The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning-based methods, it is challenging to perform information fusion from the temporal perspective due to the complexities in fusing heterogeneous sensor data and establishing long-term context correlations. This paper proposes a novel triple spectral fusion framework tailored for HAR. First, we develop an adaptive complementary filtering technique for noise suppression and organize each IMU's sensors into posture and motion modality nodes. Given that IMU nodes form a dynamic heterogeneous graph, we then apply adaptive filtering within the graph Fourier domain to merge both homogeneous and heterogeneous node information. Furthermore, an adaptive wavelet frequency selection approach is implemented to suppress context redundancy and shorten the length of features. This approach enhances both timestamp-based graph aggregation and the correlation of long-term contexts. Our framework uses adaptive filtering in the Fourier, graph Fourier, and wavelet domains, enabling effective multi-sensor fusion and context correlation. Extensive experiments on ten benchmark datasets demonstrate the superior performance of our framework.

Electric scooters (ESs) serve as smart city terminals by uploading real-time charging data to improve mobility and energy efficiency. Battery swapping service has emerged as a transformative solution for energy replenishmen of ESs. Time-of-use (TOU) pricing is commonly adopted to encourage off-peak electricity usage by offering different rates. However, the rigid work schedules of ES drivers always hinder the optimization of battery swapping, leading to escalated charging costs. Existing literature fails to account for the practical constraints of battery charging in real-world scenarios. To tackle thisparadox, we studied battery swap stations in Chengdu. Our findings reveal that the cost of swapping stations could be remarkably reduced by optimizingintrinsic, energy-greedy charging power strategies rather than by altering theextrinsic, rigid swapping schedules. Further analysis indicates 63% of charged batteries remain idle, presenting an opportunity to optimize charging power for cost savings without disrupting swapping schedules. Motivated by these insights, we propose PowerRL, anewadaptive charging power strategy empowered by spatio-temporal reinforcement learning. Specifically, we employ a spatio-temporal gated recurrent unit to predict the demand for battery swapping, which is then used to optimize charging power through reinforcement learning. Applied to the large-scale dataset, PowerRL could reduce per-unit electricity costs by 25.7% while still accommodating driver swapping schedules, thereby harmonizing TOU pricing with the rigidity of ES swapping service.

Illegal roadside parking is highly common in major cities worldwide, leading to traffic congestion & accidents and undermining human safety. Traditional methods for detecting illegal parking rely heavily on active human effort and customized sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we explore utilizing massive on-road vehicles to collect parking violation-related data (including surrounding videos and driving state information), enabling large-scale and low-cost illegal parking detection. However, the dynamic and unpredictable motion of the sensing and target vehicles, coupled with intricate driving behaviors and traffic conditions, pose great challenges to achieving accurate detection. To address these challenges, we propose$i$Patrol+, a novelrobustillegal parking detection system empowered by vehicle crowdsensing, at the heart of which liesa key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios in intricate driving conditions. Specifically, following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model via frame-to-frame feature shifts. Furthermore, this model is augmented by a feature re-projection scheme and a driving behavior-aware bias rectification scheme to effectively alleviate the negative effect of intricate driving conditions. We implement$i$Patrol+on smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233$\rm {km}$of roads in a 125$\rm {km}^{2}$urban area. The experimental results demonstrate that$i$Patrol+identifies illegal parking events with a precision of 86% in intricate driving conditions (e.g.,turns/u-turns, lane changes, traffic congestion, and waiting at traffic signals), outperforming five baselines by 17% on average.

This paper addresses theJoint deployment ofUnmanned aerial vehicles (UAVs) and surveillance caMeras withPath planning (JUMP), aiming to deploy a fixed number of UAVs and budget-limited surveillance cameras, to achieve continuous omnidirectional monitoring. Specifically, the objective is to maximize the monitoring durations of target objects in each of all horizontal directions within a given task duration. We propose an approach for JUMP, which is proved to be NP-hard. Our approach achieves a$\frac{1}{6}-\varepsilon _{1}$approximation ratio in general and$\frac{1}{4}-\varepsilon _{1}$when camera costs are uniform. Firstly, we introduce spatio-temporal discretization to approximate JUMP. Secondly, we partition the solution space of JUMP from spatial perspective and refine the space by addressing a variant of obstacle-avoiding shortest path problem spatio-temporal perspective. Thirdly, we reformulate the problem as a classical problem of Monotone Submodular set function Maximization with one partition Matroid and two Knapsack constraints (MSMMK). To address MSMMK, we propose a$\frac{1}{6(1+\varepsilon )}$approximation algorithm, which outperforms the state-of-the-art with the same time complexity in general. Specifically, our algorithm achieves a$\frac{1}{4(1+\varepsilon )}$approximation ratio for special cases. Simulation results demonstrate our proposed approach outperforms five benchmark algorithms, yielding enhancements 13%-1446%. Moreover, field experiment results indicate that our approach surpasses comparison algorithms, achieving enhancements 23%-265%.

