Compared with conventional virtual rehabilitation programs, the self-adaptive virtual rehabilitation system has the advantage of dynamically adjusting the training difficulty according to the users' real-time motion data collected, showing the potential to improve the rehabilitation experiences and assist the therapists with the flexibility provided. Movement enhancement, which visually amplifies the user's motion, exhibits significant promise in rehabilitation to improve the user's confidence and motivation. This study aims to propose a self-adaptive strategy in a virtual rehabilitation system based on movement enhancement and evaluate its effectiveness in improving user experience and performance. This study will be beneficial for the future development of virtual rehabilitation programs that combine self-adaptive systems and movement modulation, consequently helping individuals involved in virtual rehabilitation with improved user experience.

The last decade has witnessed unprecedented de-velopment in blockchain smart contracts. While smart contracts inherit the decentralization and other security properties of blockchain, they are hampered by the lack of privacy protection and poor performance of blockchain. In this paper, we propose a parallel contract execution framework, ParaEthereum, that combines blockchain and trusted execution environments (TEEs) to construct private, efficient, and scalable smart contracts. By introducing TEEs, ParaEthereum performs contract execution off the chain through enclave-enabled computing nodes and confirms the correctness of the execution results on the chain, achieving the decoupling of contract execution and consensus. To meet system availability requirements and enable concurrent exe-cution of transactions, each transaction is executed independently by a set of computing nodes determined by its execution set, and different computing nodes process transactions within the block concurrently based on the constructed transaction dependency graph. We also conducted extensive tests on our proposed scheme with respect to its efficiency and effectiveness.

This paper investigates the correlation between building material properties and indoor network coverage, encompassing both indoor Wi-Fi and outdoor 5G technologies to provide customized network services tailored to users' needs in diverse areas. We first analyze the impact of building material characteristics, with a special focus on wall materials, on the distribution of wireless signal propagation. Then, a ray-tracing-based method is introduced to synthetically generate high-quality training data that covers fine-grained network scenarios with a wide range of wall materials, extending beyond traditional materials. This dataset serves as the foundation for our proposed Global Embedding Isomorphism Network (GemNet), a machine learning framework that facilitates the prediction of optimal material parameters for customized in-building coverage. This innovation enables architects and builders to design novel, network-friendly materials, ensuring ubiquitous and on-demand network services. Extensive evaluations consistently demonstrate a re-markable prediction accuracy of 90.52% on material parameters, underscoring the framework's ability to optimize indoor wireless network planning through the lens of material engineering.

The software-based implementation of differential privacy mechanisms has been shown to be neither friendly for lightweight devices nor secure against side-channel attacks. In this work, we aim to develop a hardware-based technique to achieve differential privacy by design. In contrary to the conventional software-based noise generation and injection process, our design realizes local differential privacy (LDP) by harnessing the inherent hardware noise into controlled LDP noise when data is stored in the memory. Specifically, the noise is tamed through a novel memory design and power downscaling technique, which leads to double-faceted gains in privacy and power efficiency. A well-round study that consists of theoretical design and analysis and chip implementation and experiments is presented. The results confirm that the developed technique is differentially private, saves 88.58% system power, speeds up software-based DP mechanisms by more than$10^{6}$times, while only incurring 2.46% chip overhead and 7.81% estimation errors in data recovery.

With the ever-growing interest in modeling complex graph structures, graph neural networks (GNN) provide a generalized form of exploiting non-Euclidean space data. However, the global graph may be distributed across multiple data centers, which makes conventional graph-based models incapable of modeling a complete graph structure. This also brings an unprecedented challenge to user privacy protection in distributed graph learning. Due to privacy requirements of legal policies, existing graph-based solutions are difficult to deploy in practice. In this paper, we propose a privacy-preserving graph neural network based on local graph augmentation, named LGA-PGNN, which preserves user privacy by enforcing local differential privacy (LDP) noise into the decentralized local graphs held by different data holders. Moreover, we perform local neighborhood augmentation on low-degree vertices to enhance the expressiveness of the learned model. Specifically, we propose two graph privacy attacks, namely attribute inference attack and link stealing attack, which aim at compromising user privacy. The experimental results demonstrate that LGA-PGNN can effectively mitigate these two attacks and provably avoid potential privacy leakage while ensuring the utility of the learning model.

