the proceedings contain 29 papers. the special focus in this conference is on distributedcomputing and Artificial Intelligence. the topics include: Optimization of urban freight distribution with different time const...
ISBN:
(纸本)9783319624099
the proceedings contain 29 papers. the special focus in this conference is on distributedcomputing and Artificial Intelligence. the topics include: Optimization of urban freight distribution with different time constraints;artificial bee colony algorithms for two-sided assembly line worker assignment and balancing problem;cyclic steady state behaviour subject to grid-like network constraints;application of fuzzy logic and genetic algorithms in automated works transport organization;quality assessment of implementation of strategy design pattern;minimizing energy consumption in a straight robotic assembly line using differential evolution algorithm;statistics-based approach to enable consumer profile definition for demand response programs;feature extraction-based method for voltage sag source location in the context of smart grids;a multi-agent system for energy trading between prosumers;smart grids data management;data mining for prosumers aggregation considering the self- generation;control of accuracy of forming elastic deformable shafts with low rigidity;a negotiation algorithm for decision-making in the construction domain;deep neural networks and transfer learning applied to multimedia web mining;predicting the risk of suffering chronic social exclusion with machine learning;semantic profiling and destination recommendation based on crowd-sourced tourist reviews;robustness of coordination mechanisms in distributed problem solving against erroneous communication;a sentiment analysis model to analyze students reviews of teacher performance using support vector machines;proposal of wearable sensor-based system for foot temperature monitoring and energy analyzer emulation for energy management simulators.
sensor networks are increasingly being used for applications which require fast processing of data, such as multimedia processing. distributedcomputing can be used on a sensor network to reduce the completion time of...
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this paper presents a system architecture for mapping and real-time monitoring of a relatively large industrial robotic environment of size 10 m × 15 m × 5 m. Six sensor nodes with embedded computing power a...
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this paper presents a system architecture for mapping and real-time monitoring of a relatively large industrial robotic environment of size 10 m × 15 m × 5 m. Six sensor nodes with embedded computing power and local processing of the 3D point clouds are placed close to the ceiling. the system architecture and data processing is based on the Robot Operating System (ROS) and the Point Cloud Library (PCL). the 3D sensors used are the Microsoft Kinect for Xbox One and point cloud data is collected at 20 Hz. A new manual calibration procedure is developed using reflective planes. the specified range of the used sensor is 0.8 m to 4.2 m, while depth data up to 9 m is used in this paper. Despite the fact that only six sensors are used and that the Kinect sensors are operated beyond the specified range, a benchmark of the accuracy compared with a Leica laser distance meter demonstrates an accuracy of 10 mm or better in the final 3D point cloud.
In the applications of the Internet of things (IoT) based on wireless sensor network (WSN), an edge device depends on a battery that have limited lifetime. the validity of sensed data that indicates the change of even...
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In the applications of the Internet of things (IoT) based on wireless sensor network (WSN), an edge device depends on a battery that have limited lifetime. the validity of sensed data that indicates the change of events plays the most important role in the accuracy and reliability of IoT/WSN applications. It is to give assurance on the data quality for perfect decision making. Moreover, the cost of energy consumption to observe the measured error at the edge device should not be higher than the cost energy consumption during transmitting. In this paper, we proposed an energy dissipation model to determine the cost of detecting errors at the edge device level. the proposed model has been used to evaluate the effect of various data validation schemes on the energy consumption of edge device. the analysis showed that, selected data validation scheme at edge device level is a vital issue. In addition, the cost of energy consumptions to observe the measured error effectiveness in the sensor node. the ED-RT scheme produced the best performance overall other schemes such as ED-HD and ED-NN, in terms of detecting errors cost.
Mobile offloading overcomes the resource limitations of offloader devices by splitting resource-intensive tasks and allocating subtasks to nearby offloadee devices. In processing its subtask, each offloadee effectivel...
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ISBN:
(纸本)9781509066001
Mobile offloading overcomes the resource limitations of offloader devices by splitting resource-intensive tasks and allocating subtasks to nearby offloadee devices. In processing its subtask, each offloadee effectively executes foreign and untrusted code which might both harm the device and exhaust its resources. Given the personal nature and constrained resources of offloadee devices, such as smartphones, precise control at the offloadee over the execution environment of offloaded tasks as well as the provided and consumed resources then is a natural requirement for the success of offloading approaches. We thus contribute a mechanism for fine-grained resource control of local task execution, benefitting allocation approaches by precisely assessing, advertising, and guaranteeing offloadee processing resources. Our design protects local device integrity and usability by isolating the execution of each task in a dedicated Linux container with precisely defined resource constraints. We highlight the performance and immediate applicability of our design through a prototypical implementation using LXC containers on COTS Android smartphones that achieves controllable task execution at minimal costs: Each container starts up in only 2 ms, imposes less than 5 % computation overhead, and consumes only 10MB of memory.
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