Discrete chaotic maps in the real number field have been widely investigated and applied to various applications. However, there has been limited focus on constructing discrete chaotic maps with complicated dynamics in the complex field. In light of this, this article proposes a 1-D complex-variable chaotic model (1-D-CCM), which can produce a multitude of 1-D complex-variable chaotic maps by combining unbounded analytic functions and locally bounded analytic functions. To illustrate the effectiveness of 1-D-CCM, we construct two 1-D complex-variable chaotic maps by combining inverse trigonometric functions and hyperbolic trigonometric functions. We provide theoretical proof of a new 1-D complex-variable chaotic map as one example to demonstrate that the generated chaotic maps satisfy the chaos definition in terms of Lyapunov exponent. Property analysis reveals distinct strange attractors and hyperchaotic behaviors for the two 1-D complex-variable chaotic maps. Performance evaluations show that the example maps of 1-D-CCM model can achieve a 0–1 test value of 1.0017, a$C_{0}$complexity of 0.7253, a correlation dimension of 2.0242, and a sample entropy of 0.8288. Experimental results demonstrate superior performance indicators compared to other representative chaotic maps. We construct a hardware platform using a microcontroller to implement the attractors of the two new complex-variable chaotic maps. Finally, we design pseudorandom number generators to demonstrate the potential applications of the two 1-D complex-variable chaotic maps.

Recently, heterogeneous mobile robots (HMRs) have been widely implemented in warehouses or factories with limited space to improve efficiency. HMRs have different capabilities and sizes, which leads to two bottlenecks in the task planning for HMRs in an online environment. One is that it is hard to efficiently find high-quality task assignment results between heterogeneous tasks and HMRs because heterogeneous tasks need to be completed by HMRs of different capabilities. The other is that finding collision-free paths for HMRs of different sizes to complete assigned tasks is hard to satisfy the real-time demand. Existing works mainly consider solving these two bottlenecks separately while ignoring the mutual influence between the results of task assignment and collision-free path finding. To solve these issues, in this paper, we formally define the Online Task Planning with Collision Avoidance for HMRs (OTCH) problem, in which the platform aims to find the best task assignment and collision-free paths for HMRs to complete as many heterogeneous tasks as possible while minimizing the total cost containing the path cost and the delay cost. To solve the OTCH problem, we propose a novel task planning framework that considers task assignment and collision-free path finding simultaneously. Within this framework, we first present an aggregation algorithm to accelerate calculations. Then, we propose a network-flow-based task assignment algorithm to find a high-quality task assignment between heterogeneous tasks and HMRs in each batch. Finally, we propose a novel and efficient path finding algorithm to find collision-free paths for HMRs to complete assigned tasks while satisfying real-time demand. In addition, we not only analyze the complexity of the OTCH problem in detail but also give theoretical analysis for our proposed algorithms. Extensive experiments in a real-world dataset and two simulated datasets show that our proposed algorithms outperform the state-of-the-art while having the best scalability

Cloud-Fog computing has emerged as an essential paradigm to support the growing demand for real-time data processing driven by the Internet of Things. By integrating the extensive computing capabilities of cloud data centres with the low-latency benefits of fog nodes, this architecture increases resource utilisation and improves quality of service. However, the dynamic and heterogeneous nature of cloud fog environments poses significant workflow scheduling challenges, especially when optimising multiple trade-offs such as latency, cost, energy consumption, and resource utilisation. This paper investigates the many-objective dynamic workflow scheduling problem under deadline constraints in container-based cloud-fog computing environments (MDWS-CoCF). Unlike existing studies that primarily focus on horizontal scaling, this work considers both vertical and horizontal scaling of containers, allowing for real-time adjustments of container configurations based on task-specific requirements. To address this complex problem, we first develop a dynamic workflow scheduling simulator that models real-world scenarios, including a variety of task categories and container scalability. Based on this simulator, we propose a Cooperative Coevolution Genetic Programming (CCGP) approach that evolves specialised heuristics for task selection, resource allocation, and container deployment to facilitate adaptive and efficient scheduling in MDWS-CoCF. Extensive simulations using real-world data traces show that the proposed CCGP approach significantly outperforms existing baseline algorithms, achieving superior performance as measured by the HyperVolume and Inverted Generational Distance metrics. The results show that the evolved heuristics are robust and effective under different dynamic scenarios, ensuring balanced optimisation of many objectives.

The s -bundle, as a cohesive subgraph model which relaxes the clique, remains connected whenever fewer than n-s vertices are removed, where n is the number of vertices inside. Finding the largest s -bundle is a fundamental problem and has diverse applications in various fields such as social network analysis, graph visualization, and bioinformatics. Existing studies for solving the problem follow the same branch-and-bound framework and improve the efficiency by developing pruning techniques. As a result, all share the same worst-case time complexity of O* (2 n ), where O* suppresses the polynomial factors. In this paper, we propose a new branch-and-bound algorithm, called SymBD, which achieves improved theoretical guarantees and practical performance. It adopts the existing Symmetric-BK branching strategy whose performance highly depends on the ordering of vertices. We explore various vertex orderings for improving the performance. In particular, we propose two novel vertex orderings based on the local vertex connectivity. With the proposed vertex orderings, SymBD improves the worst-case time complexity to O* (λ n s ) where λ s is strictly less than 2. To further boost the practical efficiency, we introduce a heuristic algorithm for computing a large initial solution and a divide-and-conquer strategy. Extensive experiments on 664 graphs demonstrate that our algorithm is up to five orders of magnitude faster than existing solutions.

Community search has widespread applications in areas such as advertising, friend recommendation, and protein complex identification. In this paper, we revisit the Size-bounded Community Search (SCS) problem, which aims to identify a connected subgraph containing a query vertex q and between l and h vertices, while maximizing the minimum degree of the subgraph. Existing state-of-the-art exact solutions for SCS face challenges in practical efficiency due to ineffective strategies for searching candidate solutions and suboptimal method for finding optimal solution. To address these challenges, we propose a novel branch-and-bound algorithm that efficiently locating a subset of candidate solutions with favorable structural properties, forming the basis for three progressively refined frameworks to determine the optimal solution. Furthermore, we enhance practical performance through a new heuristic, two reduction rules, and a query decomposition technique. Extensive experiments on 12 large real-world graphs demonstrate that our most efficient framework significantly outperforms state-of-the-art methods, achieving an average speedup of two orders of magnitude while consistently identifying communities with higher cohesion.

Deterministic concurrency control (DCC) guarantees that the same input transactions produce the same serializable result. It offers benefits in both distributed databases and blockchain systems. Dozens of DCC algorithms have emerged in the past decade. However, there is a lack of comprehensive evaluations for them. To study the performance of existing DCC algorithms and discover further opportunities, we make the following contributions: First, we abstract five essential features from the existing DCC algorithms—generality, speculative mechanism, version strategy, batch strategy, and concurrency mode. Each distinct combination of these features corresponds to a specific algorithm. Second, we implement 13 DCC algorithms and conduct evaluations focused on their features by using 10 workloads, to conclude each feature’s strengths and weaknesses. Third, based on our feature analysis, we discover opportunities for improvement in two existing DCC algorithms, resulting in performance boosts of up to 2.3× and 3.4×.

The$k$-vertex connected ($k$-VC) subgraph, which remains connected with fewer than$k$vertices being removed, is an essential structure in graph mining. It has found many applications, such as survivable network design and web search optimization. However, existing studies focus on mining maximal$k$-VCs, which are excessively large yet less cohesive in real applications. In this paper, we study theminimum$k$-VC search (MinVC)problem, seeking to find a$k$-VC with the minimum number of vertices. We formally prove that this problem is NP-hard and then propose two algorithms to obtain the exact solution. The basic method, calledEnum, follows a branch-and-bound framework with some pruning rules, which directly enumerates all possible vertex sets. Nonetheless, it suffers from the efficiency issues due to the non-hereditary property of the$k$-VC model. To address this challenge, we propose an advanced method, calledVCtoB, which divides theMinVCproblem into several new sub-problems, called thefixed-size$k$-VCproblems. Each of them can be solved efficiently by exploiting the hereditary property of the$s$-bundle model. Finally, our empirical experiments on 139 real-world networks demonstrate thatVCtoBachieves performance improvement of up to six orders of magnitude over the baseline.

Deterministic concurrency control (DCC) can guarantee consistent execution results with the same input transactions, which can reduce the synchronization overhead when applied to a multi-replica distributed database. To achieve determinism, early DCC protocols require priori knowledge about execution details of transactions, such as read/write sets, for prescheduling. State-of-the-art DCC protocols like Aria and FGA address this impractical limitation. However, they struggle with performance degradation due to high abort rate under skewed and write-intensive workloads. In this paper, we present Dmucca, a practical DCC protocol that maintains ideal performance across varying transaction workloads without requiring any priori knowledge about them. Dmucca utilizes re-execution strategy to commit nearly all transactions after their second execution. To reduce the cost of re-execution, Dmucca incorporates a multiversion cache, inter-batch pipeline parallel processing, as well as a novel reordering schema based on weight prediction. Finally we evaluate Dmucca as well as other state-of-the-art DCC protocols with the two popular benchmarks, YCSB and TPC-C. And the experimental results show that Dmucca has the optimal performance in most scenarios.

