City-wide package delivery becomes popular due to the dramatic rise of online shopping. In order to speed up the package delivery process without increasing the delivery cost, a promising system has been proposed, which leverages the crowdsourced taxis. Many efforts have been done on this novel system in recent literature. However, a fundamental problem still remains open, i.e., measuring the maximum capacity of taxi-based logistics at the urban scale. In this paper, we first propose an accurate and efficient measurement mechanism to tackle this problem in the Non-stop package delivery method. The basic idea is to construct a spatial-temporal graph according to the passenger demands and calculate the maximum urban capacity by combining the results of several carefully designed max-flow problems. Then, we expand our measurement mechanism to be used in other taxi-based package delivery methods after a few adaptations, including the One-hop method and the Stop-and-wait method. At last, we evaluate our measurement mechanism and compare the maximum urban capacity of various package delivery methods with a real-world dataset from an online taxi-taking platform.

City-wide package delivery has become popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gaussian distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95 percent on average during the daytime, and is at most 46.9 percent higher than those of the benchmarks.

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

A mobile crowdsensing (MCS) platform motivates to employ participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. Recently, the appearance of data reconstruction method makes it possible to improve the platform's profit with a limited amount of sensing results in Compressive MCS (CMCS). However, It is of great challenge to the maximal profit for the CMCS platform, since it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In response to such challenges, we propose two profit-driven online participant selection mechanisms for the given task model and participant model. In ProSC, the sub-profit in each slot is maximized during the sensing period of a task, by combing a statistical-based quality prediction method and a repetitive cross-validation algorithm. In ProSC+, we jointly optimize the number of required participants and their spatial distribution to further improve the converging property. Finally, we conduct comprehensive evaluations, the results indicate the effectiveness and efficiency of our mechanisms.

WiFi networks are vulnerable to rogue AP attacks in which an attacker sets up an imposter AP to lure mobile users to connect. The attacker can eavesdrop on the communication, severely threatening users' privacy. Existing rogue AP detection solutions are confined to some specific attack scenarios (e.g., by relaying the traffic to a target AP) or require additional hardware. In this paper, we propose a crowdsensing based approach, named CRAD, to detect rogue APs in camouflage without specialized hardware requirement. CRAD exploits the spatial correlation of RSS to identify a potential imposter, which should be at a different location from the legitimate one. The RSS measurements collected from the crowd facilitate a robust profile and minimize the inaccuracy effect of a single RSS value. As a result, CRAD can filter out abnormal samples sensed in the realtime by dynamically matching the profile. We evaluate our approach with both a public dataset and a real prototype. The results show that CRAD can yield 90% detection accuracy and precision with proper crowd presence, even when the rogue AP is launched close to the legitimate one (e.g., within 1m).

This paper discusses the problem of local path planning in a static-obstacle environment by designing a PSO-based receding horizon control approach. In order to avoid obstacles, a virtual robot is first designed and moves along the boundary of obstacles. Then, in the framework of receding horizon control, a cost function is proposed where the virtual robot and the target position are integrated, which implies that mobile robots are controlled to keep a security distance and velocity consensus with virtual robots, and to move toward the target position. Next, the proposed cost function with constraints is processed by a particle swarm optimization (PSO) algorithm such that the PSO-based receding horizon control approach is developed. By solving the proposed cost function, a control sequence is obtained and then the first control input is used to enable the robot toward the target and avoid obstacles. Finally, the performance capabilities of the PSO-based receding horizon control approach are illustrated by simulation results.

How to improve consensus conditions for double-integrator multi-agent systems is investigated. First, a consensus algorithm is presented and the corresponding convergence condition is described. However, the given convergence condition is conservative such that consensus is still obtained when the parameters of the consensus algorithm violate the given convergence condition. Second, to cope with this issue, a less conservative convergence condition is proposed by using complex number and matrix properties. Finally, simulation results illustrate the effectiveness of the convergence condition through two examples.

A T-S fuzzy control scheme is designed for unicycle robots. First, a Lagrange method is used to model unicycle robots. Second, based on the established model, a T-S fuzzy approach is used to linearize the dynamics model of unicycle robots. Then, a parallel distributed compensator (PDC) is proposed, which is obtained by the linear matrix inequality approach. Third, stability criterions on unicycle robots with the proposed control scheme are expressed through using LMI tools. Finally, the effectiveness of the proposed control scheme is illustrated for unicycle robots.

Mobile crowdsensing (MCS) is a new paradigm which takes advantage of pervasive mobile devices to collaboratively collect data and analyze physical phenomenon. As mobile devices are owned and controlled by individuals with various capabilities and intentions, a main challenge MCS applications face is to ensure the credibility of the crowd contributed data. Existed works attempt to increase confidence level of the sensory measurements by validating the location. However, the required infrastructure or neighbor support may not always be available, and the unreliable form containing false sensory data with a valid location is implicitly ignored. In this paper, we propose a novel Crowd-based Credibility Improving Scheme (CCIS) to improve the credibility of data in possible false forms leveraging crowd data property and crowd participants' reputation. Based on the data clusters generated using a lightweight fixed-width clustering algorithm, CCIS is able to adequately identify and filter out the clusters constituted mainly by false data using reputation information as the classifier. We conduct simulations on a publicly available trace with crowd contributed temperature measurements, the results show that CCIS yields an improvement of overall data credibility of around 1.2 with clustering accuracy over 96%.