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the throughput and the quality of entanglement distribution in a multi-hop quantum repeater network. Compared to existing work which aims to heuristically maximize the entanglement distribution rate (EDR) and/or entanglement fidelity, our goal is to characterize the maximum achievable worst-case fidelity, while satisfying a bound on the maximum achievable expected EDR between an arbitrary pair of quantum nodes. This characterization will provide fundamental bounds on the achievable performance region of a quantum network, which can assist with the design of quantum network topology, protocols and applications. However, the task is highly non-trivial and is NP-hard as we shall prove. Our main contribution is a fully polynomial-time approximation scheme to approximate the achievable worst-case fidelity subject to a strict expected EDR bound, combining an optimal fidelity-agnostic EDR-maximizing formulation and a worst-case isotropic noise model. The EDR and fidelity guarantees can be implemented by a post-selection-and-storage protocol with quantum memories. By developing a discrete-time quantum network simulator, we conduct simulations to show the characterized performance region (the approximate Pareto frontier) of a network, and demonstrate that the designed protocol can achieve the performance region while existing protocols exhibit a substantial gap.

As a standard solution to the problem of private deep learning, differential privacy (DP) is widely used in graph neural networks (GNNs) to protect sensitive information about the input graph data. However, most existing DP algorithms for GNNs protect the privacy of every attribute for each node. This results in the need for injecting a large amount of noise, making these methods significantly underperform their non-private counterparts. We argue that in some practical scenarios, node labels serve as the only or the most sensitive attribute, where label differential privacy, a more fine-grained notion of differential privacy that only protects the labels is more appropriate. To better capture these scenarios and improve the trade-off between data privacy and model accuracy, we propose a novel method of training GNNs under label differential privacy. Instead of naively adding noise to the node labels before training the GNN, our method follows the strategy of Private Aggregation of Teacher Ensembles (PATE) to generate differentially private node labels with both high accuracy and strong privacy guarantee. We also propose a label denoising module that takes advantage of the graph structure to further improve the accuracy of the trained model. Additionally, our method is model-agnostic, making it applicable to any GNN architecture. We evaluate its performance on two commonly used benchmark datasets and demonstrate its capability to learn high-performance models while ensuring privacy.

In this paper, we present a multi-domain network slicing scheme for satellite-terrestrial edge computing networks (STECNs) that admits different slice configurations. Each slice is configured to include terrestrial-air, terrestrial-satellite, terrestrial-air-satellite, or terrestrial-air-satellite-gateway domain topologies. However, the multi-domain nature of STECNs makes slicing especially challenging since the cross-domain orchestrator has no knowledge of the resource availability in different domains. Our goal is to design an algorithm that builds a belief in resource availability to jointly optimize the slice configuration, service level agreement (SLA) decomposition, routing, and resource allocation. We model the slice/resource availability as a Markov process to track the probability of achieving the SLA per configuration. To solve the multi-domain slicing problem, the cross-domain orchestrator interacts with the configuration coordinator to define an index-based slice configuration policy based on restless multi-armed bandits (RMABs), which is aware of the network traffic. The configuration coordinator decomposes the SLA and each domain controller solves the optimum routing and resource allocation. Our slicing scheme is evaluated using five typical application scenarios for STECNs. Simulation results show that our scheme achieves six times higher reward than agnostic schemes and efficiently performs multi-domain slicing with low complexity.

Disasters are constant threats to humankind, and beyond losses in lives, they may cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring communication services is of vital importance. Although existing works have proposed many architectural and protocol designs, none of them have taken human factors and social equality into consideration. Recent sociological studies have shown that people from marginalized groups (e.g., low income) are more vulnerable to communication outages. In this paper, we make efforts in integrating human factors – extracted from our collected dataset after Hurricane Harvey in 2017 in Texas, US – into an empirical optimization model to determine strategies for post-disaster communication restoration. We cast the design into a mix-integer non-linear programming problem, which captures the essential features of the design but is proven too complex to be solved. To find approximate solutions, we leverage a suite of convex relaxations and then develop heuristic algorithms to efficiently solve the transformed optimization problem. Based on our collected dataset, we further evaluate and demonstrate how our design could prioritize communication services for vulnerable people and promote social equality compared with an existing modeling benchmark.

Long-range wide-area network (LoRaWAN) is a low-power wide-area network (LP-WAN) protocol developed for low-bandwidth, battery-operated long-range sensors. However, the LoRaWAN specification has several security issues and utilizes software cryptography, which is not energy efficient. Our proposed solution combines a modified LoRaWAN protocol with a hardware-based cryptographic coprocessor that includes secure on-chip key storage for encryption, decryption, and digital signature creation. This design reduces the energy utilization of the wireless nodes while addressing several of the LoRaWAN threat surfaces. This article provides a security analysis of the reduced threat surfaces and demonstrates the improved energy efficiency of the LoRaWAN nodes with the integrated solution.

