Embedded System Communication Research Articles

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted.

A Novel Framework for Testing High-Speed Serial Interfaces in Multiprocessor Based Real-Time Embedded System

Recent years has seen a tremendous increase in processing requirements of present-day embedded system applications. Embedded systems consist of multiple processing elements (PEs) connected to each other using different types of interfaces. Many complicated tasks are accomplished by embedded systems in varied settings, which may introduce errors during inter-processor communication. Testing such systems is tremendously difficult and challenging from testing non-real time systems. A major part of testing real time embedded systems involves ensuring accuracy and timing in synchronous inter-process communication More specifically, the synchronization and inter-processor communication of real-time applications makes testing a challenging task and due to the demand for higher data rate increases, day-by-day, making testing of such systems even more complex. This paper presents a novel frame work that uses multiple instances of simulators with physical high-speed serial interfaces to emulate any real time embedded system communication. The framework presents a testing technique that detects all faults related to synchronization of high-speed synchronous serial interfaces in a systematic manner. The novelty of our approach is to simulate communication across multiple processors in a simulation environment for detecting and localizing bugs. We verify this framework using a case study consisting of an embedded software defined radio (SDR) system. The test results show the applicability of our approach in fixing bugs that relates to synchronization issues that otherwise are very hard to find and fix in very complicated systems, such as SDR.

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted.

Penerapan Sistem Tertanam untuk Monitoring Kandang Ayam Broiler

Broilers are fast growing animals that are influenced by environmental conditions. Routine monitoring is needed, especially temperature, humidity and ammonia levels. In this study, the authors propose an embedded system that can detect temperature, humidity and ammonia levels. This system works by sending temperature, humidity and ammonia gas information routinely once a day to the henhouse operator via text message (SMS), or information will be sent if environmental conditions are above/below the threshold. The embedded system was built using microcontrollers, temperature and humidity sensors as well as ammonia sensors. The microcontroller uses Arduino. Temperature and humidity sensors use DHT11, while the ammonia level sensor uses MQ-135. Embedded system communication uses the GSM SIM800L module, this module is responsible for sending the henhouse environmental conditions to the operator via SMS. Testing is done in two stages, namely functionality and connectivity. The functionality test proves that the sensor can produce information on temperature, humidity and ammonia levels by comparing it with conventional tools. The connectivity test proves that the GSM SIM800L module can send monitoring data every day and certain conditions.

Battery Recovery-Aware Optimization for Embedded System Communications

In this paper, we consider a point-to-point wireless communications for embedded battery powered systems. We aim to provide an optimal use of all the usable capacity inside the battery before it becomes exhausted. For this purpose, we exploit the recovery effect to extend their lifetime. We consider a stochastic battery model and use both dynamic programming and reinforcement learning approaches to compute optimal transmission policies for wireless sensor networks. The obtained results show that the expected total transmitted data and the battery lifetime are maximized when all the charge units inside the battery are consumed.

IP assignment for efficient NoC-based system design using multi-objective particle swarm optimisation

Network-on-chip (NoC) is considered the next generation of communication in embedded system. In this case, an application is implemented by a set of collaborative intellectual propriety blocks (IPs). The selection of the most suited block from a library of IPs is an NP-complete problem. In this paper, we use multi-objective particle swarm optimisation (MOPSO) to yield the best selection of IP to implement efficiently a given application on a NoC infrastructure. In this purpose, MOPSO is exploited to obtain an assignment that minimises the requirements for power, hardware area and the total execution of the application. We show that the achieved solutions are better that those obtained by other multi-objective optimisation algorithms.

IP assignment for efficient NoC-based system design using multi-objective particle swarm optimisation

Network-on-chip (NoC) is considered the next generation of communication in embedded system. In this case, an application is implemented by a set of collaborative intellectual propriety blocks (IPs). The selection of the most suited block from a library of IPs is an NP-complete problem. In this paper, we use multi-objective particle swarm optimisation (MOPSO) to yield the best selection of IP to implement efficiently a given application on a NoC infrastructure. In this purpose, MOPSO is exploited to obtain an assignment that minimises the requirements for power, hardware area and the total execution of the application. We show that the achieved solutions are better that those obtained by other multi-objective optimisation algorithms.

Hardware Implementation of AES Encryption Algorithm Based on FPGA

With the development of society, the information industry has attracted more and more attention by the state. Since the emergence of prism doors, it has made countries pay great attention to the direction of information security. The question about how to protect information security has become an increasingly concerned issue. This paper introduces a widely used algorithm based on FPGA of symmetric encryption algorithm AES, because its key has three kinds of length 128bit, 192bit, 256bit, which can guarantee its difficulty in the crack, so it is relatively safe, this design can include encryption path and decryption path, you can also shield the decryption path and only include the encrypted path to reduce the use of resources in order to apply to resources insufficiently when the data is encrypted. Besides, this article can also be used for the key and data transmission using 32bit bus, multi-clock transmission. Through the Jtag Uart module to achieve the computer and embedded system communication, you can use it in the IDE integrated environment to achieve the program window to debug and monitor.