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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 $\varepsilon (1-{\mathrm{ Pr}}(\Lambda (t)=0))$ than the SCAC algorithm, where ${\mathrm{ Pr}}(\Lambda (t)=0)$ is a selection criterion calculated from the SCAC algorithm.

The aim of this article is to improve vehicular traffic in terms of both travel time and load balance. To achieve this goal, we propose an optimization model that minimizes the sum of the total travel time in the road network and a time representation of the traffic imbalance effects in the network. This paper presents an analytic formulation of the optimization problem, and an algorithm, Dynamic Interior Point Method (DIPM), that solves this optimization through driver rerouting. Unlike user-optimum traffic optimizations, DIPM leads to better fairness for drivers and works well in case of congestion. Unlike other system-wide traffic optimizations, DIPM considers the effects of the driver behavior on traffic load. Together, these features allow our system to work well in a potential real-world deployment. DIPM benefits from a central server that computes driver routes, which is reachable via cellular networks or vehicular ad hoc networks. Theoretical analysis and simulation results demonstrate that DIPM is fast and can work in real-time. The results of extensive simulations with realistic urban maps and traffic scenarios show that DIPM outperforms other dynamic rerouting algorithms in terms of travel time. DIPM also improves fairness when compared with a user-optimum approach.

A leapfrog weakly conditionally stable finite-difference time-domain method is extended to simulate general dispersive media modeled through a partial fraction model via a vector fitting tool and auxiliary difference equation. The computational accuracy and efficiency of this algorithm are demonstrated by numerical examples of metal and graphene.

In order to avoid overuse of a certain battery energy storage system (BESS) and prolong the cycle life of battery in AC microgrid, an improved SoC-based droop control based on multi-agent system (MAS) is proposed for achieving State of Charge (SoC) balance of multiple BESS units. A proportional-integral (PI) adjustment item using the average SoC is added to the P -f droop to regulate the charging and discharging power of BESS, then SoC changes towards in the direction of consistency and finally reaches equalization. The dynamic average consensus algorithm is utilized to obtain the average SoC. The proposed SoC-based droop method needs not to be changed in different operating mode of BESS and can achieve SoC balance regardless of whether the capacities of different batteries are the same, which improve the applicability of this method. A complete small-signal state space model including all BESS units is built and analyzed to select the appropriate control parameters of the improved droop. The impact of communication time delay on the improved droop method for balancing SoC is investigated and the effectiveness of the proposed strategy is verified through the simulation results of different case studies.

To improve the wear resistance of aluminium alloys, an Al/316L stainless-steel composite coating was successfully prepared on an aluminium alloy surface by two-channel-synchronization wire feeding arc spraying technology. The composite coating has a ductile (Al) and hard (stainless-steel) interpenetration structure. The interface between the substrate and the coating was fuzzy owing to the pinning interface of interpenetrating structure and the obvious diffusion layer near the interface, compared with 316L stainless-steel coating. The tribological properties of composite coatings were analysed. The wear resistance of the Al/316L stainless-steel composite coating was shown to be three times higher than that of the stainless-steel coating. The Al/316L stainless-steel composite coatings improved the wear resistance of aluminium alloy by a factor greater than nine. In the composite coating, the addition of Al splats promoted the formation of Fe3O4 and the interpenetrating structures, improving the wear resistance and crack propagation resistance of the coating.

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 paper 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 algorithm (CALC) 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, 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., 11 pages, 11 figures

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.

Time for unmanned aerial vehicle (UAV) assisted vehicular ad hoc networks (VANETs) to promote the efficient data transmission is limited. To this end, improving the endurance of UAV has become a crucial issue. In this paper, we first propose an energy optimization model to improve the endurance of UAV, which consider not only the flying energy, but also communication energy. By considering the relative position between UAV and vehicle, adaptive transmission power is applied to communication energy consumption. Second, in order to verify the existence of the solution, we use Rolle’s theorem and the monotonicity of the function to prove the objective function, and obtain the approximate solution of the objective function by using the principle of inequality. Finally, compare with optimized algorithm and algorithm without optimized communication energy, and our proposed algorithm which performance is better than the existing energy optimization algorithms.