It is often observed that people's data are scattered across various organizations and these data can be used to generate usable insights when integrated. However, data fusion from multiple data hosting sites could put user privacy at risk albeit with some security mechanisms. This paper studies a data-analytic platform that adopts the Kulldorff scan statistic to determine infectious-disease spatial hotspots by integrating and analyzing users’ health and location data that are respectively stored in two clouds. We examine the privacy threats to this platform which has a key-oblivious inner product encryption (KOIPE) mechanism in place to ensure that only coarse-grained statistical data is revealed to the honest-but-curious (HbC) entity. To protect user privacy from the designed inference attack, we exploit a game-theoretic approach to incentivize users to form anonymous clusters with a quantitative privacy guarantee. We conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of design overhead and privacy level.

Cloud outsourcing provides flexible storage and computation services for data users in a low cost, but it brings many security threats as the cloud server may not be fully trusted. Previous secure outsourcing solutions mostly assume that the server is honest-but-curious while the adversary model of a malicious server that may return incorrect results is rarely explored. Moreover, with the increasing popularity of verifiable computations, existing verification schemes are yet not efficient and cannot cater to different scenarios in practice. In this paper, we propose a blockchain-based verifiable search framework in the adversarial cloud outsourcing context. When outsourcing the encrypted data to the cloud or Interplanetary File System (IPFS), we also store the encrypted data index in a decentralized blockchain (i.e., Ethereum in this paper) which is public and cannot be modified. Once a user is authorized, he/she can flexibly obtain the query results and efficiently check the query integrity via the pre-deployed smart contract, without the need of the data owner being online. Moreover, for user's privacy protection, we construct a stealth authorization scheme to deliver the access authorization without any identity disclosure. Finally, theoretical analysis and performance evaluation validate the security and efficiency of our proposed framework.

The current privacy-preserving Graph Neural Networks (GNNs) cannot provide security and privacy guarantees against malicious adversaries without sacrificing accuracy and efficiency. For example, the Secure Multi-party Computation (MPC) can resist malicious adversaries while adding severe overhead. Trusted Execution Environment (TEE), such as Intel Software Guard Extension (SGX), can guarantee privacy and faithful execution without compromising efficiency. However, existing attacks can compromise the confidentiality of SGXs. Besides, the CPU-based structure of SGX restricts its extensibility that cannot perform collaborative computation with GPUs. To address the above issues, we propose a novel GNN training and inference framework to support data holders outsourcing their computation tasks to servers. First, we combine the advantage of MPC and the code integrity protection provided by SGXs to resist malicious adversaries without sacrificing efficiency. Second, we adopt a strategy that allows the servers to transfer the parallelizable computation task to the untrusted yet high-performance GPUs, further improving efficiency without hindering privacy. To the best of our knowledge, our proposal is the first privacy-preserving GNN framework against malicious adversaries without sacrificing accuracy and efficiency. Experiments on real-world citation datasets have demonstrated the performance of our framework regarding security, privacy, accuracy, and efficiency.

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.

It is observed that modern vehicles are becoming more and more powerful in computing, communications, and storage capacity. By interacting with other vehicles or with local infrastructures (i.e., fog) such as road-side units, vehicles and fog devices can collaboratively provide services like crowdsensing in an efficient and secure way. Unfortunately, it is hard to develop a secure and privacy-preserving crowdsensing report deduplication mechanism in such a system. In this article, we propose a scheme FVC-Dedup to address this challenge. Specifically, we develop cryptographic primitives to realize secure task allocation and guarantee the confidentiality of crowdsensing reports. During the report submission, we improve the message-lock encryption (MLE) scheme to realize privacy-preserving report deduplication and resist the fake duplicate attacks. Besides, we construct a novel signature scheme to achieve efficient signature aggregation and record the contributions of each participant fairly without knowing the crowdsensing data. The security analysis and performance evaluation demonstrate that FVC-Dedup can achieve secure and privacy-preserving report deduplication with moderate computing and communication overhead.

