Optimal Time Slot Research Articles

Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing

With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing-intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.

Comparison of decentralised fast-charging strategies for long-distance trips with electric vehicles

Despite noticeable improvements in electric vehicle (EV) technology, EV charging time remains high, and the lack of fast-charging infrastructure extends the travelling time for long-distance trips even further. Charging strategies to coordinate EVs’ charging plans dynamically will help reduce station waiting times. This paper aims to quantify and compare the performances of two decentralised strategies using regular information-sharing between EVs and stations to estimate future waiting time in charging stations accurately. The communication strategy relies only on the communication of optimal charging plan computed by EVs to stations without any reservation. In contrast, the reservation strategy enables additionally the booking of optimal charging time slots. In a dedicated multi-agent simulation framework, we tested both strategies on different levels of infrastructure saturation. The communication strategy shows the best performance especially when combined with more frequent charging stops.

Economical revenue maximization in mobile edge caching and blockchain enabled space-air-ground integrated networks

In this paper, we study an edge caching and blockchain enabled space-air-ground integrated networking (SAGIN) network, where a low-earth-orbit (LEO) satellite serves as the content provider, and multiple edge caching enabled unmanned aerial vehicles (UAVs) will cache some contents to provide user equipments (UEs) with satisfactory content access services together with the satellite. Moreover, there’s a blockchain system that is deployed on UAVs, to provide the network with trust mechanism without requiring a centralized authority. From the standpoint of the operator, we intend to maximize the long-term averaged economical revenue by providing UEs with satisfactory and secure content access services. To achieve this purpose, we will jointly optimize the content placement of each UAV, content replacement when each UAV is full, the access control of each UE, and the blockchain deployment strategy about each UAV. the concept of queues in Lyapunov optimization is utilized to represent the backlog of edge equipment, ensuring the stability of virtual queues on UAVs and satellites, while satisfying the caching capacity constraints for content caching and blockchain deployment. Due to the tight coupling of optimization in each time slot and the variables within each time slot, our problem, which involves stochastic optimization and binary integer programming, is challenging to solve. To address this issue, we initially employ Lyapunov optimization theory to transform and decouple the problem into individual time-slot optimization problems. Subsequently, we utilize an effective heuristic algorithm called the fireworks algorithm to solve these individual optimization problems. However, the original fireworks algorithm cannot be directly applied to our problem due to its binary characteristics and inter-coupling constraints. Therefore, we have redesigned the explosion and mutation operations to adapt them to our specific problem. Simulation results demonstrate that our proposed algorithm outperforms other baseline algorithms.

Resource Scheduling for Intelligent Reflecting Surface-Assisted Full-Duplex Wireless-Powered Communication Networks With Phase Errors

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless powered communication networks (FD-WPCNs). This paper investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of formulated non-convex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method, and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

An Efficient Distributed MAC Protocol for Underwater Acoustic Sensor Networks

Many marine scientific and industrial applications in underwater acoustic sensor networks (UASNs) are characterized by dynamic-high load and dense deployment. In such UASNs, the channel reservation mechanism is devised to deal with channel utilization degradation. Unfortunately, channel reservation would lead to an increase in end-to-end delay and a decrease in throughput due to long propagation delay in UASNs. To reduce end-to-end delay and improve throughput, we proposed an efficient distributed medium access control (ED-MAC) protocol for UASNs. ED-MAC reduces the impact of long propagation delays on channel reservation by removing the channel reservation confirmation process and improving channel reservation efficiency. First, the optimized time slot length for the next time frame is obtained based on the data packet number estimate and secondary transmission data packet number, which could reduce idle waiting time. Then, the channel reservation confirmation process is removed by distributed channel reservation mechanism, and efficient channel reservation is achieved through channel reservation packet collision-free transmission and channel multiplexing. Meanwhile, channel reservation is performed before packet generation. Thus, the data packet waiting delay is reduced. Finally, a distributed data packet time slot schedule scheme is proposed to achieve collision avoidance between different data packets, as well as data packets and channel reservation packets. The performance analysis and simulation results show that our proposed ED-MAC could efficiently reduce end-to-end delay and improve throughput in dynamic-high-load and dense UASNs. Simulation results show that ED-MAC can achieve a maximum 98.7% reduction in average end-to-end delay and a maximum 61.1% improvement in throughput.

