The Internet-of-Vehicles (IoV) has gained significant attention from both academia and industry, driven by its potential to enhance road safety and traffic efficiency. To fully realize this potential, securing IoV communications is essential, where detecting malicious Basic Safety Messages (BSMs) is a key challenge. To this end, machine learning (ML) has been widely employed to design Misbehavior Detection Systems (MDSs) for identifying manipulated BSMs. However, the existing MDSs mainly focus on improving detection accuracy, with little attention given to time efficiency. As safety in IoV is time-sensitive, designing a time-efficient MDS is urgently necessary. In this work, we propose BatchMDS, a time-efficient MDS using Convolutional Neural Network (CNN) and data-to-image transformation. By processing multiple BSMs simultaneously rather than individually, BatchMDS significantly reduces detection latency. Furthermore, this work proposes a novel combination of a sliding window mechanism and a Learned Feature Cache (LFC) to eliminate redundant computation during continuous detection. This work conducts extensive simulation over the VeRiMe dataset that contains five attacks. The experimental results demonstrate the effectiveness of the proposed BatchMDS, improving time efficiency of all attacks by at least 50% while remaining remarkable detection accuracy (≥ 97%).

Robots equipped with multimodal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual–haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multimodal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multimodal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. Instead of sharing raw data, FL exchanges intermediate learning parameters, such as gradients, among clients. This article presents an efficient wireless communication approach tailored for FL parameter transmission, especially for Internet of Things (IoT) devices, to facilitate model aggregation. Our study considers practical wireless channels that can lead to random bit errors, substantially affecting FL performance. Motivated by empirical gradient value distribution, we introduce a novel received bit masking method that confines received gradient values within prescribed limits. Moreover, given the intrinsic error resilience of ML gradients, our approach enables the delivery of approximate gradient values with errors without resorting to extensive error correction coding or retransmission. This strategy reduces computational overhead at both the transmitter and the receiver and minimizes communication latency. Consequently, our scheme is particularly well-suited for resource-constrained IoT devices. Our simulations demonstrate that our proposed scheme can effectively mitigate random bit errors in FL performance, achieving similar learning objectives but with the 50% air time required by existing methods involving error correction and retransmission.

Additive manufacturing is a multi-billion dollar industry 21.58 billion in 2024), so its processes should be dependable and secure. Malicious actors can inject negative spaces, known as voids, in STL files, which can have a devastating impact on a final product's quality. Current ways of detecting voids use machine sensor or simulation data, and physical verification measures after printing. However, to the best of our knowledge, no method exists for detecting hidden voids solely at the STL level. Void detection at this level is inexpensive, and has the potential to detect voids in designs en masse. In this work, we proposeAVOID, a new approach to detect hidden voids. It both detects voids, and assesses their risk of weakening the manufactured part.AVOIDperforms risk assessment based on the size and location of each detected void. We empirically evaluatedAVOIDusing several large datasets, in total thousands of STL files, each with multiple hidden voids. We found thatAVOIDis highly accurate, with 97.7% recall, 98.1% precision, and 99% F1 on average across six data sets.AVOIDis also robust, scalable, and efficient. We find that high risk voids account for approximately 7% of all detected voids inserted randomly.

With the rise of the Internet of Everything, machine-type wireless video applications like intelligent surveillance are increasingly becoming mainstream network services. However, the massive transmission of video streams heavily burdens wireless networks. Existing methods typically establish a correlation between the transmitted content and the quality of experience or service, making it challenging to balance trade-offs among decision quality, bandwidth utilization, and latency requirements. To address this issue fundamentally, this paper proposes a novel perspective by designing wireless video transmission strategies from the angle of decision quality, where only the content that impacts the decision outcomes is transmitted. The highlight of this paper is designing a lightweight yet efficient binary classifier that predicts whether the current content will change decision outcomes based on content discrepancies, measured only through pixel-level differences and macroblock-level similarity. Furthermore, these discrepancies can be used to distinguish the background, thereby further saving bandwidth by reusing the background information. Additionally, a fine-tuning module is incorporated to flexibly update the classifier model, ensuring adaptability across various scenarios. Extensive results demonstrate that the proposed strategy achieves over 81% redundant bandwidth savings while maintaining decision quality and catering to strict latency requirements on computation-constrained devices.