Wave energy is one of the most difficult energies to be captured among marine renewables. With the technical progress, wave energy converters (WECs) are being tested in relatively deeper waters, which makes floating concepts almost the only choice. In this paper, a two-body heaving WEC where the wave energy is absorbed through the relative motion between the outer annular and the inner cylindrical buoys is studied. Both experimental and numerical studies are adopted for regular wave conditions. In the physical model test, a hydraulic system is used to achieve constant power take-off (PTO) damping force. Numerical simulations, validated against experimental data, are applied using both the frequency domain and the time domains analyses. Different types of PTOs, including constant, linear and nonlinear damping forces, are undertaken to evaluate the hydrodynamic and power absorption performance of such device.

Data admission control is an important issue in wireless sensor networks (WSNs) because of the limited transmission coverage area and the limited battery capability of each sensor node. To achieve the optimum benefit of the sensor network, a reward when a data package arriving at a sensor is accepted (not rejected) for transmission is considered, but a holding cost per unit time for the accepted data package waiting in the sensor for transmission is also incurred. For the sensor with sleep and active phases, a dynamic data admission control model is developed in this paper. By constructing a suitable Markov decision process (MDP), we verify that the optimal admission control policy of when to admit or reject an arriving data packet is a control limit policy, in order to achieve the maximum expected a discounted reward in the sleep and active phase, respectively. Furthermore, we provide a formula for how to calculate the upper bound of these threshold values. For the identified model with the optimal expected discount reward, the energy consumption of the sensors in active and sleep phases, as well as the energy consumption switching from active to sleep per unit time and vice versa is investigated. Extensive simulation is implemented. The results show that the problem is effectively solved by an optimal scheme with high energy efficiency. The results of this paper can be applied to designing optimal sensor nodes in wireless sensor networks.

In this paper, we study an energy cost minimization problem in cognitive mobile wireless networks, where base stations (BSs) are powered by hybrid energy sources including external power grids (PG), harvested renewable energy (RE), distributed local generators and energy storages. Based on sleep mode techniques, idle BSs can be switched to an energy-saving mode to cut down unnecessary expenditure. We formulate the problem into an optimization programming to achieve optimal decisions for energy scheduling and sleep control. By adopting a two-timescale approach, we implement the power scheduling and data transmission in time slots and the BSs sleep-awake mode in time frames (each time frame consists of multiple time slots) to avoid the overheads for frequent sleep/awake mode switches. Based on the Lyapunov technique, we further propose a close-to-optimal algorithm which only requires mean energy price of PG in each time frame instead of the exact 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 queues. We present theoretical analysis as well as numerical simulations to demonstrate the performance of the proposed algorithm.

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.

Real-time traffic information can efficiently reflect the traffic condition in urban scenarios. However, how to design an efficient real-time information sharing mechanism to achieve the real-time traveling time estimation (TTE) and dynamic path planning still remains a challenging problem. In this paper, we first propose an effective real-time traffic information sharing mechanism based on a distributed transportation system with road-side units, which has lower computing complexity and less redundancy. Then, considering current traffic information, we present a method to estimate the traveling time from a source to a destination in the road network, called TTE, which acts as a metric for path planning. Furthermore, we propose a real-time path planning algorithm based on TTE comparison via information collection to avoid traffic congestion. We verify the complexity and redundancy of our proposed real-time information sharing mechanism via C $^{\#}$ . Moreover, we perform extensive simulations in VanetMobiSim to evaluate the proposed real-time path planning algorithm, which uses the real-world traffic scenario and urban map of Songjiang district in Shanghai. The results show that our proposed algorithm outperforms the static path planning algorithm.

The contribution of positron source for the results of a positron annihilation lifetime spectrum (PALS) is simulated using Geant4 code. The geometrical structure of PALS measurement system is a sandwich structure: the 22Na radiation source is encapsulated by Kapton films, and the specimens are attached on the outside of the films. The probabilities of a positron being annihilated in the films, annihilated in the targets, and the effect of positrons reflected back from the specimen surface, are simulated. The probability of a positron annihilated in the film is related to the species of targets and the source film thickness. The simulation result is in reasonable agreement with the available experimental data. Thus, modification of the source contribution calculated by Geant4 is viable, and it beneficial for the analysis of the results of PALS.

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.

