Flying Ad-Hoc Networks (FANETs) composed of Unmanned Aerial Vehicles (UAVs) offer innovative solutions in various fields. However, their dynamic nature and sparse topology pose significant challenges for connectivity and routing efficiency. To address these issues, we propose the Adaptive Virtual Tunnel Routing protocol (AVTR) with eliminating boundary effects for FANETs. AVTR is a location-based, on-demand protocol that introduces a hop-by-hop virtual relay tunnel (HH-VRT) to confine forwarding within a limited set of UAVs. This approach reduces unnecessary transmissions and overhead. Additionally, AVTR incorporates a novel eliminating boundary effect factor (Q) to minimize path deviation and a new link quality factor (LQ) to evaluate link stability. By considering residual energy, LQ, and hop count, AVTR optimally selects the next forwarding node, enhancing routing efficiency and connectivity. Simulation results demonstrate that AVTR outperforms existing EARVRT, Pipe, IHCR, IoDMix routing protocols across seven critical metrics, including end-to-end delay and routing overhead, validating its effectiveness in improving FANET performance.

The increasing popularity of electric passenger vehicles (EPVs) has significant implications for transportation networks and power grids. We aim to tackle the routing and charging dispatching problem for EPVs while considering charging station (CS) power limits. We formulate the problem using a clustered rolling framework and introduce an energy criterion to determine EPV availability for shuttle services. The EPV charging dispatching is modeled as a constraint programming problem under CS power limits, with a fixed charging rate assumed at the start of charging. The RCLBD algorithm, based on logic-based benders decomposition, effectively handles binary and continuous variables. The EPV routing model serves as the master problem, while the charging model acts as the sub-problem. Simulation experiments demonstrate the RCLBD algorithm’s performance and efficiency. The algorithm successfully provides efficient pickup and delivery services, minimizing waiting times for customers. It ensures successful routing and charging solutions for all arriving EPVs. The electricity cost of our proposed RCLBD algorithm is 2.13%; 10.68% lower than that of MIP and MIPC method when the number of EPVs is 750. Our proposed routing and charging algorithm shows good performance and efficiency, addressing the challenges posed by the increasing popularity of EPVs.

As the electric vehicles (EVs) become prevalent, the demand for smart charging rises. The disordered charging problem of EVs, the high cost, and the location problem of charging stations bring a great challenge to the power grids and transport networks. The Internet of Things (IoT) technology enables the IoT-based EV (IoEV) to plan the route and process the information with smart wireless charging. However, how to schedule the optimal routing and wireless charging is challenging. In this article, we consider a joint routing and wireless charging scheduling problem with a microwave power transfer system to minimize the travel distance, the charging cost, and battery degradation cost when IoEVs provide shuttle services. To solve this mixed linear programming problem for the joint routing and charging schedule of IoEVs with the integer routing variables and continuous charging variables, we propose a routing and charging customized benders decomposition (RCBD) algorithm. To increase the time efficiency of the RCBD algorithm, we propose an improved RCBD (IRCBD) algorithm with the trajectory similarity measurement method. Extensive simulation results show the effectiveness and correctness of the proposed scheduling algorithms. We compare the IRCBD algorithm with the actor–critic algorithm and the RCBD algorithm. The charging cost of the IRCBD algorithm with the threshold 0.9 of trajectory similarity is 5.56% more than that of the RCBD algorithm. The running time of the IRCBD algorithm is 50.01% less than that of the RCBD algorithm when there are 300 pickups and deliveries. The running time of the IRCBD algorithm is less than that of the RCBD algorithm and the actor–critic algorithm.

With the advances in the Internet-of-Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This article investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor–critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor–critic learning charging (CALC) algorithm by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs’ expected cost by 24.03%, 21.49%, 13.80%, compared with the eagerly charging algorithm, online charging algorithm, reinforcement learning (RL)-based adaptive energy management algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

Content‐centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content‐centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache‐enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell‐partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

With the increasing use of electric vehicles (EVs) and the development of emerging transportation network services, autonomous EVs (AEVs) may play an important role in the future of transportation. AEVs can automatically plan their route, park in the charging station, and support the vehicle-to-grid (V2G) services. However, V2G services may influence user dissatisfaction due to the task delays. There is a tradeoff between the optimization of electricity cost and user dissatisfaction. In this article, we formulate the problem to minimize the electricity cost of AEVs and the degradation cost of the charging station batteries with the constraint of V2G services and user dissatisfaction, which is a nonconvex problem and is difficult to solve. To solve the nonconvex optimization problem, we design a suboptimal charging algorithm with some constraints (SCAC) based on the Lyapunov optimization technique to find a tradeoff between the total cost and user dissatisfaction. This algorithm cannot find the optimal solution but can give a selection criterion. Furthermore, in order to get a global charging schedule, we use the criterion from the SCAC algorithm as a priori knowledge to design the charging scheduling reinforcement-learning-based (CSRL) algorithm, which is more efficient than the reinforcement learning (RL) method without any particular criterion. We do simulations by using day-ahead price and practical profiles of AEVs to evaluate the proposed algorithms. The numerical results show that the CSRL algorithm has a better performance 5.12% than the SCAC algorithm and both algorithms are 12.66% and 17.14% better than the benchmark algorithm which is the shortest path (SP)-based algorithm. The CSRL algorithm has more efficiency ε(1 - Pr(Λ(t) = 0)) than the SCAC algorithm, where Pr(Λ(t) = 0) is a selection criterion calculated from the SCAC algorithm.