Accurate channel estimation is required for various multiple input multiple output (MIMO) implementations in the next-generation wireless communication systems. Recently, Artificial Intelligence (AI) techniques have been introduced for channel state information (CSI) processing in channel estimation because of their accuracy and relatively low complexity compared to the traditional approaches. However, these AI-supported CSI processing models are usually developed with a fixed training dataset. Therefore, the performance of such approaches cannot be guaranteed in new environments. This paper focuses on enhancing AI-supported channel estimation methods with a 2-stage open set recognition scheme. New environments are detected in the first stage by identifying different characteristics between testing and training data. In the second stage, new data is filtered and further categorized to each individual environment. Simulation results using four different environment settings demonstrate that the proposed method can greatly enhance the usability of AI-supported channel estimation.

Semantic communication is of crucial importance for the next-generation wireless communication networks. Recent advancements have primarily benefited from the design of semantic communication systems based on deep learning. Nevertheless, these deep learning-based systems are vulnerable to certain security attacks, particularly backdoor attacks. A novel attack paradigm, backdoor attacks on semantic symbols (BASS), targets reconstruction tasks by manipulating the reconstructed source data or features. However, the perceivable risks associated with BASS have not been thoroughly explored. This paper investigates the perceivable risks of BASS in the context of computer vision tasks. A transform-based methodology is designed to improve the stealthiness of the poisoned reconstructed target samples in the training dataset. In addition, while various hidden triggers have been studied for traditional backdoor attacks, they cannot be applied to BASS directly due to the unaligned model problem. To address this, an iterative hidden trigger generation (IHTG) algorithm is proposed. The simulation results demonstrate the effectiveness of the proposed methods in addressing the perceivable risks in BASS.

Wireless emergency networks play a crucial role in natural disasters, enabling seamless communication between explorers (e.g., rescue robots) and remote command centers. However, unstable communication links and limited computational resources hinder explorers from directly transmitting massive real-time content for remote human observation or locally computing the latest detection results. To address this challenge, this paper introduces a cross-modal semantic communications paradigm, where our highlights are characterized by precise semantic extraction, low-complexity implementation, and progressive semantic transmission. Specifically, by simplifying the desired task from human-oriented signal recovery to the machine-oriented decision, we firstly deploy a lightweight cross-modal semantic encoder on the explorer, which precisely extracts compact semantics relevant to the decision-making based on inter-modal correlations. Then, to mitigate the impact of intermittent network connections, we develop a scalable semantic transmission strategy that encodes extracted semantics as base and enhanced semantics, progressively delivering them once the network becomes active. Numerical results indicate remarkable benefits of cross-modal semantic communications in terms of decision accuracy, model size, and transmission latency.

A vehicular communication network allows vehicles on the road to be connected by wireless links, providing road safety in vehicular environments. Vehicular communication network is vulnerable to various types of attacks. Cryptographic techniques are used to prevent attacks such as message modification or vehicle impersonation. However, cryptographic techniques are not enough to protect against insider attacks where an attacking vehicle has already been authenticated in the network. Vehicular network safety services rely on periodic broadcasts of basic safety messages (BSMs) from vehicles in the network that contain important information about the vehicles such as position, speed, received signal strength (RSSI) etc. Malicious vehicles can inject false position information in a BSM to commit a position falsification attack which is one of the most dangerous insider attacks in vehicular networks. Position falsification attacks can lead to traffic jams or accidents given false position information from vehicles in the network. A misbehavior detection system (MDS) is an efficient way to detect such attacks and mitigate their impact. Existing MDSs require a large amount of features which increases the computational complexity to detect these attacks. In this paper, we propose a novel grid-based misbehavior detection system which utilizes the position information from the BSMs. Our model is tested on a publicly available dataset and is applied using five classification algorithms based on supervised learning. Our model performs multi-classification and is found to be superior compared to other existing methods that deal with position falsification attacks.