Deterministic concurrency control is able to avoid the expensive two-phase commit in distributed databases and can solve the single-thread bottleneck of transaction processing in blockchain systems. Most existing deterministic concurrency control protocols rely on prior knowledge of the transaction’s read-write set, which is impractical in most cases. The state-of-the-art deterministic concurrency control protocols Aria and DOCC break this limitation. However, they do not perform well in multi-node and multi-core scalability. To solve the scalability issues, we propose Dodo, a novel deterministic concurrency control protocol. Dodo processes transactions in multi-batches, and each batch is divided into three phases. In the first phase, transactions are executed as read committed and staged. In the second phase, transactions are validated for the read-write conflict. In the third phase, only the continuous un-conflicted transactions at the head of the batch can be committed. For the aborted transaction that will be rerun in the next batch, we utilize its write set in the previous execution to reduce the read-write conflicts in the next batch. In this way, Dodo has the following benefits. First, Dodo does not rely on the prior knowledge of the read-write set. Second, Dodo commits transactions in pre-determined orders (TIDs) providing high multi-node scalability. Third, Dodo runs transactions in each phase concurrently, and the aborted transactions are re-executed in a conflict-less manner, enabling high multi-core scalability. Besides, we propose two optimism-based improvements, lazy decision and early-write visibility, to reduce aborts and blocking. Our evaluation shows that Dodo outperforms Aria and DOCC by up to 16.5x and 8.0x in a single-node setting and scales well in a multi-node setting.

For modern Intelligent Transportation System (ITS), data missing during traffic raster acquisition can be inevitable because of the loop detector malfunction or signal interference. Nevertheless, missing data imputation is meaningful due to the periodic spatio-temporal characteristics and individual randomness of traffic raster data. In this paper, traffic raster data collected from all spatial regions at each time interval are considered as a multiple channel image. Accordingly, the traffic raster data over a period of time can be regarded as video, on which an unsupervised generative neural network called MSST-VAE (Multiple Streams Spatial Temporal-VAE) is proposed for traffic raster data imputation, and this model can even robustly performs at varied missing rates while many other approaches fail to conduct. Two major innovations can be summarized in MSSTVAE: Firstly, it uses multiple periodic streams of Variational Auto-Encoders (VAEs) with Sylvester Normalizing Flows (SNFs), which shows strong generalization ability. Secondly, after the traffic raster data are transferred into videos, an ECB (Extraction-and-Calibration Block) consisting of dilated P3D gated convolution and multi-horizon attention mechanism is employed to learn global-local-granularity spatial features and long-short-term temporal features. Extensive experiments on three real traffic flow datasets validate that MSST-VAE outperforms other classical traffic imputation models with the least imputation error.

Clustering routing is one of the most prevailing approaches for saving energy in Wireless Sensor Networks (WSNs). However, many existing clustering protocols have the problem of node premature death. This is primarily due to the frequent selection of some advantageous nodes as cluster heads (CHs), which bear the responsibility of data aggregation and forwarding, resulting in higher energy consumption. To address this problem, this paper proposes a dual-CH clustering routing model and introduces an interval-based CH reelection mechanism to reduce the energy consumption associated with frequent cluster formation. In addition, a comprehensive consideration of residual energy and base station distance in CH election facilitates the design of an improved hybrid swarm intelligence optimization algorithm termed GWOA-CH, which combines Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) to obtain a more effective CH election scheme. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted between GWOA-CH and some state-of-the-art WSNs routing protocols, including ModifyGA, NCOGA, ARSH-FATICHS and MMROR. Experimental results demonstrate the superior energy saving capabilities of the proposed clustering protocol compared to its alternatives.

With the increasing prevalence of network cloud storage, an escalating number of users are choosing to entrust their data to the cloud. To guarantee remote data integrity and mitigate irreversible loss in case of a single point of failure, numerous multi-cloud public auditing schemes have been proposed. However, most existing studies primarily focus on storage architectures with multiple copies. In practice, users are required to distribute identical data replicas individually across multiple cloud servers (CSs), resulting in significant communication overhead and substantial consumption of storage resources on these servers. Moreover, there is a lack of secure public auditing schemes that effectively address both fault localization and data recovery challenges. To address these issues and enhance storage data reliability, this paper proposes an identity-based integrity auditing and data recovery scheme with fault localization for multi-cloud storage (hereafter referred to as IB-IADR). Specifically, we design a novel identity-based homomorphic signature to facilitate a lightweight auditing challenge-verification process. Our scheme ensures the uniform distribution of encoded data while minimizing data redundancy across multiple CSs. Additionally, IB-IADR provides robust data recovery capabilities and supports fast and accurate fault localization features, including entity position, file position and data block position. We demonstrate that our scheme is provably secure against forgery attacks on response auditing proofs, based on the hardness assumption of the standard CDH problem and DDH problem. We evaluate the proposed scheme’s performance to demonstrate its utility in multi-cloud storage environments.

Compared to conventional wireless body area networks (WBANs), the amount of data processed and the analytical capabilities offered by cloud-based WBANs are significantly more extensive. Nevertheless, the paramount consideration in such contexts remains the security and privacy ramifications. Concurrently, the process where medical cloud server (MCS) computes the response aggregation data may be opaque and there is a risk that (partially) invalid aggregation results may be presented to the task requester, either intentionally (e.g., malicious or cost-saving) or unintentionally (e.g., corruption or processing error). Furthermore, with the different roles played by each data requester, relying solely on a single data aggregation type is no longer sufficient to satisfy the diverse data aggregation requests from these requesters. To this end, this paper proposes a novel verifiable multi-dimensional encrypted medical data aggregation scheme (VMEMDA) for cloud-based WBANs, where we integrate an extended super-increasing sequence with a modified Paillier cryptosystem. Doing so allows us to ensure that each dimensional medical data collected by wireless sensor devices and corresponding square values can be encrypted into a single ciphertext with the chronological time series. This enables MCS to select various aggregation types, such as spatial/temporal data aggregation, to aggregate the multi-source encrypted medical data into a single ciphertext. Then the task requester can conduct diverse privacy-preserving statistical analyses, including sum, average, and variance. Moreover, we utilize a homomorphic hash function to guarantee the encrypted data integrity in a highly efficient way, and we design an unpredictable random sequence and integrate it into the provable data possession mechanism to achieve aggregated data correctness guarantee. Performance evaluation demonstrates that VMEMDA exhibits considerably lower computation and communication overhead compared to other existing multi-dimensional data aggregation schemes.

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.

Designing chaotic maps and fractal maps with rich dynamics in the complex field presents an interesting and challenging research topic. In this paper, we propose a novel approach called the one-dimensional multi-valued model (1D-MVM) for generating 1D complex chaotic and fractal maps by levering both single-valued and multi-valued functions. To demonstrate the effectiveness of the 1D-MVM, we present two 1D complex chaotic maps and one 1D fractal map as specific examples. Theoretical analysis confirms that the chaotic maps generated by the 1D-MVM exhibit chaotic behavior, while property analysis reveals that these chaotic maps possess hyperchaotic strange attractors, with their associated Lyapunov exponents being determined by specific system parameters. We also conduct extensive experiments to demonstrate the intricate dynamics and high performance indicators of the newly generated complex chaotic maps. A hardware platform is developed and the principal value attractors of these chaotic maps are experimentally captured. In addition, we explore the application of the hyperchaotic sequences generated by these complex chaotic maps to the pseudo-random number generators. Rigorous testing results validate the high degree of randomness exhibited by the generated pseudo-random numbers. Finally, we leverage the second branch of the 1D fractal map to generate a diverse range of fractal structures, further demonstrating the versatility and potential applications of the proposed approach.

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.

Cloud computing has been a research focus in both academic and industrial communities for decades. Along with this trend, Searchable Encryption (SE) technology emerged and developed as data privacy concerns increased. Many schemes are proposed to solve the privacy-preserving data-sharing problem in multi-user scenarios. Most existing solutions are based on the assumption that all users are trusted. However, there will be cross-user leakage when there are malicious or compromised ones. This is because of the inherent linkability of authorization information and the search result when multiple users request data from the same database. To this end, we propose a cross-user leakage mitigation scheme for authorized encrypted data sharing in a two-server model. We utilize a blinding factor to delink authorizations based on Symmetric Multi-Key Searchable Encryption (SMKSE). To break the linkability of query results, we combine the zero-sum garbled Bloom filter with the oblivious transfer technique, where each of the two servers can only know partial information. We devise a group-based Bloom filter structure in indices to improve efficiency. We perform formal security analysis and also demonstrate the efficiency through comparative experiments.

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 theTask 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%$\sim$97.19% of processing time and reduce 29.08% of the total cost at most.

In virtual machine (VM) deployment, the physical machine (PM) usually first retrieves VM image files from the central image server through block transfer, and the VM image retrieval and communication are the two main factors that consume network bandwidth resources, In this paper, we propose a heuristic-based algorithm to minimize both image retrieval cost and communication cost for VM placement in a fat-tree network. It consists of three phases: PM clustering, VM partitioning, 1) We first cluster the PMs based on the possible longest communication distance, which is estimated by a pre 2) To reduce the traffic between PM clusters, a semidefinite programming algorithm is used to place the coarsened VMs to PM Here coarsening means packing the resources of smaller VMs as a whole, so as to accelerate the solving process. 3) In each PM cluster, the VMs are mapped to PMs one by one, and the VMs with common blocks and communication traffic between each other are more likely to be placed together. Extensive simulations show that our algorithm is more effective and efficient than the state-of-the-art.

