Large number of autonomous robot solutions exists for various missions and domains. These robots are sufficient for the missions they are built for. At the same time each of them has limited functional and physical capabilities. Multi agent systems can be used to remove these limits. However it is true only in case when the system ensures effective interaction among the robots i.e. enables their social behavior. Usually it is hard to implement such capabilities directly into robots due to functional and physical limitations and heterogeneity of the team. One of possible solutions is to implement a behavior sensors management for the robots. [1] It should collect events, allocate subtasks to specific robots and monitor the execution of the assigned tasks. In order to avoid inherent drawback of fully centralized systems a significant level of autonomy has to be preserved. Intelligent agents fulfill these requirements. Therefore we propose a multi-agent system's architecture for safe road application with GPS tool. It can be used to control the car speed and to adjust it in case of danger.
Large number of autonomous robot solutions exists for various missions and domains. These robots are sufficient for the missions they are built for. At the same time each of them has limited functional and physical capabilities. Multi agent systems can be used to remove these limits. However it is true only in case when the system ensures effective interaction among the robots i.e. enables their social behavior. Usually it is hard to implement such capabilities directly into robots due to functional and physical limitations and heterogeneity of the team. One of possible solutions is to implement a behavior sensors management for the robots.It should collect events, allocate subtasks to specific robots and monitor the execution of the assigned tasks. In order to avoid inherent drawback of fully centralized systems a significant level of autonomy has to be preserved. Intelligent agents fulfill these requirements. Therefore we propose a multi-agent system's architecture for safe road application with GPS tool. It can be used to control the car speed and to adjust it in case of danger.
Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK $$\rightarrow$$ → 16-QAM) and from 0 to 43.3% for (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK $$\rightarrow$$ → 16-QAM)and (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.
Life is the main interesting thing in this existence and we are always searching to make it better and easy answering our duties as simple as it can, the purpose is easy to understand it cause we are looking for a comfortable life which handles all our routine tasks, for that today we introduce a system which can be plugged on our terminals and which it can make tasks automatically basing on the environment events. We present a dissertation project details and its application on both sides the web corner and terminal assistant, Multi-Agent System collaborates with PDDL to determinate which functions and procedures the agent has to execute at the time basing on environments events which are playing the rules of parameters in each functions or procedures.
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