Massive multiple input multiple output (MIMO) systems are integral to next-generation wireless technologies due to their ability to meet the growing demands of throughput and support a plethora of applications. An efficient operation of massive MIMO requires accurate channel state information (CSI). In a frequency division duplex (FDD) MIMO system, the base station can rely on CSI feedback that user equipment (UE) estimates from downlink CSI from orthogonal pilot sequences. Recently, artificial intelligence (AI), i.e., deep learning approaches, have been introduced to compress and reconstruct CSI matrices at UE and the base station, respectively. However, these existing approaches still rely on channel estimation at the UE side, which introduces additional errors in the autoencoder design. To address these issues, we propose to implement the autoencoder that processes the pilot sequences directly to avoid excessive processing errors. Moreover, a higher compression can be achieved due to the lower error. Evaluation results demonstrate that the proposed scheme can significantly reduce the communication overhead by using a higher compression ratio while maintaining high CSI reconstruction performance in addition to lower bit error rates compared to the existing deep learning approach.

Asynchronous federated learning aims to solve the straggler problem in an environment with heterogeneity, where certain clients may possess limited computational capacities, potentially leading to model aggregation delay. The core concept behind asynchronous federated learning is to empower the server to aggregate the model as soon as it receives an update from any client without waiting for updates from multiple clients or adhering to a predetermined waiting time, which is typical in synchronous mode. Because of the asynchronous setting, a potential concern is the emergence of a stale model issue, wherein slow clients might employ an outdated local model for their data training. Consequently, when these locally trained models are uploaded to the server, they may impede the convergence of the global training. Therefore, effective model aggregation strategies play a significant role in updating the global model. Besides, client scheduling is critical when heterogeneous clients with diversified computing capacities participate in the federated learning process. This work first investigates the impact of the convergence of asynchronous federated learning mode when adopting the aggregation coefficient in synchronous mode. Effective aggregation solutions that can achieve the same convergence result as in the synchronous mode are proposed, followed by an improved aggregation method with client scheduling. The simulation results in various cases demonstrate that the proposed algorithm converges with a similar level of accuracy as the classical synchronous federated learning algorithm but effectively accelerates the learning process, especially in its early stage.

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. This work is focused on both binary and multi-class attack detection in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated to generate novel features aimed at detecting attacks in vehicular networks accurately. Machine learning-based methods have been applied to the reformulated dataset for the detection of attacks in vehicular networks. The extensive simulation results show that the proposed scheme can detect more than 99% attacks both for binary and multi-class scenarios which is an impressive performance to enhance the security in vehicular networks.

Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adver-sarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies.

Integrating visual-haptic remote control is an important application direction in the Industrial Internet of Things (IIoT). Cross-modal communications are considered to be an effective technology to support this application. However, due to limited bandwidth and competition between modalities, the quality of visual transmission and the end user’s immersive experience cannot be guaranteed in practical scenarios. To overcome this dilemma, this paper proposes a super-resolution reconstruction strategy for cross-modal communications. Specifically, the sender only transmits low-resolution images and haptic signals, while a haptic-aided super-resolution reconstruction (HaSR) approach is designed at the receiver. This approach involves semantic correlation-based modal fusion and generative adversarial principle-based visual generation, which enable the reconstruction of high-resolution images using the received low-resolution images and haptic signals. Experimental results from a standard dataset and a practical remote industrial control platform validate the effectiveness of the proposed strategy.

To ensure the users’ immersive experience in cross-modal communication, overcoming the end-to-end (E2E) latency through prediction has attracted attention and shown its superiority. However, existing prediction schemes encounter formidable challenges in the presence of multi-modal signals, primarily to adapt and satisfy the prediction requirements of diverse multi-modal services, as well as to fully exploit and effectively utilize the correlation features of multi-modal signals for precise prediction. To this end, this work presents a flexible prediction scheme for low-latency cross-modal communication. Specifically, we first propose an adaptive prediction-aware cross-modal communication framework, which reduces the delay by predicting and transmitting the future multi-modal signals in advance, and flexibly adjusts the prediction horizon to satisfy the prediction accuracy of different multi-modal services. Next, we design an information gain-assisted graph attention (IGGA) method for cross-modal signal prediction, which leverages the graph attention block to extract the intra-modal, inter-modal spatial and temporal correlation features, and effectively optimize and utilize these features with the information gain (IG), thereby facilitating precise cross-modal signal prediction. Finally, numerical experiments conducted on a self-built dataset, a public dataset, and a multi-modal acupuncture platform demonstrate the superiority of the proposed scheme in low-latency cross-modal communication.