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.

Multi-cloud is a promising paradigm due to its advantages such as avoiding vendor lock-in and optimising costs. This article focuses on dynamic flexible workflow scheduling with minimum total monetary cost in multi-clouds, considering multiple categories of services for each cloud with different configurations and billing methods. Existing studies generally ignore the characteristics and states of each individual cloud when making schedules, which may be ineffective regarding cost savings and quality of service. To address this issue, we propose to introduce a cloud selection decision on top of the existing task selection and resource selection decisions to help us select appropriate resource for task in an overall cost-effective cloud. To automatically learn the task, cloud and resource selection rules simultaneously, we propose a new genetic programming with multi-tree representation based on a customised discrete event-driven dynamic workflow scheduling simulator. Simulation results based on two real-world data traces show that the proposed algorithm performs significantly better than the state-of-the-art algorithms in terms of reducing the rental costs and deadline deviation, and improving the success rate. The results also show that the superiority of the proposed algorithm lies in the ability to select an appropriate cloud resource for a task.

Smart grid is widely deployed in the modern smart city. However, the frequent network attacks on smart grid today have raised concerns about the privacy and security of individual fine-grained information collection. Moreover, once the secret key held by the data control center is disclosed, any adversary could easily break the indistinguishability of ciphertexts, thereby revealing users privacy. To this end, we propose an enabling efficient multidimensional encrypted data aggregation scheme for fog-cloud-based smart grid by using the improved Boneh-Goh-Nissim cryptosystem and the super-increasing vector technique, which can realize efficient multi-source multidimensional encrypted data aggregation in the fog-cloud architecture. The security analysis demonstrates that our scheme can satisfy security requirements of smart grid, i.e. it achieves the usage data privacy protection and confidentiality, multidimensional aggregation data integrity, and key-disclosure resistance. The performance evaluations show that our scheme is more efficient than related data aggregation schemes.

With the prevalence of credit cards worldwide, credit card fraud detection becomes increasingly meaningful and necessary for ensuring financial security. However, approaches that directly mine fraud patterns are not practical due to the lack of fraud transaction samples for training and the unpredictability of fraud behavior. Meanwhile, most existing works, lacking the ability to model the complex spatio-temporal dependencies in transaction sequences, fail to show satisfactory detection performance. To solve the problems above, this work proposes a Credit Card Fraud Detection model based on Generative Adversarial Network (CCFD-GAN). Instead of modeling the fraud transaction directly, CCFD-GAN learns the normal transaction distribution and detects samples outside the normal transaction distribution as fraudulent, therefore, no fraud transaction training samples are needed. Meanwhile, for effectively modeling the complex dependencies, not only Convolutional GRU (Conv-GRU) is utilized in CCFD-GAN to capture the spatio-temporal dependencies together, but temporal aggregated features and multi-scope outlier degrees are also employed to obtain a larger feature space that provides richer information. Moreover, in order to solve inaccurate detections caused by generating data outside the normal manifold, the penalty mechanism based on complementary distribution is introduced in the training phase to lead the GAN model towards the normal manifold. Experimental results show that our model outperforms classical credit card fraud detection models.

It is undeniable that rumors abound on online social networks and rumors can cause many disastrous consequences. Effective controlling of rumors is of great significance in social networks. However, the existing research only selects boosting users who are more likely to adopt the truth or select blocking users to terminate the spread of rumors. The former tends to correct the rumor after the spread is over but with high controlling cost, while the latter blocks the rumor without considering the truth transmission. In this article, we focus on how to select boosting–blocking users to control rumors when the rumor and truth are spreading together. We propose a boosting-truth blocking-rumor cascade (BTBRC) model. Under this model, given the rumor seed set and truth seed set, the boosting rumor controlling (BRC) problem aims to find a boosting–blocking seed set with$k$users such that the number of users influenced by the truth can be maximized. In order to solve it, we design a multihop neighbor boosting (MHNB) algorithm, which can get effective results with a data-parameter-dependent approximation ratio. Based on the above model, we also propose a positive boosting-truth blocking-rumor cascade (PBTBRC) model and design a connected multihop neighbor boosting (CMHNB) algorithm to solve the connected positive boosting rumor controlling (CPBRC) problem that requires a seed set to be connected under this model. Finally, extensive theoretical analysis and experimental results show the superiority of our algorithms over other comparison methods.

Cloud computing promises great advantages in handling the exponential data growth. Secure deduplication can greatly improve cloud storage efficiency while protecting data confidentiality. In the meantime, when data are outsourced to the remote cloud, there is an imperative need to audit the integrity. Most existing works only consider the support for either secure deduplication or integrity auditing. Recently, there have been some research efforts aiming to integrate secure deduplication with integrity auditing. However, prior works are unsatisfactory in that they suffer from the leakage of ownership privacy and forgeability of auditing results for low-entropy data. In this paper, we propose a new scheme that delicately bridges secure deduplication and integrity auditing in encrypted cloud storage. In contrast with prior works, our scheme protects the ownership privacy and prevents the cloud service provider from forging the auditing results for low-entropy data. Furthermore, we propose a blockchain-based mechanism that helps to ensure key recoverability and reduce local storage cost of keys. Formal analysis is provided to justify the security guarantees. Experiment results demonstrate the modest performance overhead of our scheme.

The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large volumes of distributed data at vehicles, which can be harnessed to build models for intelligent applications. Federated learning has recently received wide attentions, which allows model training over distributed datasets without requiring raw datasets to be shared out. However, federated learning is known to be vulnerable to poisoning attacks, where malicious clients may manipulate the local datasets or model updates to corrupt the global model. Such attacks have to be countered when federated learning is adopted in IoV systems, given that the training process is distributed among a large number of vehicles in an open environment. In addition, IoV systems present a hierarchical architecture in practice where other types of nodes sit between the cloud server and vehicles, allowing intermediate aggregation for reducing overall training latency. Yet the intermediate aggregation nodes may also pose threats. In this paper, we propose a robust hierarchical federated learning framework named RoHFL, which allows hierarchical federated learning to be suitably applied in the IoV with robustness against poisoning attacks. We develop a robust model aggregation scheme that contains a logarithm-based normalization mechanism to cope with scaled gradients from malicious vehicles. We integrate the notion of reputation into the aggregation process and develop a scheme for reputation updating. We provide a formal analysis of RoHFL’s convergence guarantees. Experiment results over several popular datasets demonstrate the promising performance of RoHFL, which is superior to prior work in the robustness against poisoning attacks.

Graphs are widely used to model complex structured data in many applications. With the proliferation of cloud computing, it is popular to store and query graphs in the cloud. Among others, graph similarity search, which aims to retrieve from a graph database graphs similar to a query graph, has received wide attentions and benefited various domains such as cheminformatics, computer vision, and more. Deploying graph similarity search services on the cloud, however, raises critical privacy concerns on the information-rich graphs. In this article, we initiate the first study on privacy-preserving graph similarity search in cloud computing. We design, implement, and evaluate PrigSim, a novel system allowing the cloud to host an outsourced encrypted graph database and support secure graph similarity search, where the graph similarity is measured by the well-known metric called graph edit distance. PrigSim is built from a customized and delicate synergy of insights on graph modelling, lightweight cryptography, and data encoding and padding, providing protections for the confidentiality of data content associated with graphs, as well as hiding the connections among vertices. Extensive experiments demonstrate that the security design of PrigSim is accuracy-preserving, and presents modest performance overheads (with$9\times$-$15\times$higher query latency than the plaintext baseline).

The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted outsourced databases, where little work has been done. Skyline query is an advanced kind of database query which is important for multi-criteria decision-making systems and applications. We propose SecSkyline, a new system framework building on lightweight cryptography for fast privacy-preserving skyline queries. SecSkyline ambitiously provides strong protection for not only the content confidentiality of the outsourced database, the query, and the result, but also for data patterns that may incur indirect data leakages, such as dominance relationships among data points and search access patterns. Extensive experiments demonstrate that SecSkyline is substantially superior to the state-of-the-art in query latency, with up to$813\times$improvement.

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM$^{2}$, a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.

Cloud computing is an emerging computational infrastructure for cost-efficient workflow execution that provides flexible and dynamically scalable computing resources at pay-as-you-go pricing. Workflow scheduling, as a typical NP-Complete problem, is one of the major issues in cloud computing. However, in the cloud scenario with unlimited resources, how to generate an efficient and economical workflow scheduling scheme under the deadline constraint is still an extraordinary challenge. In this paper, we propose a hybrid heuristic algorithm called enhanced task type first algorithm (ET2FA) to solve deadline-constrained workflow scheduling in cloud with new features such as hibernation and per-second billing. The objectives to be minimized include the total cost and total idle rate. ET2FA involves three phases: 1) Task type first algorithm, which schedules tasks based on topological level and task types, and utilizes a compact-scheduling-condition based VM selection method to assign each task. 2) Delay operation based on block structure, which further optimizes total cost and total idle rate based on block structure properties. 3) Instance hibernate scheduling heuristic, which sets an instance to hibernate if idle for a duration. Extensive simulation experiments based on seven well-known real-world workflow applications show that ET2FA delivers better performance in comparison to the state-of-the-art algorithms.

Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.

High-dimensional (HD) chaotic map has wide applications in various research fields such as neural networks and secure communication. Designing HD chaotic maps with expected dynamics and robust hyperchaotic behaviors is an interesting but challenging topic. In this article, we propose an$n$-dimensional hyperchaotic map$(n\text{D}$-HCM) generation method on the basis of the Gershgorin-type theorem. First, the general form of the proposed$n\text{D}$-HCM is built using$n$parametric polynomials. Then, the entity and coefficient parameter matrices are configured according to the Gershorin-type theorem. Theoretical analysis shows that the generated$n\text{D}$-HCM has$n$positive Lyapunov exponents and thus can show robust hyperchaotic behaviors. Two examples of hyperchaotic map with specified equations are provided and their properties are analyzed to show the availability of the proposed method. Performance evaluations display that our$n\text{D}$-HCM possesses abundant properties and complex behaviors, and it can outperform some representative HD chaotic maps. Moreover, to show the application of our$n\text{D}$-HCM, we apply it to a secure communication scheme and the experimental results exhibit that it shows much better performance than these representative HD chaotic maps in resisting transmission noise.

In Internet of Things (IoT), smart devices continuously collect various spatio-temporal data via different kinds of sensors, whose anomaly detection is crucial for the smooth operation of the whole system and the decisionmaking afterwards. Recently, traditional Deep-Learning based methods (i.e., CNN) for anomaly detection gain significant progress in the Euclidean space, but they fail to capture the spatial dependencies of spatio-temporal data from non-Euclidean domain. Besides, there might be few or even no abnormal data available for training when dealing with anomaly detection issue. In this paper, an end-to-end deep learning framework 3G-Net is proposed to address the problem above. The core idea of 3G-Net is to learn the normal pattern of IoT data considering both the long-short-term temporal dependencies and non-Euclidean spatial dependencies, and the more an observation differs from normal pattern, the more likely it is anomalous. Besides, an offline anomaly detection workflow based on Correcting Mechanism and Anomaly Score is also put forward in 3G-Net. Experiments conducted on different real-world and public datasets demonstrate that, major techniques in 3G-Net are effective in IoT scenes and it outperforms other state-of-theart approaches overall.

Coroutine is able to increase program concurrency and processor core utilization. However, for adapting the coroutine-to-transaction model, the existing coroutine package has the following disadvantages: (1) Additional scheduler threads incur synchronization overhead when the load between scheduler threads and worker threads is unbalanced. (2) Coroutines are swapped out periodically to prevent deadlocks, which will increase the conflict rate by adding suspended transactions. (3) Supporting only the swap-out function (yield, await, etc.) cannot flexibly control the transaction swap-in time. In this paper, we present SGPM, a coroutine framework for transaction processing. To adapt to the coroutine-to-transaction model, SGPM has the following properties: First, it eliminates scheduler threads and the periodic coroutine switch. Second, it provides a variety of coroutine scheduling strategies to make all types of concurrency control protocols run on SGPM reasonably. We implement eight well-known concurrency control on SGPM and, particularly, we use SGPM to optimize the performance of four wound-wait concurrency control among them, including 2PL, SS2PL, Calvin, and EWV. The experiment result demonstrates that after SGPM optimization 2PL and SS2PL outperform OCC and MVCC, and the throughput of Calvin and EWV is also improved by 1.2x and 1.3x respectively.

Designing high-dimensional chaotic maps with expected dynamic properties is an attractive but challenging task. The dynamic properties of a chaotic system can be reflected by the Lyapunov exponents (LEs). Using the inherent relationship between the parameters of a chaotic map and its LEs, this paper proposes an$n$-dimensional polynomial chaotic system ($n\text{D}$-PCS) that can generate$n\text{D}$chaotic maps with any desired LEs. The$n\text{D}$-PCS is constructed from$n$parametric polynomials with arbitrary orders, and its parameter matrix is configured using the preliminaries in linear algebra. Theoretical analysis proves that the$n\text{D}$-PCS can produce high-dimensional chaotic maps with any desired LEs. To show the effects of the$n\text{D}$-PCS, two high-dimensional chaotic maps with hyperchaotic behaviors were generated. A microcontroller-based hardware platform was developed to implement the two chaotic maps, and the test results demonstrated the randomness properties of their chaotic signals. Performance evaluations indicate that the high-dimensional chaotic maps generated from$n\text{D}$-PCS have the desired LEs and more complicated dynamic behaviors compared with other high-dimensional chaotic maps. In addition, to demonstrate the applications of$n\text{D}$-PCS, we developed a chaos-based secure communication scheme. Simulation results show that$n\text{D}$-PCS has a stronger ability to resist channel noise than other high-dimensional chaotic maps.

Much work has already been studied on the interrelation between the epidemic spreading and awareness spreading to prevent infections in a social network. By selecting seed users to spread awareness, we can control epidemic spreading. However, selecting seed users with the maximum influential users may not be the best solution in location-based social networks. Therefore, it is challenging to determine users to spread the information (the awareness of prevention) in these networks. The minimized epidemic infection (MEI) problem aims to find a seed set with$k$seed users such that the infection users can be minimized. In this article, we propose a piecewise function to measure the probability of each user being infected, which considers the distance and time. Then, we propose an algorithm called location-infected-greedy (LIG) to solve theMEIproblem by finding the seed nodes that consider the probability of infection, time of check-in, location information, and influence of users. In the meantime, LIG can obtain an upper bound of the data-dependent approximate ratio, and it runs in$O(kn^{2})$, where$n$is the total number of nodes and$k$is the number of seed nodes. Finally, extensive contrast experiments on real-world location-based social networks show that our algorithm is efficient and effective.

In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured data in various applications. Such trend of outsourced graph analytics, however, is accompanied with critical privacy concerns regarding the information-rich and proprietary attributed graph data. In light of this, we design, implement, and evaluate OblivGM, a new system aimed at oblivious graph analytics services outsourced to the cloud. OblivGM focuses on the support for attributed subgraph matching, one popular and fundamental graph query functionality aiming to retrieve from a large attributed graph subgraphs isomorphic to a small query graph. Built from a delicate synergy of insights from attributed graph modelling and advanced lightweight cryptography, OblivGM protects the confidentiality of data content associated with attributed graphs and queries, conceals the connections among vertices in attributed graphs, and hides search access patterns. Meanwhile, OblivGM flexibly supports oblivious evaluation of varying subgraph queries, which may contain equality and/or range predicates. Extensive experiments over a real-world attributed graph dataset demonstrate that while providing strong security guarantees, OblivGM achieves practically affordable performance (with query latency on the order of a few seconds).

When high-dimensional chaotic systems are applied to many practical applications, they are required to have robust and complex hyperchaotic behaviors. In this article, we propose a novel$n$D chaotic system construction method using the Pascal-matrix theory. First, a parametric Pascal matrix is constructed. Then, an$n$D chaotic system can be generated by using the parametric Pascal matrix as the parameter matrix of the system. Theoretical analysis shows that the generated$n$D chaotic systems have robust and complex chaotic behaviors, and they become$n$D Arnold Cat maps by fixing the parameters as some special values. Performance evaluations demonstrate that the$n$D chaotic systems have more complex chaotic behaviors and better distribution of outputs compared with existing HD chaotic systems. A 4-D Arnold Cat map and a 4-D chaotic map with hyperchaotic behaviors are generated as two examples. The two chaotic maps are then simulated on a microcontroller-based hardware platform and the chaotic sequences are tested to show good randomness.

By providing rich context of lane information on roads, lane-level maps play a vital role in intelligent transportation systems. Since Global Positioning Systems (GPS) have been widely applied to vehicles, vehicle-based crowdsourcing offers an economical way to the lane-level map building by collecting and analyzing the GPS trajectories of vehicles. However, existing works cannot directly extract lane-level road information from raw and interleaved crowdsourcing trajectories, and moreover they are time-consuming and inaccurate. In this article, we propose a lane-level map building scheme, which can directly extract lane-level road information from raw crowdsourcing GPS trajectories with both efficiency and accuracy improvement. Consider the global similarity between trajectories, we design an efficient trajectory segmentation and clustering algorithm based on improved discrete Fréchet distance and entropy theory, which can directly and accurately deal with the interleaved and messy trajectories. To improve the efficiency, we employ the Least Square Estimate (LSE) to constrain Gaussian Mixture Model (GMM) and design an efficient and accurate lane-level road information extraction algorithm. Comprehensive comparative experiments and performance evaluation on a real-world trajectory dataset show that the proposed scheme outperforms the state-of-the-art works in terms of both efficiency and accuracy.

Workflow scheduling is one of the most challenging problems in cloud computing. This paper proposes a heuristic algorithm task type first algorithm (T2FA) for solving deadline-constrained workflow scheduling in cloud with multicore resource (DWS_CMR). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total costs. Firstly, resource model and workflow application model are introduced. Resource model has the configurations of multicore, processing capacity, bandwidth and leasing price, and workflow application model is described by directed acyclic graph (DAG). Based on above models, the mathematical model of DWS_CMR is established, which allows multiple tasks to run concurrently on multicore resources. Secondly, to exploit the characteristics of the problem, the structures of DAG are decomposed and formulated. Merging tasks conforming to the first structure into task blocks can simplify DAG. Four special types of tasks are extracted from the second and third structures, and are preferentially scheduled in task scheduling stage. Then, a new interrelated calculation method of estimated start time and actual start time of tasks is proposed, which can complete the task-to-resource mapping. Finally, T2FA is devised, which incorporates two important phases, including pre-processing and task scheduling. Experimental results show that T2FA can achieve significantly better schedules in most test cases compared to several existing algorithms.

Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.

Federated Learning enables the collaborative learning in cross-client scenarios while keeping the clients' data local for privacy. The presence of non-IID data is one of major challenges in federated learning. To deal with this statistic challenge, federated multi-task learning considers the local training for each client as a single task. However, all the clients must participate in each training round, and it is inapplicable to mobile or IOT devices with constrained communication capability. To achieve the communication-efficiency and high accuracy with non-IID data, we propose a clustered federated multi-task learning by exploring client clustering and multi-task learning. We measure the similarities of local data among clients indirectly through their models' parameters, and design a client clustering strategy to enable clients with similar data distribution into a same group. The limitation of full-participation can be eliminated through the way of model training for groups instead of individual clients. The convergence analysis and experimental evaluation on real-world datasets shows that our work outperforms the basic federated learning in accuracy and is also more communication-efficient than the existing federated multi-task learning.

As a critical problem in time series data mining, time series classification (TSC) has always been a challenging task for high-dimensional and high-frequency time sequences in internet of things (IoT), Recently, convolutional neural networks (CNNs) have exhibited great superiority on TSC tasks in deep learning models, but not effective in capturing the long-term temporal dependency of time series. In this paper, we transform time series into GM-images by Gramian Angular Field (GAF) and Markov Transition Field (MTF), which can well preserve the temporal dependency and transition statistics of raw time series, respectively. We further propose an efficient Adaptive Dila-DenseNet (ADDN) to extract various and discernable patterns from GM-images for TSC. In ADDN, we devise an adaptive feature aggregation method for combining static and dynamic information flexibly from GAF and MTF representations. Moreover, inspired by the dilated residual networks, we design the Dila-Dense block in ADDN to preserve local spatial information for GM-images. The significant decrease of GM-images resolution in common deep learning models may lead to performance degradation. Nevertheless, our Dila-Dense block can address this resolution issue without decreasing the receptive field. Experiments evaluated on 24 benchmark datasets demonstrate that our approach shows greater efficiency and efficacy over the compared deep learning baselines, in IoT.

Chaotic systems are suitable for image encryption owing to their numerous intrinsic characteristics. However, chaotic maps and algorithmic structures employed in many existing chaos-based image encryption algorithms exhibit various shortcomings. To overcome these, in this study, we first construct a two-dimensional logistic tent modular map (2D-LTMM) and then develop a new colour image encryption algorithm (CIEA) using the 2D-LTMM, which is referred to as the LTMM-CIEA. Compared with the existing chaotic maps used for image encryption, the 2D-LTMM has a fairly wide and continuous chaotic range and more uniformly distributed trajectories. The LTMM-CIEA employs cross-plane permutation and non-sequential diffusion to obtain the diffusion and confusion properties. The cross-plane permutation concurrently shuffles the row and column positions of pixels within the three colour planes, and the non-sequential diffusion method processes the pixels in a secret and random order. The main contributions of this study are the construction of the 2D-LTMM to overcome the shortcomings of existing chaotic maps and the development of the LTMM-CIEA to concurrently encrypt the three colour planes of images. Simulation experiments and security evaluations show that the 2D-LTMM outperforms recently developed chaotic maps, and the LTMM-CIEA outperforms several state-of-the-art image encryption algorithms in terms of security.

In a cloud data center, servers are always over-provisioned in an active state to meet the peak demand of requests, wasting a large amount of energy as a result. One of the options to reduce the power consumption of data centers is to reduce the number of idle servers, or to switch idle servers into low-power sleep states. However, the servers cannot process the requests immediately when transiting to an active state. There are delays and extra power consumption during the transition. In this paper, we consider using state-of-the-art servers with multi-sleep modes. The sleep modes with smaller transition delays usually consume more power when sleeping. Given the arrival of incoming requests, our goal is to minimize the energy consumption of a cloud data center by the scheduling of servers with multi-sleep modes. We formulate this problem as an integer linear programming (ILP) problem during the whole period of time with millions of decision variables. To solve this problem, we divide it into sub-problems with smaller periods while ensuring the feasibility and transition continuity for each sub-problem through a Backtrack-and-Update technique. We also consider using DVFS to adjust the frequency of active servers, so that the requests can be processed with the least power. Our simulations are based on traces from real world. Experiments show that our method can significantly reduce the power consumption for a cloud data center.

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspectbased sentiment analysis (TABSA).However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA.This may result in targets or aspects having the same vector representations in different contexts and losing the contextdependent information.To address this problem, we propose a novel method to refine the embeddings of targets and aspects.Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context.Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors.Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.

Edge servers are densely deployed in the future mobile edge networks to meet the rapid increasing demand of mobile users. Since the distribution and traffic demand of user equipment (UE) fluctuate in time and over space, a number of edge servers may be underutilized which causes a great deal of energy waste. Therefore, we intend to reduce the energy cost of mobile edge networks, by dynamically switching on/off edge servers according to the variation of UEs' distribution. We formulate the energy saving problem in mobile edge networks as the minimum energy consumption (MinEn) problem which involves two critical issues: (1) cooperative service caching and UE association of adjacent BSs; (2) switching on/off edge servers. To solve the MinEn problem, we propose a dynamic server switching algorithm along with a lightweight UE distribution prediction mechanism. Simulation results show that our algorithm can greatly reduce the energy consumption of mobile edge networks compared with existing methods.

With the growing demand for satellite network technology in military and commercial applications, a large number of LEO(Low Earth Orbit) satellites have been launched in various countries around the world. One of the most important tasks is to download the data collected by these satellites to the Earth Stations(ESs) for processing. Previous research efforts have focused on how to download satellite-collected data to ESs. They didn't consider the situation that the satellites continuously collect data when downloading data. In this paper, our goal is to optimize the data download from the satellites to the ES in the case of dynamic collection of satellite data. We use inter-satellite links (ISLs) to offload data from heavily loaded satellites to the light ones. We build a topology map based on the interaction time between satellites and the interaction time between the satellite and the ES, and allocate the download time to the satellite carrying the largest amount of data. Then, the time slice is dynamically divided and the inter-satellite scheduling is determined by a way of classifying the idle satellites. Finally, the maximum flow algorithm is applied to determine the specific inter-satellite transmission scheme. In this way, the ES idle time is minimized. Simulations results show that our solution can greatly improve the data download efficiency from the satellites to the ES.

For those satellites in low earth orbit(LEO) satellite networks, due to limited storage capacity and other factors, an important task is to download their data collected in the space to earth stations (ESs). To maximize the throughput of data downloading from satellite to ESs, thus far, the existing research work has extensively studied how to efficiently allocate download time of ESs to satellites. However, a considerable part of satellites have barely contact time with ESs, because of the uneven contact between satellites and ESs.In this paper, we aim at optimizing data downloading from satellites to multiple ESs by using our inter-satellite link(ISL) routing method to offload data from the satellites that have surplus data to the satellites that have surplus contact time with ESs. First the entire satellite operation period is divided into several time intervals, a time interval is a time unit of data transmission. Then we allocate the download time between satellites and ESs. Lastly, the topology of satellites is modeled as Twisted Manhattan topology and our offloading algorithm is proposed to offload data among the satellites. Extensive simulations have been conducted to evaluate the effectiveness of our proposed scheme. The simulation results show that our scheme can significantly increase the overall system throughput of data downloading.

Chaos is known as a natural candidate for cryptography applications owing to its properties such as unpredictability and initial state sensitivity. However, certain chaos-based cryptosystems have been proven to exhibit various security defects because their used chaotic maps do not have complex dynamical behaviors. To address this problem, this paper introduces a cosine-transform-based chaotic system (CTBCS). Using two chaotic maps as seed maps, the CTBCS can produce chaotic maps with complex dynamical behaviors. For illustration, we produce three chaotic maps using the CTBCS and analyze their chaos complexity. Using one of the generated chaotic maps, we further propose an image encryption scheme. The encryption scheme uses high-efficiency scrambling to separate adjacent pixels and employs random order substitution to spread a small change in the plain-image to all pixels of the cipher-image. The performance evaluation demonstrates that the chaotic maps generated by the CTBCS exhibit substantially more complicated chaotic behaviors than the existing ones. The simulation results indicate the reliability of the proposed image encryption scheme. Moreover, the security analysis demonstrates that the proposed image encryption scheme provides a higher level of security than several advanced image encryption schemes.

Energy saving is critical for the cloud radio access networks (C-RANs), which are composed by massive radio access units (RAUs) and energy-intensive computing units (CUs) that host numerous virtual machines (VMs). We attempt to minimize the energy consumption of C-RANs, by leveraging the RAU sleep scheduling and VM consolidation strategies. We formulate the energy saving problem in C-RANs as a joint resource provisioning (JRP) problem of the RAUs and CUs. Since the active RAU selection is coupled with the VM consolidation, the JRP problem shares some similarities with a special bin-packing problem. In this problem, the number of items and the sizes of items are correlated and are both adjustable. No existing method can be used to solve this problem directly. Therefore, we propose an efficient low-complexity algorithm along with a context-aware strategy to dynamically select active RAUs and consolidate VMs to CUs. In this way, we can significantly reduce the energy consumption of C-RANs, while do not incur too much overhead due to VM migrations. Our proposed scheme is practical for a large-size network, and its effectiveness is demonstrated by the simulation results.

