Hao Yue

Spectrum Management for Proactive Video Caching in Information-Centric Cognitive Radio Networks

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.

RAAC: Robust and Auditable Access Control With Multiple Attribute Authorities for Public Cloud Storage

Data access control is a challenging issue in public cloud storage systems. Ciphertext-policy attribute-based encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained, and secure data access control for cloud storage with honest-but-curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence, it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low efficiency of the system. Although multi-authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper, we propose a novel heterogeneous framework to remove the problem of single-point performance bottleneck and provide a more efficient access control scheme with an auditing mechanism. Our framework employs multiple attribute authorities to share the load of user legitimacy verification. Meanwhile, in our scheme, a central authority is introduced to generate secret keys for legitimacy verified users. Unlike other multi-authority access control schemes, each of the authorities in our scheme manages the whole attribute set individually. To enhance security, we also propose an auditing mechanism to detect which attribute authority has incorrectly or maliciously performed the legitimacy verification procedure. Analysis shows that our system not only guarantees the security requirements but also makes great performance improvement on key generation.

Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach

Device-free wireless localization and activity recognition (DFLAR) is a new technique, which could estimate the location and activity of a target by analyzing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and promising technique for many emerging smart applications. The key question of DFLAR is how to characterize the influence of the target on wireless signals. Existing work generally utilizes statistical features extracted from wireless signals, such as mean and variance in the time domain and energy as well as entropy in the frequency domain, to characterize the influence of the target. However, a feature suitable for distinguishing some activities or gestures may perform poorly when it is used to recognize other activities or gestures. Therefore, one has to manually design handcraft features for a specific application. Inspired by its excellent performance in extracting universal and discriminative features, in this paper, we propose a deep learning approach for realizing DFLAR. Specifically, we design a sparse autoencoder network to automatically learn discriminative features from the wireless signals and merge the learned features into a softmax-regression-based machine learning framework to realize location, activity, and gesture recognition simultaneously. Extensive experiments performed in a clutter indoor laboratory and an apartment with eight wireless nodes demonstrate that the DFLAR system using the learned features could achieve 0.85 or higher accuracy, which is better than the systems utilizing traditional handcraft features.

Spectrum-Aware Anypath Routing in Multi-Hop Cognitive Radio Networks

Cognitive radio networks (CRNs) have been emerging as a promising technique to improve the spectrum efficiency of wireless and mobile networks, which form spectrum clouds to provide services for unlicensed users. As spectrum clouds, the performance of multi-hop CRNs heavily depends on the routing protocol. In this paper, taking the newly proposed Cognitive Capacity Harvesting network as an example, we study the routing problem in multi-hop CRNs and propose a spectrum-aware anypath routing (SAAR) scheme with consideration of both the salient spectrum uncertainty feature of CRNs and the unreliable transmission characteristics of wireless medium. A new cognitive anypath routing metric is designed based on channel and link statistics to accurately estimate and evaluate the quality of an anypath under uncertain spectrum availability. A polynomial-time routing algorithm is also developed to find the best channel and the associated optimal forwarding set and compute the least cost anypath. Extensive simulations show that the proposed protocol SAAR significantly increases packet delivery ratio and reduces end-to-end delay with low communication and computation overhead, which makes it suitable and scalable to be used in multi-hop CRNs.

Energy-Aware Scheduling for Multi-Hop Cognitive Radio Networks

Cognitive radio (CR) technology, which enables unlicensed secondary users to opportunistically access the unused licensed spectrum, has attracted more and more attention from both academia and industry due to its potential to significantly improve the spectrum utilization. Considering both temporal and spatial variations of spectrum availability, this paper focuses on improving the energy efficiency in CR networks by opportunistically serving the delay-tolerant data only when enough spectrum is available. Based on this idea, a stochastic optimization problem is formulated to integrate the power control, link scheduling, and routing, which minimizes the expected power consumption while guaranteeing the system stability. To obtain the solution, we use the Lyapunov optimization technique and design an online algorithm, which solves a sub-problem without future knowledge of the related stochastic models (e.g., random data arrival and spectrum supply). Besides, in view of the NP-hardness of the sub-problem, we also develop a heuristic algorithm based on branch-and-bound framework to obtain the approximate solution with low computing complexity. Theoretical analysis shows that our algorithm offers an explicit tradeoff between energy consumption and delay performance. Numerical results also confirm the effectiveness of our solutions.