360-degree virtual reality videos greatly improve the video experience by providing users with a more immersive and interactive environment than standard streaming video. However, 360-degree videos suffer from bandwidth limits. Existing bandwidth-efficient solutions mainly focus on spatially cutting 360-degree video into tiles, and only provide video content in the Field-of-View (FoV) of users with high quality to reduce bandwidth consumption. Although existing tile-based schemes can reduce the bandwidth consumption, the bandwidth and transmission delay provided by a single-path TCP may still not meet the high requirements of 360-degree videos. Multipath TCP (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the mobile devices with various radio interfaces to aggregate multipath bandwidth and improve the throughput. In this paper, by taking the advantage of MPTCP, we propose an MPTCP-based adaptive streaming framework for 360-degree Virtual Reality videos, named MP-VR. MP-VR dynamically selects the appropriate tile bitrate according to the bandwidth and transmission delay of different subflows. Then it schedules the video segments to subflows to improve QoE of users. We conduct experiments on a testbed in our lab and simulations on NS-3. Evaluation results show that MP-VR outperforms existing tile-based strategies when network fluctuations or errors in FoV predictions occur.

Edge artificial intelligence (AI) achieves real-time local data analysis for IoT systems, enabling low-power and high-speed operation, but comes with privacy-preserving requirements. The memristor-based computing system is a promising solution for edge AI, but it needs a low-cost privacy protection mechanism due to limited resources. In this article, we propose a noise distribution normalization (NDN) method to add Gaussian distributed noise through hardware implementation, thereby achieving differential privacy in edge AI. Instead of using traditional algorithmic noise-insertion methods, we take advantage of inherent cycle-to-cycle variations of memristors during the weight-update process as the noise source, which does not incur extra software or hardware overhead. In one case study, the proposed method realizes ultralow-cost differentially private stochastic gradient descent (DP-SGD) for edge AI in IoT systems, achieving a 3.5%-15.5% average recognition accuracy improvement under different noise levels, as compared with a baseline mechanism.

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.

To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, as in-network content caching can be directly utilized to respond users’ requests, multimedia content retrieval is beyond content providers’ control and makes it hard for them to implement access control and service accounting. In this paper, we propose a Fine-grained Accountable and Space-Efficient access control scheme, called FASE, for multimedia content distribution. FASE allows content providers to be fully offline while making the best of in-network caching. In FASE, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure secure fine-grained access control. Our scheme is efficient in both space and time. By designing one time chameleon signature (OTCS), users can keep anonymous during the authentication, and their privileges can be conveniently revoked when needed. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users’ request process. Through formal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices’ energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

It is a known issue that low-latency communication is hard to achieve when using multiple network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN) simultaneously. A main underlying cause of this issue is that the packets with lower sequence number are stalled on a high-latency path, thus the early arriving packets with higher sequence number become “out-of-order (OFO)” packets. These OFO packets may excessively consume receiver’s buffer, causing long reordering delay and unnecessary packet retransmission. In this paper, we present a novel design of packet scheduling for Multipath TCP (MPTCP), called OverLapped Scheduler (OLS), able to tackle the OFO-packet problem more effectively. OLS can guarantee sufficient throughput on demand of upper layer applications, and utilizes the remaining bandwidth to reduce OFO-packets. To do so, OLS schedules packets according to their arrival time and sends a controlled number of redundant packets to avoid the impact of inaccurate arrival-time estimations due to network jitter. We implement OLS in a Linux kernel, and the experiments show that in asymmetric networks with or without jitter, OLS can effectively reduce OFO-packets and transmission latency while maintaining a sufficient throughput, which makes it fully capable to meet the requirements of applications such as live video streaming.

Vehicular Edge Computing (VEC) is an integration of Mobile Edge Computing with traditional vehicular networks, which aims to shift computing, communication, and storage resources to the edge of networks and is more close to vehicles. Due to the high mobility of vehicles, connection interruption and network changes may frequently occur. To tackle this issue, cache-based content delivery is regarded as a promising solution to achieve efficient data sharing in VEC. However, privacy-preserving access control and fair incentive distribution are rarely taken into account in prior VEC-oriented studies. In this paper, we propose an efficient and secure access control scheme for providing cache-based content delivery in VEC. Specifically, we construct two layer access control to enable flexible access control and fair incentive distribution with the assistance of edge nodes. Moreover, we construct a group signature-based scheme to achieve anonymous authentication and conditional revocation. The performance analysis shows that our secure scheme has an acceptable effect on network performance.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

