LoRa technology plays a vital role in IoT applications by enabling low-power and long-range communication. Since the LoRa signal may transmit hundreds of meters to tens of kilometers under different environments, estimating the path loss of LoRa links can bring great benefits to the network, such as device localization and network planning. Recent years have seen some mechanisms to improve the LoRa link estimation by analyzing the environments based on satellite images. However, they may be inaccurate when we consider the actual network in the three-dimensional (3D) scene. This paper proposes 3DLoRa, the first framework to refine LoRa link estimation through exploiting 3D point clouds. We generate a 3D map by conducting semantic segmentation and voxelization based on the point clouds. We then propose both model-based and learning-based methods to estimate the path loss of a link according to the environments obtained from the 3D map. We test the 3D map generation process based on 3D point clouds in a campus area and show an accuracy of about 97% for environment categorization. We also deploy a LoRa network on the campus and collect data to evaluate the performance of link estimation. Experimental results indicate that 3DLoRa can significantly improve the accuracy of link estimation in various environments compared to previous works. Specifically, the model-based method can reduce the estimation error to about$7dBm$with low computational overhead, while the learning-based method can reduce the estimation error to about$2.5dBm$with the cost of higher computational overhead. These results can serve as a reference for how to deploy link estimation methods in different network situations.

Lesion localization using machines greatly assists doctors in diagnosing diseases and providing better treatment for patients, which is significant for intelligent healthcare. Unlike natural images, the background and target objects in medical images are often difficult to distinguish, and obtaining medical annotations for training models is challenging. This makes accurate lesion localization extremely difficult. In this paper, we propose a counter-factual generation prompt learning framework for lesion localization in medical images. First, we employ the class association embedding method for separating lesion-related information from lesion-irrelevant information in medical images. By embedding different lesion-related information, we generate counterfactual samples, and obtain lesion-related knowledge based on comparison. We further process the lesion-related knowledge and obtain a prior prompt, which is then fed into a well-known segmentation network for more accurate and detailed lesion localization. To accurately acquire lesion-related knowledge, we propose an irrelevant feature similarity transfer method to reduce the interference of irrelevant knowledge. Experimental results show that our method achieves excellent lesion localization results without requiring pixel-level annotations for training, and also outperforms other existing localization algorithms.

Federated Learning (FL) Services enable customers (requesters) to outsource their FL tasks to the FL service provider, who will recruit a group of clients with appropriate datasets to complete the FL task. For a given FL task, how to select appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a new client selection, scheduling, and rewarding scheme to ensure fairness through a three-stage process: 1) multicriteria initial client pool selection, 2) data quality-oriented perround client scheduling, and 3) performance-based rewarding. Specifically, we first define a client selection metric with multiple criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. Furthermore, we formulate the per-round client selection problem into a data quality-oriented scheduling problem that aims to improve model quality and guarantee fairness. We propose a heuristic algorithm to divide the pool into several subsets such that the federated dataset in a subset is close to an independent and identical distribution (iid) while guaranteeing each client is selected at least once in a scheduling period. In addition, we propose a performance-based payment adjustment protocol with a bonus and punishment mechanism, such that the final payment reflects the actual performance of each selected client. Our fairness analysis and experimental results show that our scheme not only can guarantee fairness but also can improve the model quality especially when data are non-iid.

Image classification is a primary task in data analy-sis where explainable models are crucially demanded in various applications. Although amounts of methods have been proposed to obtain explainable knowledge from the black-box classifiers, these approaches lack the efficiency of extracting global knowl-edge regarding the classification task, thus is vulnerable to local traps and often leads to poor accuracy. In this study, we propose a generative explanation model that combines the advantages of global and local knowledge for explaining image classifiers. We develop a representation learning method called class association embedding (CAE), which encodes each sample into a pair of separated class-associated and individual codes. Recombining the individual code of a given sample with altered class-associated code leads to a synthetic real-looking sample with preserved individual characters but modified class-associated features and possibly flipped class assignments. A building-block coherency feature extraction algorithm is proposed that efficiently separates class-associated features from individual ones. The extracted feature space forms a low-dimensional manifold that visualizes the classification decision patterns. Explanation on each individual sample can be then achieved in a counter-factual generation manner which continuously modifies the sample in one direction, by shifting its class-associated code along a guided path, until its classification outcome is changed. We compare our method with state-of-the-art ones on explaining image classification tasks in the form of saliency maps, demonstrating that our method achieves higher accuracies. The class-associated manifold not only helps with skipping local traps and achieving accurate explanation, but also provides insights to the data distribution patterns that potentially aids knowledge discovery. The code is available at https://github.com/xrtll/xAI-CODE.

Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. In this paper, we propose a counterfactual generation framework that not only achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations, but also provides explainability. Our framework effectively separates class-related features from class-unrelated features of the samples, and generate new samples that preserve identity features while altering class attributes by embedding different class-related features. We perform topological data analysis on the extracted class-related features and obtain a globally explainable manifold, and for each abnormal sample to be segmented, a meaningful normal sample could be effectively generated with the guidance of the rule-based paths designed within the manifold for comparison for identifying the tumor regions. We evaluate our proposed method on two datasets, which demonstrates superior performance of brain tumor segmentation. The code is available at https://github.com/xrt11/tumor-segmentation.

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent difference reward based actor-critic with V2I bandwidth allocation (MDRAC-VBA) solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of MDRAC-VBA.

With the rapid growth of Internet of Things, the number of heterogeneous wireless devices working in the same frequency band increases dramatically, leading to severe cross-technology interference. To enable coexistence, researchers have proposed a large number of mechanisms to manage interference. However, existing mechanisms have severe modifications in either the physical or MAC (medium access control) layers, making them very different from the standard. In this paper, we design and implement SledZig to boost cross-technology coexistence for low-power devices through both enabling more transmission opportunities and avoiding interference. SledZig is fully compatible with the standard in both physical and MAC layers. It decreases the WiFi signal power on the channel of low-power devices while keeps the WiFi transmission power unchanged, through making constellation points on the overlapped subcarriers have the lowest power, which can be achieved by just encoding the WiFi payload. We implement SledZig on hardware testbed and evaluate its performance under different settings. Experiment results show that SledZig can effectively increase ZigBee transmissions and improve its performance over a WiFi channel under various WiFi data traffic, with as low as 6.94$\%$WiFi throughput loss.

With the deployment of Internet of Things, we have seen many wireless devices working in the crowded unlicensed 2.4GHz band, leading to severe cross-technology coexistence problem. The sensing of signal type and channel can assist wireless devices with more efficient transmission strategy to improve the network performance. In this paper, we present DeepSpectrum, a wireless signal identification system based on convolutional neural networks, to identify the type and estimate the channel of four wireless signals, including Wi-Fi, Zigbee, Lora and Bluetooth. DeepSpectrum consists of a master model for the initial identification of the wireless signals, and an auxiliary model to further improve the accuracy of the Lora channel estimation. We thoroughly evaluate DeepSpectrum using various test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at $0\mathrm{~dB}$ SIR, the average identification accuracy for the four signals is over $\mathbf{9 9 \%}$ and the average error of central frequency is only 21KHz, which indicates that we can get the channel of Wi-Fi, Zigbee, and Bluetooth correctly. As for Lora with flexible central frequencies, the average bandwidth error is only 76KHz.

Leveraging emerging mobile edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive applications (e.g. virtual reality and cloud gaming) onto GPU-based edge servers to reduce the user experienced latency. A task offloading problem arises, that is where to offload rendering tasks such that each user will experience tolerable delay and meanwhile the cost of used servers is minimized. The multi-dimensional resource sharing feature of rendering tasks makes the problem challenging. We formulate the task offloading problem into a boolean linear programming. We propose a sharing-aware offloading algorithm which decomposes the problem into two subproblems (user assignment and server packing) and solves them alternately and iteratively. We compare our algorithm with the one without resource sharing in consideration, and the simulations demonstrate that our method can effectively reduce cost as well as satisfy delay requirement.

Towards the large amount of data generated everyday, data selective sharing and acquisition is one of the most significant data services in cloud-based systems, which enables data owners to selectively share their data to some particular users, and users to selectively acquire some interested data. However, it is challenging to protect data security and user privacy during data selective sharing and selective acquisition, because cloud servers are curious about the data or user’s interests, and even send data to some unauthorized users or some uninterested users. In this paper, we propose an efficient and provably secure Data selective Sharing and Acquisition (${\sf DSA}$) scheme for cloud-based systems. Specifically, we first formulate a generic data selective sharing and acquisition problem in cloud-based systems by identifying several design goals in terms of correctness, soundness, security and efficiency. Then, we propose the${\sf DSA}$scheme to enable data owners to control the access of their data in a fine-grained manner, and enable users to refine the data acquisition without revealing their interests. Technically, a brand new cryptographic framework is developed to integrate attribute-based encryption with searchable encryption. Finally, we prove that the proposed${\sf DSA}$scheme is correct, sound, secure in the random oracle model, and efficient in practice.

