and larger, as a result the attention time limits for individual animals smaller and smaller. Thus, video based monitoring system will become an emerging technology approaching to an era of intelligent monitoring system. In this context, image processing is a promising technique for such challenging system because it is relatively low cost and simple enough to implement. One of important issues in the management of group-housed livestock is to make early detection of abnormal behaviors of a cow. Particularly failure in detecting estrus in timely and accurate manner can be a serious factor in achieving efficient reproductive performance. Another aspect is concerned with health management to identify unhealthy or poor health such as lameness through analysis of measured motion data. Lameness is a one of the biggest health and welfare issue in modern intensive dairy farming. Although there has been a tremendous amount of methods for detecting estrus, still it needs to improve for achieving a more accurate and practical. Thus in this paper, a general intelligent video surveillance system framework for animal behavior analysis is proposed to be by using (i) various types of Background Models for target or targets extraction, (ii) Markov and Hidden Markov models for detection of various types of behaviors among the targets, (iii) Dynamic Programming and Markov Decision Making Process for producing output results. As an illustration, a pilot experiment will be performed to confirm the feasibility and validity of the proposed framework.

The Design of Neural Network Fuzzy Controller in Washing Machine

Abstract: Wasting of electric and water, when we use washing machine, has become an important issue in life, how to reduce water consumption charge electric washing machine is an important task. The washing machine fuzzy controller neural network is researched deeply, which is based on fuzzy logic, neural network and its learning algorithm. The BP neural network is combined with fuzzy control and experiments are simulated by MATLAB. Water level, flow intensity and the washing time are pre-set. Fuzzy control rules and membership functions are automatically generated. These parameters can be adjusted real-time to improve the performance of washing machines and achieve better water-saving effect of energy saving.

A Dynamic Distributed Energy Management Algorithm of Home Sensor Network for Home Automation System

Abstract: Internet of Things (IoT) applications have become a prominent subject in computational science, embedded system and network technology. It is also the next big thing to influence complete electronic industries and human life. Many researches focus on smart home applications to develop varied home sensor network and topology. In the heterogeneous communication environment, power supply of home sensor network is the basic condition to maintain system operation. How to enlarge required power of sensor network as long as possible is the critical point to decide network life. There are more and more renewable energy deployed in home energy management system (HEMS) at modern residence. We develop an adaptive classification scheme (ACS) to classify power source type of home automation sensors in smart home, and further propose dynamic distributed energy management algorithm (DDEM) to adjust energy distribution for establishing intelligent home automation management system. Finally, the proposed scheme can effectively prolong operation life of home sensor network.