The emerging paradigm - Software-Defined Networking (SDN) and Network Function Virtualization (NFV) - makes it feasible and scalable to run Virtual Network Functions (VNFs) in commercial-off-the-shelf devices, which provides a variety of network services with reduced cost. Benefitting from centralized network management, lots of information about network devices, traffic and resources can be collected in SDN/NFV-enabled networks. Using powerful machine learning tools, algorithms can be designed in a customized way according to the collected information to efficiently optimize network performance. In this paper, we study the VNF placement problem in SDN/NFV-enabled networks, which is naturally formulated as a Binary Integer Programming (BIP) problem. Using deep reinforcement learning, we propose a Double Deep Q Network-based VNF Placement Algorithm (DDQN-VNFPA). Specifically, DDQN determines the optimal solution from a prohibitively large solution space and DDQN-VNFPA then places/releases VNF Instances (VNFIs) following a threshold-based policy. We evaluate DDQN-VNFPA with trace-driven simulations on a real-world network topology. Evaluation results show that DDQN-VNFPA can get improved network performance in terms of the reject number and reject ratio of Service Function Chain Requests (SFCRs), throughput, end-to-end delay, VNFI running time and load balancing compared with the algorithms in existing literatures.

A practical WiFi system only achieves a discrete data rate adjustment due to hardware constraints while channel signal-to-noise ratio (SNR) is continuous. This mismatch leads to the SNR gaps. In this paper, we introduce a novel communication mechanism, CoS (Communication through Silent subcarriers), which turns the wasted SNR gaps into new opportunities for transmitting control messages for free. Compared with traditional piggybacking schemes, CoS is more reliable to transmit control messages from one node to many nodes. In CoS, silent subcarriers are inserted into data packets and the intervals between adjacent silent subcarriers are utilized to encode information. Since the wasted SNR gap results in under-utilization of the channel code, the data bit errors induced by silent subcarriers are corrected by the correcting capability of the existing channel code as long as we carefully design the total number of inserted silent subcarriers. Based on CoS, we design CoS-MAC to validate the effectiveness of CoS. We measure the throughput of free control messages achieved by CoS under various channel conditions and conduct simulations to show the throughput gain achieved by CoS-MAC over the existing schemes.

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.

In order to cope with the explosive growth of mobile traffic, many traffic offloading schemes such as heterogenous networks have been developed to enhance network capacity of the Radio Access Network (RAN). Among them, networking of Low-Earth Orbit (LEO) satellites promises to significantly improve the RAN performance due to its economical prospect and advantages in high bandwidth and low latency. In this paper, by introducing the cache-enabled LEO satellite network as a part of RAN, we propose an integrated satellite/terrestrial cooperative transmission scheme to enable an energy-efficient RAN by offloading traffic from base stations through satellite's broadcast transmission. Considering energy-constraints of satellites, we then formulate a nonlinear fractional programming problem aiming at optimizing transmission energy efficiency of the system. In order to effectively solve this problem, we transform it into an equivalent one, and then adopt iteration and sub-problem decomposition to obtain the optimal solution for each optimization variable, i.e., block placement, power allocation, and cache sharing variable. Numerical results show that compared with traditional terrestrial scheme, our cooperative transmission scheme achieves significant performance improvement in terms of traffic offloading and energy efficiency, especially in an environment of high request consistency degree.

Bluetooth Low Energy (BLE) has become an intrinsic wireless technology for the Internet of Things (IoT). With the proliferation of BLE-embedded IoT devices, it is important to study the security and privacy implications of BLE. The forefront attack to BLE devices is the wireless sniffing attack, which would lead to more detrimental threats like jamming, encryption cracking or system penetration. Existing sniffing attacks are based on the correct detection of BLE connection initiation state, but they become ineffective for BLE long-lived connections. In this paper, we focus on the adversary setting with a low-cost single radio and develop a suite of real-time algorithms to determine the key parameters necessary to follow and sniff a BLE connection in the connected state. We implement our algorithms in the open source platform - Ubertooth One and evaluate its performance in terms of sniffing overhead and accuracy. By comparing with state-of- the-art schemes, experimental results show that our sniffer achieves much higher sniffing accuracy (over 80%) and better stability to BLE operational dynamics.

Outsourcing storage and computation to clouds is popular but also raises security concerns. Most existing solutions mainly focus on an honest-but-curious cloud server, while security designs against a malicious server have not drawn enough attention. Although there are a few works addressing the issue of verifiable designs that enable the data owner to verify the integrity of search results. Unfortunately, these verification schemes are not efficient and or applicable from one scenario to another. Motivated by this, in this paper, we propose a publicly verifiable search framework for outsourced encrypted data based on blockchain. In our framework, we store the encrypted index in a decentralized blockchain (Ethereum) while outsourcing the corresponding encrypted data to the cloud or Interplanetary File System (IPFS). Thus, once a user is authorized, he/she can get the query results and check the query integrity efficiently by the designed smart contract anytime without data owner being online. Besides, to guarantee the privacy of the data user in the Ethereum, we construct a stealth authorization scheme to achieve access authorization delivery. The security analysis and performance evaluation show that the proposed scheme is secure and practical for verification of encrypted data.

