Indoor location-based services (LBS) have witnessed rapid growth in applications such as user tracking, healthcare monitoring, and smart facility management, driving the critical need for efficient and pervasive indoor localization. Traditional WiFi fingerprinting methods face significant challenges: multi-site localization (MSL) relies on densely deployed static WiFi sites, incurring high infrastructure costs and conflicting with the Integrated Sensing and Communication (ISAC) paradigm; single-site localization (SSL) requires complex hardware; and single mobile site localization (SMSL) suffers from poor real-time performance due to long traversal paths. To address these limitations, this paper proposes a Multi-Agent Deep Reinforcement Learning-based Collaborative Indoor Localization (MADRL-CIL) framework. MADRL-CIL leverages multiple collaborative mobile sites to dynamically acquire Received Signal Strength (RSS) fingerprints. By modeling each mobile site as an agent, the framework formulates the path selection and fingerprint acquisition task as a Multi-Agent Deep Reinforcement Learning (MADRL) problem under a Centralized Training with Decentralized Execution (CTDE) paradigm, facilitating effective collaboration among multiple mobile sites to optimize localization accuracy while minimizing localization time. Additionally, a Multi-Site Fingerprint Matching (MS-FM) model is specifically designed to process collaboratively collected RSS fingerprints, enabling fine-grained localization accuracy. Experimental evaluations in a real-world indoor environment demonstrate that MADRL-CIL achieves localization accuracy comparable to dense multi-static site deployments while provides good real-time performance.

Cooperative Perception (CP) has been a promising paradigm to enhance single-vehicle awareness by enabling perception sharing among connected vehicles. However, existing studies often overlook the impact of constrained and heterogeneous edge resources, leading to synchronization bottlenecks and limited deployment efficiency. To address these challenges, this paper proposes EI-Cooper, an Edge Intelligence (EI)-enhanced cooperative framework for adaptive and efficient CP in heterogeneous vehicular networks. The novelty of EI-Cooper is fourfold. First, we leverage key EI techniques including selective cooperation, model pruning and bandwidth allocation to jointly coordinate the perception, computation, communication within the CP pipeline. To the best of our knowledge, EI-Cooper represents the first attempt to extend CP with EI capabilities. Secondly, we formulate aSynchronization-EfficientCooperativePerception (SECP) problem, which jointly determines edge selection, pruning ratios and bandwidths to balance end-to-end synchronization efficiency and perception accuracy. Thirdly, to tackle the closed-box nature and computational NP-hardness of SECP, we decompose it into two interpretable subproblems, respectively capturing macro-level spatial completeness and micro-level semantic retention. Finally, we develop aTwo-StageHierarchicalOptimization (TSHO) algorithm, where the first stage maximizes coverage via submodular node selection with a$(1-1/e)$approximation, and the second stage performs alternating optimization of pruning and bandwidth allocation under convergence guarantees. Extensive experiments on public datasets and a real-world prototype demonstrate the superiority of EI-Cooper.

Accurate driving behavior recognition and reasoning are critical for autonomous driving video understanding. However, existing methods often tend to dig out the shallow causal, fail to address spurious correlations across modalities, and ignore the ego-vehicle level causality modeling. To overcome these limitations, we propose a novel Multimodal Causal Analysis Model (MCAM) that constructs latent causal structures between visual and language modalities. Firstly, we design a multi-level feature extractor to capture long-range dependencies. Secondly, we design a causal analysis module that dynamically models driving scenarios using a directed acyclic graph (DAG) of driving states. Thirdly, we utilize a vision-language transformer to align critical visual features with their corresponding linguistic expressions. Extensive experiments on the BDD-X, and CoVLA datasets demonstrate that MCAM achieves SOTA performance in visual-language causal relationship learning. Furthermore, the model exhibits superior capability in capturing causal characteristics within video sequences, showcasing its effectiveness for autonomous driving applications. The code is available at https://github.com/SixCorePeach/MCAM.

Owing to the growing population and rapid urbanization, intersections, where traffic converges from various directions, have become major bottlenecks for road capacity due to frequent congestion. Recent advances in Connected and Autonomous Vehicle (CAV) technology enable signal-free intersections, where CAVs collaborate to cross intersections without collisions. Most existing signal-free intersection control methods focus on accommodating conflicts among vehicles inside the intersection and fixed-direction lanes are commonly adopted. However, the use of fixed-direction lanes is a legacy from conventional signalized intersections, where turning lanes are predetermined and fixed, so as to direct vehicles with different turning intentions to different lanes and avoid collisions. In this paper, we aim to make full utilization of the capacity of signal-free intersections by making use of free-direction lanes, which allow vehicles to make right, straight or left turns from any lane. To this end, we propose a cooperative multi-agent Deep Reinforcement Learning (DRL)-based control method for signal-free intersections with free-direction lanes. Specifically, we first study the problem of cooperatively determining coordination of vehicles inside the intersection and lane changes of vehicles on the incoming arms. Then, a multi-agent DRL-based control method for cooperatively determining coordination and lane-change of vehicles for signal-free intersections with free-direction lanes, named CD-CLC, is proposed for maximizing non-conflicting vehicles crossing the intersection simultaneously while taking vehicle fairness into consideration, to minimize travel delays of vehicles and improve traffic efficiency. Extensive experiments have been conducted to compare CD-CLC with other state-of-the-art methods to demonstrate the effectiveness of the proposed approach.

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user’s device and using information element (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely predeployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single-station-based user identification (MS.Id) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. MS.Id includes mobile single-station localization (MSL) and MAC de-randomization based on users’ spatiotemporal location and IE information (DR.LIE). MSL can be implemented on a standard mobile Wi-Fi station without extensive predeployment. DR.LIE performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that MS.Id outperforms previous IE-based user identification methods and multistation localization techniques. MSL achieves a localization error of 1.15 m which is better than multistation with 12 APs of 1.40 m. DR.LIE demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%.

Leveraging the widespread deployment of existing WiFi sites, WiFi-based techniques offer substantial potential for achieving pervasive indoor localization among various indoor localization techniques. Conventional WiFi-based indoor localization techniques primarily focus on providing fine-grained accuracy. However, previous techniques are not pervasive due to the following constraints: 1) they can hardly be implemented in environments with limited resources of WiFi sites; and 2) they are constrained by high hardware requirements, such as the need for multiple antennas. In this paper, we propose a novel technique called Mobile Single-site Localization (MS-Loc), which leverages a mobile single-site to perform indoor localization. Specifically, MS-Loc utilizes existing hardware at off-the-shelf mobile WiFi sites to achieve pervasive localization rather than relying on multiple sites or multiple antennas. Moreover, in MS-Loc, a tailor-designed path planning algorithm guides the movement of the mobile single-site to locate targets quickly and accurately. We conducted extensive experiments using a real-world testbed. The experimental results demonstrate that MS-Loc presents a competitive localization accuracy compared to previous techniques but is pervasive.

Federated Learning (FL) mitigates data leakage by sharing only local machine learning models instead of raw data. However, it remains vulnerable to differential attacks. Differential Privacy (DP) addresses this concern by introducing noise to make it challenging for adversaries to reconstruct training samples. Nonetheless, clients often have varying attitudes toward data privacy, quantified by their privacy budgets. Low privacy budgets indicate the stringent privacy requirements of clients, requiring high compensations to incentivize their participation. Focusing solely on privacy budgets, however, can introduce selection bias, potentially compromising model generalization. Therefore, it is essential to emphasizes the fairness of client participation, ensuring that clients with lower privacy budgets also have opportunities to contribute to the training process. To tackle the above challenges, this paper formulates a novel DP-based incentive problem in FL, aiming to optimize the utilities of both the server and the clients. Specifically, we propose an auction mechanism that jointly selects participants based on their heterogeneous privacy budgets and determines appropriate payments. The proposed auction mechanism is proven to achieve several desirable properties, including computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed optimization performance. Finally, simulation results validate the effectiveness of the proposed mechanism.

EdgeAI represents a compelling approach for deploying DNN models at network edge through model partitioning. However, most existing partitioning strategies have primarily concentrated on homogeneous environments, neglecting the effect of device placement and their inapplicability to heterogeneous settings. Moreover, these strategies often rely on either data parallelism or model parallelism, each presenting its own limitations, including data synchronization and communication overhead. This paper aims at enhancing inference performance through a pipeline system of devices through leveraging both parallel and sequential relationships among them. Accordingly, the problem of Multi-Device Cooperative DNN Inference is formulated by optimizing both device placement and model partitioning, taking into account the unique characteristics of heterogeneous edge resources and DNN models, with the goal of maximizing throughput. To this end, we propose an evolutionary device placement technique to determine the pipeline stage of devices by enhancing a variant of particle swarm optimization. Subsequently, an adaptive model partitioning strategy is developed by combining intra-layer and inter-layer model partitioning based on dynamic programming and the input-output mapping of DNN layers, respectively, to accommodate edge resource limitations. Finally, we construct a simulation model and a prototype, and the extensive results demonstrate that our proposed algorithm outperforms current state-of-the-art algorithms.