Receive Buffer Pre-division Based Flow Control for MPTCP

Multipath TCP (MPTCP) enables terminals utilizing multiple interfaces for data transmission simultaneously, which provides better performance and brings many benefits. However, using multiple paths brings some new challenges. The asymmetric parameters among different subflows may cause the out-of-order problem and load imbalance problem, especially in wireless network which has more packet loss. Thus it will significantly degrade the performance of MPTCP. In this paper, we propose a Receive Buffer Pre-division based flow control mechanism (RBP) for MPTCP. RBP divides receive buffer according to the prediction of receive buffer occupancy of each subflow, and controls the data transmission on each subflow using the divided buffer and the number of out-of-order packets, which can significantly improve the performance of MPTCP. We use the NS-3 simulations to verify the performance of our scheme, and the simulation results show that RBP algorithm can significantly increase the global throughput of MPTCP.

FFRD: Fragment forwarding and reassembly decoupling based chunk transmission in NDN

In-network caching is an inherent feature of Named Data Networking (NDN), and the basic data unit of naming and caching in NDN is called “chunk”. However, a chunk needs to be further fragmented into fragments when its size is larger than the link layers Maximum Transmission Unit (MTU). Furthermore, fragments also need to be reassembled into the original chunk at intermediate routers so that subsequent requests can be satisfied by the cached copy. The current NDN design adopts a coupled hop-by-hop reassembly mechanism where fragments can be forwarded to the next hop only if the chunk has been fully reassembled, which leads to a significant end-to-end delay when large chunks are transmitted due to the processing delay at intermediate routers. In this paper, we propose a reliable and fast chunk transmission protocol based on Fragment Forwarding and Reassembly Decoupling (FFRD) at intermediate routers in NDN. In FFRD, fragments are forwarded to the next hop upon being received and reassembly occurs after all fragments are received. Meanwhile, FFRD can timely detect and recover packet losses at intermediate routers to minimize the transmission delay. The simulation results show that FFRD can significantly reduce chunk retrieval delay and decrease end-to-end Interest packet retransmission times, especially over lossy networks with non-negligible packet losses.

Incentive cooperative caching for localized information-centric networks

In-network caching has the potential to improve network efficiency and content distribution performance by satisfying user requests with cached content in Information-Centric Networking (ICN). Due to the fact that users in the same network domain can easily share their cached content with each other via home network devices, to reduce the transmission cost for obtaining content from the core network, caching cooperation home networks are constructed. Derived from the analysis of the economic relations among ICN entities, we propose an efficient incentive cooperative caching mechanism for content retrieval in localized ICNs in this paper. Particularly, access networks give rebates to those users who provide locally cached content for content retrieval in cache cooperation. To minimize the cost (including rebate and transmission cost) of obtaining a piece of content in the proposed caching framework, we also formulate an optimal caching problem as a multiple-choice knapsack problem. Furthermore, a sub-optimal caching scheme is proposed to handle the optimal content placement problem efficiently. Simulation results verify the superiority and efficiency of the proposed sub-optimal caching scheme compared with random caching.

Context Awareness with Ambient FM Signal Using Multi-Domain Features

Context awareness plays an important role in many emerging applications, such as mobile computing and smart space. Since FM signal is ubiquitous, it has been recognized as an attractive and promising technique to realize context awareness. When a target is at different locations or performs different activities, it will exert different influence on the FM signal around it. Therefore, it is possible to deduce its location and activity by analysing its influence on the FM signal. However, FM signal is extremely weak and noisy, which makes it a challenging task to achieve high-performance context awareness. In this paper, we propose a new method for improving the performance of an FM-based context-aware system using multi-domain features. Specifically, we extract signal features not only from the time domain, but also from the wavelet domain, the frequency domain, and the space domain, and construct robust and discriminative multi-domain features to characterize the FM signal. Furthermore, we also model context awareness as a classification problem and develop a robust iterative sparse representation classification algorithm to efficiently solve this problem. Extensive experiments performed in a 7.2m×10.8m clutter indoor laboratory with one multi- channel FM receiver demonstrate that the proposed schemes could achieve more than 90% accuracy of location estimation and activity recognition when 3 antennas are used.