UAV has attracted a significant amount of attention for its low-cost and diverse applications like video surveillance, auxiliary communication, etc. In this paper, the UAV fire surveillance network is proposed and the maximization of the UAV-centric energy efficiency (EE) is investigated by jointly taking the source/channel rate control and flow routing into account. The design is cast into a cross-layer optimization problem, which is proven to be difficult to solve. In light of it, a parametric transformation approach is adopted to convert the original problem into a tractable form and further decouple it into two independent subproblems. An efficient algorithm consisting of a two-layer iterative algorithm with an inner loop and an outer loop is proposed to solve the transformed problem. Simulation results show the impact of the network configuration on the network-wide EE and the performance of the proposed algorithm.

Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

Offloading cellular traffic to WiFi networks plays an important role in alleviating the increasing burden on cellular networks. However, excessive traffic offloading brings severe packet collisions into a WiFi network due to its contention-based medium access scheme, which significantly reduces the WiFi network's throughput. In this paper, we propose DAO, a device-to-device (D2D) communications-assisted traffic offloading scheme to improve the amount of traffic offloaded from cellular to WiFi in integrated cellular and WiFi networks. Specifically, in an integrated cellular-WiFi network, the cellular network exploits D2D communications in licensed cellular bands to aggregate traffic from cellular users before offloading it to the WiFi network to reduce the number of contending users in WiFi access. The traffic offloading process in DAO is formulated as an optimization problem that jointly takes into account the activations of aggregation nodes (ANs) and the connections between ANs and offloading users to maximize the offloaded traffic while guaranteeing the long-term data rates required by the offloading users. Extensive simulation results reveal the significant performance gain achieved by DAO over the existing schemes.

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations, while keeping privacy preservation and achieving the verification requirements: freshness, authenticity, and completeness. Finally, we extend our scheme by dividing the accumulation tree into different small accumulation trees to make our scheme scalable. The security analysis and performance evaluation show that the proposed scheme is secure and practical.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

A typical application of unmanned aerial vehicles (UAVs) is surveillance of distant targets, where data collected by its sensors need to be transmitted back to a ground terminal (GT) for further processing in a timely manner. Due to the limited battery capability of the UAV, the sensed data could be preprocessed in a UAV to reduce the amount of data transmitted, which could potentially reduce the average power consumption at the UAV, especially when the transmission link quality is poor. In this paper, a probabilistic approach is adopted to schedule the transmission and computing of the data tasks based on the UAV and GT's buffer states. The joint transmission and computing problem can be modeled as a four-dimensional Markov chain, based on which the average delay of each task and the average power consumption at the UAV can be obtained. Our design goal is to minimize the average power consumption under the delay constraints. To do that, a delay-constrained power minimization problem is solved by an proposed method to obtain the power-optimal joint transmission and computation scheduling (JTCS) policy efficiently. Finally, the optimization results are validated with extensive simulations.

Nowadays, vehicles are powerful enough to carry communications, computing and storage capabilities. By interacting with each other and with local (i.e., fog) infrastructures like road-side units, a cohort of vehicles and fog devices could collaboratively provide services like crowdsensing in an unprecedentedly secure and efficient way. However, it has been widely recognized as a challenging work in the vehicular system to develop a secure and efficient sensing task allocation and data de-duplication mechanism. In this paper, we attempt to develop a scheme to address this challenge. Specifically, we use the Elliptic Curves Cryptography (ECC) algorithm to realize secure allocation of location-dependent tasks. During the report submission phase, we adopt the improved message-lock encryption to realize privacy-preserving data de-duplication and to resist the duplicate-faking attacks. Besides, we present a novel signature scheme that can efficiently record the contributions of each vehicle. The security analysis and performance evaluation demonstrate that the proposed scheme can achieve secure and privacy-preserving report de-duplication with moderate computation and communication overhead.