Handling product configuration changes during assembly

Configuration changes of customizable products during assembly are disruptive for the assembly line and are therefore often avoided. Nevertheless, allowing changes to customer-specific product configurations until late steps into the final assembly can be a competitive advantage as long as the negative effects on the assembly line can be mitigated. This paper proposes a method to efficiently handle such configuration changes while keeping the disruption of the assembly line as minimal as possible. The method assesses the criticality of change requests based on their severity and the assembly progress of the product. Based on this categorization an optimal time slot during the assembly process is identified to perform any additional work associated with a change request. These additional work contents can occur from necessary disassembly and different or additional assembly processes. The validation of the method for a final assembly line of large and complex products showed that it can reduce the additional workload by 26 % and the prolongation of the throughput time by 78 % on average.

Intelligent Reflecting Surfaces for Sum-Rate Maximization in Cognitive Radio Enabled Wireless Powered Communication Network

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

Energy-Aware Offloading and Power Optimization in Full-Duplex Mobile Edge Computing-Enabled Cellular IoT Networks

The incorporation of full-duplex (FD) communication into task offloading offers significant advantages for mobile edge computing (MEC). In this article, we investigate joint optimization of multiuser computation offloading, transmitting power, time slot, and computation resources in FD MEC-aided cellular Internet of Things (IoT) networks. For the sake of minimizing the energy consumption of the IoT nodes, we develop mixed-integer nonlinear programming with coupling constraints. To handle the coupling constraints, we derive the decoupling conditions and simplify the formulated problem into the decoupling and coupling cases. For the decoupling case, we convert the original problem equivalently into several independent subproblems. Then, for each subproblem, the transmitting power and computation offloading are optimized efficiently by the optimal condition of the objective function. After that, we derive the optimal time slot allocation by Karush–Kuhn–Tucker conditions. For the coupling case, we formulate the feasibility problem of the obtained results from the decoupling case as a two-level knapsack problem. Based on that, we develop an efficient algorithm to modify the solution of decoupling case so that its feasibility is guaranteed for the coupling constraints. Extensive simulations indicate that our proposed algorithm can effectively reduce the total energy consumption of the IoT nodes and fully improve the efficiency of MEC with respect to existing algorithms.

Artificial intelligence-enabled probabilistic load demand scheduling with dynamic pricing involving renewable resource

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.

Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks

In order to efficiently compute the primary data and support the secondary quality-of-service (QoS) requirement, we propose a cooperative hybrid non-orthogonal multiple access (NOMA) scheme for mobile edge computing (MEC) assisted cognitive radio networks. In the proposed scheme, the primary computation task is securely offloaded to the secondary base station, and the hybrid NOMA technique is adopted to provide secondary spectrum access and secure the primary offloading simultaneously. The weighted energy consumption minimization problem for both the primary and secondary systems is first studied under the constraints of the primary system’s secure outage probability and the secondary system’s QoS requirements, and a two-stage algorithm is proposed to derive the optimal power, time slot and computation task allocation. To motivate the secondary system’s cooperation, we optimally allocate the transmit power, time slot and computation task, such that the average secondary system’s rate is maximized under the primary system’s security requirement, and we derive closed-form expressions for the optimal resource allocations. Numerical results demonstrate the performance superiority of the proposed scheme compared with the full-offloading scheme in terms of the energy consumption and the average secondary rate.

Time Allocation and Optimization in UAV-Enabled Wireless Powered Communication Networks

Unmanned aerial vehicles (UAVs) have attracted great research attention due to their flexibility. In this paper, the use of UAVs in wireless sensor networks as an energy transmitter and a data collector is investigated. The UAV is first charged from a charging station, such as a base station (BS), before it flies to the sensors for data collection. Upon arrival, the UAV first charges the sensors via wireless power transfer (WPT) in the downlink, followed by data transmission from the sensors in the uplink. After that, the UAV flies back to the BS to offload data to the BS. We aim to maximize the amount of data offloaded to the BS by allocating optimal time slots to different tasks in this process, given a fixed total time. The maximization is solved in two steps as two convex optimization problems. In the first step, the time allocation between WPT to sensors and data collection from sensors is optimized. In the second step, the time allocation of BS charging, the total time in the first step, and BS data offloading is optimized. Unlike the previous works, our study takes into account the charging process from the BS to the UAV, the propulsion consumption at the UAV and the data offloading process to the BS. Both distance-dependent path loss and small-scale fading are considered. Numerical results show that the optimal time allocation can maximize the amount of data at the BS without wasting any time and energy.