In this paper, an adaptive active power optimal allocation strategy for minimizing power loss during power transmission in islanded microgrids is proposed. A secondary regulated variable is added to the P-f droop control as a frequency offset to regulate power output reasonably among multiple distributed generation (DG) units. The rate of change of the total power loss during power transmission is calculated. The frequency offset is adaptively generated in turn among multiple DG units according to the rate of change of power loss. Thus the power flowing through different transmission lines is optimized and the power loss is minimized. Simulation results validate the effectiveness of the proposed strategy.

With the development of renewable energy technology and communication technology in recent years, many residents utilize renewable energy devices in their residences with energy storage systems. However, it is a great challenge to share residents' energy with others in the smart community for minimizing the total cost of all residents. In this paper, we investigate the problem of energy management and task scheduling for a smart community with residential combined heat and power system (resCHP) and renewable energy to pay the least bill. We take heterogeneous task arrival into consideration, which widely exists in the community. We formulate the minimum cost problem of a non-cooperative community as a random non-convex optimization problem with physical constraints. Our objective is to minimize the community time-average cost, including the cost of the external grid and natural gas. We adopt the Lyapunov optimization theory to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on Sarsa Algorithm. Finally, we present extensive simulations to validate the proposed algorithms by using real trace data.

In this paper, we study an energy cost minimization problem in cellular networks, where base stations (BSs) are supplied with hybrid energy sources including harvested recyclable energy (RE), external power grids (PGs), distributed local generators (LGs) and power storages, and operate with sleep mode techniques. We formulate the problem into an optimization programming to achieve optimal decisions for energy scheduling and sleep control. To avoid frequent switching, we implement BS sleep mode techniques on a larger timescale by adopting a two-timescale approach. Based on the Lyapunov technique, we further propose a close-to-optimal algorithm which only requires mean price of PG energy in each time frame instead of future information about stochastic inputs (e.g., the amount of RE harvesting and user demand for data traffic). The proposed algorithm can achieve approximately minimal energy cost and ensure the stability of workload and battery virtual queues. We present theoretical analysis as well as numerical simulations to demonstrate the performance of the proposed algorithm. The results present the stability of queues and the reduction in system cost.

Mobile-edge computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for an MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on the Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.

We consider a wireless information surveillance in UAV network, where a legitimate unmanned aerial vehicle (UAV) proactively eavesdrops communication between two suspicious UAVs. However, challenges arise due to lossy airborne channels and limited power of the UAV. In this paper, we study an emerging legitimate eavesdropping paradigm that the legitimate UAV improves the eavesdropping performance via jamming the suspicious communication. Moreover, a power efficient legitimate eavesdropping scheme, PELE, is proposed to maximize the number of eavesdropped packets from the legitimate UAV while maintaining a target signal to interference plus noise ratio at the suspicious link. Numerical results are shown to validate the performance of PELE. Additionally, four typical fading channel models are applied to the network so as to investigate their impact on PELE.

In this paper, we focus on the link predication problem in social networks. Our approach is based on the observation that there is a large amount of social behavior taking place every day which contains substantial information about user intrinsic characteristics that influence the dynamics of social networks. In order to obtain a deeper understanding of user behavior, we introduce the concept of latent factor to capture the motivation behind social activities. Since user relationships are often asymmetric, we also take into account bilateral user wishes with respect to friend as preferences, which is beyond traditional approaches or overall measurements. Two combination modes are proposed, independent fusion and interdependent fusion, to integrate these hybrid metrics with traditional measurements for link inference. In order to quantify the sensitivity of each element in metrics we use information theory. Experimental results on several real datasets show that our approach has better performance than previous methods.

Petri nets are a powerful formalism in modeling workflows. A workflow determines the flow of work according to pre-defined business process. In many situations, business processes are constrained by scarce resources. The lack of resources can cause contention, the need for some tasks to wait for others to complete, which slows down the accomplishment of larger goals. In our previous work, a resource-constrained workflow model was introduced and a resource requirement analysis approach was developed for emergency response workflows, in which support of on-the-fly workflow change is critical [14]. In this paper, we propose a Petri net based approach for recourse requirements analysis, which can be used for more general purposes. The concept of resource-oriented workflow nets (ROWN) is introduced and the transition firing rules of ROWN are presented. Resource requirements for general workflows can be done through reachability analysis. An efficient resource analysis algorithm is developed for a class of well-structured workflows, in which when a task execution is started it is guaranteed to finish successfully. For a task that may fail in the middle of execution, an equivalent non-failing task model in terms of resource consumption is developed.