In vehicular ad hoc networks (VANETs), vehicles' mobility and urban obstacles may cause frequent communication disconnections and sudden network changes. As a result, the traditional IP-based node-to-node content delivery mechanism does not adapt well to such changes in VANETs. To solve this problem, in this paper, we study the Named Data Networking (NDN) architecture to support efficient and secure data forwarding in urban VANETs. To meet security requirements, we adopt the encryption-based name obfuscation to achieve Interest-based access control. Moreover, the revocation of illegal vehicles and the updated operation are addressed by proxy re-encryption method, which saves the main communication overhead during the process. Finally, we design an incentive scheme to guarantee the utility of NDN in VANETs. The security analysis shows that the proposed secure scheme can satisfy security requirements of the data forwarding in VANETs. The performance analysis indicates that the overhead caused by the proposed secure scheme is low and acceptable.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

The Internet of Things (IoT) becomes a novel paradigm as more and more devices are connected to the Internet, enabling several innovative applications such as smart home, industrial automation, and connected health. However, the cyber-attack to these applications is a big issue and countermeasures are in dire need to provide system security and user privacy. In this paper, we address the traffic analysis attack to smart homes, where adversaries intercept the Internet traffic from/to the smart home gateway and profile residents' behaviors through digital traces. Traditional cryptographic tools may not work well due to the effectiveness of adversaries' machine learning algorithms in classifying encrypted traffic, so here we propose a privacy-preserving traffic obfuscation framework to achieve the goal. To be specific, we leverage the smart community network of wirelessly connected smart homes and intentionally direct each smart home's traffic to another home gateway before entering the Internet. The design jointly considers the network energy consumption and the resource constraints in IoT devices, while achieving strong differential privacy guarantee so that adversaries cannot link any traffic flow to a specific smart home. Besides, we consider a hostile smart community network and develop secure multihop routing protocols to guarantee the source/destination unlinkability and satisfy each user's personalized privacy requirement. To evaluate the effectiveness of our framework in protecting privacy and reducing network energy consumption, extensive simulations are conducted and the results demonstrate that our design outperforms other differential privacy mechanism in preserving privacy and minimizing network utility cost.

Given the fact that roughly 800 000 children are reported missing in the United States every year, how to assist parents to track their children becomes an important problem. Even though many children tracking systems have been proposed, the high cost and energy limitation of locators are the stumbling blocks which limit the application of those systems. To address this challenge, we design a children tracking system based on RFID technology, where children carry RFID tags and the system is responsible for locating the children by aggregating the readings from the deployed readers. Noting the importance of localized processing for efficient children tracking, we further study how the locally available computing resource, such as the mobile devices carried by the park employees and visitors, can be utilized for service provisioning. Since mobile devices have limited energy, we study an energy efficiency optimization problem by jointly considering the resource allocation and user association. The formulated problem is solved by a dynamic updating matching approach. Through extensive simulations, we have demonstrated the effectiveness of our proposed solution.

Efficient design of wireless networks benefits from the exchange of control messages. However, control message itself consumes scarce channel resources. In this paper, we propose CoS (Communication through symbol Silence), a novel communication strategy that conveys control messages for free without consuming extra channel resources. CoS inserts silence symbols in data packets and leverages the intervals between inserted silence symbols to encode information. The silence symbols can be located by energy detection at the granularity of symbols and the intervals are interpreted into transmitted control messages. Based on our key insights that the channel code is under-utilized in current wireless networks and the distribution of symbol errors within a data packet is predictable in indoor wireless transmissions, the symbols erased by silence symbols are recovered by the coding redundancy that is originally used to correct symbol errors. A rate adaptation scheme is designed to dynamically adjust the rate of free control messages according to channel conditions so that the transmission of free control messages does not harm the original data throughput. We implement CoS on our software defined radio platform to validate the feasibility of CoS. The extensive results show that the control messages are delivered with close to 100% accuracy in a large SNR range. In addition, we measure the achievable capacity of free control messages in various channel conditions.

In cognitive radio networks (CRNs), spectrum database has been well recognized as an effective means to dynamically sharing licensed spectrum among primary users (PUs) and secondary users (SUs). In spectrum database, the protected incumbents (a.k.a. PUs) and the CRs (a.k.a. SUs) are required to register in database their operational specifications such as transmitting power, antenna height, time of operation and etc. so as to provide an up-to-date radio map for public queries and avoid possible interference. However, it poses potentially serious privacy problems especially when governmental and military systems participate in spectrum sharing through spectrum database. Most recent research works in database-driven CRNs, however, only focused on protecting user's location privacy but merely studied preserving PUs' operational specifications. In this paper, we propose a secure and privacy-preserving scheme using hidden policy-assisted attribute-based encryption technique to protect sensitive PUs' operational privacy without affecting database's accessibility and spectrum utilization efficiency. The security and performance analysis demonstrates that our scheme is secure and computationally efficient. Additionally, our policy-assisted scheme is practical and promising because of its consistency with FCC/NTIA's rule in spectrum regulation in database-driven CRNs.