Wireless sensor networks (WSNs) are used to cover destination areas for a lot of practical applications. To enhance the performance of the WSN, the virtual backbone based on the connected dominating set is an efficient way with respect to the routing cost between sensors, lifetime of entire network, and so on. In this paper, especially for the WSN with different transmission radii among different sensors, we study the problem of constructing the minimum ρ-range connected dominating set under the constraint α-times of the minimum routing cost (αMOC-ρCDS), where α ) 5 and ρ is the ratio of the maximum-to-minimum transmission radius. Our contributions are three folds. First, we propose a polynomial time approximation scheme which generates the αMOC-ρCDS with the size of at most (1 + ϵ) times of the optimum solution, where ϵ is the error parameter. Second, we propose a polynomial time algorithm and prove that it has two approximation ratios (6ρ+1)2(2ρ+1)2and 10[(2π/θ)I[(ln 3ρ/(ln(1/ cos θ)))] [(ln ρ/(ln(2 cos(π/5))))], where θ <; arcsin(1/3ρ). Finally, we propose the distributed version of the constant approximation ratio algorithm which has both the time complexity and message complexity O(n3), where n is the number of sensor nodes. Besides, the simulation results demonstrate the efficiency of our algorithms.

Mobile edge computing (MEC) can improve the quality of services and save the bandwidth of backhual networks, by placing application services in the base stations (BSs), which are endowed with computing resources and are in close proximity to user equipments (UEs). Since the capacity of an individual BS is limited, only a small number of service instances can be allowed for each BS at the same time. Meanwhile, in a densely deployed network, the coverage areas of adjacent BSs are overlapped. Therefore, these capacity-limited BSs can collaboratively optimize their service placements to improve the performance of MEC. In this paper, we investigate the collaborative service placement (CSP) problem in MEC, which aims to minimize the traffic load caused by service request forwarding. The CSP problem involves several difficult issues, including correlations of adjacent BSs' service placement decisions, joint service placement and UE association, and joint allocation of computing and radio resources. This makes the CSP problem be a complex combinatorial optimization problem. To solve the CSP problem, we propose an efficient decentralized algorithm based on the Matching Theory. It can optimize the decisions of service placement and BS-UE association for BSs, according to local interactions between BSs and UEs. Our proposed algorithm is practical for large-size networks, and its effectiveness is demonstrated by the simulation results.

Location-based services (LBS) enables a user to query on spatial-temporal data with certain query criteria. With the popularity of mobile devices such as mobile phones and navigators, LBS has shown increasingly importance in various real world applications. However, in a traditional LBS system, the service provider maintains a geographic database (Geo-DB) and acquires the users' real-time locations. This raises the concerns of users privacy. This paper presents a privacy-preserving LBS scheme. In our scheme, the data owner outsources Geo-DB to the server in encrypted form, and the user enjoys the LBS without disclosing its location to the server. We construct a secure index to data objects in the Geo-DB by using the Encrypted Coded Quad-tree (ECQtree), such that user's queries can be efficiently located over the encrypted data. We also propose a Bloom filter based method for users to generate query trapdoors by hashing user's query regions into a Bloom filter vector, which is able to hide users' locations and query regions from the server. We also give security proof and analysis. Extensive simulations have been conducted and the results have shown the superior performance of our proposed scheme.

In tradition, the problem of Multi-Agent Path Finding is to find paths for the agents without conflicts, and each agent execute one-shot task, a travel from a start position to its destination. However, making just one planning for the agents may not satisfy the requirement in dynamic environments such as logistics sorting center, where the paths of the agents may constantly need to be adjusted according to the incoming tasks. The challenging issue is to dynamically adjust the already planned paths while make planning for the agents ready to execute new incoming tasks. In this paper, we formulate it into Dynamic Multi-Agent Path Finding (DMAPF) problem, the goal of which is to minimize the cumulative cost of paths. To solve this problem, we propose an algorithm called Lifelong Planning Conflict-Based Search (LPCBS), which can efficiently and optimally make planning for the new incoming tasks while adjusting the already planned paths. Experiment results show that the LPCBS performs much better than the existing works in each planning.

In recent years, automated guided vehicle (AGV) is becoming increasingly important for logistic center, which usually has tens of thousands of express packages to sort and transport every day. In order to enhance the efficiency of sorting, multiple AGVs have been used to transport as many express packages as possible in a given time. However, there may exist collisions and deadlocks when two or more AGVs are trying to pass through a shared area at the same time. In this paper, we study collision-free and deadlock-free path planning of multiple AGVs with the objective of minimizing the makespan, that is, the maximum time of the AGVs to finish a round of delivery. To solve the problem, we first propose a candidate path generation algorithm to generate several paths for each AGV, from which a combination of the paths is selected based on optimal path combination selection (OPCS) algorithm, such that the collisions and deadlocks can be minimized. Then, we construct a colored resource-oriented timed Petri net (CROTPN) model for dynamic changing AGV routes after slicing the scheduling period into timeslots. Through Slot-Control policy, the collision and deadlock can be effectively avoided. Our experiment is simulated based on CPN tools. Experiment results show that our method can achieve a high sorting throughput using the least AVGs.

Caching application services at the edge of mobile networks can both reduce the traffic load in core networks and improve the quality of services. Since the capacity of a single BS is constrained, only a small number of service can be executed simultaneously by each BS. However, when the BSs are densely deployed in the network, the BSs that are close to each other can cooperatively cache the services to improve the performance of the system. Moreover, we should avoid frequent service switching when the users' service requests always change. In this paper, we study the dynamic service caching (DSC) problem in mobile edge networks. Our objective is to minimize the traffic load that needs to be forwarded to the cloud, as well as considering service switching cost of BSs. This DSC problem involves two important issues, which include cooperative service caching of adjacent BSs and service switching in adjacent time slots. To solve the DSC problem, we propose a dynamic service caching algorithm for the BSs to cooperatively cache the services in an online manner. The simulation results show that our algorithm can greatly reduce the forwarded traffic load without frequently changing the service caching of BSs.

In sparse wireless sensor networks, a UAV is used to collect the sensing data. Each sensor node has a limited transmission range and the UAV has to traverse the transmission range of all sensor nodes to collect data without exhausting energy of the UAV. To minimize the total energy consumption of the UAV on the flying and collecting data, it is essential to consider the tradeoff between path length and data collection time when planning path. In this paper, we show that the optimization problem can be regarded as the traveling salesman problem with neighborhood, which is known to be NP-hard. To address the problem, we decompose it into two subproblems, 1) weighted set cover problem; and 2) a combined optimization problem. Then we solve the first one by a greedy algorithm and the second by an improved shuffled frog-leaping algorithm. We also simulate the proposed algorithm to evaluate its performance.

Single micro UAV is limited by its endurance and suffers the problem of single point failure. An autonomous fleet of cooperating UAVs can render more efficient and robust ability to complete the missions. In order to make multiple UAVs fly cooperatively in an autonomous way without control from the ground station, one of the key techniques is communication network technology. In this paper, we propose a distributed algorithm to construct a clustered-backbone to support both broadcast and unicast communications for UAV networks. In our algorithm, UAVs clustering and virtual backbone construction only need the information exchange among adjacent UAVs. We also propose a strategy to dynamically maintain this clustered-backbone network. We test our scheme using four commercial UAVs enhanced by an ARM-based embedded computer to demonstrate the distributed control and dynamic maintenance features of the algorithm.

To securely communicate information in different networks, this paper introduces a novel secure communication scheme using non-coherent modulation. The transmitter is to generate transmitted signal by modulating the chaotic sequences and information bits, while the receiver can recover the information bits without generating a synchronized duplication of the chaotic sequence. Each frame can transmit two information bits. Thus, it can achieve a high transmission rate. Performance analysis demonstrates that the proposed communication scheme has high performance in resisting Gaussian noise and simulation results shows that it has better bit-error rate performance than a newly developed method in the additive-white-Gaussian-noise channel.

There has been explosive growth of satellite applications in the past decades. Broadcast is a fundamental operation in satellite networks, which is required for satellites self-organization, routing discovery, coordination and collaborations. In this paper, we study the problem of delay bounded and energy efficient broadcast in satellite networks. Broadcast routing is a challenging issue in satellite networks due to highly dynamic change and intermittent connectivity of the network. We propose a method that is based on a Multi-power Level Multi-Transmission (MLMT) Space-Time Graph. The algorithm repeatedly finds the most energy efficient `caterpillars' in the graph, and merges caterpillars into an energy efficient broadcast tree. Simulations have been conducted in the real satellite constellations and the results show our proposed scheme has achieved good performance compared with other broadcast algorithms.