Vehicular Crowdsensing (VCS) is a promising paradigm for supporting urban sensing services, where Service Providers (SPs) engage Mobile Vehicles (MVs) to perform data sensing tasks with specific objectives. However, existing studies have predominantly focused on data sensing quality in terms of data collection completeness and geographic fairness, while largely neglecting the important aspect of data freshness. Moreover, effective mechanisms for optimizing data freshness through coordination of the behaviors of both SPs and MVs are still lacking. Accordingly, this paper proposes a Freshness-Aware Data Sensing (FDS) model by considering heterogeneous data freshness, varying sensing capabilities of MVs, and limited budgets of SPs. The FDS is formulated as a two-stage game model, where SPs and MVs iteratively determine their pricing and sensing strategies in a self-interested manner to maximize their individual gains. Further, we develop a multi-agent reinforcement learning-based approach to learn the pricing strategies based on historical observations, which allows SPs to make pricing decisions without global knowledge. Additionally, given the pricing strategies of SPs, the optimal solution for each MV is derived. Finally, we build the simulation model based on realistic vehicular traces, where the simulation results demonstrate the superiority of the proposed algorithm in various scenarios.

Coordinating traffic lights and vehicles in urban environments is essential for optimizing road traffic efficiency and reducing congestion. Traditional traffic control strategies, which typically schedule traffic lights and vehicles independently, limit the potential for enhanced overall travel efficiency. In this study, we jointly control traffic lights and vehicle management to optimize traffic flow. Specifically, a dedicated signal control process at each intersection is managed by specialized agents, while vehicles within the vicinity are modeled as agents contributing to a unified optimization process. To facilitate effective collaboration among these heterogeneous agents, we propose a novel Heterogeneous Attention Reinforcement Learning (HARL) algorithm, where Graph Attention Networks (GAT) are designed to generate weighted vectors from traffic observations across intersections, accurately capturing and utilizing real-time traffic conditions. The integration of GAT improves the representation of traffic states in complex urban scenarios, thereby enhancing traffic management efficiency. We conduct extensive experiments on various real-world urban scenarios and the simulation results show that the proposed HARL significantly reduces the queue length by more than 16.13% compared to other methods.

Recent years have witnessed significant advancements in collaborative perception, particularly in enhancing accuracy and reducing communication overhead. However, due to the limited computation capacity of vehicles and dynamic bandwidth environment, traditional methods are constrained by fixed computational strategies, posing a challenge to providing low delay services in heterogeneous Vehicular Edge Computing (VEC). Considering this, we present a framework to accelerate collaborative perception, where vehicles adaptively partition the inference models and jointly offload them to the edge node. Further, we model the total delay by considering task arrival order, as well as the heterogeneous capacities of each node. Then, we formulate the Model Partitioning and Offloading (MPO) problem aiming to minimize the total delay of collaborative perception tasks. On this basis, we propose the Partitioning and Offloading Points Selection (POPS) algorithm, leveraging dynamic Thompson Sampling to select the optimal offloading points for each vehicle. By actively adjusting exploration intensity and passive parameter update rules, the POPS algorithm is highly adaptable to dynamic bandwidth environments. Finally, we conduct extensive performance evaluations, and the results demonstrate the superiority of our algorithm.

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent difference reward based actor-critic with V2I bandwidth allocation (MDRAC-VBA) solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of MDRAC-VBA.

Edge computing has emerged as a powerful technology for enhancing the performance of on-device video analytics, which is critical to support real-time applications. Nevertheless, there still lack of effective metrics to guide the offloading decision of video analytics tasks between device and edge server. Additionally, these existing optimization mechanisms either presume prior knowledge of the ground-truth of previous inferences or involve high training overheads, thereby rendering them unsuitable for real-time situations. To address these challenges, this paper presents a system model of edge-assisted online video analytics, where a lightweight object tracking module and a complex DNN-based model are deployed at the device and edge server, respectively. We formulate the resolution and deviation-based offloading (RDO) problem by considering heterogeneous computation resources and dynamic network bandwidth, aiming at maximizing inference accuracy and processing rate concurrently. We propose a context-aware offloading (CO) algorithm based on Bayesian optimization, which learns the optimal parameter settings by evaluating reward based on Gaussian process. Notably, the CO is proved to offer near-optimal solution with sublinear regret. Finally, we build a testbed and test algorithm performance on three realistic video datasets. The simulation results illustrate that the proposed CO outperforms other existing solutions in various service scenarios.

Mobile edge computing (MEC) is expected to support real-time services at wireless networks, where task replication is applied to guarantee job completion within a strict deadline through replicating multiple copies to different edge servers. Most of previous works focused on guaranteeing the reliability of individual task in MEC-based networks with the assumption of homogeneous task execution distribution. Further, these algorithms cannot suit dynamic network scales, due to overhigh communication or retraining overhead. Therefore, this paper formulates the problem of heterogeneous task replication in a finer level by modeling outage probability of individual replication, where the decisions of all tasks are jointly optimized within the constraints of both mobile users and MEC servers for minimizing job outage probability. To adapt to varying network scales, we develop centralized and distributed algorithms, respectively. The centralized algorithm is developed based on Interior Point Method, which obtains the optimal solution of relaxed model and then approximates to the solution of original problem. Further, the distributed algorithm decomposes the HTR into multiple subproblems and parallelly compute each local solution based on Distributed ADMM. Finally, we build a simulation model and conduct comprehensive results, which demonstrates that the proposed algorithms can achieve high-accuracy solution with fast convergence.

This work investigates the truthful auction for dependent task offloading in vehicular edge computing by considering the selfishness and rationality of participating nodes. Specifically, we first illustrate a truthfulness-guaranteed dependent task offloading architecture. Then, we formulate the Truthfulness-Guaranteed Dependent Task Offloading problem, aiming at maximizing the system utility (SU) while ensuring truthfulness and individual rationality in dynamic environments. Further, we design both centralized and distributed auction mechanisms to derive the optimal and approximate solutions, respectively. For centralized auction mechanism, we adopt the branch-and-price algorithm to determine the offloaded nodes, which yields maximum SU. Then, we adopt VCG mechanism to determine the payment of buyers. For distributed auction mechanism, each seller independently chooses the winning bid, and the buyer greedily chooses the offloaded node with maximum utility. Then, a novel payment mechanism regarding the cost of failed buyers is designed to guarantee the truthfulness and individual rationality. Finally, we build the simulation model and conduct the performance evaluation based on realistic vehicular trajectories. The results demonstrate that the proposed distributed auction mechanism achieves performance within approximately 4% of the optimal method, while significantly reducing computational complexity. Additionally, it significantly outperforms other methods in terms of system utility across various task requirements.

Sensor-rich smartphones have facilitated a lot of services and applications. Indoor/Outdoor (IO) status serves as a critical foundation for various upstream tasks, including seamless pedestrian navigation, power management, and activity recognition. Nevertheless, achieving robust, efficient, and accurate IO detection remains challenging due to environmental complexities and device heterogeneity. To tackle this challenge, some researchers have turned to deep learning for IO detection, which can deal with complex scenarios and achieve high detection accuracy. However, deep learning methods are often blamed for their expensive computational cost. Therefore, in this paper, we introduce a novel efficient IO detection method-DeepSIO, which can detect IO status accurately and efficiently. Specifically, different from existing IO detection methods, DeepSIO is developed based on spiking neural networks (SNN) that are more biologically plausible and computationally efficient than other deep neural networks. To better capture useful features, we propose to utilize dense connections between SNN layers. Extensive experiments are conducted in three typical scenarios, and experimental results demonstrate that DeepSIO outperforms state-of-the-art methods, achieving an accuracy of about 99.7%. Moreover, it has better generalization ability and can adapt well to new environments and devices.

Visual odometry is important for plenty of applications such as autonomous vehicles, and robot navigation. It is challenging to conduct visual odometry in textureless scenes or environments with sudden illumination changes where popular feature-based methods or direct methods cannot work well. To address this challenge, some edge-based methods have been proposed, but they usually struggle between the efficiency and accuracy. In this work, we propose a novel visual odometry approach called EdgeVO, which is accurate, efficient, and robust. By efficiently selecting a small set of edges with certain strategies, we significantly improve the computational efficiency without sacrificing the accuracy. Compared to existing edge-based method, our method can significantly reduce the computational complexity while maintaining similar accuracy or even achieving better accuracy. This is attributed to that our method removes useless or noisy edges. Experimental results on the TUM datasets indicate that EdgeVO significantly outperforms other methods in terms of efficiency, accuracy and robustness.

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.

Mobile edge computing (MEC) is envisioned as a promising solution to real-time services in Internet of Vehicles (IoV) by enabling edge caching, computing and communication. However, it is still challenging to implement multimedia streaming in MEC-based IoV due to dynamic vehicular environments and heterogeneous network resources. In this paper, we present an MEC-based architecture for adaptive-bitrate-based (ABR) multimedia streaming in IoV, where each multimedia file is segmented into multiple chunks encoded with different bitrate levels. Then, we formulate a joint resource optimization (JRO) problem by synthesizing heterogeneous edge cache and communication resource constraints, which aims at achieving both smooth play and high-quality service by optimizing chunk placement and transmission. For chunk placement, a multi-armed bandit (MAB) algorithm is proposed for online scheduling with low overhead but slow convergence. Further, a deep-Q-learning algorithm is proposed to improve cache reward and speed up convergence by using replay memory for repeatedly training. For chunk transmission, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which determines bandwidth allocation and quality level based on a benefit function incorporating quality level, available playback time, and freezing delay. Lastly, we build the simulation model and give comprehensive performance evaluation, which demonstrates the superiority of proposed algorithms.

In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, i.e., turning a transmitter from OFF to ON, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.

