Computation offloading has been extensively studied in recent years for the internet of vehicles (IoV), where roadside units (RSUs) are deployed to assist computation offloading. However, it is still challenging to decide when to offload regarding to multiple factors, such as load differences among RSUs, a vehicle’s moving speed, and a vehicle’s energy constraint. In this paper, an optimal offloading decision method is proposed based on optimal stopping theory (OST) to decide when to offload considering the aforementioned factors. Firstly, two offloading decision problems with and without energy constraint are constructed to find the optimal RSU which can minimize expected cost, where the expected cost is determined by the decision on offloading to the current RSU or continuing observing the next RSU. Then, OST is utilized to solve these two problems. Specifically, a sequence of thresholds are pre-calculated based on the OST. An offloading decision can be made by comparing the load of current RSU with the threshold. Moreover, some facts on a vehicle’s moving speed in the environment without energy constraint and the number of observations on RSUs in the environment with energy constraint are revealed. What’s more, the optimal moving speed which can minimize the expected cost is also provided. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed method. The effects of a vehicle’s moving speed and the number of observations on the performance of the proposed method are also verified. Comparing to the benchmarks, the proposed method can achieve superior performance in terms of cost and hit ratio, and has comparable performance with the best offloading method which has full RSUs’ load information. Moreover, the proposed method is robust to the estimation deviation of RSUs’ load distribution.

In the mobile edge computing (MEC)-enabled air-terrestrial integrated network, unmanned aerial vehicles (UAVs) serve as air edge nodes with the purpose to collaboratively train the high-availability prediction model by federated learning (FL). Nonetheless, in view of the significance of data freshness for an accurate training model, UAVs suffer from the stochastic and intermittent nature in energy harvesting (EH). This paper formulates an inter-UAV cooperative federated computing (IUCFC) problem to jointly optimize prediction accuracy, age of information (AoI) in flight region, and overall energy consumption of EH-enabled UAVs for edge data processing. To address the intricate IUCFC problem, a deep reinforcement learning (DRL) based cooperative UAV intelligent decision (CUID) algorithm is proposed, which leverages a dual Actor-Critic architecture, in pursuit of the collective tuning of hybird actions. Further, the Ornstein-Uhlenbeck (OU) noise is engaged in continuous action spaces to prompt exploration, while a conditional iteration dropout (CID) scheme mitigates the infeasible actions caused by the noise introduced, thereby bolstering the exploration efficiency and quality of CUID algorithm. Considering the non-stationary environments originated from UAV mobility, priority experience replay (PER) is adopted to dynamically modify experience priority. Extensive experiments show that CUID attains superior performance over those advanced algorithms, upgrading system utility by 8.79%, while augmenting FL model accuracy by 3.68% in dynamic scenarios with heterogeneous data distributions.

Vehicle accidents result in a large number of fatalities and substantial economic losses annually. A significant portion of vehicle accidents are attributed to driver negligence, particularly drowsy driving (often caused by fatigue), which can seriously weaken drivers' alertness in steering control, lane keeping, and maintaining a safe distance from other vehicles. Thus, effective drowsiness detection is essential for preventing accidents. The fatigue state of drivers can be inferred from videos captured by in-vehicle cameras. To achieve accurate and rapid detection, both personal and common fatigue features should be learned from historical driving videos and jointly exploited for state recognition. In this paper, we propose an edge AI-enabled drowsiness detection framework. We design a dual-branch architecture for feature fusion that integrates the local models to capture personalized fatigue features and the regional models to extract common fatigue features, thereby improving the detection accuracy while reducing detection latency. Furthermore, to lower computational overhead, we identify key frames in videos and represent facial landmarks with relative polar coordinates, which accelerates feature extraction. Extensive experiments demonstrate that our approach performs well in terms of accuracy, AUC, and F1 score (0.9986, 0.9999, and 0.9986, respectively). Moreover, only 85 frames (45 I-frames and 40 P-frames) are required per detection instead of the original 795 frames, resulting in a detection delay of approximately 517 ms.

Unmanned Aerial Vehicles (UAVs) have recently been widely used in various fields. However, both cooperative charging scheduling and insufficient charging facility problems in UAV charging scenarios have been rarely studied. This paper studies parallel cooperative charging scheduling of UAVs. We adopt cooperative charging to reduce the total cost and parallel scheduling to enable UAVs can be charged even if the number of UAVs is more than the number of charging facilities. We formulate the Parallel Cooperative Charging Scheduling for UAVs Problem (PCCSUP) for optimizing the total cost of whole charging system. We first investigate the special case of PCCSUP with single charging station, and use the approximation algorithm for Uniform Parallel Machines Scheduling Problem (UPMSP) to solve the special case. Then, a greedy approach based approximation algorithm is proposed to solve the PCCSUP, where we use the approximation algorithm for UPMSP to obtain the charging arrangements and the Set Covering Problem (SCP) optimization framework to obtain the charging groups. The results of extensive simulations demonstrate that our algorithm can reduce up to 59.81% total cost compared with the benchmark algorithms. Finally, we discuss and design the algorithms for three related problems: PCCSUP with different arrival times, PCCSUP withK-anonymity, and charging arrangements for excluded UAVs.