Computational resource allocation is a critical issue for the baseband unit (BBU) pool in a cloud radio access network (C-RAN). There are multiple resources in a BBU, including CPU, memory, disk, etc. The virtual machines (VMs) have diverse requirements along these resources to handle the baseband signal processing of corresponding remote radio units (RRUs). Consolidating VMs to BBUs based on single resource incurs over-allocation of the resources that are not explicitly allocated. Therefore, we study the multi-resource allocation problem in CRANs, which aims to minimize the number of active BBUs that are required to serve all users in the network. Since the RRU can be set to an idle state when its traffic is low, the number of VMs and their resource demands are all adjustable. We propose an efficient algorithm to solve this problem. This algorithm selects active RRUs and associates users with these RRUs in an iterative way. It adapts a heuristic for the multi-dimensional bin packing problem to assign VMs to BBUs. Our proposed method can significantly reduce the number of required active BBUs, while satisfying the VMs' demands for multiple computational resources. Simulation results demonstrate the effectiveness of our proposed algorithm.

In smart grids, smart meters are the metering devices that are responsible for collecting users' information about their electricity consumption. Smart meters provide an effective means for control center (CC) to monitor the power supply and consumption in a timely and accurate fashion. However, the collection of users' metering reports through an untrusted public network raises a serious privacy concern of users. Even though a lot of privacy-preserving approaches that have been proposed in the literature for secure data transmission, they are not directly applicable to the scenario of collecting metering reports in smart grids due to the limited computational resources of smart meters and the networking environment of smart grids. In this paper, we propose a lightweight privacy-preserving scheme for metering data collection in smart grids. There are three advantages of our proposed scheme: 1) Lightweight. There is no need for users' metering devices to carry out intensive encryption and decryption operations. 2) No need of a trusted third party for key generation and key management. 3) Scalability. Our scheme can cater for large scale of users connected via public networks. Experiment results show that the proposed scheme is efficiently and lightweight and is suitable for privacy-preserving data collection in smart grids.

Summary Infrastructure‐as‐a‐Service cloud providers always charge users in 2 ways: on‐demand billing and reservation billing with different discounts. On‐demand billing is more flexible for short‐term use but with higher cost, while reservation billing is much cheaper for long renting period but may cause inefficiency because of underutilized capacity. As middle agent, cloud broker always tries to reserve a large number of virtual machine (VM) instances from cloud providers to meet the requirement of all users, while making high profit through reducing its cost by leveraging long‐term reservations and time‐multiplexing of instances. Different from existing work, we believe that the rental time of each user's request should be served consecutively in one VM instance, rather than partitioned into different VM instances by cloud broker to enhance time‐multiplexing ratio. In this paper, we first try to smooth users' unordered and time‐overlapping requests using demand graph, based on which we propose two reservation algorithms for cloud broker, one for off‐line and the other for online. We also consider cloud broker with multiple service providers, finding that much more cost can be saved by leveraging the various reservation discounts of those providers. Extensive simulations have been done on the traces from real cloud platforms. Experiment results show that the broker cost can be substantially saved using our off‐line and online algorithms.

Satellite systems have attracted great attention from academic and industrial communities in recent years. There are many satellites that have been launched for weather forecast, environment monitoring, and target surveillance. One important task of the satellites is to download the data they have collected in space to the ground servers via earth stations (ESs). Since satellites move at high speed along their own orbits and have very limited contact time with ESs, satellites may not be able to download all the data they have to the ground on time. In this paper, we propose a collaborative scheme that allows satellites to offload data among themselves using inter-satellite links (ISLs) before they come into contact with the ES, such that satellites will carry the right amount of data according to the length of their contact time with the ES and the throughput of data downloading at the ES is maximized. We develop an iterative optimization algorithm that jointly schedules data offload among the satellites and data downloading from satellites to the ES. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offload can be increased significantly. In many cases, the throughput reaches close to 100% of the capacity of the ES.

In recent years a large number of LEO (Low Earth Orbit) satellites have been launched into space for various applications, such as weather forecast, environment monitoring and military surveillance. One important task of these satellites is to collect data and download them to Earth Stations (ESs) for further processing. Since LEO satellites fly at high speeds and have very limited contact time with ESs, they may not have sufficient time to download data to the ground. Existing work focused on scheduling data downloading from satellites to the ES such that the throughput of the downloading is maximized. However, what the scheduling algorithms can do is very limited in this scenario due to the disparity of the contact time a satellite has and the amount of data it carries for downloading. In this paper, we propose a novel scheme that uses inter-satellite links (ISLs) to offload data among satellites before they contact with the ES. A satellite with large amount of data but little contact time can offload data to other satellites that have surplus contact time. By offloading data among satellites on-the-fly in space, each satellite can carry the right amount of data when it comes to download its data to the ES, such that its contact time can be fully utilized. Extensive simulations have been conducted to evaluate the effectiveness of our proposed method. The simulation results show that the data downloading throughput by using ISL data offloading can be increased significantly.

In this paper, we study the issues of scheduling of user requests to geographically distributed data centers. The data centers are powered by three types of energy: wind, solar, and brown energy. We consider using energy storage devices (ESDs) to store energy when its supply is abundant or the price is low, and discharge it when the energy supply is in short or expensive. By introducing ESDs in data centers, it brings some new challenges regarding the scheduling of user requests, such as when and how much to charge the energy into ESDs and when to discharge it, how this charge and discharge of ESDs affects the QoS and total energy cost. We focus on two optimization problems: 1) Schedule the servers and energy usage from different energy sources, such that the QoS requirement is met and the total energy cost is minimized. 2) Minimize total carbon emissions for given a budget of energy cost. The problems are formulated as mixed integer linear programming problem (MILP). We use Cplex to solve the formulated problems. We have run extensive experiments on traces from real world. Experiments show that our scheduling methods using ESDs can significantly reduce both carbon emissions and total cost.

Satellite systems have attracted much attention from academic and industrial communities in the past 20 years. There are many satellites that have been launched for all kinds of applications. Broadcast is a fundamental operation in satellite networks. It is frequently used for satellites self-organization, coordination and collaboration. The problem of our concern is, given an Low earth orbit (LEO) satellite network and a delay-bound, to find a broadcast schedule that delivers a message to all satellites within the delay-bound and the total number of transmissions is minimized. In this paper, we propose a broadcast routing and transmission scheduling algorithm for satellite networks. We first develop a space-time graph that models the dynamic connectivity of the satellite network as time progresses forward. The broadcast message is carried from one position to the other where it is forwarded to other recipients. A greedy algorithm with back-track search is designed to find the best transmitting nodes and the best transmission time in order to achieve the minimal number of transmissions subject to the constraint of delay-bound. Simulations have been conducted in the real LEO satellite constellations. Simulation results show good performance of our proposed scheme.

In this paper, we consider the real-time pricing problem for a small scale local power supplier (LPS) in a smart energy community. The LPS supplies power to the residential users (RUs) in a local area and sells the remaining power to the main grid. Since the selling price to the main grid is relative low, LPS intends to sell more power to the RUs with an appropriate price. The LPS determines the price based on the proposed pricing scheme to maximize its revenue. The price is informed to RUs through the communication infrastructure. According to the announced price of LPS, each RU schedules its power consumption to maximize its utility. We model the interactions between the local power supplier and all users as a one-leader multi-followers Stackelberg game, where the LPS acts as the leader and RUs act as the followers. To address this problem, a distributed algorithm based on information exchange between the LPS and RUs is proposed. Simulation results show that the distributed algorithm converges to the Stackelberg equilibrium.

In wireless sensor networks, Virtual Backbone (VB) construction based on connected dominating set is a competitive issue for routing efficiency and topology control. Transmission ranges of sensors are not always equivalent. A sensor networks is modeled as a directed graph while sensors have different transmission ranges. In this paper, we will try to find a special Strongly Connected Bidirectional Dominating Set (SCBDS) within minimum routing cost for each pair of nodes in directed graphs. The SCBDS forms a VB of the networks whose sensors have different transmission radius. For any pair of sensors, the length of the shortest path they communicate with each other through VB should be no more than a constant times the length of the shortest path without using VB. We propose a constant approximate scheme to construct the SCBDS with the bounded size 3*(8ρ+1)2(2ρ+1)2/2 opt. A centralized and a distributed algorithm with the same performance ratio are presented to show the details to construct the SCDS in directed graphs. Simulation results show that the average shortest path length through our algorithms is reduced greatly compared with other algorithms.

The high power consumption of cloud data centers has aroused great concern on environmental implications such as global warming. Therefore, cloud service providers like Facebook and Green House Data have built their own wind or solar farms to power the data centers to reduce both energy cost and carbon emissions. In this paper, we propose an optimization- based framework to make planing for green cloud data centers, where the objective functions range from minimizing energy cost to minimizing carbon emissions. Our planning is based on the optimized scheduling of the users' requests to each data center, while considering time-varying and location-varying electricity prices and the supply of sustainable energy under different weather conditions. Our plan includes: 1) How many servers each data center should have. 2) How many wind turbines and solar panels should be used to power each data center. 3) What capacity of energy storage device (ESD) should be equipped for each data center. We formulate each problem as a constraint optimization problem, and solve it using Cplex. Extensive experiments have been done on traces from real world. As far as we know, our work is the first to explore the issue of planning for green cloud data centers through optimized scheduling methods.