In this paper, we investigate the multicast capacity for vehicular ad hoc networks with directional antennas and the end-to-end delay constraint. We consider a torus of unit area with $n$ vehicles (nodes), there are $n_{s}$ multicast sessions and each session contains one source vehicle which is associated with $p$ destinations. We study the 2D and 1D random walk mobility models with two different time scales, i.e., fast and slow mobility. Given a delay constraint $D$ and assuming that each vehicle is equipped with a directional antenna, we obtain the multicast capacity of the two mobility models with two different time scales in the order of magnitude, respectively. We then characterize the impact of the network parameters (i.e., the end-to-end delay constraint $D$ , the beamwidth of directional antenna $\theta$ , and the number of destinations $p$ in each session) on the multicast capacity. Moreover, we find that the unicast capacity can be considered as a special case of our multicast results when the beamwidth of directional antenna $\theta$ tends to $2\pi$ and the number of destinations $p$ tends to 1 in the sense of probability.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV&rsquo, s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

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.

With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents&rsquo, energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted 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 State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical 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.

Vehicular Ad hoc Networks (VANETs) which is a special form of Mobile Ad hoc Networks (MANETs) has promising application prospects in the future. Due to the rapid changing of topology structure, how to find a route which can guarantee Quality of Service (QoS) is an important issue in VANETs. This paper presents an improved Greedy Perimeter Stateless Routing (GPSR) protocol based on our proposed next-hop node selection mechanism. Firstly, we define the link reliability in two cases which take the movement direction angle between two vehicles into consideration. Then we propose a next-hop node selection mechanism based on a weighted function which consists of link reliability between the sender node and next-hop candidate node, distance between next-hop candidate node and the destination, movement direction angle of next-hop candidate node. At last, an improved GPSR protocol is proposed based on the next-hop node selection mechanism. Simulation results are presented to evaluate the performance of the improved GPSR protocol, which shows that the performance including packet delivery ratio and average end-to-end delay of the proposed protocol is better in some situations.

In this paper, we investigate the data delivery delay from source to destination for VANETs on bi-directional roadways. The topology includes bi-directional roadways, left-turn lane, straight through lane, right-turn lane with traffic lights deployed at the intersections. Due to the multi-hop feature of data delivery and the limited range of radio transmission, the roadways’ topology and the switch operations of traffic lights will jointly affect the data delivery delay. By employing the queuing theory and analyzing the traffic lights operations, we propose the formulae of data delivery delay for different scenarios and extend the analysis to a more general scenario. We find that in the scenario of single intersection, the data delivery delay can be reduced by appropriately selecting the relay nodes. Specifically, in the case of green traffic light at the intersection, opposite lane vehicles can be used to reduce the delay, while for red traffic light, left-turn lane vehicles are used to reduce the delay in a similar approach, i.e., by using multi-hop transmission. The proposed algorithm is verified on single lane roadway, opposite-only, and left-turn-only lane for different simulation time slots, the number of mobile nodes and the value of $R/W$ ( $R$ is the wireless communication range and $W$ is the length of the road intersection) through VanetMobiSim and NS-2. Numerical results show that for single intersection, the data delivery delay can be reduced by choosing appropriate relay nodes. Besides, the successful packet delivery rate of bi-directional roadway is better than that of single line roadway scenario.

Vehicular Ad-Hoc Networks (VANETs) as a part of intelligent transportation systems (ITS) are a new perspective to settle the traffic problem. Because of the high velocity, variational topology and such other characters, the link between VANETs and road side unit (RSU) is unstable. Besides, on account of the high cost of RSU, some places can’t be covered completely which make the Internet accessing difficult and accessing delay long. In this paper, we propose a hierarchical link scheme to help vehicles access the Internet based on delay analysis for congestion scenario. We select some moving accessing help points (AHPs) from vehicles in VANETs. Two different hierarchical schemes under the circumstances of whole road covered or not are proposed. When the RSUs are enough to cover the whole road fully, we propose the inward progressive hierarchical method. When the RSUs are not enough to cover the whole road, we propose the outward extend hierarchical method. According to the delay analysis, we can get the optimal hierarchy number. This hierarchical link scheme can decrease delay and make all vehicles access the Internet with delay guarantee. The simulation results indicate that our proposed hierarchical link scheme in VANETs can decrease the link delay effectively.

With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA’s prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models.