The variation of wireless signal in dynamic indoor parking environments may seriously compromise the performance of fingerprint-based localization methods. In this regard, this paper investigates the problem of robust WiFi fingerprint-based vehicle tracking in dynamic indoor parking environments, aiming at designing an online learning framework to continuously train the localization model and counteract the effect of signal variation. Specifically, a Hidden Markov Model (HMM) based Online Evaluation (HOE) method is firstly proposed to assess the accuracy of localization results by measuring the inconsistency of locations inferred by WiFi fingerprinting and Dead Reckoning (DR). Further, an Online Transfer Learning (OTL) algorithm is designed to improve the robustness of the fingerprinting localization, which consists of a weight allocation scheme to combine two classification models (i.e., the batch model and the online model) and an instance-based transferring scheme to resample the offline fingerprints and retrain the batch model. Finally, we implement the system prototype and give comprehensive performance evaluation, which demonstrates that the proposed solutions can outperform the state-of-the-art localization algorithms around 28%$\sim$58% on vehicle tracking accuracy in dynamic indoor parking environments.

This paper explores on accelerating Deep Neural Network (DNN) inference with reliability guarantee in Vehicular Edge Computing (VEC) by considering the synergistic impacts of vehicle mobility and Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. First, we show the necessity of striking a balance between DNN inference acceleration and reliability in VEC, and give insights into the design rationale by analyzing the features of overlapped DNN partitioning and mobility-aware task offloading. Second, we formulate the Cooperative Partitioning and Offloading (CPO) problem by presenting a cooperative DNN partitioning and offloading scenario, followed by deriving an offloading reliability model and a DNN inference delay model. The CPO is proved as NP-hard. Third, we propose two approximation algorithms, i.e., Submodular Approximation Allocation Algorithm (SA3) and Feed Me the Rest algorithm (FMtR). In particular, SA3 determines the edge allocation in a centralized way, which achieves 1/3-optimal approximation on maximizing the inference reliability. On this basis, FMtR partitions the DNN models and offloads the tasks to the allocated edge nodes in a distributed way, which achieves 1/2-optimal approximation on maximizing the inference reliability. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solutions.

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

Traditional traffic event monitoring and detection solutions mainly rely on roadside surveillance cameras. However, existing solutions cannot be applied for traffic event augmentation due to both restricted monitoring angles and limited camera coverage. Therefore, this paper investigates a novel architecture for traffic event augmentation via vehicular edge computing. In particular, multiple vehicles can collaborate with roadside infrastructures for detecting, re-identification and augmenting certain traffic event via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To enable such an application, we formulate the problem of multi-view augmentation task offloading (MATO) by considering the heterogeneous capabilities of vehicles and edge servers, which aims at minimizing average request delay. On this basis, we design the offloading scheduling framework and propose an adaptive real-time offloading algorithm (ARTO), which makes online offloading decision of object detection and re-identification, by balancing real-time workload among heterogeneous devices. Finally, we implement the hardware-in-the-loop testbed for performance evaluation. The comprehensive results demonstrate the superiority of the proposed algorithm in various realistic traffic scenarios.

Greedy selection is a widely used idea for solving many machine learning problems. But greedy selection algorithms often have high complexities and thus may be prohibitive for large-scale data. In this paper, we consider two fundamental optimization problems in machine learning: k-center clustering and convex hull approximation, where they both can be solved via greedy selection. We propose sublinear time algorithms for them through combining the strategies of randomization and greedy selection. Our results are similar in spirit to the linear time stochastic greedy selection algorithms for submodular maximization, but with several important differences. Our runtimes are independent of the number of input data items n. In particular, our runtime for k-center clustering significantly improves upon that of the uniform sampling approach, especially when the dimensionality is high. Our sublinear algorithms can also reduce the computational complexities for various applications, such as data selection and compression, active learning, and topic modeling, etc.

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

Age of Information (AoI) has become a new performance metric that quantifies the freshness of information in the Internet of Things (IoT). To optimize the AoI, the latest information should be frequently sampled and timely updated by source nodes (SNs), which, however, contradicts with the fact that the resources of both the SNs and destination node are limited. To consider this issue, this article formulates a more general multisource information update problem, taking into account both the budget constraint of destination node and the limited sampling/updating capabilities of the SNs. Besides that, two different sampling models, namedsampling-predeterminedandsampling-at-willmodels, have been studied, respectively. Particularly, for the information update problem under the sampling-predetermined model, we prove that it is an NP-hard problem. Then, we propose a greedy algorithm to select the appropriate SNs, and theoretically analyze the bound of the AoI achieved by the proposed algorithm. For the sampling-at-will model, we theoretically derive the minimum AoI that can be achieved under given number of updates, based on which, we design an optimal SNs selection and updating time determination mechanism, to achieve the minimum AoI. Finally, we conduct simulations to evaluate the effectiveness of the proposed algorithms.

Sharing transportation systems can significantly promote travelers convenience and efficiency. As a vital part, bike-sharing system (BSS) has effectively solved “the-last-mile” problem during transportation interchange, but bike imbalance between docks severely deteriorates operational efficiency of BSS. In this paper, we present multi-objective optimization and predictive control approaches to tackle the bike rebalancing problem, where optimal redistributing strategies can maximize the operational efficiency of BSS with respects to equilibrium state and redistribution cost. A dock-based BSS dynamic network is modeled based on a proximity graph, in which connection relation, bike usage, and redistribution flow are formulated. To measure the operational efficiency, a performance metric is presented to consider both benefits of user and operator. To satisfy bike renting, returning, and redistributing requirements, model predictive control (MPC) is then employed to compute feasible and optimal redistribution strategies based on the network model. The effectiveness of multi-objective optimization and MPC is verified on different topologies of BSS. Experimental results show that when the BSS reaches the equilibrium state, the operational efficiency will be maximized in the proposed approaches.

Ride-hailing systems (RHSs) provide passengers with convenient and flexible mobility services, have played an important role in modern urban transportation. With the limited vehicles, RHSs wish to optimize the dispatch of vehicles to requests with the objective of serving as many requests as possible. To address such a city-scale vehicle dispatch problem with thousands of vehicles and requests in each epoch, existing algorithms always take a tradeoff between effectiveness (i.e., real-time) and efficiency (i.e., service rate), such as ignoring future demands to guarantee real-time or solving a complex combinatorial optimization to improve service rate. To guarantee the service rate in a real-time fashion, this paper proposes two novel network flow-based vehicle dispatch algorithms. A network flow-based algorithm (NFBA) is provided to deal with offline scenarios. By constructing the vehicle-shareability network, a min-cost flow is built to find the optimal dispatch of vehicles to requests. To improve the request service rate in real-time, an efficient multi-sample multi-network flow-based algorithm (MNFBA) is proposed for the online scenarios. Each min-cost flow is utilized for a sample of future requests, and online vehicle dispatch policy is averaged over these flows. Extensive simulations based on real-world trip datasets in New York City are conducted. The experimental results show that compared to the benchmarks, our proposed algorithm can generate the dispatch of vehicles to requests within seconds, but can greatly increase the daily request service rate.

Leveraging emerging edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive multimedia applications (e.g., virtual reality and cloud gaming) onto edge servers. For high resource utilization, multiple rendering tasks run in the same GPU server and compete against each other for the computation resource. Each task has its requirement for performance, i.e., QoS target. A significant problem is how to schedule tasks so that each preset QoS is met and the performance of all tasks are maximized. We make the following contributions. First, we formulate the problem into a QoS constrained max-min utility problem. Second, we find that using the common natural logarithm as a utility function overly promotes one performance but demotes another. To avoid this phenomenon, we design a special utility function. Third, we propose an efficient scheduling algorithm, consisting of a resolution adjustment algorithm and a frame rate fair scheduling algorithm, both of which interact with each other. The former selects resolutions for tasks and the latter decides which task to process. We evaluate our method with actual rendering data, and the simulations demonstrate that our method can effectively improve task performance as well as satisfy QoS simultaneously.

Mobile edge computing (MEC) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

Efficient optimization is a key enabler for emerging intelligent applications in Internet of Vehicles (IoV). However, existing studies in IoV only focus on solving a single domain-specific optimization problem at a time, which undermines their efficiency on tackling various cross-domain optimization tasks in IoV. In this paper, we make the first effort on investigating a novel optimization framework in IoV for cross-domain tasks via vehicular edge computing. Specifically, two typical cross-domain tasks in IoV are presented, namely, the data dissemination (DD) task and the computing offloading (CO) task. Then, a cross-domain problem called DD-CO is formulated to facilitate the sharing of task features and knowledge during the solution searching. On this basis, we propose an evolutionary multitasking approach named EMA, which consists of an integer based unified representation scheme for encoding both the DD and CO tasks in a single solution, a corresponding decoding operator for task-specific solution evaluation, and a new population evolution mechanism for better adaptation to the cross-domain problem optimization. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrate the advancement of the new optimization framework via vehicular edge computing and the effectiveness of the proposed EMA method.

A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors. This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.

This study investigates the synergy between centralized and decentralized (i.e., ad hoc) data scheduling in vehicular ad hoc networks (VANETs) for offloading and balancing the workloads of roadside units (RSU) in bidirectional road scenarios. In the centralized scheduling, an RSU schedules data dissemination using a hybrid of infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications. Specifically, RSUs cooperate with each other by transferring unserved requests, and each RSU schedules the data services based on its locally received requests and the transferred requests. In the decentralized scheduling, vehicles driving in opposite directions share the cached data items via V2V communication when out of the coverage of RSU. The ad hoc scheduling can be benefited from the cooperation among RSUs since the chance for V2V data sharing could be enhanced when considering transferred requests into scheduling at each RSU. We formulate a hybrid of centralized and ad hoc data scheduling (HCA) problem, aiming at best exploiting the synergistic effects of I2V and V2V communication based on centralized data broadcast and ad hoc data sharing. On this basis, we propose an RSU Cooperation-based Adaptive Scheduling (RCAS) algorithm that consists of three mechanisms, including a centralized scheduling mechanism at each RSU, an ad hoc scheduling mechanism for vehicles, and a cluster management mechanism. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution under a variety of circumstances.