Wireless sensor networks face significant challenges in large-scale multitask scenarios (LSM-WSNs), where traditional data collection methods lead to high energy consumption and packet collisions. Mobile data collection, employing mobile collectors, mitigates these issues but introduces electricity shortages due to limited battery capacity. This paper addresses the core research problem of minimizing charger deployment costs while ensuring uninterrupted power for mobile collectors in long-running LSM-WSNs. We formulate the Minimum-Cost Charger Deployment (MCCD) problem, prove its NP-hardness, and establish its equivalence to the Overflow-Free MCCD (OF-MCCD) problem. By refining constraints and transforming OF-MCCD into a Minimum-Cost Submodular Cover (MCSC) problem, we propose a greedy algorithm with an approximation ratio of$ln\gamma +1$, where$\gamma$is a system parameter. Further, we extend MCCD problem to adjustable power allocation case, showing that continuous power allocation reduces to linear programming, while discrete power allocation remains NP-hard, for which we devise an efficient$ln\gamma +1$-approximation solution. Additionally, we tackle the Charger Deployment Extension (CDE) problem for evolving network requirements. The unique contributions include the first systematic study of cost-optimal charger deployment for LSM-WSNs, novel problem formulations, and theoretically grounded algorithms with provable guarantees. Simulations validate that our solutions outperform baselines across diverse scenarios.

The deployment of Unmanned Aerial Vehicle (UAV) cluster is an available solution for object detection missions. In the harsh environment, UAV cluster could suffer from some significant threats (e.g., forest fire hazards, electromagnetic interference, and ground-to-air attacks), which could lead to the destruction of UAVs and loss of data. To this end, we propose a Hierarchical Federated Learning Framework for Object Detection (HFL-OD) to enhance the robustness of UAV cluster conducting object detection missions. In HFL-OD, UAVs are grouped through a Three-Dimensional (3D) graph coloring method, and an intragroup backup mechanism is provided to prevent the data loss caused by the destruction of UAVs. Besides, a dynamic server selection mechanism deals with the potential destruction of servers (cluster server and group servers) by adaptively reassigning the server roles. To further improve the robustness and mission efficiency of UAV cluster, a twotier federated learning framework is introduced to make a proper trade-off between object detection accuracy and communication/computational overhead. This framework is built on the concept of hierarchical federated learning by implementing both intragroup parameter aggregation and global parameter aggregation. Extensive simulations and comparisons demonstrate the superior performance of our proposed HFL-OD, i.e., the robustness of UAV cluster conducting object detection missions can be significantly improved, and the communication/computational overhead is effectively reduced.

At present, Unmanned Aerial Vehicle (UAV) swarm has been extensively applied in various fields. In the application of detection and localization of electronic signals, some UAVs could become disabled due to some abnormal events (e.g. electromagnetic interference and battery electricity exhaustion), and the topology connectivity of UAV swarm could be impaired, i.e., the topology of UAV swarm could be partitioned. For the topology recovery issue, we first propose Robust Topology Recovery Algorithm of UAV swarm (RTRA) to recover the topology connectivity of UAV swarm and enhance the topology robustness (reduce the number of potential topology recoveries in future) by relocating some UAVs to new positions with shortest flight distance. Furthermore, we note that the relocated UAVs are easy to exhaust the battery electricity and fail due to the extra flight movements for the topology recoveries, which affects the topology robustness. To this end, we present Cascading Robust Recovery Topology Algorithm of UAV swarm (CRTRA), which adopts a cascading movement strategy to share the flight movements among multiply relocated UAVs, thus avoiding the battery electricity exhaustion of the relocated UAVs. Extensive simulations and comparisons demonstrate that our proposed CRTRA can effectively recover the topology connectivity of UAV swarm while enhancing the topology robustness and shortening the flight distance of relocated UAVs, and CRTRA is especially suitable for some missions such as the detection and localization of electronic signals where UAVs are prone to fail.

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average.

Abstract Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S‐MBSA for the shared bus with strong endurance and E‐MBSA for the large‐scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S‐MBSA and E‐MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue.

Machine Learning (ML) techniques can facilitate the automation ofmalicious software(malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and defense effectiveness. In this article, we propose a new adversarial training framework, termedPrincipledAdversarial MalwareDetection (PAD), which offers convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations to protect malware detectors from adversaries, whereby for smooth detectors, adversarial training can be performed with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, at the price of suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal against realistic adversarial malware.