Mobile crowdsourcing is an emerging network architecture that can outsource tasks to a group of people or devices. Most recent literature studied the location privacy of users during task allocation in mobile crowdsourcing. But in some cases, a dispute may occur when users have an argument about the payment of the task. To adjudicate the dispute, users need to provide their private information such as credentials to the third party. Furthermore, finding a trusted third party for mobile crowdsourcing is hard in real life, raising the opportunity for decentralized disputes. However, existing work cannot avoid information leakage during decentralized dispute adjudication, and these schemes can also lower the efficiency of arbitrating. In this paper, we propose a privacy-preserving and decentralized dispute arbitration protocol, which allows the dispute adjudication to proceed without a trusted third party but prevents users' private information from disclosing. Specifically, we first propose a secure task allocation protocol for mobile crowdsourcing, which preserves users' location privacy while enabling efficient task release and allocation. Then, in order to avoid employing a trusted third party, we design a privacy-preserving and decentralized dispute arbitration protocol, which does not reveal any private information during the dispute adjudication. Security and privacy discussions show our protocol can resist forgery attacks but preserve privacy during the decentralized dispute adjudication. In addition, performance evaluation validates the efficiency of our protocol.

Automated Internet of Things (IoT) devices generate a considerable amount of data continuously. However, an IoT network can be vulnerable to botnet attacks, where a group of IoT devices can be infected by malware and form a botnet. Recently, Artificial Intelligence (AI) algorithms have been introduced to detect and resist such botnet attacks in IoT networks. However, most of the existing Deep Learning-based algorithms are designed and implemented in a centralized manner. Therefore, these approaches can be sub-optimal in detecting zero-day botnet attacks against a group of IoT devices. Besides, a centralized AI approach requires sharing of data traces from the IoT devices for training purposes, which jeopardizes user privacy. To tackle these issues in this paper, we propose a federated learning based framework for a zero-day botnet attack detection model, where a new aggregation algorithm for the IoT devices is developed so that a better model aggregation can be achieved without compromising user privacy. Evaluations are conducted on an open dataset, i.e., the N-BaIoT. The evaluation results demonstrate that the proposed learning framework with the new aggregation algorithm outperforms the existing baseline aggregation algorithms in federated learning for zero-day botnet attack detection in IoT networks.

The Internet of Things (IoT) plays a critical role in enabling the seamless integration of disparate devices. Future IoT will rapidly expand its coverage to offer future worldwide omnipresent applications and services by merging communications in diverse spatial domains to build the space–air–ground–ocean-integrated network (SAGOI-Net). SAGOI-Net will include a significant number of battery-powered network nodes, such as satellites, unmanned vehicles, and underwater devices, due to the extremely vast geographic reach and dynamics in free space. Given the battery limitations, high-energy-efficiency communications and networking will be critical to the future system. Green SAGOI-Net seeks to not only bring ubiquitous connectivity to every corner of the globe but also to deliver more data with the same amount of energy.

With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a new trend to encourage mobile nodes to participate in cooperative data collection in various Internet of Things (IoT) applications. In location-dependent MCS, location information of mobile nodes are collected and analyzed by service provider to assist in task allocation. If the service provider is not fully trusted, mobile node's privacy is leaked and accessed by unauthorized parties. How to preserve privacy while maintaining task allocation accuracy and efficiency becomes challenging. To this end, we propose a learning-based mechanism that involves two parts: 1) privacy-preserving task release and task allocation; 2) accurate and efficient task allocation. In the first part, we design a location-based symmetric key generator, which enables two parties to self-generate a symmetric key without depending on fully trusted authorities. By utilizing this key generator and Proxy Re-encryption, we propose a privacy preserving protocol to protect location information in task release and task allocation. In the second part, we design a reinforcement learning based task allocation algorithm to optimize the winners selection, which obtains high accuracy and efficiency. The performance analysis reveals that our proposed mechanism achieves accurate and efficient task allocation while preserving privacy in location-dependent MCS.