Mobile cloud computing (MCC) as an emerging computing paradigm enables mobile devices to offload their computation tasks to nearby resource-rich cloudlets so as to augment computation capability and reduce energy consumption of mobile devices. However, due to the mobility of mobile devices and the admission of cloudlets, the connection between mobile devices and cloudlets may be unstable, which will affect offloading decision, even cause offloading failure. To address such an issue, in this paper, we propose a robust computation offloading strategy with failure recovery (RoFFR) in an intermittently connected cloudlet system aiming to reduce energy consumption and shorten application completion time. We first provide an optimal cloudlet selection policy when multiple cloudlets are available near mobile devices. Furthermore, we formulate the RoFFR problem as two optimization problems, i.e., local execution cost minimization problem and offloading execution cost minimization problem while satisfying the task-dependency requirement and application completion deadline constraint. By solving both optimization problems, we present a distributed RoFFR algorithm for CPU clock frequency configuration in local execution and transmission power allocation and data rate control in cloudlet execution. Experimental results in a real testbed show that our distributed RoFFR algorithm outperforms several baseline policies and existing offloading schemes in terms of application completion cost and offloading data rate.

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme.

This paper makes the first effort on proposing a fog computing empowered architecture together with a dedicated scheduling algorithm for data dissemination in software defined heterogeneous vehicular ad-hoc networks (VANETs). Specifically, the architecture supports both the logically centralized control via the cloud node in the core network and the distributed data dissemination via the fog nodes at the network edge. A problem calledfog assisted cooperative service(FACS) is formulated, which takes network coding and vehicular caching into consideration, and aims at minimizing the overall service delay via the cooperation of vehicle-to-cloud (V2C), vehicle-to-fog (V2F) and vehicle-to-vehicle (V2V) communications. Further, we derive an equivalence problem of FACS and prove that FACS is NP-hard. On this basis, we propose a Clique Searching based Scheduling (CSS) algorithm at the SDN controller, which considers the heterogeneous communication interfaces and vehicle mobility in scheduling, and enables the collaborative data encoding and transmission among the cloud, fog nodes and vehicles. The complexity analysis demonstrates the feasibility of the proposed algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation based on real vehicular trajectories extracted from different time and space. The simulation results conclusively demonstrate the superiority of the proposed solution.

In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID‐19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real‐time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two‐stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero‐one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.

Ahstract— With the rapid development of vehicular applications and mobile devices, demands for resources to process time-critical and computation-intensive tasks are increasingly prominent. In this paper, we propose a two-layer Vehicular Fog Computing (VFC) architecture, including the client layer and the fog layer. Vehicles may generate tasks as clients, which are further assigned to the nodes in the fog layer for processing. The fog layer aggregates available resources of vehicles and infrastructures by exploiting their communication, computation and storage capabilities. Each task requires certain amount of resources for processing at the fog nodes. We formulate a distributed task allocation (DTA) problem, which takes deadline, vehicle mobility and fog capacity into consideration, and aims at maximizing the overall resource utilization of system, via the cooperation of vehicles and fog nodes. We linearize DTA into a 0–1 integer linear programming (ILP) problem to obtain the optimal solution. Further, we design a heuristic algorithm to obtain near-optimal performance with low computational overhead, which decomposes DTA into two subprocess and schedules tasks in each fog node independently. Finally, we build the simulation model and conduct a series of experiments based on real-world vehicle trajectories, which demonstrate the effectiveness and scalability of the proposed algorithm.

Solving dynamic multi-objective optimization problem (DMOP) requires optimizing multiple conflicting objectives simultaneously. When a dynamic is detected in the changing environment, most of existing prediction-based strategies predict the trajectory of changing Pareto-optimal solutions (POS), based on the historical solutions obtained in the solution space. In this paper, we present a new prediction method to track the moving optima for solving DMOP. In contrast to existing approaches, we propose to build the prediction model in the objective space. As the evaluation for solving a DMOP is based on the Pareto-optimal front (POF), to predict directly in the objective space could provide more useful information than the prediction in the solution space. In particular, to efficiently capture the complex relationships among POFs found along the evolutionary search, here we build a prediction model in Reproducing Kernel Hilbert Space, which holds a closed-form solution. To evaluate the performance of the proposed method, empirical studies have been conducted by comparing against three state-of-the-art prediction-based strategies on fourteen commonly used DMOP benchmarks. The results obtained by using different optimization solvers confirmed the superiority of the proposed method for solving DMOP in terms of both solution quality and time efficiency.

The popularity of the Internet has brought profound influence to electronic commerce. A kind of review-oriented consumption mode is gradually expanding in the market and consumers will refer to the reviews provided by consumers who bought the product in the past. How to accurately analyze users' sentiments from massive data of e-commerce reviews has become one of the key issues for e-commerce platforms. Current standard sentiment analysis classifies overall sentiment of e-commerce reviews without an extended description of the entity. We set up an optimized Aspect-based sentiment analysis (ABSA) that includes four elements: aspect, category, polarity, and opinion. Aiming at the above problems, this paper proposes a Chinese e-commerce reviews sentiment analysis algorithm based on BERT. By using pre-training model, we use the BIO(B-begin,I-inside,O-outside) data labeling pattern to label entities and study sentiment analysis by the annotation data. Experimental results on the Taobao cosmetics review datasets show that compared with the ordinary deep learning methods, our approach in the accuracy rate and the F1 score has significant improvement.

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. Then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

In the last decades, evolutionary algorithms (EAs) have demonstrated strong search capabilities in solving multi-objective optimization problems (MOPs). To improve the search performance of EAs, as problems seldom exist in isolation, transferring knowledge from related problems have attracted considerable attentions in recent years. In this paper, we present a preliminary study to enhance existing evolutionary algorithms (MOEAs) by transferring knowledge from the process of solving the single objectives involved in a given MOP of interest. As the single objectives are the objectives of the MOP, they naturally share great similarity with the given MOP, which thus could yield useful traits for enhancing the problem-solving of the MOP. To the best of our knowledge, this work severs as the first attempt to improve evolutionary multi-objective optimization via transferring knowledge from single objective problems. To evaluate the performance of the proposed method, empirical studies using a popular MOEA, i.e., NSGAII, on commonly used multi-objective benchmarks are conducted. The obtained results confirmed the efficacy of the proposed method in terms of both convergence speed and solution quality.

Remote sensing by unmanned aerial vehicles (UAVs) is significantly important in emergency rescue applications and operations. Particularly, the on‐site images from UAVs can provide valuable information for hazard identification and disaster assessment. In this study, the authors propose a novel method by using back propagation neural networks with feature fusion to detect landslides from UAV images. Specifically, the authors first construct a fundamental shape model of landslides and devise a scale‐invariant feature transform algorithm for feature matching and transformation. By fusing the spatial shape features and spectral features of the landslide, the suspected landslide object from UAV images can be detected initially. Next, the change features of a pre/post‐landslide object are extracted by using the satellite sensing images (before landslide) and the UAV image (after landslide). The authors further feed the change features into the proposed model to enhance the precision and accuracy of landslide detection. They conduct numerous experimental studies with aerospace remote sensing data in two real‐world landslide scenarios. The evaluation results show that the proposed method outperforms baseline algorithms by achieving over 91% accuracy in landslide detection.

Although indoor localization has been studied over a decade, it is still challenging to enable many IoT applications, such as activity tracking and monitoring in smart home and customer navigation and trajectory mining in smart shopping mall, which typically require meter-level localization accuracy in a highly dynamic and large-scale indoor environment. Therefore, this article aims at designing and implementing an adaptive and scalable indoor tracking system in a cost-effective way. First, we propose a zero site-survey overhead (ZSSO) algorithm to enhance the system scalability. It integrates the step information and map constraints to infer user's positions based on the particle filter and supports the auto labeling of scanned Wi-Fi signal for constructing the fingerprint database without the extra site-survey overhead. Further, we propose an iterative-weight-update (IWU) strategy for ZSSO to enhance system robustness and make it more adaptive to the dynamic changing of environments. Specifically, a two-step clustering mechanism is proposed to delete outliers in the fingerprint database and alleviate the mismatch between the auto-tagged coordinates and the corresponding signal features. Then, an iterative fingerprint update mechanism is designed to continuously evaluate the Wi-Fi fingerprint localization results during online tracking, which will further refine the fingerprint database. Finally, we implement the indoor tracking system in real-world environments and conduct a comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of the proposed algorithms.

With the recent development of wireless communication, sensing, and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Nevertheless, it is challenging to process time-critical tasks due to unique characteristics of IoV, including heterogeneous computation and communication capacities of network nodes, intermittent wireless connections, unevenly distributed workload, massive data transmission, intensive computation demands, and high mobility of vehicles. In this article, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog, and the mobile fog on processing time-critical tasks in IoV. Then, we give a motivational case study by implementing a prototype of a traffic abnormity detection and warning system, which demonstrates the necessity and urgency of developing adaptive task offloading mechanisms in such a scenario and gives insight into the problem formulation. Furthermore, we formulate the offloading model, aiming at maximizing the completion ratio of time-critical tasks. On this basis, we propose an adaptive task offloading algorithm (ATOA). Specifically, it adaptively categorizes all tasks into four types of pending lists by considering the dynamic requirements and resource constraints, and then tasks in each list will be cooperatively offloaded to different nodes based on their features. Finally, we build the simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of ATOA.