In cognitive radio networks (CRNs), primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, while SUs will in return obtain more spectrum access opportunities, leading to cooperative CRNs (CCRNs). Prior research works in CCRNs mainly focus on providing ubiquitous access and high throughput for users, but have rarely taken energy efficiency into consideration. Besides, most existing works assume that the SUs are passively selected by PUs regardless of SUs' willingness to help, which is obviously not practical. To address energy issue, this paper proposes an energy-efficient cooperative strategy by leveraging temporal and spatial diversity of the primary network. Specifically, SUs with delay-tolerant packets can proactively make the cooperative decisions by jointly considering primary channel availability, channel state information, PUs' traffic load, and their own transmission requirements. We formulate this decision-making problem based on the optimal stopping theory to maximize SUs' energy efficiency. We solve this problem using a dynamic programming approach and derive the optimal cooperative policy. Extensive simulations are then conducted to evaluate the performance of our proposed strategy. The results show significant improvements of SUs' energy efficiency compared with existing cooperative schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for SUs.

When equipped with multiple antennas, secondary users in cognitive radio networks are able to communicate even when neighboring primary users are active by transmitting in the null space of the communication channel occupied by primary users. In this case, the throughput of a secondary link is limited by the transmission power and the dimension of the null space, i.e., the number of active primary users nearby. Since the number of active primary users is time-varying, the required transmission power to support certain data rate changes from time to time. Thus, secondary users could adapt their transmission to the variation of the primary traffic to improve energy efficiency. In view of that, we develop an energy-efficient traffic scheduling scheme for secondary users equipped with multiple antennas. By formulating the traffic scheduling problem as a Markov decision problem, an energy-efficient transmission scheme is derived from linear programming. The analytical results are verified by simulations and the impacts of various parameters are discussed. The superiority of the derived scheme is also shown by comparing with a randomized scheme.

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations while keeping privacy-preservation and achieving the verification requirements: authenticity, freshness, and completeness. The security analysis and performance evaluation show that the proposed scheme is privacy-preserving and practical.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

With the development of wireless broadband access, OFDMA technology is widely used for the next generation telecommunication systems. In this paper, we focus on analyzing the performance of contention-based services in IEEE 802.16 OFDMA networks with bulk services. We derive various performance measures such as queue utilization in an subscriber station, probability of unsuccessful bandwidth request, and the mean service time of a packet. The accuracy of the proposed analytical model is validated by extensive simulations.

The IEEE 802.16-based WiMAX technology has great potential for the fourth-generation mobile networks. Some of its service classes use the contention-based broadcast polling mechanism to request resources. In this paper, we investigate the performance experienced by these services when the network is unsaturated. In particular, we model each subscriber station as an M/G/1 queue where the service time is determined by the parameters of the network configuration and the binary exponential backoff contention resolution algorithm. We develop a fixed-point analysis to derive analytical expressions for network throughput and packet access delay. The accuracy of the analytical model is validated by comparing it with simulation over a wide range of operating conditions. The implications of various different parameter configurations on the performance are investigated using the analytical model. Moreover, we show that the model can be degenerated to the saturated condition. The utility of both the unsaturated and saturated models is further demonstrated by finding the optimal set of parameter values that maximize the network throughput.

In this paper, an approximated model is proposed to analyze the performance of the contention based services via broadcast polling in unsaturated IEEE 802.16 networks with channel errors. The main idea is that each subscriber station with buffer capacity K can be treated as a M/G/1/K queue with service time determined by the backoff process of broadcast polling. Using this model, the normalized network throughput and the distribution of the packet delay are derived. This proposed analytical model is useful for performance evaluation and optimization of best effort or contention-based non-real time polling services. Our simulator written in C++ verifies the accuracy of the proposed analytical model. Furthermore, we show that the model gives good approximations for network performance with a more realistic bursty arrival process at light load, while providing conservative performance measures at medium and high loads.

In this paper, we propose a general model for the broadcast polling protocol of unsaturated IEEE 802.16 networks in which each subscriber station has a finite buffer. A subscriber station can be modelled as a M/G/1/K queue with its service time determined by the broadcast polling protocol. The buffer overflow probability, network throughput and packet delay performances are analyzed. The proposed model is validated by simulation confirming its accuracy for various scenarios studied.

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed.>