The data center networks including multiple geo-distributed data centers begin to surge for meeting the ever-increasing Internet demand. To reduce the electricity bills in data centers, the current efforts center on developing sustainable data centers and improving energy efficiency. In this paper, we power data center with a microgrid which have conventional generators (CG), renewable energy sources (RES) and connected with electricity markets. To smooth the RES uncertainty, we integrate energy storage devices into microgrid. Besides, we consider any kind of energy can be charged into batteries or directly cover the demand of data centers. A stochastic program is formulated by integrating the geo-distributed load balancing, the energy management in microgrid and server configuration while guaranteeing the quality of service experience by end users. We design an online algorithm using Lyapunov optimization techniques without foreseeing any future information. The numerical evaluations based on real-world data sets corroborate the superior performance in reducing cost compared with previous works.

To achieve accurate reactive power sharing and voltage frequency and amplitude restoration in low-voltage microgrids, a control strategy combining an improved droop control with distributed secondary power optimization control is proposed. The active and reactive power that each distributed generator (DG) shares is calculated by extracting load information and utilizing a power sharing ratio, and is reset to be the nominal power to recalculate droop gains. The droop control curves are reconstructed according to the nominal active and reactive power and the recalculated droop gains. The reconstructed active power-frequency droop control can regulate active power adaptively and keep frequency at a nominal value. Meanwhile, the reconstructed reactive power voltage droop control can reduce voltage amplitude deviation to a certain extent. A distributed secondary power optimization control is added to the reconstructed reactive power voltage droop control by using average system voltage. The average system voltage is obtained by using a consensus algorithm in a distributed, sparse communication network which is constituted by all controllers of DGs. As a result, accurate reactive power sharing is realized, average system voltage is kept at a nominal value, and all voltage amplitude deviations are further reduced. Due to the absence of a microgrid central controller, the reliability of the strategy is enhanced. Finally, the simulation results validate the proposed method.

Along with the explosive growth of mobile terminal (MT), wireless network has been used more and more extensive. However, the Wireless Local Area Network (WLAN) in some scenarios such as large-area and mass MT, it can not satisfy the users demands. Different from the WLAN communicate frequency and characteristic, TV signal frequency has more advantages due to the low frequency. To solve the wireless spectrum shortage problem, Federal Communication Commission (FCC) permit the unlicensed user to use the TV white space (TVWS)since 2008. In this paper, we first propose Fast-Fine Real-Time Sensin(FFRTS) method to measure and process the TVWS resource. The FFRTS is based on real-time TVWS sensing which includes both fast and fine sensing. Then we design a algorithm to choose the maximum average throughput point as the priority choices to deploy access point (AP). At last, we use the Universal Software Radio Peripheral (USRP) and GNU Radio to collect the real-time data, and process the data by Python module Pandas.

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.

Dynamic path planning can efficiently enhance the validity and reliability of real-time navigation for vehicles to avoid unexpected traffic congestions and to shorten the whole traveling time. In this paper, we present a dynamic path planning method in which the density of road links is set as the main measurement for traffic status. By analyzing the traveling time and waiting time, we propose a mechanism for vehicles to decide whether to revise the current path. Our method aims at offering a path with smallest time cost for vehicles towards the destination. Furthermore, we give the discussion on time complexity and convergence of our method. Finally, simulation is conducted to evaluate the proposed method.

Nowadays, directional sensor networks (DSNs) have drawn a lot of attentions, which are made up of a large number of tiny directional sensors that are different from traditional omnidirectional sensors. Directional sensor is characterized by working direction and angle of view (AOV). In this paper we study area coverage of DSNs. We exploit Voronoi theory to divide sensors into polygons, by optimizing the local coverage in each polygon to achieve the overall coverage. We take full use of Voronoi vertexes and edges to judge whether a sensor gets full coverage inside in current polygon, if not, then the sensor calls Move Inside Cell Algorithm (MIC) and Rotate Working Direction Algorithm (RWD) algorithms we have designed. Compared to the similar methods to solve this question our algorithms are relatively simple and moving distance is shorter. Simulation results reveal that our algorithms outperform some existing methods in term of the area coverage.