At present, mobile charging stations (MCSs) are taken as an important complement of fixed charging stations. Currently, the strategy of MCSs is to move towards the electric vehicles to be charged (EVCs) only after being requested. To shorten the charging delay of EVCs and enhance the proportion of charged EVCs, idle MCSs should actively move to the areas with large potential charging demand rather than remaining stationary. The distribution of idle MCSs in different areas should be taken into account to prevent excessive idle MCSs from moving into the same areas simultaneously. To this end, we introduce the concept of charging demand force to depict the potential charging demand of EVCs, and then propose the Placement Strategy for Idle Mobile Charging Stations (PS-IMCS). In PS-IMCS, each idle MCS can measure the potential charging demand in neighboring areas through obtaining the resultant force composed of attraction force and repulsion force, and an MDP model is specially designed to make placement decisions for idle MCSs. Extensive simulations and comparisons demonstrate the performance superiority of PS-IMCS, i.e., the charging delay of EVCs can be significantly shortened, and the proportion of charged EVCs can be effectively enhanced.

Mobile charging stations (MCSs) have become an indispensable complement of fixed charging stations. In the regions where fixed charging stations are sparsely deployed or even absent, the main concern is that how to properly schedule MCSs to charge the electric vehicles with insufficient electricity (EVCs). In this paper, we focus on the scheduling of idle MCSs and pending EVCs. To increase the charging revenue of MCSs and enhance the proportion of successfully charged EVCs, we schedule idle MCSs to proactively track some EVCs with potential charging demand, and schedule pending EVCs to approach some busy MCSs for potential charging opportunities. To this end, a Scheduling Approach based on Multi-Agent Deep Reinforcement Learning (SA-MADRL) is proposed to train the scheduling models for agents (idle MCSs and pending EVCs). In SA-MADRL, the agents obtain the local observations to make the scheduling decisions. Both idle MCSs and pending EVCs can independently make the scheduling decisions, and thus SA-MADRL can realize the fully distributed scheduling and has a good scalability. Extensive simulations and comparisons demonstrate the performance superiority of SA-MADRL, i.e., the charging revenue of MCSs can be significantly increased, and the proportion of successfully charged EVCs can be effectively enhanced.

As the gasoline taxis are gradually restricted due to the increased environmental awareness, electric taxis (E-taxis) have become a more environmentally friendly choice to provide the transportation service. When some E-taxis are vacant, they typically cruise along roads without any specific destinations, and two major concerns should be considered for vacant E-taxis: In order to increase the business profits of E-taxis, it is vital to recommend the profitable cruising routes along which vacant E-taxis could pick up passengers as early as possible and earn more profits. Besides, the residual electricity of E-taxis is continuously consumed on travels, and E-taxis must be timely charged before their residual electricity is exhausted (i.e. the breakdowns of E-taxis). Thus, the cruising route recommendation for vacant E-taxis should take into account both passenger-carrying demand and charging demand, and the carrying-charging demand balance should be properly made. To this end, we propose a cruising Route Recommendation Method based on Carrying-charging Demand Balance (RRM-CDB) for vacant E-taxis. The passenger-carrying demand and charging demand are first formulated to reflect their changes and interrelationships, and the historical cruising trajectories of vacant E-taxis (with the two types of demand) are locally learned to recommend the future cruising routes, because the historical cruising trajectories contain the distribution of taxi demand of passengers and the trend of vacant E-taxis gradually approaching the charging stations with the decrease of residual electricity. Particularly, in RRM-CDB each vacant E-taxi trains a local learning model in a distributed manner, thus significantly reducing the computational complexity of cruising route recommendation. Extensive simulations and comparisons demonstrate that RRM-CDB can help to increase the business profits of E-taxis and avoid the breakdowns of E-taxis as much as possible.