The most efficient way to save energy in cellular networks is to switch ON/OFF base stations (BSs) dynamically according to the distribution of user equipment (UE) at real time. When a BS is switched ON/OFF, there is a switching energy cost incurred, which is a significant amount and cannot be ignored. By considering this switching cost, we formulate the energy saving problem of BSs in cellular networks as the minimum energy cost problem (MECP). The objective of MECP is to choose the BSs to be active during a period of time and determine the levels of transmission power of the active BSs according to the UEs that are served by the BSs, such that the total energy cost of the BSs is minimized. We propose a scheme to solve the MECP in two steps. In the first step, we aim to minimize the energy cost of all BSs in a time unit independently, without considering the switching ON/OFF BSs across adjacent time units. In the second step, we consider the switching cost of state transitions of BSs by introducing a state transition graph a BS over an entire time period, and transform the MECP into a minimum energy cost flow problem. A minimum cost flow algorithm is developed to solve this problem. Simulation results show that our proposed scheme can achieve significant energy cost reduction of the cellular network, compared with the existing methods.

In this paper, we study the issue of minimizing the total energy cost for green cloud data centers with time varying and location varying electricity prices and supply of renewable energy. Given the budget of energy cost, schedule the requests, servers, and power usage of different sources, such that the total cost can be minimized. We formulate the problem during the whole period of time as an MILP problem. We use Cplex to solve the problem. Experiment shows that our method can significantly reduce total cost after using ESDs.

In this paper, we investigate the residential load scheduling problem within a smart energy community, which is powered by a primary utility along with a small scale local power supplier. As a premise, unit prices set by these two suppliers are different and both are time-varying. Therefore, users are motivated to control their household appliances' operation time and calculate appropriate portions of power purchased from these two suppliers to achieve bill curtailments. The capacity constraint of local power supplier, arising from the renewable energy source and the limited storage capability, also should not be violated. We formulate a residential load scheduling problem to address this situation. Distributed scheme based on information exchange among users is proposed, without over revealing individual user's load profile. Then we propose a distributed algorithm to solve this scheduling problem. Simulation results show that the proposed approach can reduce energy cost of the community and cut down electricity payments of users, and the peak-to-average ratio in load demand is also decreased.

The aim of influence maximization problem is to find a k-size seed set that has the maximum influence. In previous works the modification of user's attitude is seldom paid attention to. However from the psychology research, we know that people's opinions are affected by their friends. Base on this, we present a new Linear Threshold model with Instant Opinions (LT-IO). We devise an attitude function Atuthat describes node u's attitude at time t, and the broadcast attitude which is the attitude when a node becomes active. To simulate information propagation in real world, we define a trust threshold η to justify whether a node follows or opposes the influence from its neighbor. We propose a heuristic algorithm IMLT-IOA to solve our problem, prove its submodularity and monotonicity and then obtain its approximation ratio which is (1 - 1/e). To the best of our knowledge, this is the first work that focuses on the influence maximization with user's attitude modification. To verify our IMLT-IOA algorithm, we conduct extensive experiments on a large data collection obtained from real social networks, the results show that IMLT-IOA reduces the running time and meanwhile keeps effectiveness comparing to other algorithms.

Servers and network contribute about 60% to the total cost of data center in cloud computing. How to efficiently place virtual machines so that the cost can be saved as much as possible, while guaranteeing the quality of service plays a critical role in enhancing the competitiveness of service cloud provider. Considering the heterogeneous servers and the random property of multiple resources requirements of virtual machines, the problem is formulated as a multi-objective nonlinear programming in this paper. Virtual machine cluster with higher traffic is made staying together. This reduces the communication delay while saving the inter-server bandwidth consumption, especially the relatively scarce higher level bandwidth, by exploiting the topology information of data center. At the same time, statistic multiplex and newly defined “similarity” techniques are leveraged to consolidate virtual machines. The violation of resource capacity is kept at any designated minimal probability. Thus the quality of service will not be deteriorated while saving servers and network cost. An offline and an online algorithms are proposed to address this problem. Experiments compared with several baseline algorithms show the validity of the new algorithms: more cost is cut down at less computation effort.

Online Social Networks (OSNs) have recently emerged as an effective medium for information sharing. Unfortunately, it has been frequently observed that malicious rumors being spread over an OSN are not controllable, and this is not desirable. This paper proposes a new problem, namely the γ - k rumor restriction problem, whose goal is, given a social network, to find a set S of nodes with k protectors (γ * k protectors from the contaminated set, and (1 - γ) * k protectors from the decontaminated set) to protect the network such that the number of decontaminated nodes is maximum. We show that the objective function of the γ - k rumor restriction problem is submodular, and use this result to design a greedy approximation algorithm with performance ratio of 1 - 1/e for the problem under the linear threshold model and independent cascade model, respectively. To verify our algorithms, we conduct experiments on real word social networks including NetHEPT, WikiVote and Slashdot0811. The results show that our algorithm works efficiently and effectively.

Security policies are one of the most fundamental elements of computer security. Current security policy design is concerned with the composition of components in security systems and interactions among them. Consequently, in a modular specification and verification of a policy, the composition of the modules must consistently assure security policies. A rigorous and systematic way to predict and assure such critical properties is crucial. This paper addresses the problem in a formal way. It uses colored Petri net process (CPNP) to specify and verify security policies in a modular way. It defines fundamental policy properties, i.e., completeness, termination, consistency, and confluence in Petri net terminology and gets some theoretical results. According to the eXtensible Access Control Markup Language (XACML) combiners and property preserving Petri net process algebra (PPPA), several policy composition operators are specified and property preserving results are stated for the policy correctness verification. As an application, the approach is illustrated for the design of Chinese Wall Policy.

Security policies are one of the most fundamental elements of computer security. For secure interoperation and sharing resources among heterogeneous systems, local policies should correspondingly be integrated for designing a global policy. This paper addresses the problem in a formal way. It uses extended Petri net process to specify and verify security policies in a modular way. It defines four types of policy compositions such that the integrated policy is capable of handling resources sharing, simultaneously executing operations and embedding sub-policies into main policies in multiple heterogeneous systems. Furthermore, the global policy can preserve the fundamental policy properties, i.e.,completeness, termination, consistency and confluence, and satisfy policy autonomy and security principles that are required for secure interoperation.

In this paper, we focus on the need for secure and efficient localization for wireless sensor networks in adversarial settings. An attack-resistant and efficient localization scheme is developed, which extends the scheme proposed in [1]. The method offers strong defense against not only distance reduction attacks but also distance enlargement attacks. Furthermore, our method does not employ any device-dependent variables, hence yields more accurate localization. An attack-driven model is also specified using Petri net. It provides a formal method for the verification of our scheme when considering distance enlargement attacks. The state analysis shows that the potential insecure states are unreachable, implying that the model can offer strong defense against these attacks. To the best of our knowledge, it is the first time that the Petri net has been introduced to validate security scheme for wireless sensor networks in the literature.

The reachability graph technology is the basic and important method of analysis and verification of a system but it suffers from state explosion problem. To cope with this problem, this paper introduces transition vectors which can detect all the enabled transitions at given state of system and identify them as dependent or independent. Transition vectors have been found useful and functional for simultaneous execution of concurrently enabled transitions. An efficient algorithm based on transition vectors has been presented to generate the reduced reachability graph and compared with the reduced reachability graph constructed by stubborn set method.

As the price of hardware decreases, Wireless Mesh Networks (WMNs) will become practical commodity in a few years. By equipping each node with multiple network interface cards, multiple channels can be applied to improve the performance of WMNs. With the advantages of both wireless LAN and Ad Hoc, WMN also has challenges. Connectivity of the communication network and the interferences between the links of the network are two most important points to be considered carefully. This paper presents some efficient technologies to handle the two points. It firstly simplifies the topology of the communication network and gets a bi-connected network; then for the resulted network, it greedily assigns channels such that the interference of the network is as small as possible. The simulation results show that, by applying the technologies presented in this paper, a bi-connected network with minimum interference is created.

An important goal of the design of WDM (wavelength division multiplexing) networks is to use less wavelengths to serve more communication needs. According to the wavelength conflict rule, we know that the number of wavelengths required in a WDM network is at least equal to the maximal number of channels over a fiber (called maximal link load) in the network. By placing wavelength converters at some nodes in the network, the number of wavelengths needed can be made equal to the maximal link load. In this paper we study the problem of placing the minimal number of converters in a network to achieve that the number of wavelengths in use is equal to the maximal link load. For duplex communication channels, we prove that an optimal solution can be obtained in polynomial-time. For unidirectional communication channels, which was proved to be NP-complete, we develop a set of lemmas which lead to an efficient approximation algorithm whose approximation ratio is two.

In multihop systems of wavelength division multiplexing (WDM) networks, wavelength conversion is required at the conjunction of two lightpaths if they use different wavelengths. We consider the problem of assigning wavelengths to the lightpaths by using a limited number of wavelengths, so that the overall number of wavelength conversions in the whole system is minimal. The problem is formulated as a maximum clique cover problem. An approximation algorithm is proposed to solve it. Our proposed theory also illustrates the tradeoff relationship between the number of wavelengths and the number of conversions in the system.

Investigates the issues of placing a limited number of Web proxies in an environment where the Web server is replicated (i.e. mirrored Web servers). Two different objectives are considered: (a) minimizing the overall access cost by all clients of the Web server, and (b) minimizing the longest delay for any client to access the Web server. The problems are formulated and solved by using a dynamic programming method. This work can: (1) alleviate the amount of Internet traffic incurred by fast-growing Web accesses; (2) improve the response time of Web server accesses; and (3) maximize Web server performance by using a limited number of proxies.