Solid-state drives (SSDs) have been widely adopted in embedded systems, data centers, and cloud storage due to its well-identified advantages. Inside SSD, random access memory (RAM) is adopted as the built-in cache for achieving better performance. However, due to the volatility characteristic of RAM, data loss may happen when sudden power interrupts. In order to solve this issue, a capacitor has been equipped inside emerging SSDs as an interim power supplier. But due to the capacitor aging issue, which will result in capacitance decreases over time, there still may exist data loss when power interruption occurs. Once the remaining capacitance drops to the threshold value where all dirty pages in the cache can not be written back to flash memory, data loss happens. To solve the above issue, an efficient cache management scheme for capacitor equipped SSDs is proposed in this article. The basic idea of this scheme is to bound the number of dirty pages in a cache within the capability of the equipped capacitor. The proposed scheme includes three steps: 1) a periodical dirty page budget detection (DPBD) scheme is proposed to acquire the maximal number of dirty pages that can be written back within current capability of equipped capacitor; 2) a smart dirty page synchronizing scheme is proposed during normal run time to bound the number of dirty pages in the cache; and 3) when power supply interrupts, an efficient writing back method is applied to further reduce the capacitance consumption of capacitor. The simulation results show that the proposed scheme achieves encouraging improvement on lifetime and performance while power interruption induced data loss is avoided.

Deep learning is a popular direction in computer vision and digital image processing. It is widely utilized in many fields, such as robot navigation, intelligent video surveillance, industrial inspection, and aerospace. With the extensive use of deep learning techniques, classification and object detection algorithms have been rapidly developed. In recent years, with the introduction of the concept of “unmanned retail,” object detection, and image classification play a central role in unmanned retail applications. However, open-source datasets of traditional classification and object detection have not yet been optimized for application scenarios of unmanned retail. Currently, classification and object detection datasets do not exist that focus on unmanned retail solely. Therefore, in order to promote unmanned retail applications by using deep learning-based classification and object detection, in this article we collected more than 30 000 images of unmanned retail containers using a refrigerator affixed with different cameras under both static and dynamic recognition environments. These images were categorized into ten kinds of beverages. After manual labeling, images in our constructed dataset contained 155 153 instances, each of which was annotated with a bounding box. We performed extensive experiments on this dataset using ten state-of-the-art deep learning-based models. Experimental results indicate great potential of using these deep learning-based models for real-world smart unmanned vending machines.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the `right' fog nodes at `right' period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

Solid state drives (SSDs) are constructed with multiple level parallel organization, including channels, chips, dies, and planes. Among these parallel levels, plane level parallelism, which is the last level parallelism of SSDs, has the most strict restrictions. Only the same type of operations that access the same address in different planes can be processed in parallel. In order to maximize the access performance, several previous works have been proposed to exploit the plane level parallelism for host accesses and internal operations of SSDs. However, our preliminary studies show that the plane level parallelism is farfrom well utilized and should be further improved. The reason is that the strict restrictions of plane level parallelism are hard to be satisfied. In this article, a from plane to die parallel optimization framework is proposed to exploit the plane level parallelism through smartly satisfying the strict restrictions all the time. In order to achieve the objective, there are at least two challenges. First, due to that host access patterns are always complex, receiving multiple same-type requests to different planes at the same time is uncommon. Second, there are many internal activities, such as garbage collection (GC), which may destroy the restrictions. In order to solve above challenges, two schemes are proposed in the SSD controller: First, a die level write construction scheme is designed to make sure there are always N pages of data written by each write operation. Second, in a further step, a die level GC scheme is proposed to activate GC in the unit of all planes in the same die. Combing the die level write and die level GC, write accesses from both host write operations and GC induced valid page movements can be processed in parallel at all time. To further improve the performance of SSDs, host write operations blocked by GCs are suggested to be processed in parallel with GC induced valid page movements, bringing lesser waiting time cost of host write operations. As a result, the GC cost and average write latency can be significantly reduced. Experiment results show that the proposed framework is able to significantly improve the write performance without read performance impact.

OD bundling is a promising method to identify key origin-destination (OD) patterns, but the bundling can mislead the interpretation of actual trajectories traveled. We present OD Morphing, an interactive OD bundling technique that improves geographical faithfulness to actual trajectories while preserving visual simplicity for OD patterns. OD Morphing iteratively identifies critical waypoints from the actual trajectory network with a min-cut algorithm and transitions OD bundles to pass through the identified waypoints with a smooth morphing method. Furthermore, we extend OD Morphing to support bundling at interaction speeds to enable users to interactively transition between degrees of faithfulness to aid sensemaking. We introduce metrics for faithfulness and simplicity to evaluate their trade-off achieved by OD morphed bundling. We demonstrate OD Morphing on real-world city-scale taxi trajectory and USA domestic planned flight datasets.

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for the solving of continuous optimization problems. In the last decades, a plethora of DE variants have been proposed in the literature for enhanced optimization performance. However, most of these DE variants are designed to solve a single problem in a single run. Recently, a multifactorial DE (MFDE) has been proposed to conduct evolutionary search on multiple tasks simultaneously. Benefitting from the implicit knowledge transfer among different tasks, MFDE has demonstrated a superior performance against the single-task DE in terms of convergence speed and solution quality. In MFDE, the knowledge transfer is realized via the mutation operation conducted on solutions with different skill factors. However, despite a lot of mutation strategies suggested in the literature, the current MFDE takes DE/rand/1 as the only strategy for knowledge transfer. The impacts of different mutation strategies on the performance of MFDE is still unexplored. Taking this cue, in this paper, we embark a study to investigate how different mutation strategies for knowledge transfer affect the performance of MFDE. In particular, besides DE/rand/1, another four commonly-used mutation strategies are adapted for the purpose of multitask optimization. Further, towards effective mutation for knowledge transfer in MFDE, a new mutation strategy called DE/best/1+ρ, which is able to adjust its behavior along the search process is proposed. Lastly, comprehensive empirical studies are conducted to investigate the performance of existing and the new proposed mutation strategies on the 9 single-objective multitasking benchmarks.

Mobile edge computing (MEC) has been an emerging paradigm to support low-latency applications in vehicular networks by offloading resources at network edge. However, it is still challenging to apply MEC- based architecture to implement multimedia services due to varying wireless communication, high vehicle mobility and heterogeneous resource integration. In this paper, we investigate adaptive-bitrate (ABR)-based multimedia services (MS) in MEC-based vehicular networks, where each multimedia file is divided into multiple chunks and can be requested at different bitrate levels. Further, MEC servers can satisfy local vehicular requests by integrating heterogeneous cache and communication resources. Based on the above observation, we formulate joint resource optimization (JSO) problem by synthesizing cache placement, wireless bandwidth allocation and chunk quality adaptation. On this basis, we propose a reinforcement- learning-based cache placement (RLCP) algorithm, which determines the optimal offloaded chunks by learning the global knowledge of cache reward in an iterative way. Further, we design an adaptive-quality- based chunk selection (AQCS) algorithm, which can be adaptive to time-varying wireless channel by dynamically adjusting bandwidth allocation and quality level based on real-time service workload. Lastly, we build the simulation model and conduct an extensive performance evaluation, which demonstrates the superiority of proposed algorithms.

With the ever-increasing demand of location-based services (LBS), Wi-Fi based indoor localization has attracted increasing attentions. This paper is dedicated to addressing two critical problems: a) signal fluctuation due to unforeseeable interferences during the offline training phase; b) insufficient real-time signal measurements at certain point due to the target movement during the online localization phase. Specifically, we first give an intensive analysis on the characteristics of received signal strength indicator (RSSI) in indoor environments with respect to both time-domain and frequency-domain. Then, inspired from the advantages of Stacked Denosing Autoencoder (SDA) in terms of recognizing and stabilizing the original features, we propose a dual-band SDA (DBSDA) based model to create more distinguishable fingerprints by extracting the RSSI features at each reference point (RP). In this model, both 2.4GHz and 5GHz RSSIs are exploited to train the SDA neural network and construct the offline fingerprint database. On this basis, we propose a data generation scheme, which is designed based on the observation that environmental interferences are similar in proximate spots. So, the designed scheme can generate signal values at certain point based on its nearby RSSI measurements when there are not enough inputs for the SDA neural network. Finally, we propose a locally weighted liner regression (LWLR) based method to predict the coordinate of the target. For performance evaluation, we implement the system prototype and give comprehensive experiments in real-world environments, which demonstrate the effectiveness and robustness of the proposed solutions.

Mobile edge computing (MEC) is an emerging paradigm to offload the server-side resources closer to the mobile terminals compared with cloud-based computing. However, due to highly vehicular mobility and limited wireless coverage, it is challenging to apply off-the-shelf MEC-based architecture to support the real-time services in vehicular networks, especially when the vehicle density changes dynamically. Hence, this paper investigates a novel service scenario in an MEC-based architecture, where the local MEC server has to complete the real-time services of mobile vehicles in its service range. On this basis, we formulate a novel problem of distributed real-time service scheduling (DRSS) by comprehensively considering the delay requirements of real-time services, the heterogeneous computing capabilities of MEC servers and the mobility features of vehicles, which targets at maximizing the service ratio. To resolve such an issue, we propose a multi-agent reinforcement learning algorithm called Utility-based Learning (UL), in which each local MEC server selects the optimal solution by learning the global knowledge online. Specifically, a utility table is established to determine the optimal solution by estimating the pending delay of service request at each MEC server and it will be updated periodically based on the feedback signal from the assigned MEC server. Lastly, we build the simulation model and conduct an extensive performance evaluation, which demonstrates the superiority of the proposed algorithm.

