Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c); definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations were correct only for special case of p_i=d_

Store and Forward CubeSat using LoRa Technology and Private LoRaWAN-Server

In the THAIIOT project, we built a 3U CubeSat with LoRa module and private LoRaWAN-server payload. The main goal of this project was to develop the payload for CubeSat in the low-earth orbit to receive uplink data via LoRa technology between LoRa module and private LoRaWAN-server on CubeSat to store data and then use S-band transceiver to downlink the data to ground station. In addition, we improved network security between ground LoRa nodes and the private LoRaWAN-server by using Advanced Encryption Standard (AES). In this paper, we present the efficiency of THAIIOT payload, focusing on store and forward data via LoRa technology. From the calculation results, the calculated link budget showed that the LoRa technology can transmit and receive data at a distance up to 2000 km. By assuming the transmitted data size of 35 bytes, the possible maximum data rate was 292 bps, which required 1.81 s of Time on Air (ToA). Moreover, the experimental results verify the capability of the THAIIOT payload to successfully transmit the data up to 2,200 km

Optimizing communication and computation for multi-UAV information gathering applications

Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme

Energy-efficient optimal control for real-time computing systems

Moving toward ubiquitous Cyber-Physical Systems - where computation, control and communication units are mutually interacting - this thesis aims to provide fundamental frameworks to address the problems arising from such a system, namely the real-time multiprocessor scheduling problem (RTMSP) and the multi-UAV topology control problem (MUTCP). The RTMSP is concerned with how tasks can be scheduled on available computing resources such that no task misses a deadline. An optimization-based control method was used to solve the problem. Though it is quite natural to formulate the task assignment problem as a mixed-integer nonlinear program, the computation cost is high. By reformulating the scheduling problem as a problem of first determining a percentage of task execution time and then finding the task execution order, the computation complexity can be reduced. Simulation results illustrate that our methods are both feasibility optimal and energy optimal. The framework is then extended to solve a scheduling problem with uncertainty in task execution times by adopting a feedback approach. The MUTCP is concerned with how a communication network topology can be determined such that the energy cost is minimized. An optimal control framework to construct a data aggregation network is proposed to optimally trade-off between communication and computation energy. The benefit of our network topology model is that it is a self-organized multi-hop hierarchical clustering network, which provides better performance in term of energy consumption, reliability and network scalability. In addition, our framework can be applied to both homogeneous and heterogeneous mobile sensor networks due to the generalization of the network model. Two multi-UAV information gathering applications, i.e. target tracking and area mapping, were chosen to test the proposed algorithm. Based on simulation results, our method can save up to 40% energy for a target tracking and 60% for an area mapping compared to the baseline approach.Open Acces