Federated learning (FL) is a promising paradigm of distributed machine learning, since it enables the collaborative training of machine learning models across multiple edge devices without compromising privacy. However, when deploying the FL framework in real-world scenarios, the global model is plagued by the aggregate loss issue, which can result in inefficient and unfair model training. Existing works in the literature either fail to ensure high efficiency in model updates or can not achieve fair model updates. Inspired by the art of “Less is more”, we propose FedBFO, a Federated learning framework with Bi-level First-order Optimization. The key idea of FedBFO is to treat model aggregation as the second optimization of local models, which utilize knowledge trimming to design a more tailored global model for each device. To this end, we first propose a knowledgeaware trimming scheme to adaptively aggregate tailored global models for local devices. We then present a anomaly-aware trimming mechanism to iteratively select qualified local models. The theoretical analysis shows that FedBFO can ensure the efficiency, fairness, and convergence of models. Experimental results demonstrate that FedBFO outperforms six state-of-the-art baselines with a 6.7x convergence speed than FedAvg, achieving better tradeoff convergence performance, efficiency, and fairness.

IoT device identification is vital for network asset and security management. However, existing methods use statistical features that can not identify IoT devices accurately in complex network environments.GraphIoTproposes using non-statistical features and building a heterogeneous graph neural network to identify IoT devices accurately. However, heterogeneous graph neural networks lack interpretability, which reduces trust in the model. Besides, it is difficult to deploy on resource-constrained devices, limiting the broad application of IoT device identification. To make IoT device identification interpretable, easy to deploy, and with high accuracy, we get the interpretation results ofGraphIoTthrough interpretability and further build the rule set based on the interpretation results. Considering there is no suitable interpreter forGraphIoTwith many nodes and edges, we proposeHGExplainer, which reduces the time complexity by splitting the interpretation target into important relation solving and edge solving and uses a novel solution method, ExpandTree. Then, we also designed a rule extractor, which can build rule sets based on the interpretation results. Experimental results on Yourthings and UNSW datasets show thatHGExplainercan build high fidelity, concise sample-level explanations in less than 3 seconds, and the established rule set can precisely identify IoT devices.

Unmanned Aerial Vehicles (UAVs) equipped with cameras provide a promising way for large-scale urban crowd surveillance due to their convenient deployment and flexible mobility. However, UAVs are constrained by limited power and computing resources, hindering existing work in achieving efficient UAV-based crowd surveillance,i.e., long flight time, high accuracy, and low latency. To this end, we propose EagleEye, alow-power,high-precision, andlow-latencycrowd surveillance system empowered by edge-assisted UAVs. It leverages lightweight devices on UAV-side to compress video information edge-independently, then transmits key video information instead of raw high-definition videos. Furthermore, we propose anovel spatio-temporal Compressive-Sensing-based video feature compression algorithmto achieve efficient, low-latency video compression. It can reduce video volumes greatly while minimizing the loss of crowd-surveillance-related information. Specifically, inspired by the Compressive Sensing theory, we compress the video content from both thetemporalandspatialperspectives by accounting forinter-frame redundancyandintra-frame information saliency, respectively. Finally, we implement a prototype system and conduct extensive experiments based on four large-scale datasets with over 20,000 frames. The experimental results demonstrate that EagleEye can reduce transmission latency by 31.4% only with no more than 4% of accuracy loss in urban crowd detection.

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate anaggregation gapcaused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we proposepFedCal, a novelpersonalizedfederated learning with lightweight adaptivecalibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods.