With rapid development of Internet of Things (IoT) and pervasive computing, indoor localization and tracking has attracted considerable attentions. This work aims at designing an effective and scalable indoor tracking system based on smart phones embedded with Wi-Fi interfaces and inertial sensors. Specifically, we first propose a zero site-survey overhead algorithm (ZSSO), which includes a step detection mechanism, a map constraint construction method and a customized particle filter. The step detection mechanism is used to count user steps based on raw data extracted from inertial sensors. The map constraint construction method is adopted to generate obstacle constraints of the indoor environment based on a two-step conversion method designed for indoor map. Finally, a customized particle filter is proposed to track user's positions continuously. Further, we propose an enhanced version of ZSSO (i.e., E-ZSSO) to improve tracking performance by incorporating with Wi-Fi fingerprint based localization technique. First, an automatic Wi-Fi fingerprint collection mechanism is developed for building the fingerprint database without extra site-survey overhead. Then, the Wi-Fi fingerprint based localization results are further adopted to speed up the convergence of the particle filter as well as to better calibrate the localization results. We have implemented the indoor tracking system in real-world environments and conducted comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of our proposed algorithms.

With recent development of wireless communication, sensing and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Services with low communication latency and high reliability are necessary to enable safety-critical applications in IoV. Nevertheless, it is challenging to satisfy the service requirement due to unique characteristics of IoV, including limited wireless communication bandwidth, high vehicle mobility, massive data transmission, and overwhelming computation overhead. In view of this, we propose a novel vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog and the mobile fog by defining corresponding service modes. On this basis, we further formulate a task offloading model, which quantitatively analyzes the characteristics of the three service modes and enables task offloading based on particular service requirements. Finally, we implement a traffic abnormity detection and warning system based on the proposed architecture as a case study. The hardware-in-the-loop performance evaluation not only demonstrates the effectiveness of the proposed architecture, but also enlightens future research directions on developing adaptive task offloading for dynamic IoV applications.

Heterogeneous network integration is a promising technique to support efficient data services in vehicular networks. However, due to highly dynamics of vehicular mobility and heterogeneous performance of wireless interfaces, it is still challenging to design an efficient scheduling policy for information services in vehicular networks. In this paper, we propose a centralized service architecture for managing heterogeneous network resources. Particularly, we comprehensively investigate the heterogeneity of networks, as well as the diversity of service requests. On this basis, we formulate the heterogeneous network resource management (HNRM) problem as a multiple-objective problem, which aims at minimizing both the service delay and the network access cost simultaneously. Then, we propose a packet-encoding based multi-objective algorithm (PEMA), which consists of two components: packet encoding for data broadcast and multiobjective algorithm for network interface selection. Specifically, for improving bandwidth efficiency, we develop a multiple-packet encoding (MPE) technique to serve more requests simultaneously. For network selection, we propose a multi-objective evolutionary mechanism to further minimize both the service delay and the network access cost via population evolution. Finally, we give a comprehensive performance evaluation to demonstrate the superiority of PEMA under a wide range of scenarios.

With recent advances in wireless communications, vehicular networks have attracted great interests in both industry and academia. This work aims at proposing a novel vehicular fog computing paradigm including both the system architecture and the scheduling algorithm. Specifically, we present a hierarchical architecture, which integrates the paradigm of both fog computing and the software defined networking (SDN). Then, we formulate a novel problem called Cooperative Service in Vehicular Fog Computing (CS-VFC), which aims at maximizing the bandwidth efficiency by coordinating the service in both the fog layer and the cloud layer. We prove that CS-VFC is NP-hard. On this basis, we propose an on-line scheduling algorithm, which incorporates with the network coding and makes scheduling decisions at SDN controller. In particular, it will determine the coding policy for each cloud node, and then it will implement both the intra and inter cooperation strategies at the fog layer. Finally, we build the simulation model by implementing NS3 simulator and SUMO. A comprehensive simulation is carried out to demonstrate the superiority of the proposed system architecture and the solution.

Heterogeneous network resources are expected to cooperate with each other to support temporal data services in vehicular networks. However, it is challenging to implement an efficient data scheduling strategy due to the following factors: first, there are different time constraints on services, which are imposed by the application requirements of both temporal data quality and transmission delay; second, the heterogeneity of wireless interfaces further complicates the transmission task assignment in dynamic vehicular environments. Therefore, this paper proposes an software-defined network-based architecture to enable unified management on heterogeneous network resources. Then, we formulate the cooperative temporal data dissemination (CTDD) problem by considering the property of temporal data, the heterogeneity of wireless interfaces, and the delay constraints on service requests. Further, we prove the NP-hardness of the CTDD by constructing a polynomial-time reduction from a well know NP-hard problem, classical knapsack problem. On this basis, we design a heuristic algorithm called priority-based task assignment (PTA), which synthesizes dynamic task assignment, broadcast efficiency, and service deadline into priority design. Accordingly, PTA is able to adaptively distribute broadcast tasks of each request among multiple interfaces, so as to improve overall system performance. Last but not least, we build the simulation model and implement the proposed algorithm. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.

A cyber–physical system (CPS) consists of physical devices and operations that are closely controlled and monitored by computational processes. This concrete connection involves the real-time actuation of physical devices, real-time sensing of physical quantities, and modeling and control of the overall system. A CPS may be connected to the Internet of Things (IoT) and, if so, should be considered in that context; the IoT is essential to realize a vision of future CPSs, where numerous devices are connected over the Internet, allowing them to collect information about the real world in real time, and share it with other systems and physical devices.

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm calledMO-TDUD, which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation forTDUDsolution encoding, and an evolutionary operator designed forTDUDsolution reproduction. The proposedMO-TDUDcan be adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implementMO-TDUDfor performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.

The vehicular sensor network (VSN) technology empowers intelligent transportation systems (ITSs) to support a wide range of road safety and traffic management applications. By taking advantage of the information collection and communication capabilities offered by VSNs, information, such as speed, travel time, dash-camera video, and so on, can be gathered from sensors embedded in vehicles and then delivered to the infrastructure to support ITS applications. The explosive growth in the availability and variety of sensor instruments as well as the number of vehicles provides us with the opportunity to create large-scale ITS applications, which demand large-scale data processing. In order to support large-scale data processing, Google proposed the MapReduce framework. The MapReduce framework provides scalability in a large-scale data cluster by performing aggregate computations as close to the data source as possible. However, supporting ITS applications over VSN is not just a matter of simply applying the existing MapReduce framework to VSN due to the limited wireless bandwidth and the highly dynamic network topology. In this paper, we propose an analytical processing framework for VSNs called Vehdoop. Vehdoop utilizes the computing capability of vehicles to efficiently process sensor data in parallel across a large number of vehicles in a decentralized manner. We conducted extensive experiments using vehicle trajectories generated from Simulation of Urban MObility (SUMO) and a network simulator, NS-3, to simulate vehicle-to-vehicle and vehicle-to-infrastructure communications. The experimental results demonstrate the superiority of Vehdoop.

In the last decades, evolutionary search has been extensively studied in the literature, and successfully applied in many real-world applications for solving complex optimization problems. Traditional evolutionary search methods generally start the search from scratch, and ignore the relationship between the current problem of interests and past solved problems. However, problems seldom exist in isolation. Recently, the research topic of enhancing the search performance of evolutionary search by transferring knowledge from past solved related problems has attracted many attentions. In particular, the autoencoding evolutionary search paradigm is one of the latest approaches proposed to leverage the useful knowledge embedded in past search experiences for enhanced optimization performance. The efficacy of the autoencoding evolutionary search has been confirmed on both benchmarks and real-world applications. However, as there is no problem selection process, the original autoencoding evolutionary search transfers knowledge from all the available past problems without the consideration of problem relationships, which could cause negative transfer. It is also computational expensive if there are huge number of past problems. Taking this cue, in this paper, we embark a preliminary study to improve the autoencoding evolutionary search by proposing a similarity measure between problems based on the Spearman's rank correlation coefficient. Empirical studies with commonly used evolutionary solvers on benchmark problems are presented to verify the effectiveness of the proposed method.

Dynamic multi-objective optimization problem (D-MOP) is widely existed in many real-world applications. Over the years, DMOP has attracted many research attentions in the literature. The adaptive indicator-based evolutionary algorithm (IBEA2) is a recently proposed multi-objective evolutionary algorithm (MOEA). It has demonstrated strong search capability on commonly used multi-objective benchmarks over state-of-the-art MOEAs. However, as the adaptation of parameter$k$is based on the selected solutions with maximum hypervolume, this mechanism will be inappropriate if the problem changes over time. The reason is that the solutions with high hypervolume at one particular time instance may not be with high hypervolume at another if the problem changed. Keeping this in mind, inspired by the recent autoencoding evolutionary search, which is able to transfer the past search experiences to improve the evolutionary search on unseen problems, in this paper, we propose to extend the IBEA2 by adapting k with transferred high hypervolume solutions obtained before the dynamic change occurs, for solving DMOP. To evaluate the proposed method, empirical comparisons on the commonly used Farina-Deb-Amato (FDA) DMOP benchmarks, against both the IBEA2 and one recently proposed dynamic MOEA, are presented.