Vehicular Ad Hoc Networks (VANETs) play a significant role in preventing traffic accidents on the highway. But it is a challenge to reduce the messages transmission delay under emergency condition. In this paper, an emergency event driven routing algorithm for bi-directional highway is proposed. Each vehicle maintains a real-time special neighbor nodes set which includes the next-hop vehicle and vehicle ID in three different directions. In particular, when there is no vehicle ahead or behind the accident vehicles we use the vehicles from reverse direction to reduce the intermittent link. And the emergency events of vehicles are divided into two types, according to the different influence of events on vehicles ahead and behind. And different emergency events launch different transmission algorithms. This ensures that the emergency messages (EMs) can transmit to the vehicles affected much more by the emergency events. Furthermore, we derive the transmission delay formula based on the proposed algorithm. Finally, the algorithm is verified by the simulation of the transmission delay formula. The results show that the proposed emergency event driven routing algorithm can reduce the transmission delay effectively.

In this paper, we investigate the capacity scaling laws of content-centric hybrid wireless networks, where users aim to retrieve content stored in the network rather than maintain source-destination communication. n nodes that have limited-capacity content store are assumed to be independently and uniformly distributed in the network area. The content store equipped in each node is used to cache contents according to the proposed caching schemes. m base stations are regularly placed and act as relays during the content retrieving process. We consider heterogenous caching access scheme where the cached probability of contents is different and the requested contents follow a Zipf content popularity distribution. We present the closed form capacity formulae for the heterogenous caching access scheme in order sense.

In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio.

In this paper, we analyze the data delivery delay at the road intersection, which is influenced by switch operation of traffic lights and the topology of roadways. By employing the arrival rate and departure rate and analyzing the traffic lights operations, we propose the data delivery delay formulae for different cases, respectively. We find that adding left-turn lane to relay the packets can reduce the data delivery delay at intersections. At last, by using extensive simulations via VANET Mobisim and NS-2, we compare the average end-to-end delay of single lane roadway with our results of adding left-turn lane in different conditions. The reduction of data delivery delay proves that our theoretical analysis results perform better.

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.

Efficient parking spot search has been a hot research top over the past decades. Most published work is based on the assumption that inter-vehicular communication through VANETs is available for vehicles. Major concerns over VANETs are potential overwhelming messaging and compromise of drivers' privacy. In this paper, we present a new approach that requires only minimal communication between vehicles and parking service providers. Our goal is to help vehicles to get parked with the least amount of time. Therefore, we propose a new parking model based on Timed Petri Nets (TPNs) which considers the parking time, the amount of time spent in a parking lot waiting for an unoccupied spot, and the driving time, the amount of time spent on the road to the chosen parking lot. The proposed approach is low-cost, while modeling with TPNs brings several noticeable benefits: it supports simulation directly; it is a mathematical model so that one can perform timeliness analysis based on the model; it is scalable and refineable. Base on the proposed model, we demonstrate the real-time traffic and the real-time average parking time of a parking lot affect drivers' choice. Finally, we give out some simulations which demonstrate the effectiveness of our proposed model.

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

Disaster Area Wireless Networks (DAWNs) are widely deployed in natural or man-made disaster scenes, since the communication infrastructure may be completely destroyed by the disaster. This paper proposes a hybrid network architecture for DAWNs due to the mobility of first responders and refugees. Based on the link characterization of DAWNs, we choose four essential criteria and propose a multi-criteria decision-making method for emergency communication protocol (MCDM-ECP), which utilizes the analytic hierarchy process (AHP) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to find the optimal next-hop node in DAWNs. Routing discovery and routing maintenance processes are included in the novel protocol. The simulation results show that MCDM-ECP performs better than other classical protocols both in energy consumption and packet received rate (PRR) for long-term emergency communications.

This paper studies an energy management problem for a typical grid-connected microgrid system that consists of renewable energy sources, Combined Heat and Power (CHP) co-generation, and energy storages to satisfy electricity and heat demand simultaneously. We formulate this problem into a stochastic non-convex optimization programming to achieve the minimum microgrid’s operating cost, which is difficult to solve due to its non-convexity and coupling feature of constraints. Existing approaches such as dynamic programming (DP) assume that all the system dynamics are known, which results in a high computational complexity and thus are not feasible in practice. The focus of this paper is on the design of a real-time energy management strategy for the optimal operation of microgrids with low computational complexity. Specifically, derived from a modified Lyapunov optimization technique, an online algorithm with random inputs (e.g., the charging/discharging of energy storage devices, power from the CHP system, the electricity from external power grid, and the renewables generation, etc.), which requires no statistic system information, is proposed. We provide an implementation of the proposed energy management algorithm and prove its optimality theoretically. Based on real-world data traces, extensive empirical evaluations are presented to verify the performance of our algorithm.

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.