Resource Allocation for Intelligent Reflecting Surface Assisted Wireless Powered IoT Systems With Power Splitting

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

A Novel Transmission Policy for Intelligent Reflecting Surface Assisted Wireless Powered Sensor Networks

This paper proposes a novel transmission policy for a wireless powered sensor network (WPSN) assisted by an intelligent reflecting surface (IRS) to enhance the performance of wireless energy transfer (WET) and wireless information transfer (WIT) with certain phase shifts. To achieve its self-sustainability, IRS collects energy from the energy station to support its own control circuit operation. Our proposed policy for the considered WPSN is called IRS assisted harvest-then-transmit time switching (IRS-HTT-TS), which is capable to schedule the transmission time slots by switching between energy collection and energy reflection modes. We provide a joint design of the transmission time slots, the power allocation as well as the discrete phase shifts of the WET and WIT, aiming to maximize the system sum throughput. This formulates a problem as a mixed-integer non-linear program (MINLP), which is NP-hard and non-convex. To effectively solve this problem, we first relax it to the one with continuous phase shifts, and then propose a two-step approach and decompose the original problem into two sub-problems. We address the first sub-problem in closed form with respect to the phase shifts of the WIT. For the second sub-problem, we apply the Lagrange dual method and Karush-Kuhn-Tucker conditions to derive the optimal closed-form transmission time slots, power allocation, and phase shift of the WET, respectively. The optimal discrete phase shifts can be obtained by quantizing the continuous values. Numerical results demonstrate the effectiveness of the proposed policy and validate the beneficial role of the IRS in comparison to the benchmark schemes.

Diagnosis of sleep disorders in optimized time slot based machine learning techniques on EEG energy level patterns

A better consciousness depends on how well your sleep is. Study shows that, sleep disorder is become a crucial problem day by day due to work pressure and other different causes. Sleep patterns can be measured through the EEG signals as EEG data has proved to reflect the activities of brain over all the section with respect to human activities. It has been found that the percentage energy level may be considered as parameter to distinguish healthy and defected EEG data due to sleep disorder. S0 is proved to be helpful in diagnosis of RBD sleep disorder because in this stage the percentage energy level is found to be decreased for alpha waves for patients having sleep disorder. The average percentage energy level in RBD patient is 0.1139 while in normal cases it is 0.1530 during alpha activity. We consider an optimized time slot for this experiment because to measure sleep quality through the systems currently available is not only complicated but also to a large extent expensive. To summarize the systems available at present. This work includes to identify Optimized Time Slot towards Frequency based EEG Sleep Disease Signal Patterns Evaluation using Machine Learning Techniques. With the results it is found that the slot 1 (12:00 PM – 1:20 PM) is the most important one in the processes of measuring sleep quality while considering sleep period 07:00 PM – 06:00 AM.

Joint rate maximization of downlink and uplink in NOMA SWIPT systems

The combination of Non-orthogonal multiple access (NOMA) and Simultaneous wireless information and power transfer (SWIPT) is an emerging technology that can meet the requirements of spectral efficiency and energy efficiency. In this paper, we study the achievable rate maximization for jointly downlink and uplink network with power splitting-based SWIPT. We propose two different models. By using Single-Input Single-Output (SISO)-NOMA in conjunction with SWIPT, a joint uplink and downlink resource allocation scheme is proposed with the optimization objective to maximize the sum rate. We prove the objective function is strictly concave and found the optimal power splitting factor of each user. By using Multiple-Input Multiple-Output (MIMO)-NOMA in conjunction with SWIPT, we add timeslot allocation and propose a power splitting control and a timeslot allocation scheme. The optimization problem is divided into two subproblems to reduce solving complexity. In more detail, we first design the power splitting control scheme to maximize both the achievable rate and harvested energy in the downlink. Then timeslot optimization method is designed to improve the sum throughput. The simulation results show that a relatively high downlink rate can be achieved, while the required energy is simultaneously harvested by the users for the uplink.

A New Traffic Flow Control Method for Terminal Control Area Using Dioid Algebra

In this article, the system model of the terminal control area is built considering the constraints between different departure routes. The balance for the departure routes in the terminal control area is achieved and the optimal control inputs for the departure flights are also acquired. In particular, the dioid algebra is employed to describe all kinds of concurrent events in the terminal control area and define the interactions between different flight trajectories. In this way, the new optimization model and solution procedure is introduced in order to improve the operation effect of air traffic flow. Finally, different scenarios such as deterministic and stochastic cases are given to demonstrate the performance of the system model. The essence of the system model is allocating optimal time slots to flights coming from different airports.