The variation of wireless signal in dynamic indoor parking environments may seriously compromise the performance of fingerprint-based localization methods. In this regard, this paper investigates the problem of robust WiFi fingerprint-based vehicle tracking in dynamic indoor parking environments, aiming at designing an online learning framework to continuously train the localization model and counteract the effect of signal variation. Specifically, a Hidden Markov Model (HMM) based Online Evaluation (HOE) method is firstly proposed to assess the accuracy of localization results by measuring the inconsistency of locations inferred by WiFi fingerprinting and Dead Reckoning (DR). Further, an Online Transfer Learning (OTL) algorithm is designed to improve the robustness of the fingerprinting localization, which consists of a weight allocation scheme to combine two classification models (i.e., the batch model and the online model) and an instance-based transferring scheme to resample the offline fingerprints and retrain the batch model. Finally, we implement the system prototype and give comprehensive performance evaluation, which demonstrates that the proposed solutions can outperform the state-of-the-art localization algorithms around 28%$\sim$58% on vehicle tracking accuracy in dynamic indoor parking environments.

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.

With the rapid growth of Internet of Things, the number of heterogeneous wireless devices working in the same frequency band increases dramatically, leading to severe cross-technology interference. To enable coexistence, researchers have proposed a large number of mechanisms to manage interference. However, existing mechanisms have severe modifications in either the physical or MAC (medium access control) layers, making them hard to be deployed on commercial devices. In this paper, we design and implement SledZig to boost cross-technology coexistence for low-power devices through both enabling more transmission opportunities and avoiding interference. SledZig is fully compatible with the standard in both physical and MAC layers. It decreases the WiFi signal power on the channel of low-power devices while keeps the WiFi transmission power unchanged, through making constellation points in the overlapped subcarriers have the lowest power, which can be achieved by just encoding the WiFi payload. We implement SledZig on hardware testbed and evaluate its performance under different settings. Experiment results show that SledZig can effectively increase ZigBee transmissions and improve its performance over a WiFi channel under various WiFi data traffic, with as low as 6.94% WiFi throughput loss.

Public auditing schemes for cloud storage systems have been extensively explored with the increasing importance of data integrity. A third-party auditor (TPA) is introduced in public auditing schemes to verify the integrity of outsourced data on behalf of users. To resist malicious TPAs, many blockchain-based public verification schemes have been proposed. However, existing auditing schemes rely on a centralized TPA, and they are vulnerable to tempting auditors who may collude with malicious blockchain miners to produce biased auditing results. In this article, we propose a blockchain-based decentralized public auditing (BDPA) scheme by utilizing a decentralized blockchain network to undertake the responsibility of a centralized TPA, and also mitigate the influence of tempting auditors and malicious blockchain miners by taking the concept of decentralized autonomous organization (DAO). A detailed security analysis shows that BDPA can preserve data integrity against tempting auditors and malicious blockchain miners. A comprehensive performance evaluation demonstrates that BDPA is feasible and scalable.

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.

The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories,physical-layer CTCandpacket-level energy modulation(PLEM). Thephysical-layer CTCachieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

Leveraging emerging edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive multimedia applications (e.g., virtual reality and cloud gaming) onto edge servers. For high resource utilization, multiple rendering tasks run in the same GPU server and compete against each other for the computation resource. Each task has its requirement for performance, i.e., QoS target. A significant problem is how to schedule tasks so that each preset QoS is met and the performance of all tasks are maximized. We make the following contributions. First, we formulate the problem into a QoS constrained max-min utility problem. Second, we find that using the common natural logarithm as a utility function overly promotes one performance but demotes another. To avoid this phenomenon, we design a special utility function. Third, we propose an efficient scheduling algorithm, consisting of a resolution adjustment algorithm and a frame rate fair scheduling algorithm, both of which interact with each other. The former selects resolutions for tasks and the latter decides which task to process. We evaluate our method with actual rendering data, and the simulations demonstrate that our method can effectively improve task performance as well as satisfy QoS simultaneously.

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.