Taxis can provide convenient and flexible transportation services for citizens. The proper cruising routes should be recommended to vacant taxis, so as to help them to pick up passengers as early as possible, and thus increase their business profits. To this end, we propose a Cloud-edge-end Collaboration Framework for the Cruising Route Recommendation of vacant taxis (CCF-CRR). In CCF-CRR, each vacant taxi trains a local model based on its historical cruising route segments, and the local model parameters of the vacant taxis in the same region are periodically uploaded to an edge server for parameter aggregation. Then, the aggregated model parameters are released by the edge server to vacant taxis for their use. In addition, the future waiting time of passengers is predicted by the edge servers in different regions and is uploaded to the cloud server, and then the cloud server can measure the potential taxi demand in regions and dispatch vacant taxis among regions to achieve the taxi demand-supply equilibrium. Extensive simulations and comparisons demonstrate the superior performance of our proposed CCF-CRR, i.e., with the cloud-edge-end collaboration framework, the business profits of taxis can be significantly increased, and the pick-up distance of taxis can be largely shortened.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) have been deployed to complement fixed charging stations. Typically, MCSs are assigned to charge the electric vehicles with insufficient electricity which have made charging requests (termed IEVs). Moreover, there are some electric vehicles with insufficient electricity which have not made charging requests (termed quasi-IEVs). If idle MCSs are allowed to actively track quasi-IEVs according to their potential charging demand, then more IEVs could be promptly charged, and thus the charging profits of MCSs could be increased. However, due to the private ownership of electric vehicles, some private information cannot be provided in the potential charging demand of quasi-IEVs (e.g., the destinations and residual electricity), making the potential charging profits of idle MCSs hard to be evaluated, and thereby the proper assignments of idle MCSs are difficult to decide. To this end, we introduce the profit-maximizing heat maps to depict the potential charging demand of quasi-IEVs and evaluate the potential charging profits of idle MCSs. A profit-maximizing heat map remarks the positions around quasi-IEVs and displays them as continuous areas. Specifically, the different shades of colours are used to distinguish the quantities of potential charging profits of idle MCSs, and the sizes of coloured areas are used to indicate the possibility of quasi-IEVs passing through these positions. In this paper, we propose a Profit-Maximizing Assignment Strategy of Idle MCSs (PMASIM) to properly assign the idle MCSs to charge IEVs at selected charging positions, or track some quasi-IEVs according to the profit-maximizing heat maps. Extensive simulations and comparisons demonstrate the superior performance of PMASIM, i.e., with the profit-maximizing heat maps, the charging profits of MCSs are increased, and the proportion of charged IEVs is enhanced as well.

Wireless Power Transfer (WPT) technology has been developed rapidly in recent years. The cooperative charging model and corresponding scheduling methods have been proposed to save the charging cost in paid charging service. However, the state-of-the-art methods ignore the spatial occupation issue of rechargeable devices. Moreover, the cooperative charging scheduling in directional wireless charging has not been studied yet. This paper studies the cooperative scheduling for directional wireless charging with spatial occupation. We formulate the Cooperative Charging Scheduling with Spatial occupation (CCSS) problem of Mobile Rechargeable Sensor Devices (MRSDs) for optimizing the total cost of whole charging system. We first investigate the properties of optimal arrangement of MRSDs in charging group and calculate the tight intervals of charging angles of MRSDs. We show that it is sufficient to bound the error by conducting angle discretization for only two MRSDs in each charging group. Then, a$(\ln n+1)(1+\varepsilon)$-approximation algorithm of the CCSS problem is proposed based on greedy approach, where$n$is the number of MRSDs, and$\varepsilon$is the discretization error. The results of extensive simulations and field experiments demonstrate that our algorithm can reduce at most 42.5% total cost comparing with the benchmark algorithms.

Wireless Power Transmission (WPT) has been widely used to replenish energy for wireless rechargeable sensor networks. This paper concerns the fundamental issue of robust fault-tolerant placement of wireless chargers for directional charging. Following the general directional charging model, we formulate theCharger Placement for Robust Coverage (CPRC)problem, which has continuous and infinite constraints, for resisting the wireless charger failure. We transform the problem to the equivalent integer program problem without performance loss by area partition and dominating strategy extraction. We show that the greedy algorithm achieves the logarithmic approximation ratio. We further formulate theCharger Placement for Robust Utility (CPRU)problem for resisting the sensor node failure. This problem also has continuous and infinite constraints. We transform the problem to the combinational optimization problem with finite strategy space through the techniques of charging power approximation, area discretization and dominating strategy extraction. We present the algorithm, which utilizes the combination of binary search and greedy algorithm, to solve theCPRUproblem. We conduct both simulations and field experiments to validate our theoretical results. The simulation results show that the proposed algorithms forCPRCandCPRUcan outperform comparison algorithms by at least 17.48% and 21.15%, respectively.

Due to the low cost and agile service provision, mobile charging stations (MCSs) have been deployed to complement fixed charging stations (FCSs). In the Internet of Electric Vehicles (IoEV) with MCSs, a major concern is to enhance the charging efficiency of MCSs. The charging efficiency of MCSs can be improved by prolonging the charging durations of MCSs, i.e., MCSs should undertake the charging tasks as more as possible, which can increase the charging profits of MCSs and reduce the charging expenses of IEVs (EVs with insufficient electricity). Besides, EVs and MCSs are selfish in terms of charging expenses and charging profits, respectively. In this article, we propose a Bayesian game based Bidding Scheme for Mobile Charging enabled Electric Vehicles (BBS-MCEV). In BBS-MCEV, each IEV first calculates the maximum charging price (MCP) according to the potential expense if charged by nearby FCSs, and then the optimal charging price (OCP) is determined by the Bayesian game model. Each MCS accepts the charging request with the largest charging profit. Extensive simulations and comparisons demonstrate the superior performance of our proposed BBS-MCEV, i.e., with the Bayesian game model, IEVs can rationally bid for the mobile charging services from MCSs, and thus BBS-MCEV can increase the charging profits of MCSs and reduce the charging expenses of IEVs effectively. Besides, a proper tradeoff between the charging profits of MCSs and the charging expenses of IEVs can be achieved.

