Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.

Dynamic Uplink Transmission Scheduling for Satellite Internet of Things Applications

Satellite communication systems provide a cost-effective solution for global internet of things(IoT)applications due to its large coverage and easy deployment.This paper mainly focuses on Satellite networks system,in which low earth orbit(LEO)satellites network collect sensing data from the user terminals(UTs)and then forward the data to ground station through geostationary earth orbit(GEO)satellites network.Considering the limited uplink transmission resources,this paper optimizes the uplink transmission scheduling scheme over LEO satellites.A novel transmission scheduling algorithm,which combined Algorithms of Simulated Annealing and Monte Carlo(SA-MC),is proposed to achieve the dynamic optimal scheduling scheme.Simulation results show the effectiveness of the proposed SA-MC algorithm in terms of cost value reduction and fast convergence.

Adaptive Application Offloading Decision and Transmission Scheduling for Mobile Cloud Computing

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

An Anti-Eavesdrop Transmission Scheduling Scheme Based on Maximizing Secrecy Outage Probability in Ad Hoc Networks

In this paper,we consider a wireless ad hoc network consisting of multiple source nodes transmitting to their respective destinations,where an eavesdropper attempts to intercept their transmissions.We propose an optimal transmission scheduling scheme to defend against the eavesdropper,where a source node having the highest secrecy rate is scheduled to access the wireless medium for transmitting to its destination in an opportunistic manner.To be specific,the secrecy rate between a pair of the source and destination in the presence of an eavesdropper varies temporally due to the wireless fading effect.The proposed optimal transmission scheduling scheme opportunistically selects a source node with the highest secrecy rate to transmit its data for the sake of maximizing the security of the ad hoc network against eavesdropping attacks.For comparison purposes,we also consider the conventional round-robin scheduling as a benchmark,where multiple source nodes take turns in accessing their shared wireless medium for transmitting to their respective destinations.We derive closed-form secrecy outage probability expressions of both the round-robin scheduling and the proposed optimal scheduling schemes over Rayleigh fading environments.Numerical results show that the proposed transmission scheduling scheme outperforms the conventional round-robin method in terms of its secrecy outage probability.Additionally,upon increasing the number of source-destination pairs,the secrecy outage probability of the round-robin scheme keeps unchanged,whereas the secrecy outage performance of the proposed transmission scheduling significantly improves,showing the security benefits of exploiting transmission scheduling for protecting wireless ad hoc networks against eavesdropping.

Q-learning-based energy transmission scheduling over a fading channel

To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is considered.According to the channel state information(CSI)and the battery state,the charging duration of the battery is determined to jointly minimize the energy consumption of ES,the battery's deficit charges and overcharges during energy transmission.Then,the joint optimization problem is formulated using the weighted sum method.Using the ideas from the Q-learning algorithm,a Q-learning-based energy scheduling algorithm is proposed to solve this problem.Then,the Q-learning-based energy scheduling algorithm is compared with a constant strategy and an on-demand dynamic strategy in energy consumption,the battery's deficit charges and the battery's overcharges.The simulation results show that the proposed Q-learning-based energy scheduling algorithm can effectively improve the system stability in terms of the battery's deficit charges and overcharges.

Approach for earth observation satellite real-time and playback data transmission scheduling

The scheduling of earth observation satellites(EOSs)data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization(QDPSO)is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.

Energy-efficient data transmission with non-ideal circuit power for downlink cellular networks

The downlink energy-efficient transmission schedule with non-ideal circuit power over Wreless networks involving a single transmitter and multiple receivers was investigated. According to the special structure of the problem, a novel algorithm called OOSCPMR （the optimal offine scheduling with non-ideal circuit power for multi-receivers） is proposed, and the optimal offine solutions to optimize the energy- efficient transmission policy are found. The packets to be transmitted can be divided into two types where one type of packet is determined to be transmitted using the enrgy- efficient tansmission time, and the other type of packet is determined by the ID moveright algorithm. Finally, an energy-efficient online schedule is developed based on te proposed OOSCPMR algoriAm. Simulation results show that the optima offline transmission schedule provides te lower bound performance for the online tansmission schedule. The proposed optimal offline and online policy is more energy efficient than the existing schemes tat assume ideal circuit power.

Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.

Efficient Broadcast Retransmission Based on Network Coding for InterPlaNetary Internet

In traditional wireless broadcast networks,a corrupted packet must be retransmitted even if it has been lost by only one receiver.Obviously,this is not bandwidth-efficient for the receivers that already hold the retransmitted packet.Therefore,it is important to develop a method to realise efficient broadcast transmission.Network coding is a promising technique in this scenario.However,none of the proposed schemes achieves both high transmission efficiency and low computational complexity simultaneously so far.To address this problem,a novel Efficient Opportunistic Network Coding Retransmission(EONCR)scheme is proposed in this paper.This scheme employs a new packet scheduling algorithm which uses a Packet Distribution Matrix(PDM)directly to select the coded packets.The analysis and simulation results indicate that transmission efficiency of EONCR is over 0.1,more than the schemes proposed previously in some simulation conditions,and the computational overhead is reduced substantially.Hence,it has great application prospects in wireless broadcast networks,especially energyand bandwidth-limited systems such as satellite broadcast systems and Planetary Networks(PNs).

Statistics and Analysis on Reliability of HVDC Transmission Systems of SGCC

Reliability level of HVDC power transmission systems becomes an important factor impacting the entire power grid.The author analyzes the reliability of HVDC power transmission systems owned by SGCC since 2003 in respect of forced outage times,forced energy unavailability,scheduled energy unavailability and energy utilization eff iciency.The results show that the reliability level of HVDC power transmission systems owned by SGCC is improving.By analyzing different reliability indices of HVDC power transmission system,the maximum asset benef its of power grid can be achieved through building a scientif ic and reasonable reliability evaluation system.

Analysis on the gear-shifting schedule of hydraulic automatic transmission

With an advanced foreign hydraulic automatic transmission as the objective,an analytical method for the gear-shifting schedule is proposed.First the demanded maximum gradient of test is estimated.Then a test scheme and analytical procedure is formulated by initial test and hypothetical shift parameters.Finally through gear-shifting tests under different road conditions,load,accelerator pedal position limitation,throttle opening and output shaft speed are found to be the gear-shifting parameters.Under a common road condition,the gear-shifting schedule is a double-parameter schedule.Based on the driver＇s demands on braking and dynamic performance,different shift schedules are made under downhill,uphill and quick releasing acceleration pedal conditions.The operation criteria of down-shift schedule on abrupt grade are proposed.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.