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Recently, heterogeneous automated guided vehicles (AGVs) are widely deployed to improve the work efficiency of the logistics warehouse with limited space. While completing various tasks to improve efficiency and reduce transport costs, heterogeneous AGVs also bring new challenges to task planning in warehouses because of their different shapes, speeds, and capabilities. Briefly, it is difficult to efficiently calculate the costs for heterogeneous AGVs to complete tasks and assign heterogeneous AGVs suitable tasks in real time. Inspired by this, in this paper, we study the Task Planning for Heterogeneous AGVs (TPHA) problem, in which the warehouse assigns tasks to suitable heterogeneous AGVs to minimize the total cost containing the travel cost and the tardiness cost. To solve the TPHA problem, we propose a novel framework that considers the route planning and task assignment simultaneously. Within this framework, we first present an efficient cost computation algorithm to calculate costs between tasks and heterogeneous AGVs. Then, we present two task assignment algorithms to assign tasks to suitable heterogeneous AGVs. Finally, we propose an improved route planning algorithm for further improving the processing time and reducing the total cost. Compared with state-of-the-art, extensive experiments on both real and synthesized datasets examine that our proposed algorithms can save 91.83%~97.19% of processing time and reduce 29.08% of the total cost at most.

To implement encrypted data deduplication in a cloud storage system, users must encrypt files using special encryption algorithms (e.g., convergent encryption (CE)), which cannot provide strong protection. The confidential level of an outsourced file is determined by the user himself/herself subjectively or by the owner number of the file objectively. These files owned by a few users are considered strictly confidential and require strong protection. In this paper, we design, analyze and implement LSDedup, which attains a high storage efficiency while providing strictly confidential files (SCFiles) with strong protection. LSDedup allows cloud users to securely interact with cloud servers to check the confidential level of an outsourced file. Users encrypt the SCFiles using standard symmetric encryption algorithms to achieve a high security level, whereas encrypting the less confidential files (LSFiles) using CE such that cloud servers can perform deduplication. LSDedup is designed to prevent cloud servers reporting fake confidential level and a fake file user claiming the ownership of the file. Formal analysis is provided to justify its security. Besides, we implement an LSDedup prototype using Alibaba Cloud as backend storage. Our evaluations demonstrate that LSDedup can work with existing cloud service providers’ APIs and achieves modest performance overhead.

Mobile crowdsensing (MCS) has rapidly emerged as a popular paradigm for sensory data collection and benefited various location-based services and applications like road monitoring, smart transportation, and environmental monitoring. In practice, there often exist data-missing regions in the target sensing area, due to factors like limited budget, large area size, and scarcity of participants. This poses a demand for data recovery, which is commonly done based on the compressive sensing (CS) technique. However, CS-based data recovery requires access to sensory data tagged with locations, raising critical concerns on participants’ location privacy. While a plethora of location privacy techniques exist, most of them breach the data correlation inherently required by CS-based data recovery. Meanwhile, existing works mostly focus on protecting locations and overlook sensory data which may also indirectly lead to location leakages. In this paper, we propose SecDR, a new system design supporting secure, efficient, and accurate data recovery for location-based MCS applications. SecDR protects both locations and sensory data, and is built from a delicate synergy of CS-based data recovery and lightweight cryptography techniques. Extensive evaluations demonstrate that SecDR achieves promising performance and, even with stronger security guarantees, outperforms the state-of-the-art, with accuracy close to the plaintext domain.

There is a huge amount of information generated in online social networks, which is filled with a lot of rumors. The spread of a rumor often leads to the generation of a causal related rumor, and when users believe the first kind of rumor, the probability of being influenced by another causal related rumor is larger. Therefore, the influence probability will change with the process of rumor spreading. In this paper, we design the Causal Rumors Enhance Cascade ( $CREC$ ) model to describe the spreading process of causal related rumors. Then we attempt to select a set of seed users that minimizes the number of users expected to be influenced by rumors, which we call the Causal Related Rumors Controlling (CRRC) problem. The main challenges of this problem are that the influence probability is constantly changing during the spread process, so the reverse sampling technique cannot be used, and the greedy mechanism is not suitable for massive-scale datasets. For the sake of overcoming these challenges and solving the problem, we put forward the Degree Trigonometric Metrology (DTM) algorithm, which uses the property of three-directed circles in the directed network to select seed nodes. Finally, experiments on three massive-scale datasets show that our algorithm outperforms the other algorithms.