Illegal roadside parking is a common problem faced by large-scale cities, leading to traffic congestion & accidents, and hindering fire rescue. Traditional methods for detecting illegal parking rely highly on active human efforts and particular sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we consider employing massive on-road vehicles to collect the vehicular sensory data (including recording video of the surroundings and driving state information), thereby enabling a large-scale, fine-grained illegal parking detection at a low cost. However, the dynamic and complex movement of the sensing and target vehicles, coupled with complex traffic situations and environmental factors, presents challenges for achieving accurate detection. To address these challenges, we propose iPatrol, an illegal roadside parking detection system leveraging on-road vehicle crowdsensing, at the heart of which lies a key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios. Following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model, using video feature’s change to estimate the relative speed of the two vehicles. Furthermore, this model is utilized to identify the parking status of the target vehicle and estimate its position by utilizing the graph rigidity theory and the non-convex optimization scheme. We implement iPatrol on Android smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233 km roads in an urban area about 125 km2. The experimental results demonstrate that iPatrol detected a total of 162 illegal parking events while achieving a detection accuracy of 87.1% which outperforms three baselines by 21.9% on average.

With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results. Therefore, we assume a task-scheduled execution scenario that combines the above two conditions. In the pre-allocation process, an original time location constraints (ORTA) allocation algorithm is first proposed. Then it is optimized (OPTA) to fully utilize the remaining time of the workers and increase the matched number. In addition, the design of the incentive mechanism is an effective means to improve the task completion rate of the platform. To efficiently utilize the limited platform budget in the long run, a Q-learning-based algorithm is proposed to identify target inspire tasks and subsequently increase their reward to attract workers’ participation. Finally, comparison experiments are conducted on real datasets to verify the effectiveness of our algorithm. Furthermore, the experiments on a Raspberry Pi local terminal are conducted under a satellite-based environment.

As a promising battery-sharing service, battery swapping has become one of the most important electric-scooter (called e-scooter) energy refueling mechanisms. However, by analyzing a battery swapping dataset of 108,574 e-scooter drivers and 41,358 batteries and surveying 224 of these drivers, we find that drivers are facing insufficient energy replenishment during the battery swapping process, i.e., swapping out an undercharged battery from the battery swapping station. To identify the root cause, we perform a data-driven analysis and reveal an imbalance in spatial and temporal swapping behavior patterns, which causes a significant decrease in battery charging time. Inspired by these findings, we design iSwap, a novel spatio-temporal battery swapping recommendation system, which improves drivers’ swapping experience by proactively coordinating spatio-temporal unbalanced behavior patterns. iSwap considers not only drivers’ individual temporal preference for swapping time recommendation (i.e., when to swap) but also the complex mutual influence among drivers for swapping station recommendation (i.e., where to swap). To perform the two tasks efficiently, we propose a swapping behavior-aware phase demand balancing schema and a submodularity-based near-optimal swapping station recommendation algorithm. Finally, through the evaluation, iSwap improves the probability of swapping out a fully charged battery by 42.1% and driver satisfaction by 16.8% compared to the ground truth.

We propose the design, implementation, and evaluation of an instantaneous angular speed (IAS) measurement system, namely Wi-Rotate, using commercial-off-the-shelf (COTS) WiFi hardware. Wi-Rotate exploits the Channel State Information (CSI) of WiFi signals to extract the physical characteristics of the rotation object to achieve accurate contact-free IAS measurements. Wi-Rotate contains three main components: Wi-Fresnel model, Wi-Phase model, and a combination model. Wi-Fresnel model explores the signal amplitude variation features when the rotating object cuts the Fresnel zone boundary to track target rotation. Wi-Phase model leverages signal phase variation and formalizes the problem of determining IAS as a linear programming problem. The combination model combines the IAS values obtained by Wi-Fresnel and Wi-Phase and utilizes a clustering method to further improve measurement accuracy. Comprehensive experiments are conducted to demonstrate the advantages of Wi-Rotate in terms of accuracy, sensing range, and system latency. Wi-Rotate is able to achieve real-time rotation measurements at an accuracy higher than 99% when the target is within 2 meters. Even when the target is 3 meters away, Wi-Rotate can still achieve an accuracy of 94%, demonstrating the long-range tracking capability which is critical for industrial applications.

Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus onstaticalservice provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users’ demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, i.e., how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the$q$-independence system constraint, where$q$is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a$1/(1+q)$approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

With the popularity of Mobility-on-Demand (MOD) vehicles, a new market called MOD-Vehicular-Crowdsensing (MOVE-CS) was introduced for drivers to earn more by collecting road data. Unfortunately, MOVE-CS failed after two years of operation. To identify the root cause, we survey 581 drivers and reveal its simple incentive model based on blindly competitive rewards. This model brings most drivers few yields, resulting in their withdrawals. In contrast, a similar market termed MOD-Human-Crowdsensing (MOMAN-CS) remains successful thanks to a complex model based on exclusively customized rewards. Hence, we wonder whether MOVE-CS can be resurrected by learning from MOMAN-CS. Despite considerable similarity, we can hardly apply the incentive model of MOMAN-CS to MOVE-CS, since MOD drivers are also concerned with passenger missions that dominate their earnings. To this end, we analyze a large-scale dataset of 12,493 MOD vehicles, finding that drivers have explicit preference for short-term, immediate gains as well as implicit rationality in pursuit of long-term, stable profits. Therefore, we design a novel driver-oriented incentive mechanism for MOVE-CS, calledLSTAloc, at the heart of which lies a spatial-temporal differentiation-aware task allocation scheme empowered by submodular optimization. Applied to the dataset, our design would essentially benefit both the drivers and platform to incentivize MOD vehicular crowdsensing efficiently, thus possessing the potential to resurrect MOVE-CS.

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput – the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer with$1 \le s\le K$, e.g.,$s=3$. We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers’ collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers’ data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/ e ) ¼ -approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

In this paper we study the deployment of a UAV (unmanned aerial vehicle) network that consists of multiple UAVs to provide emergent communication services to people trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of users. Unlike most existing studies focusing on homogenous UAVs, we consider the deployment of heterogeneous UAVs, where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a connected UAV network such that the number of users served by the UAVs is maximized, subject to the constraint that the number of users served by each UAV is no greater than its service capacity, assuming that the maximum number of users can be served by a UAV is given. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer, e.g.,$s=3$. We finally evaluate the performance of the approximation algorithm. Experimental results show that the number of users served by all UAVs in the approximate solution is improved by 22% compared with the solutions delivered by state-of-the-arts.

Traditional path-finding studies basically focus on planning the path with the shortest travel distance or the least travel time over city road networks. In recent years, with the increasing needs of diverse routing services in smart cities, the bi-criteria optimum path-finding problem (i.e., minimizing path distance and optimizing extra cost or utility according to users’ preference) has drawn wide attention. For instance, in addition to distance, the previous studies further find routes with more scenery (utility) or less crime risk (cost). However, existing works are scenario-oriented which optimize specific cost or utility, ignoring that the routing planner should be universal to deal with both cost and utility in different real-life scenarios. To fill this gap, this paper proposes a generic bi-criteria optimum path-finding framework (cuRL) based on deep reinforcement learning (DRL). Specifically, we design a novel state representation and reward function for the DRL model ofcuRLto overcome the challenges that 1) the cost and utility should be optimized with minimal path distance in a unified manner; 2) the diverse distributions of cost and utility in various scenarios should be well-addressed. Then, a transition preprocessing method is proposed to enable the efficient training of DRL and avoid detours. Finally, simulations are performed to verify the effectiveness ofcuRL, where two criteria (i.e., solar radiation and crime risk) are modelled based on the real-world data in downtown New York. Comparing with a set of baseline algorithms, the evaluation results demonstrate the priority of the proposed framework for its generality.

IoT devices bring great convenience to a person's life and industrial production. However, their rapid proliferation also troubles device management and network security. Network administrators usually need to know how many IoT devices are in the network and whether they behave normally. IoT device identification is the first step to achieving these goals. Previous IoT device identification methods reach high accuracy in a closed environment. But they are not applicable in the continuously changing environment. When new types of devices are plugged in, they cannot update themselves automatically. Besides, they usually rely on supervised learning and need lots of labeled data, which is costly. To solve these problems, we propose a novel IoT device identification model namedAutoIoT, updating itself automatically when new types of devices are plugged in. Besides, it only needs a few labeled data and identifies IoT devices with high accuracy. The evaluation on two public datasets shows thatAutoIoTcan identify new device types only using 1.5$\sim$2.5 hours’ traffic and still have high accuracy after updating. Moreover, it has a better performance than other works when there are only a few labeled data, especially in an environment with scanning traffic.

Thanks to the increasing number and massive coverage, delivery drones, equipped with various sensors, have demonstrated significant but unexplored potentials for large-scale and low-cost urban sensing during package delivery. In this paper, we propose novel studies on the reutilization of such delivery drone resources to fill this void in urban crowdsensing. Accounting for interdependency between flying/sensing and drone delivery weight, we jointly optimize route selection, sensing time, and delivery weight allocation, to maximize delivery and sensing utility under drones’ energy constraints. This problem is formulated as a non-convex mixed-integer non-Linear programming problem, which is proved to be NP-hard. To address this intricate problem, we propose near-optimal algorithms that leverage the equivalent objective function construction, the local search scheme, and the alternating iteration technique. Theoretical analysis indicates that our algorithms can achieve$\frac{1}{4+\varepsilon }$-approximation ratio (where$\varepsilon$is an arbitrarily small positive parameter) and the convergence guarantee in polynomial time, for the scenarios of fixed and adjustable delivery weights, respectively. Extensive trace-based simulations, field experiments, and the real-world application demonstrate that ours can significantly improve the delivery & sensing utility by$124.7\%$and the energy utilization rate by$72.2\%$on average, compared with the drone delivery without reusing.

With the easy access to mobile networks and the proliferation of video applications, video traffic is occupying a great portion of the network traffic, which poses a new challenge of how to alleviate the heavy backhaul traffic and ensure the high quality of experience for video services. As a promising solution towards addressing this challenge, video caching in edge networks has recently received significant attention, which mostly considers the video popularity and the user preference for the video. However, few studies consider the user behavior and the user preference for different parts of the video that indeed have an essential impact on caching efficiency. Hence, this article proposes a new caching and resource scheduling scheme for adaptive bitrate videos by incorporating these fine-grained factors. We first model the video service problem as a nonlinear integer programming problem, which can be divided into a cache placement problem and an online resource scheduling problem. Then, we design efficient algorithms based on several techniques, including greedy strategy, relaxation, and rounding, to solve the two problems. Extensive experimental results based on two real-world datasets show that the proposed solution achieves superior performance compared with several state-of-the-art caching approaches.

With the popularity of Mobility-on-Demand (MOD) vehicles, a new market called MOD-Vehicular-Crowdsensing (MOVE-CS) was introduced for drivers to earn more by collecting road data. Unfortunately, MOVE-CS failed after two years of operation. To identify the root cause, we survey 581 drivers and reveal its simple operation model based on blindly competitive rewards. This model brings most drivers few yields, resulting in their withdrawals. In contrast, a similar market termed MOD-Human-Crowdsensing (MOMAN-CS) remains successful thanks to a complex model based on exclusively customized rewards. Hence, we wonder whether MOVE-CS can be resurrected by learning from MOMAN-CS. Despite considerable similarity, we can hardly apply the operation model of MOMAN-CS to MOVE-CS, since drivers are also concerned with passenger missions that dominate their earnings. To this end, we analyze a large-scale dataset of 12,493 MOD vehicles, finding that drivers have explicit preference for short-term, immediate gains as well as implicit rationality in pursuit of long-term, stable profits. Therefore, we design a novel operation model for MOVE-CS, at the heart of which lies a spatial-temporal differentiation-aware task recommendation scheme empowered by submodular optimization. Applied to the dataset, our design would essentially benefit both the drivers and platform, thus possessing the potential to resurrect MOVE-CS.

The prediction of taxi-passenger’s destination with the partial GPS trajectory left by moving taxis is an important yet challenging research issue. The high uncertainty of human mobility and limited clue provided by the unfinished trajectory are two major barriers to developing effective predictors. In general, such a prediction task is often converted to the identification one among given candidate destinations. Hence, how to extract the discriminative knowledge from the partial trajectory becomes crucial. It is well-recognized that the sequence of visited locations by the taxi has inherent relationship with the heading destination. Inspired by the idea, we propose a novel approach that jointly combines the GPS embedding and attention-based BiLSTM techniques for the prediction of passenger’s destination. Specifically, we propose two GPS embedding methods to encode the geographic proximity and multi-scale spatiality of GPS points into embedding vectors, so as to reveal the spatial context of visited locations in the urban space. After converting GPS trajectories into embedding sequences, we further establish an attention-based dual BiLSTMs neural network to model the relationship between the heading destination and the bidirectional sequential context of visited locations. Meanwhile, the discriminative capability of visited locations in determining the destination can be captured by the attention mechanism. In addition, the OT (origin and time) information is aggregated into the neural network as auxiliary features. Stepping closer to smarter passenger services, rather than telling destinations in terms of drop-off clusters, our proposed model outputs the destinations in terms of historical passengers’ destination clusters. Finally, we evaluate the system performance based on two real large-scale datasets. Results show the superior performance of our proposed model.

The recent breakthroughs in Wireless Power Transfer (WPT) facilitate supporting rechargeable sensors to enrich a series of energy-consuming applications. However, most charging scheduling schemes in rechargeable wireless sensor networks (WSNs) focus on sensing tasks instead of charging utility, which leaves a considerably high performance gap in the optimal result. Moreover, the charging scheduling is usually non-flexible, in which a full or nothing charging policy suffers from relatively low charging coverage as well as low efficiency. In this article, we focus on how to efficiently improve charging utility when introducing charging-oriented sensor placement and flexible scheduling policy. We formulate a general maximization optimization problem under a general routing constraint, which generates great difficulty. We utilize area partition and charging discretization methods to transform into the scope of maximizing a submodular function problem. Thus, a constant approximation algorithm is delivered to construct a near optimal charging tour. We analyze the performance loss from the discretization to guarantee that the output of the proposed algorithm has more than (1-ɛ)(1-1/ e )/4 of the optimal solution, where ɛ is an arbitrarily small positive parameter (0 < ɛ < 1). Both simulations and field experiments are conducted to evaluate the performance of our proposed algorithm.

With increasing expansion and urbanization of cities, the gap is constantly widening between the current provision of urban public toilets and the fast-growing toileting demand. Building new ones becomes a promising way to alleviate such issue. Nevertheless, where to build them in a city is challenging. Different from other location selection (e.g., commercial sites) problems, the selection of public toilet locations is hard to quantify and evaluate. On one hand, the toileting demand that determines whether the new public toilet is needed cannot be measured accurately. On the other hand, the modeling of the toileting demand is also complicated, being influenced by multiple factors, e.g., human mobility, human activity, and geographical characteristics. In this article, we propose a novel data-driven framework named ToiletBuilder to address it, which consists of three components, i.e., region identification, region representation, and region classification. Specifically, region identification obtains many reachable regions with the reasonable size. Region representation extracts city-specific features from multiple urban data to characterize location selection influencing factors for each region. A deep embedding model is further applied to learn a high-order and concise semantic representation. By labeling some regions with the true positive label (i.e., having public toilets served in these regions) in advance, region classification trains a positive-unlabeled (PU) learning model from these samples to identify unlabeled positive ones. Finally, we conduct extensive experiments based on four real-world data sets including road network, river network, taxi trajectory, and POI data, in the city of Chongqing, China. Results demonstrate the effectiveness of our proposed approach.

For effective crowdsensing, it is essential to incentivize the interactions of participants and platforms. Existing approaches do not tailor users’ bidding to their preferences, i.e., personalized bidding (PB). To meet this need, we design an incentive mechanism, called Picasso, that achieves not only the expressiveness and description efficiency of PB for users, but also minimal social cost, computational efficiency, and strategy proof for platform owners. This design is, however, challenging due to the intrinsic conflicting goals of the platform owner and users. To handle these conflicts, Picasso represents bids in a novel 3-D expression space by orchestrating three logical operations to balance among expressiveness, computational complexity, and description efficiency. Moreover, we equivalently decompose and recombine the complex task dependencies of bids originated from the expressiveness of PB, thus achieving a constant-factor approximation of optimal task allocation with strategy proof in polynomial time. These properties of Picasso are proven theoretically. In addition to a detailed simulation study, our trace-driven evaluations show that, compared to existing approaches, Picasso can enable each user to bid$9.7\times $more tasks, on average, and decrease the description length by 74%, thus encouraging more users’ participation. Picasso also reduces the platform owner’s payment by more than 61%, hence yielding a win–win solution for incentivizing platform–user interactions.

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3 percent accuracy improvement (e.g., 2.2 percent MAPE) in predicting nearby traffic volume. We believe that this work can open a new gate of reusing building sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation.

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.

Wireless power transfer (WPT) has emerged as a promising paradigm to charge devices due to the high reliability and efficiency of continuous power supply. Recent studies usually focus on relatively general charging patterns and metrics but neglect the collaborated task execution of nodes that incur charging inefficiency. In this article, we respect the energy requirement diversity among nodes to investigate the collaborated and tasks-driven mobile charging problem. Our goal is to maximize the overall task utility that concerns sensor selection and task cooperation. To address this problem, we propose a$(1-1/e)/4$-approximation algorithm. First, we propose a novel energy allocation scheme with a specific theoretical analysis of the submodularity and gap property for the surrogate function. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint and propose a greedy algorithm to address this problem. We conduct extensive simulations to validate our theoretical results and the results show our algorithm can achieve a near-optimal solution covering at least 84.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in an office room and a soccer field environment are implemented, respectively, to validate our proposed algorithm.

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks. However, in real cases, especially in the era of smart cities where many kinds of transportation-related data become easily available, recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria, e.g., finding the route with the highest accumulated scenic score along (utility) while not exceeding the given travel time budget (cost). Such problem can be viewed as a variant of Arc Orienteering Problem (AOP), which is well-known as an NP-hard problem. In this paper, targeting a more realistic AOP, we allow both scenic score (utility) and travel time (cost) values on each arc of the road network are time-dependent (2TD-AOP), and propose a memetic algorithm to solve it. To be more specific, within the given travel time budget, in the phase of initiation, for each population, we iteratively add suitable arcs with high scenic score and build a path from the origin to the destination via a complicate procedure consisting of search region narrowing, chromosome encoding and decoding. In the phase of the local search, each path is improved via local-improvement-based mutation and crossover operations. Finally, we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively, and the experimental results demonstrate that it outperforms the baselines.

Doors as densely-deployed natural landmarks play an important role in improving indoor positioning systems. However, the state-of-the-art door event detection works are based on either vision or infrastructure, thus incurring non-trivial device or management cost. To address these problems, we present a Light-weight Magnetic-based Door Event Detection method, called LMDD. It leverages built-in magnetic sensors of common smartphones to achieve infrastructure-free door event detection. After analyzing the special features of sensors' readings changes caused by the door, we design LMDD scheme with three main components, including data acquisition, events identification and events denoising. Moreover, an improved and robust door event detection framework based on a majority-voting model is proposed to fuse multiple-dimensional sensing data from non-magnetic built-in sensors. We have implemented a prototype of LMDD on Android-based platform. Experimental results show that LMDD with only magnetic sensor achieves door event detection accuracy of around 80 percent on average, ranging from 70 to 87 percent in various typical indoor environments. The enhanced LMDD based on the fusion of heterogeneous sensors can achieve a much higher door event detection accuracy of 90 percent on average.

Mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity- limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system is acting as a 'data center in a box' to support compute-intensive mobile applications. The mobile cloudlets can provide in-proximity services by executing the workloads for nearby devices. Nevertheless, load balancing in mobile cloudlet network is of great importance, as it has a huge impact on task response time. Existing methods for cloudlet load balancing basically rely on the strategic placement or user cooperation. However, the above solutions require the global task load information from the whole network, which is costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose 'CTOM', a Collaborative Task Offloading Mechanism for mobile cloudlet networks. Our solution is based on the balls-and-bins theory and can balance the task load only requiring limited information. Extensive simulations and evaluation based on mobility trace demonstrate that, our CTOM outperforms the conventional random and proportional allocation schemes by reducing the task gaps among mobile cloudlets by 65% and 55% respectively. Meanwhile, CTOM's performance is close to that of the greedy algorithm but with much lower computing complexity.

Mobile advertising has played an important role with the prevalence of smart mobile devices. Most of the previous studies focus on location-based or content-based mobile advertisement propagation and distribution, which are suffered by the low propagation efficiency, because advertisements could not be available to mobile users within limited time span. Conventional offloading schemes could perfectly distribute advertisements according to user's interest, but have not fully respected the time sensitivity in mobile advertisement distribution. In response to this stalemate, we introduce the advertisement platform's expected income maximization problem (EIMP), and prove its NP-hardness. To our knowledge, ours is even harder than conventional 0-1 mutlidimensional and multiple knapsack problem. But inspiringly we find that it could be transformed into a maximizing monotone submodular set function, being subjected to partition matroid constraints. Then a simple but effective greedy algorithm (TIMAO, time-sensitive mobile advertisement offloading)is proposed to solve the EIMP with approximation ratio of 1/3. Finally, the evaluation results show that TIMAO could double the platform's expected income comparing with the random selection method and reach 99.2% of the near optimal values achieved by CPLEX tool-box. At the same time, it increases the time duty cycle by about average 10% compared with the random selection.

Summary This paper investigates the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperation among users to achieve balanced resource utilization in a platform‐centric view. In achieving fairly low communication and computational overhead, this work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the Proportional Allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users. Even with limited information, the balancing performance could be improved significantly. Moreover, theoretical analysis has been presented in convergence property. Extensive evaluations have been made to show that Chance‐Choice outperforms several existing algorithms. Typically, comparing with Proportional Allocation scheme, it could decrease the load gap between the maximum and the minimal in system by 50% to 80% and reduce the overhead complexity from O(n) to O(1) comparing with the Max‐weight Best Response algorithm, where n denotes the number of mobile users in a crowdsourcing system. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract With the pervasive use of smart mobile devices and increasing wireless networking technologies, collaborations among mobile users are becoming deeper and ubiquitous. Appropriate task collaborations among mobile users could effectively improve the network processing ability with so called ‘mobile cloud’ or ‘cloudlet’. However, task allocations confront with the security issues. The possible collusion or re‐collaborations among the mobile users would possibly merge the allocated tasks of the specific users. Moreover, considering the delivery reliability and task execution efficiency, replications are applied for enhancement, which would also lead to more sever security threat for users. We investigate how to secure the security when task collaborations are allowed for mobile users. Our security scheme is built upon the load balancing scheme, and our intuitive solution is, if the tasks could be effectively balanced among users, the security issues could be guaranteed, because averaging the task assignment could effectively raise the threshold for collusion among potential malicious users. In this work, we propose ‘SAFE‐CROWD’: a secure task offloading and reassignment scheme among mobile users. The basic idea is simple, we leverage the ‘ball and bin’ theory for task assignment, wheredmobile users in contact range are investigated, and we select the least loaded ones among them. It has been proved that such simple cases can effectively reduce the largest queueing length from to . Inspired by this theoretical result, we develop a task reassignment policy for security issues. Simulation and trace‐driven studies have shown that our simple but effective scheme could enhance the security for mobile users, when the tasks are collaboratively executed among mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, the sensors on smart phones are prone to the unknown measurement errors, requiring automatic calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by expectation maximization (EM) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. The key insight is that, only based on the participatory observations, we can “calibrate sensors without explicit or cooperative calibrating process”. Theoretical analysis proves that, our method can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized. Also, extensive simulations show that, ours improves the estimation accuracy of sensor bias up to 20 percent and that of sensor noise deviation up to 30 percent, compared with three baseline methods.

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, sensors on smart phones are prone to the unknown measurement errors, requiring automatical calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by EM (Expectation Maximization) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. Our algorithm can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized.

In the participatory sensing network, the sensor noise dominates the quality of sensing data as well as the processing efficiency. Previous works focus on evaluating sensing accuracy with expectations, and fails to quantify the sensor noise with variance estimations, which will inevitably suffer from the dynamics and the incompleteness of the sensing data. In this paper, we propose FSP (Feeling Sensors' Pulse) method, which quantifies the sensor noise using the confidence interval. Specifically, we first use EM (Expectation Maximization) based iterative estimation algorithm to compute the maximum likelihood estimation (MLE) of sensor noise. Second, on the basis of these estimations, we leverage the asymptotic normality of MLE and the Fisher information to compute the confidence interval. The extensive simulations show that, FSP can achieve 90% success rate where the true values of sensor noise fall into the 95% confidence interval, at the cost of the polynomial time complexity only.