Computation offloading over fog and cloud is critical to improve service quality and efficiency of future networks. Mobile vehicles have also been considered as potential fog nodes by sparing their computation capability to nearby users. In this paper, we propose a multi-layer computation offloading architecture, consisting of the user layer, mobile fog layer, fixed fog layer and cloud layer. Multiple wireless roadside units (RSUs) are deployed in the network to collect computation tasks from user layer, and offload the tasks to other layers. Each layer has distinct multi-dimensional characteristics, such as different transmission rates and computation capabilities. The computation tasks may consume different communication and computation resources when they are uploaded to different layers. However, the available resources of each layer are limited. Consider that each user will pay for the offloaded computation tasks according to their sizes, we aim to maximize the total profits of computation offloading from the infrastructure perspective. Specifically, the offloading problem is formulated as a generalized multidimensional multiple knapsack problem (MMKP), in which each layer is considered as a large knapsack and the computation tasks are treated as items. We propose a modified branch-and-bound algorithm to obtain the optimal solution, and a heuristic greedy method to obtain approximate performance with much lower computational overhead. A comprehensive simulation is conducted to compare the proposed two algorithms. Simulation results demonstrate that the proposed computation offloading architecture together with the task allocation algorithms can achieve the purpose of maximizing the total profits of offloaded tasks.

Heterogeneous network resources are expected to cooperate with each other to support data services in vehicular networks. However, individual wireless interface cannot complete services within short vehicular dwelling time. Further, the network heterogeneity further complicates the transmission task assignment among multiple wireless interfaces. To resolve such an issue, we propose a novel architecture, where a scheduler is able to manage heterogeneous network resources in a centralized way. Then, we formulate the heterogeneous wireless interface management (HWIM) problem by considering both the heterogeneities of wireless interfaces and the delay constraints of service requests. On this basis, we design a heuristic algorithm called Adaptive Task Assignment (ATA), which synthesizes mobility feature, broadcast efficiency and service deadline into priority design. Accordingly, ATA is able to adaptively distribute broadcast task of each request among multiple interfaces, so as to improve overall system performance. Last but not the least, we build the simulation model and implement the proposed algorithm. The comprehensive simulation results show the superiority of the proposed algorithm.

The raw vehicle trajectory data gathered by GPS devices is typically large and needs to be compressed online. However, GPS devices have limited resources, and cannot afford such burdensome task. To alleviate this issue, we design an online trajectory compression system consisting of Trajectory Mapping, Trajectory Compressing and Front-End Visualizer, which is implemented in the mobile phone to migrate the computation burdens. The proposed trajectory compression method does not need extra data during compressing suitable for online applications. Experiment results demonstrate our system has excellent performances regarding effectiveness, efficiency and so on.

This work aims to design and implement a scalable and easy-deployed indoor localization system based on particle filter and Wi-Fi fingerprint techniques. Specifically, our system leverages particle filter to estimate user's location and automatically scans Wi-Fi fingerprints. Then, we utilize the collected fingerprints to speed up the convergence of particles. Finally, the system iteratively refines the collected fingerprints by evaluating their performance duration the on-line localization phase, which is able to further enhance the positioning accuracy. We implement the system on Android platform and give a comprehensive performance evaluation by setting up the system in our lab area and comparing the algorithm with conventional fingerprint-based solutions. Experimental results demonstrate the scalability and effectiveness of the proposed solution.

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

This paper embarks the first study on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in heterogeneous vehicular networks by presenting a service architecture that exercises the software defined network concept. In particular, we consider the scenario where vehicles request a set of information and they could be served via heterogeneous wireless interfaces, such as roadside units and base stations (BSs). We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Efficient data dissemination is critical for enabling emerging applications in vehicular ad hoc networks. As a typical traffic scenario, the bidirectional road scenario of highways bring unique challenges on well exploiting the benefit of vehicle-to-vehicle (V2V) communication for data sharing among vehicles driving in opposite directions. This paper is dedicated to investigating the characteristics of data services in such a scenario and exploring new opportunities for enhancing overall system performance. Specifically, we present a system architecture to enable the road-side unit assisted data scheduling via vehicle-to-infrastructure communication. Then, we give a theoretical analysis on the opportunity of successful data sharing among vehicles driving in opposite directions based on the analysis of signal-to-interference-noise-ratio of V2V communication. On this basis, we propose a clustering mechanism based on the design of a time division policy and the derivation of the optimal cluster length. In addition, a cluster association strategy is designed to enable vehicles to dynamically join or leave a cluster based on their real-time velocities. Furthermore, a two-phase backoff mechanism is designed for distributed data sharing based on V2V communication, and a cooperative scheduling algorithm is proposed for selecting sender vehicles as well as the corresponding data items for broadcasting. Finally, we build the simulation model and give a comprehensive simulation study, which demonstrates that the proposed solutions can effectively improve the overall system performance.

Non-stationarity is a key feature of wireless channels in high-mobility environments, such as high speed railway, which is one of the barriers on improving the communication quality if not being well treated in channel estimation. Traditional Kalman Filter (KF)- based channel estimation methods that assume constant time correlation coefficients based on Jakes model are no longer valid in non-stationary channel. To address the challenges of channel estimation in high-mobility OFDM communications, a joint design of channel estimation, detection and decoding is proposed on the basis of Extended Kalman Filter (EKF) and Iterative Detector & Decoder (IDD) technology in this paper. The dynamics of non-stationary channel is characterized as a time-varying autoregressive (AR) process and a Decision-directed EKF is developed to track both the channel frequency response (CFR) and the time correlation coefficients of the time-varying AR process. In order to mitigate the error propagation with Decision-directed at data symbols, the IDD is adopted and integrated with the EKF (IDD-EKF). And, instead of Decision-directed, this paper proposes a novel log-likelihood ratio (LLR)-directed mechanism, in which the a posteriori LLRs are utilized to update the weighting matrix of EKF. The simulation results of estimating a typical non-stationary WINNER-II channel show that, compared with traditional methods, the proposed method effectively overcomes the error propagation problems and improves the channel estimation accuracy. The overall system performance in terms of signal-to-noise ratio (SNR) gain is about 3dB at 200km/h.

With recent advances in wireless communications, vehicular networks are envisioned as a promising paradigm on achieving breakthroughs in transportation safety, efficiency, and sustainability. This work investigates data broadcast via Infrastructure-to-Vehicle (I2V) communication by exploiting the vehicular caching and network coding for enhancing bandwidth efficiency of the road-side unit (RSU). Specifically, we present an architecture for providing real-time data services via I2V communication in the service range of a RSU. Then, we investigate the problem of cache-aided data dissemination with network coding and prove that it is NP-hard. Further, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Memetic algorithms (MAs), which have materialized as a fusion of population based global search and individual lifetime learning (i.e., local search) in the literature, have been widely used in real world applications to solve complex optimization problems. The balance of global and local search in MA plays a key role in defining the performance of MA in problem solving. The probabilistic memetic framework (PMF) was thus introduced to model MA as a process involving the decision of embracing the separate actions of global or local search. PMF balances these two actions by governing the local search intensity of each individual based on a theoretical upper bound derived while the search progresses. To use PMF for solving combinatorial optimization problems, according to our previous study [1], we note that the appropriate selection of a distance metric for estimating the local search intensity is a critical role. Nevertheless, to the best of our knowledge, little or no research works in the literature has studied on suitable distance metric for PMF in the context of combinatorial optimization problems. In this paper, we attempt to fill this gap by presenting a preliminary study on the selection of distance metric in PMF for capacitated arc routing problem (CARP). In particular, we first analyze the suitability of 4 existing popular distance metrics used in combinatorial optimization for solving CARP. Subsequently a score based on closeness of neighborhood and fitness landscape correlation is proposed to quantify the suitability of a distance metric in estimating the local search intensity for PMF in the context of combinatorial optimization. Experimental study on 24 egl CARP benchmark instances highlighted the significance of choice of appropriate distance metric in PMF for solving combinatorial optimization problems, with 4 new best known CARP solutions established in the present study.

Real-time and temporal information services are intrinsic characteristics in vehicular networks, where the timeliness of data dissemination and the maintenance of data quality interplay with each other and influence overall system performance. In this work, we present the system architecture where multiple road side units (RSUs) are cooperated to provide information services, and the vehicles can upload up-to-date information to RSUs via vehicle-to-infrastructure (V2I) communication. On this basis, we formulate the distributed temporal data management (DTDM) problem as a two-objective problem, which aims to enhance overall system performance on both the service quality and the service ratio simultaneously. Further, we propose a multiobjective evolutionary algorithm called MO-DTDM to obtain a set of pareto solutions and analyze how to fulfill given requirements on system performance with obtained pareto solutions. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed optimization method.

In a participatory sensing system, data sensed from smartphone users are shared with the general public who requests data through submitting tasks. When multiple tasks request the data from a mobile user, the mobile user can make a transmission schedule to achieve the balance between the amount of data transmitted and energy consumption. Intuitively, reducing the amount of data transmitted by making use of data sharing between the tasks can save the energy consumption. However, due to the convexity of rate-power function for rate-adaptive transmitting devices, a schedule purely minimizing the amount of data transmitted may not always be the optimal one minimizing the energy consumption. Thus, there exists a tradeoff between the amount of data transmitted and energy consumption. This paper formulates the problem as a bi-objective optimization problem to simultaneously minimize the amount of data transmitted and the energy consumption. Two task models are studied, first-in-first-out (FIFO) task model and arbitrary deadline (AD) task model, respectively. We first provide optimal algorithms for the off-line case. We then study the online case where requests arrive dynamically without prior information. For FIFO tasks, we develop an online algorithm that is O(ln L)-competitive with respect to both the amount of data transmitted and energy consumption, where L is the longest length of the time duration of the tasks. For AD tasks, we devise an online algorithm that is O(ln2L)-competitive with respect to both the amount of data transmitted and energy consumption. Our simulation results validate the efficiency of our online algorithms.