With the recent development of wireless communication, sensing, and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Nevertheless, it is challenging to process time-critical tasks due to unique characteristics of IoV, including heterogeneous computation and communication capacities of network nodes, intermittent wireless connections, unevenly distributed workload, massive data transmission, intensive computation demands, and high mobility of vehicles. In this article, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog, and the mobile fog on processing time-critical tasks in IoV. Then, we give a motivational case study by implementing a prototype of a traffic abnormity detection and warning system, which demonstrates the necessity and urgency of developing adaptive task offloading mechanisms in such a scenario and gives insight into the problem formulation. Furthermore, we formulate the offloading model, aiming at maximizing the completion ratio of time-critical tasks. On this basis, we propose an adaptive task offloading algorithm (ATOA). Specifically, it adaptively categorizes all tasks into four types of pending lists by considering the dynamic requirements and resource constraints, and then tasks in each list will be cooperatively offloaded to different nodes based on their features. Finally, we build the simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of ATOA.

Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem-how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused.

Many big-data computing applications have been deployed in cloud platforms. These applications normally demand concurrent data transfers among computing nodes for parallel processing. It is important to find the best transfer scheduling leading to the least data retrieval time-the maximum throughput in other words. However, the existing methods cannot achieve this, because they ignore link bandwidths and the diversity of data replicas and paths. In this paper, we aim to develop a max-throughput data transfer scheduling to minimize the data retrieval time of applications. Specifically, the problem is formulated into mixed integer programming, and an approximation algorithm is proposed, with its approximation ratio analyzed. The extensive simulations demonstrate that our algorithm can obtain near optimal solutions.

Software Defined Networking has been adopted to improve data center network efficiency. In SDN, the controllers are responsible for exchanging information with the switches to perform specific operations such as data forwarding. The transmission of control traffic usually uses networks different from data networks. However, building an additional wired network for the control traffic leads to high cabling complexity. Since a wireless network involves almost no cabling and is easy to install, we propose a wireless solution where switches make a wireless connection with controllers via wireless access points (AP). In this design, switches are divided into clusters, and an AP is placed at the center of each cluster. An important issue is to determine the minimum number of APs such that a given control traffic demand can be met. We propose an analytical model to evaluate the system throughput for possible clusterings, and an efficient algorithm to search for the optimal one. The extensive simulations demonstrate that our method can reduce cabling complexity significantly.

Virtual machine migration has been touted as one of the crucial technologies in improving data center efficiency, such as reducing energy cost and maintaining load balance. However, traditional approaches could not avoid the service interruption completely. Moreover, they often result in longer delay and are prone to failures. In this paper, we leverage the emerging named data networking (NDN) to design an efficient and robust protocol to support seamless virtual machine migration in cloud data center. Specifically, virtual machines (VMs) are named with the services they provide. Request routing is based on service names instead of IP addresses that are normally bounded with physical machines. As such, services would not be interrupted when migrating supported VMs to different physical machines. We further analyze the performance of our proposed NDN-based VM migration protocol, and optimize its performance via a load balancing algorithm. Our extensive evaluations verify the effectiveness and the efficiency of our approach and demonstrate that it is interruption-free.

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of transmissions for data collection by using pure CS is still large. The hybrid method of using CS was proposed to reduce the number of transmissions in sensor networks. However, the previous works use the CS method on routing trees. In this paper, we propose a clustering method that uses hybrid CS for sensor networks. The sensor nodes are organized into clusters. Within a cluster, nodes transmit data to cluster head (CH) without using CS. CHs use CS to transmit data to sink. We first propose an analytical model that studies the relationship between the size of clusters and number of transmissions in the hybrid CS method, aiming at finding the optimal size of clusters that can lead to minimum number of transmissions. Then, we propose a centralized clustering algorithm based on the results obtained from the analytical model. Finally, we present a distributed implementation of the clustering method. Extensive simulations confirm that our method can reduce the number of transmissions significantly.

Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of data transmissions required in CS method is still large. It is observed that there are many zero elements in the measurement matrix. In each round of data transmission in CS method, the sensor nodes corresponding to the zero elements in the measurement matrix do not have their own data to transmit. To further reduce the number of data transmissions in the network, we aim to compute a data gathering tree by taking advantages of these zero elements in the measurement matrix, such that the total number of data transmissions is minimized. We formulate the problem as linear programming with boolean variables. The problem is NP-hard. We propose heuristic algorithm to compute the Minimum Transmission Tree (MTT) for data gathering in CS methods. The MTT algorithm constructs a spanning tree by iteratively including the edge whose average incremental transmission cost is minimum. The simulation results demonstrate that our algorithm can reduce the number of transmissions significantly, compared with the methods using minimum spanning tree (MST), shortest path tree and the CS method with nonzero measurement coefficient using MST.