The multi-hop wireless charging technology can largely extend the charging service range of chargers, thus has promising prospect in sustainable energy replenishment for wireless rechargeable sensor network. This paper proposes a new cost criterion, termed comprehensive cost consisting of energy cost and deployment cost, to measure the actual expenditure of wireless charging. We present a multi-hop wireless charging model and formulate the problem of minimizing the comprehensive cost such that the energy demand of all sensor nodes can be fulfilled by the energy capacitated chargers. We propose a (ln n+1)-approximation algorithm for the optimization problem, where n is the number of sensor nodes. Then, we propose a straightforward cost sharing mechanism, which ensures that no subset of sensor nodes can benefit by breaking away from the current charging tree for any fixed charger position, to realize the paid charging service of multi-hop wireless charging. Furthermore, to keep the magnetic fields of transmitters from the interfering, the conflict avoidance schemes are proposed in both central and distributed situations. Finally, we discuss the distributed scheme for minimizing the comprehensive cost without support of central server. Through extensive simulations, we demonstrate the significant superiority of the proposed algorithms in terms of comprehensive cost.

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem for the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. We present three system models for two-tiered social crowdsourcing system based on the arrival modes of registered users and social neighbors: offline model, semi-online model, and full-online model. We consider the tasks are associated with different end times. We present an incentive mechanism for each of three system models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness. We further show that our incentive mechanisms for semi-online model and full-online model can obtain averagely 51.1$\%$and 39.7$\%$value of approximate optimal untruthful offline algorithm, respectively.

The multi-hop wireless charging technology has attracted a lot of attention, as it largely extends the charging range of chargers. Different from the existing work with single cost optimization, the objective of this article is to optimize the comprehensive cost, which is the combination of energy cost and deployment cost. We decompose the target problem into two sub-problems. The first sub-problem aims to minimize the deployment cost with energy capacity constraints. The proposed algorithm follows the greedy strategy, where the subset of sensor nodes for any charger is determined by finding the capacitated minimum spanning tree. The second sub-problem, which aims to maximize the reduction of comprehensive cost by adding chargers to the solution of the first sub-problem, is proved to be an unconstrained submodular set function maximization problem and can be solved by a 1/2-approximation randomized linear time algorithm for its equivalent problem. Through extensive simulations, we demonstrate that the proposed solution can reduce the comprehensive cost by 57.55% comparing with the benchmark algorithms.

Task graphs are widely used to represent data-intensive applications. To efficiently execute these applications on heterogeneous systems, each task must be properly scheduled on the processors of the system. The NP-completeness of the task scheduling problem has motivated researchers to propose various heuristic methods. Recently, Quality of Service (QoS) aware scheduling is becoming an active research area in heterogeneous systems because the end-user has different QoS requirements. Generally, time and cost are the most relevant user concerns. However, it is challenging to find a feasible scheduling plan which minimizes the total execution time of the user’s application (makespan) while satisfying both budget and deadline constraints. In this paper, we present a novel heuristic algorithm called Budget-Deadline-Aware-Scheduling (BUDA) that addresses task graphs scheduling under budget and deadline constraints in heterogeneous systems. The novelty of the BUDA algorithm is based on a Heterogeneous Time-Cost Matrix (HTCM) that is used to prioritize tasks and for processor selection. In addition, we introduce a new Heterogeneous Time-Cost Trade-off factor (HTCT) that tries to adjust the time and cost for the current task among all processors. The experiments based on randomly generated graphs and real-world applications graphs show that the BUDA algorithm outperforms the state-of-the-art algorithms in terms of makespan, time efficiency, and success rate.

Abstract Opportunistic underwater sensor network (OUSN) is deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. Particularly, the OUSN in military applications may be invaded by some underwater spy‐robots termed eavesdroppers. The eavesdroppers could move around some OUSN nodes and eavesdrop on their communication channels silently, and these eavesdropping actions are difficult to be perceived by OUSN nodes. To reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages, we conceive the idea that each data message is encoded into several message pieces, and then the message pieces are disseminated to sink node individually. Besides, a lightweight encryption method is adopted to encrypt the message pieces before the dissemination. Such mechanism can protect the data messages from being stolen by eavesdroppers effectively. In this article, we propose a message piece dissemination approach (MPDA) for the OUSN invaded by some underwater spy‐robots. In MPDA, OUSN nodes disseminate the held message pieces to some selected neighboring nodes at each time slot, and a data message is considered to be delivered when all pieces of this data message have been delivered to the sink node. Extensive simulations and comparisons demonstrate the preferable performance of MPDA, that is, MPDA can reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages.