Recent advances in autonomous vehicles and vehicular communications are envisioned to enable novel approaches to managing and controlling traffic intersections. In particular, with intersection controller units (ICUs), passing vehicles can be instructed to cross the intersection safely without traffic signals. Previous efforts on autonomous intersection control mainly focused on guaranteeing the safe passage of vehicles and improving intersection throughput, without considering the quality of the travel experience from the passengers' perspective. In this paper, we aim to design an enhanced autonomous intersection control mechanism, which not only ensures vehicle safety and enhances traffic efficiency but also cares about the travel experience of passengers. In particular, we design the metric of smoothness to quantitatively capture the quality of experience. In addition, we consider the travel time of individual vehicles when passing the intersection in scheduling to avoid a long delay of some vehicles, which not only helps with improving intersection throughput but also enhances the system's fairness. With the above considerations, we formulate the intersection control model and transform it into a convex optimization problem. On this basis, we propose a new algorithm to achieve an optimal solution with low overhead. Finally, we build the simulation model and implement the algorithm for performance evaluation. Comprehensive simulation results demonstrate the superiority of the proposed algorithm.

Vehicle-to-vehicle/vehicle-to-infrastructure (referred to as V2X) communications have potential to revolutionize current road transportation systems with respect to vehicle safety, transportation efficiency, and travel experience. This paper puts the first effort on applying network coding in cooperative V2X communication environments to improve bandwidth efficiency and enhance data service performance. Specifically, we investigate new arising challenges on network-coding-assisted data dissemination by considering both communication constraints and application requirements in vehicular networks. We present the system model and give an insight into the characteristics of cooperative data dissemination with network coding. On this basis, we formulate the problem and propose a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications and exploit their joint effects on providing efficient data services. We design a cache strategy that allows vehicles to retrieve their unrequested data items. This strategy not only increases the opportunity of data sharing among vehicles but also gives higher probability of packet decoding, which in turn enhances the data service performance. We give an intensive analysis on the scheduling overhead, which shows the scalability of the algorithm. Finally, we build the simulation model and conduct a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.

In a wireless system, when multiple applications can share data transmitted by rate-adaptive wireless devices, there exists a trade-off between transmission redundancy and energy efficiency. This paper conducts the first theoretical analysis on such a trade-off. We formulate the problem as a bi-objective optimization problem to simultaneously minimize the transmission redundancy and the energy consumption. In the offline setting that the full information is known in advance, we provide optimal algorithms for the bi-objective optimization problem. In the online setting, we provide an online algorithm with proven performance bound to approximate the optimal solution without relying on any assumed distribution or future information. The proposed online algorithm is proved O(ln T)-competitive with respect to transmission redundancy and also O(ln T)-competitive with respect to energy consumption, where T is the number of time slots. That is, the output of the algorithm always approximates the optimal solution within a logarithmic factor over all possible inputs. Our simulation results further validate the efficiency of our online algorithm.

Converge cast is a critical communication paradigm for data collection in wireless sensor networks, where both energy and bandwidth are scarce resources. Previous converge cast algorithms only focused on minimizing the energy cost without considering the constraint of wireless bandwidth. This article shows that constructing a congestion-free converge cast tree cannot ignore the bandwidth constraint. Considering the adjustable transmission power of sensor nodes, it will affect not only the topology of networks but also the bandwidth of wireless links. In this paper, we formulate the Minimum Total Transmission Power (MTTP) problem, which aims to address the issue of constructing a congestion-free converge cast tree in WSNs with adjustable transmission power of sensor nodes. We transform MTTP to an Integer Linear Programming (ILP) model, by which the optimal solution to MTTP is derived. To strike a balance between scheduling overhead and system performance, we propose a heuristic algorithm called Nearest-to-Sink, which searches viable paths in a greedy way and achieves near optimal performance. We build the simulation model and give a comprehensive performance evaluation, which demonstrates the feasibility and the effectiveness of the proposed algorithm.

Recent advances in infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are envisioned to enable a variety of emerging applications in vehicular networks, where it is imperative to provide efficient data services via cooperative vehicular communications. In this work, we present the data dissemination system via cooperative I2V and V2V communications. We formulate the problem by investigating both the communication constraint and the application requirement on data dissemination. The goal is to maximize the system performance by exploiting the joint effects of I2V and V2V communications. On this basis, we propose an on-line scheduling algorithm to enable scalable, fair and robust data dissemination. The algorithm makes scheduling decisions by transforming the data dissemination problem to the maximum weighted independent set (MWIS) problem and approximately solving MWIS using a greedy method. We build the simulation model based on realistic traffic and communication characteristics. A comprehensive simulation study demonstrates that the proposed solution is able to effectively strike a balance between I2V and V2V data services and maximize system performance in terms of scalability, fairness and robustness.

Timely and efficient data dissemination is one of the fundamental requirements to enable innovative applications in vehicular cyber-physical systems (VCPS). In this work, we intensively analyze the characteristics of temporal data dissemination in VCPS. On this basis, we formulate the static and dynamic snapshot consistency requirements on serving real-time requests for temporal data items. Two online algorithms are proposed to enhance the system performance with different requirements. In particular, a reschedule mechanism is developed to make the scheduling adaptable to the dynamic snapshot consistency requirement. A comprehensive performance evaluation demonstrates the superiority of the proposed algorithms.

Efficient data dissemination is one of the fundamental requirements to enable emerging applications in vehicular cyber-physical systems. In this paper, we present the first study on real-time data services via roadside-to-vehicle communication by considering both the time constraint of data dissemination and the freshness of data items. Passing vehicles can submit their requests to the server, and the server disseminates data items accordingly to serve the vehicles within its coverage. Data items maintained in the database are periodically updated to keep the information up-to-date. We present the system model and analyze challenges on data dissemination by considering both application requirements and communication characteristics. On this basis, we formulate the temporal data dissemination (TDD) problem by introducing the snapshot consistency requirement on serving real-time requests for temporal data items. We prove that TDD is NP-hard by constructing a polynomial-time reduction from the Clique problem. Based on the analysis of the time bound on serving requests, we propose a heuristic scheduling algorithm, which considers the request characteristics of productivity, status, and urgency in scheduling. An extensive performance evaluation demonstrates that the proposed algorithm is able to effectively exploit the broadcast effect, improve the bandwidth efficiency, and enhance the request service chance.

The ability to link two physical smart spaces in real-time, and apply the knowledge acquired from designing and managing the small densely instrumented space to the larger and less densely monitored space opens up a whole host of possibilities in terms of how architects and engineers approach building design, building environment modeling, energy resource optimization, and building control. We leverage and extend the EcoSense framework to link smart spaces. This provides the ability to `scale' environmental profiles derived from optimized simulation models and verified in a small physical building testbed to a much larger building space with relatively fewer sensors. We have implemented a prototype linkage framework connecting two real physical building testbeds to validate this concept.

Recent advances in wireless communication technologies have spawned many new applications in vehicular networks. Data dissemination via roadside-to-vehicle communication is a vital approach to enabling most of these applications. In this work, we investigate in the scenario where data items are broadcasted from the road-side unit (RSU) in response to requests submitted by passing vehicles. Data items are associated with temporal constraints and updated periodically to reflect dynamic states of traffic information. Each request may ask for multiple temporal data items, and it is associated with a deadline, which may either be specified by the driver or imposed by the time when the vehicle drives through the service region. In particular, we develop a real-time data dissemination model based on roadside-to-vehicle communication by formulating the time-constraint of requests and the consistency requirement of retrieving temporal data items. On this basis, we propose an online scheduling algorithm to enhance the system performance in terms of maximizing request service and improving bandwidth utilization. Lastly, we build a simulation model to evaluate the algorithm performance in a variety of situations. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms significantly in both request serving and bandwidth utilization.

Accurate vehicular localization is important for various cooperative vehicle safety (CVS) applications such as collision avoidance, turning assistant, etc. In this paper, we propose a cooperative vehicular distance measurement technique based on the sharing of GPS pseudorange measurements and a weighted least squares method. The classic double difference pseudorange solution, which was originally designed for high-end survey level GPS systems, is adapted to low-end navigation level GPS receivers for its wide availability in ground vehicles. The Carrier to Noise Ratio (CNR) of raw pseudorange measurements are taken into account for noise mitigation. We present a Dedicated Short Range Communications (DSRC) based mechanism to implement the exchange of pseudorange information among neighboring vehicles. As demonstrated in field tests, our proposed technique increases the accuracy of the distance measurement significantly compared with the distance obtained from the GPS fixes.

Abstract On‐demand broadcast is an effective data dissemination approach in mobile computing environments. Most of the recent studies on on‐demand data broadcast assume that clients request only a single‐data‐object at a time. This assumption may not be practical for the increasingly sophisticated mobile applications. In this paper, we investigate the scheduling problem of time‐critical requests for multiple data objects in on‐demand broadcast environments and observe that existing scheduling algorithms designed for single‐data‐object requests perform unsatisfactorily in this new setting. Based on our analysis, we propose new algorithms to improve the system performance. Copyright © 2010 John Wiley & Sons, Ltd.

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this work, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration.