Communication, Localization, Coverage, Error and Control, Time Synchronization, Naming and Addressing, and Cross-Layer Issues

Author(s):  
Anuradha Pughat ◽  
Parul Tiwari ◽  
Vidushi Sharma ◽  
Neeta Singh
2016 ◽  
Vol 1 (1) ◽  
pp. 20
Author(s):  
Elvira Handayani

Jambi city is one of the cities that began to develop on the island of Sumatra in the field of infrastructure and the economy, so development is one activity that is enhanced by the local government in the framework of the welfare and prosperity of the people of Jambi. In the process of development is very common problems causing delays in work, many factors causing delays become very common problem faced by the parties involved, especially the Contractor which act as implementers.From the research the factors causing delays in construction work in the city of Jambi as perceived by the contractor, a number of conclusions, among others:Factors Materials and Materials obtain a total score of 7.18,Environmental Factors obtain a total score of 5.96,Financial Factor obtain a total score of 5.99,Factors Changes obtain a total score of 6.95,The Labor get highest total score is 7.29,Factors and Control Time to get a total score of 5.93,Factors Hardware obtain a total score of 7.15


2013 ◽  
Vol 787 ◽  
pp. 978-981
Author(s):  
Sen Mao Huang ◽  
Guang You Yang ◽  
Zhi Yan Ma ◽  
Zheng Zhang

ZigBee technology is more and more used in complex and bad industrial monitoring and control environment. At the same time, ZigBee nodes are usually powered by batteries, so prolonging the working time and reducing the power consumption of the nodes is very important. If the wireless nodes can turn into sleep mode in spare time of communication, it will further reduce the node power consumption. But in sleep period, the node can't communication with other node, we need to synchronous awaken and dormancy, so precise time synchronization for wireless sensor network application is particularly important. This paper will apply FTSP algorithm in the ZigBee network and realize the network time synchronization. At the same time, it doesn't increase power consumption of the network.


2012 ◽  
Vol 220-223 ◽  
pp. 1871-1876
Author(s):  
Feng Mei Liang ◽  
Bin Liu

Due to energy restrictions, node distribution density and hardware computing power etc., the traditional time synchronization mechanism is not suitable for wireless sensor network. The paper discussed the main reason that caused asynchronization and proposed an improved time synchronization algorithm based on cross layer optimization for wireless sensor network. Considering the stability of crystal oscillation and the linearity of crystal deviation in the physical layer, the improved time synchronization mechanism implemented a self-correction by the cross-layer MAC protocol. Estimating the crystal oscillation drift, the crystal deviation had been self-corrected just by a few times data broadcast. The experiment on the MCU Si1000 physical layer platform has demonstrated the practicability of the algorithm. The synchronization algorithm is able to keep a stable network operation in the way of extending the synchronization period and reducing the synchronization cost. The synchronization mechanism is applicable to the active acquisition network, especially the realtime one.


Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos

Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the unobserved causal parameter from observed data. Control time series are chosen so as to render plausible threats to internal validity implausible. An appropriate control time series may not exist, however, an ideal time series may be possible to construct. Synthetic control group models construct a control time series that optimally recreates the treated unit’s preintervention trend using a combination of untreated donor pool units.


2018 ◽  
Author(s):  
Marcus. R. Watson ◽  
Voloh Benjamin ◽  
Thomas Christopher ◽  
Hasan Asif ◽  
Womelsdorf Thilo

1Abstract1.1BackgroundThere is a growing interest in complex, active, and immersive behavioral neuroscience tasks. However, the development and control of such tasks present unique challenges.1.2New MethodThe Unified Suite for Experiments (USE) is an integrated set of hardware and software tools for the design and control of behavioral neuroscience experiments. The software, developed using the Unity video game engine, supports both active tasks in immersive 3D environments and static 2D tasks used in more traditional visual experiments. The custom USE SyncBox hardware, based around an Arduino Mega2560 board, integrates and synchronizes multiple data streams from different pieces of experimental hardware. The suite addresses three key issues with developing cognitive neuroscience experiments in Unity: tight experimental control, accurate sub-ms timing, and accurate gaze target identification.1.3ResultsUSE is a flexible framework to realize experiments, enabling (i) nested control over complex tasks, (ii) flexible use of 3D or 2D scenes and objects, (iii) touchscreen-, button-, joystick- and gaze-based interaction, and (v) complete offline reconstruction of experiments for post-processing and temporal alignment of data streams.1.4Comparison with Existing MethodsMost existing experiment-creation tools are not designed to support the development of video-game-like tasks. Those that do use older or less popular video game engines as their base, and are not as feature-rich or enable as precise control over timing as USE.1.5ConclusionsUSE provides an integrated, open source framework for a wide variety of active behavioral neuroscience experiments using human and nonhuman participants, and artificially-intelligent agents.2GlossaryActive task: Experimental tasks which involve some combination of realistic, usually moving, stimuli, continuous opportunities for action, ecologically valid tasks, complex behaviours, etc. Here, they are contrasted with static tasks (see below)Arduino: A multi-purpose generic micro-processor, here used to control inter-device communication and time synchronization.Raycast: A game-engine method that sends a vector between two points in a virtual three-dimensional environment, and returns the first object in that environment it hits. Often used to determine if a character in a game can see or shoot another character.State Machine (also Finite State Machine): A way of conceptualizing and implementing control in software, such that at any one moment the software is in one, and only one, state. In hierarchical state machines, as used in the present software suite, these can be organized into different levels, such that each level can only be in one state, but a state can pass control to a lower level.Static task: Experimental tasks like those traditionally used in the cognitive neurosciences. Simple, usually stationary, stimuli, limited opportunities for action, simple behaviours, etc. Here, they are contrasted with active tasks (see above).Unity: One of the most popular video game engines. Freely available.Video game engine: A software development kit designed to handle many of the common issues involved in creating video games, such as interfacing with controllers, simulating physical collisions and lighting, etc.


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