Opportunistic Underwater Sensor Networks (OUSNs) are deployed for various underwater applications, such as underwater creature tracking and tactical surveillance. In an OUSN invaded by some eavesdroppers, the data messages disseminated by sensor nodes are probably stolen (captured and cracked) by the eavesdroppers. The data messages are disseminated through acoustic waves which could be altered by the environmental noises, i.e., the acoustic waves containing data messages could be superimposed by the environmental noises. To protect the data messages from being stolen by eavesdroppers and guarantee the required delivery ratio of data messages, we propose a Noise-based-protection Message Dissemination Method (NMDM). In NMDM, the acoustic waves containing data messages are superposed by the environmental noises and converted into some pseudo data messages. The environmental noises around source nodes are identified, encoded, and encrypted into some noise messages. Then, the pseudo data messages and noise messages are individually disseminated to the sink node. Such mechanism makes the eavesdroppers difficult to steal the data messages. Besides, the required delivery ratio of data messages is achieved by measuring the similarities between the nodes and the sink node, i.e., the pseudo data messages and noise messages are preferentially disseminated to the nodes with larger similarities to the sink node. Finally, simulation results demonstrate the superior performance of NMDM. NMDM can reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages effectively.

The success of online shopping accelerates the development of express delivery business with economic and efficient service. Current express delivery systems usually deliver the packages in noncooperation mode and cannot jointly optimize the express fee and moving cost of users. This paper proposes the cooperative package assignment system by lumping packages at the same express station to share the express fee, and proposes a novel pricing structure to stimulate the express stations to join the system without revenue loss by introducing cooperation cost. We formulate thecooperative package assignment (CPA)problem with heterogeneous express stations for joint optimization of users’ express fee and moving cost. Then, an approximate algorithm,CPAA, is proposed for theCPAproblem based on the greedy approach using submodular function minimization. We show that the designed algorithm achieves computational efficiency and guaranteed approximation. Furthermore, we model the large-scaleCPAproblem asCPA-gameand present a game theoretic algorithm,CPAGA. We show thatCPA-gamehas at least oneNash Equilibrium, andCPAGAfinally converges to a pureNash Equilibrium. Through extensive simulations, we demonstrate thatCPAAandCPAGAshow great advantages in terms of comprehensive cost, which is 28.1% and 19.9% lower than that in noncooperation mode on average, respectively. Moreover,CPAGAshows great scalability and is more suitable for large-scale cooperative package assignment systems.

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively.

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot deal with the crowdsourcing with copiers, who copy a part (or all) of data from other workers. This article aims at designing crowdsourcing incentive mechanism for truth discovery of textual answers with copiers. We formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery and design an three-stage incentive mechanism. In contextual embedding and clustering stage, we construct and cluster the content vector representations of textual crowdsourced answers at the semantic level. In truth discovery stage, we estimate the truth for each task based on the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery methods show prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.

Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. The data dissemination in OUSNs differs significantly from those in terrestrial wireless sensor networks or delay-tolerant networks, due to the signal irregularity in underwater communications and the limited storage capacity of the nodes in OUSNs. To alleviate the storage overflows on nodes and make room for the newly arriving data packets, some stored data packets ought to be actively discarded by nodes. This research begins with the construction of a differential equation set to describe the propagation process of data packets in OUSNs, and the storing-discarding equilibrium is investigated such that each data packet is expected to propagate and disappear during the allowable dissemination time slots. After that, the optimal storing probabilities and discarding probabilities are obtained for the nodes with different in-degrees to maximize the delivery ratio of data packets. Then, we propose an Adaptive Data Dissemination Algorithm (ADDA) for the storage-limited OUSNs with signal irregularity, where at each time slot the newly arriving data packets are stored and the stored data packets are discarded by nodes according to the obtained storing probabilities and discarding probabilities, respectively. Simulation results demonstrate the excellent performance of ADDA, showing that it can enhance the delivery ratio of data packets and reduce the number of storage overflows.

Wireless Power Transmission (WPT) has been widely used to replenish energy for Wireless Rechargeable Sensor Networks. However, the charging service model, which is of the essence to commercial WPT, has not emerged so far. In this paper, we present a wireless charging service model from the perspective of cooperative charging economics, and formulate the Cooperative Charging Scheduling (CCS) problem for joint optimization of rechargeable devices' charging cost and moving cost. We first propose two intragroup cost sharing schemes to sustain the cooperation among devices. Then, the approximation algorithm CCSA of the CCS problem is proposed based on greedy approach and submodular function minimization. Furthermore, we model the large-scale CCS problem as a coalition formation game and present a game theoretic algorithm CCSGA. We show that CCSGA finally converges to a pure Nash Equilibrium. We conduct simulations, and field experiments on a testbed consisting of 5 chargers and 8 rechargeable sensor nodes. The results show that the average comprehensive cost of CCSA is 27.3% lower than the noncooperation algorithm and is only 7.3% higher than the optimal solution on average. In field experiments, CCSA outperforms the noncooperation algorithm by 42.9% in terms of comprehensive cost on average. Moreover, CCSGA is much faster than the approximation algorithm and is more suitable for large-scale cooperative charging scheduling.

In recent years, mobile crowdsensing has become an effective method for large-scale data collection. Incentive mechanism is fundamentally important for mobile crowdsensing systems. Many mobile crowdsensing systems expect to optimize multiple objectives simultaneously. Most of the existing works transform the multiobjective problem into a single objective problem through constraints or scalarization method. However, due to the uncertain importance (weights) of objectives and the instable quality of crowdsensed data, such transformation is usually unrealizable. In this article, we aim to optimize the worst performance of two objective functions in mobile crowdsensing in order to improve the system robustness. We model an auction-based biobjective robust mobile crowdsensing system, and design two independent objective functions to maximize the expected profit and coverage, respectively. We formulate the robust user selection (RUS) problem, and design an incentive mechanism, which utilizes the combination of binary search and greedy algorithm, to solve the RUS problem. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed incentive mechanisms satisfy desirable properties of computational efficiency, individual rationality, truthfulness, and constant approximation to the tightened RUS problem. Moreover, the proposed incentive mechanism can be easily extended to multiobjective robust mobile crowdsensing systems, and all desirable properties still hold. The simulation results reveal that our incentive mechanism achieves 11% improvement of the platform’s utility, compared with the greedy algorithm for biobjective mobile crowdsensing systems on average.

The development of Electric Vehicle (EV) helps to ease energy crises and deduce vehicle exhaust emissions. However, it also brings a great impact on both transportation networks and power grids. There are some serious impediments in terms of energy charging to the popularization of EV, such as high deployment cost of charging stations, low charging efficiency, and voltage deviation of power grid. To address these issues, we design a new EV charging system, which levers the bus network in urban areas through the integration of OnLine Electric Vehicle (OLEV) system and Microwave Power Transfer (MPT) system. We formulate the EV route scheduling problem based on this new charging system to maximize the total residual energy subject to all EVs can arrive to their destinations before deadlines. Then, we propose an approximation algorithm, RSA, to solve the route scheduling problem. To relieve the traffic congestion, we further formulate the conflict-free EV route scheduling problem, and use the matching based algorithm, FRSA, to find the EV route schedules with the maximal residual energy. Through the extensive simulations, we demonstrate that RSA and FRSA can increase the average residual energy by 67.66% and 50.36% compared with the solution without the designed wireless charging system, respectively. Moreover, RSA reduces 22.22% of travel time and outputs 77.23% of residual energy, and FRSA can obtain 83.51% residual energy with 3.62% of extra travel time of the corresponding optimal solutions on average, respectively.

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

The Internet of Things has such a profound impact that we have witnessed crowdsensing has emerged as the most popular sensing paradigm where participants sense and aggregate data to the platform by smart devices. However, the participants may not be willing to involve in data sensing and aggregation if they are not sufficiently compensated or their personalized private information are disclosed. In order to overcome the above issues, this article proposes a payment-privacy protection level (PPL) game, where each participant submits his sensing data with a specified PPL while the platform chooses a corresponding payment to the participant. Additionally, we derive the Nash equilibrium point of the game. Considering that the payment-PPL model is unknown in practice, we employ a reinforcement learning technique, i.e., Q-learning to obtain the payment-PPL strategy in a dynamic payment-PPL game. We further use the deep Q network (DQN), which combines a deep-learning technique with Q-learning to accelerate the learning speed. Through extensive simulations, we verify that our proposed algorithm using DQN achieves superior performance in terms of utilities of both platform and participants and data aggregation accuracy compared with the one using Q-learning.

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques, e.g., k-anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attention, provable good algorithms considering the tradeoff between location privacy and location information quality from the optimization perspective in crowdsensing are lacking in the literature. In this article, we study two related optimization problems from two different perspectives. The first problem is to minimize the location quality degradation caused by the protection of users' location privacy. We present an efficient optimal algorithm OLoQ for this problem. The second problem is to maximize the number of protected users, subject to a location quality degradation constraint. To satisfy the different requirements of the platform, we consider two cases for this problem: 1) overlapping and 2) nonoverlapping perturbations. For the former case, we give an efficient optimal algorithm OPUMO. For the latter case, we first prove its NP-hardness. We then design a (1-E)-approximation algorithm NPUMNand a fast and effective heuristic algorithm HPUMN. Extensive simulations demonstrate that OLoQ, OPUMO, and HPUMNsignificantly outperform an existing algorithm.

Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them takes into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We study two bid models and formulate the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships. We design two incentive mechanisms, MCT - M and MCT - S, for the compatibility cases. Both of MCT - M and MCT - S consist of two steps: compatible user grouping and reverse auction. We further present a user grouping method through neural network model and clustering algorithm. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, and truthfulness. Moreover, MCT - M can output the optimal solution. By using neural network and clustering algorithm for user grouping, the proposed incentive mechanisms can reduce the social cost and overpayment ratio further with less grouping time.

The D2D (Device-to-Device) communication has been very popular as it is a promising and low-cost solution to reduce the burden on the cellular network. However, there are rare concerns about the distribution and pairing of DUEs(D2D user equipments), which have a significant impact on QoS (Quality of Service) of D2D communication. In this paper, we propose a novel algorithm based on the coalitional game to optimally adjust the distribution of DUEs. The proposed algorithm aims to form the optimal coalition structure, which achieves a balance between the throughput and power consumption of each coalition, obtaining the enhanced QoS of D2D. We show that our algorithm is superior to the benchmark models in terms of the throughput and energy efficiency of the DUE coalition. To further improve the QoS, we also propose a method to predict and maximize the pairing probability of DUEs. The proposed prediction method adopts the Logistic Regression to model the global pairing probability according to the communication parameters of DUEs. Experimental results show that the proposed prediction method is significantly superior to the benchmark methods in terms of prediction accuracy. In addition, the pairing probability maximization algorithm proposed also significantly improves the pairing probability.

Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to dealing with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting of truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.

Mobile crowdsensing emerges as a new paradigm that takes advantage of pervasive sensor-embedded smartphones to collect sensory data. Many incentive mechanisms for mobile crowdsensing have been proposed. However, none of them takes into consideration the spatio-temporal tasks in mobile crowdsensing systems, where the sensing areas of tasks can have overlaps, and the collective sensing time for each task needs to meet the specified time duration. In this paper, we present two system models for location sensitive users and location insensitive users, respectively, and formulate the social optimization problem for each model. We design two reverse auction based truthful incentive mechanisms to minimize the social cost subject to the constraint that each of the spatio-temporal tasks can be completed with its collective sensing time no less than a minimum sensing time required by the platform. Through both theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation.

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem in the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. In the two-tiered social crowdsourcing system, the tasks are associated with different end times, and both the registered users and their social neighbors have different arrival/departure times. An online incentive mechanism, MTSC, which consists of two steps: Agent Selection and Online Reverse Auction, is proposed for this novel mobile crowdsourcing system. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanism achieves computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness.

With the rapid growth of smartphones, mobile crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in the mobile crowdsensing applications and systems. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. In this paper, we design two frameworks for privacypreserving auction-based incentive mechanisms that also achieve approximate social cost minimization. In the former, each user submits a bid for a set of tasks it is willing to perform; in the latter, each user submits a bid for each task in its task set. Both frameworks select users based on platform-defined score functions. As examples, we propose two score functions, linear and log functions, to realize the two frameworks. We rigorously prove that both proposed frameworks achieve computational efficiency, individual rationality, truthfulness, differential privacy, and approximate social cost minimization. In addition, with log score function, the two frameworks are asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance of the two frameworks and demonstrate that our frameworks achieve bid-privacy preservation although sacrificing social cost.

Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them takes into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We consider that the mobile crowd sensing is launched in an online community. We study two bid models and formulated the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships. We design two reverse auction based incentive mechanisms, MCT-M and MCT-S. Both of them consist of two steps: compatible user grouping and reverse auction. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality and truthfulness. In addition, MCT-M can output the optimal solution.

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness.

Mobile crowdsensing can enable numerous attractive novel sensing applications due to the prominent advantages such as wide spatiotemporal coverage, low cost, good scalability, pervasive application scenarios, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate more potential smartphone users and to achieve good service quality. In this paper, we focus on exploring truthful incentive mechanisms for a novel and practical scenario where the tasks are time window dependent, and the platform has strong requirement of data integrity. We present a universal system model for this scenario based on reverse auction framework and formulate the problem as the Social Optimization User Selection (SOUS) problem. We design two incentive mechanisms, MST and MMT. In single time window case, we design an optimal algorithm based on dynamic programming to select users. Then we determine the payment for each user by VCG auction; while in multiple time window case, we show the general SOUS problem is NP-hard, and we design MMT based on greedy approach, which approximates the optimal solution within a factor of In|W| + 1, where |W| is the length of sensing time window defined by the platform. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.