Intelligent reflecting surface (IRS) is a promising technology for the 6G networks and attracts much attention. However, existing research seldom considers its energy demand. In this paper, we study an IRS assisted multiple-input single-output downlink broadcasting system where there exist one access point (AP), multiple users and a wireless powered IRS. We focus on the broadcasting data transmission which includes two phases. In the first phase, the AP transmits broadcasting data to users and energy signals for IRS energy harvesting (EH). In the second phase, the AP broadcasts messages with the assistance of the IRS. We aim at maximizing the transmission throughput by designing the phase duration scheduling, the transmit beamforming at the AP in each phase, the energy signal covariance matrix and the IRS reflect beamforming with discrete phase shifts. We first propose a semidefinite relaxation (SDR) based transmission design by also employing one-dimensional line search and further proposing a randomization process. Then, a low complexity transmission design has been further developed. Simulation results demonstrate that the SDR based design can almost achieve the optimal and the low complexity design can perform close to the SDR based design with much lower complexity.

As the key element of the Intelligent Transportation System, Internet of Vehicles (IoV) is expected to reduce the traffic congestion and improve the road safety. For road safety, connected vehicles need to broadcast basic safety messages frequently, which contain location, speed, acceleration, steering information, etc. Due to the wireless environment and ad-hoc nature, the vehicular communications are vulnerable to various attacks. It is vital to protect the correctness of the exchanged messages, in which the misbehavior detection mechanisms play an important role. In this paper, we propose a data-centric misbehavior detection method based on a multi-objective learning model. Different from current machine learning based misbehavior detection system, our work exploits a new direction to improve the detection performance by maximizing the recall and specificity. To show the effectiveness of the proposed model, we conduct experiments on the VeReMi Dataset and compare the detection results with existing approaches.

Motivated by the ever-increasing concerns on per-sonal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parame-ter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.

The massive multi-input-multi-output (MIMO) system will greatly enhance the performance of the next-generation wireless communications for many applications e.g., high-mobility users. The orthogonal time frequency space (OTFS) is a promising technique for high-mobility massive MIMO use cases. However, the MIMO-OTFS system requires accurate downlink channel information for optimal performance. This paper studies an uplink-aided downlink channel estimation scheme targeting high-mobility user scenario based on a frequency division duplex massive MIMO-OTFS system. Most of the existing work over-looks the change in the delay, Doppler, and angle domain during a channel estimation process due to high mobility. In this work, we analyze the reciprocity between an uplink and a downlink channel and derive the estimation error due to the latency in processing the uplink channel estimates. Simulation results demonstrate that an uplink channel may change significantly in a high-mobility massive MIMO-OTFS system, given a reasonably small amount of processing latency. Such a change will lead to high error in downlink channel estimation. With the proof of concept, our future work will focus on refining the channel estimation framework with a reduction of the processing latency.

An intelligent transportation system aims to provide various traffic safety and navigation services, and mainly relies on local perception and vehicular communication technologies. However, the vehicular communication technologies can be a target of wide range of attacks including position falsification, Sybil and denial-of-service (DoS) attacks which can lead to disastrous traffic accidents and jams. As a viable solution, misbehavior detection systems can be used in vehicular networks. Different from other works, in this paper, we propose a misbehavior detection system that utilizes both local perception and basic safety messages (BSM). Our work shows the methodology for generating realistic vehicular network data sets that include both local perception and BSM. In addition, we compare and show that the propose scheme is better compared to the previous scheme utilizing only beacon information for accurately identifying misbehavior in intelligent transportation system.

Mobile crowdsensing enables collaborative data sensing between cloud server and mobile nodes. To participate in the sensing task, mobile nodes upload their locations to the centralized cloud for task allocation. However, revealing locations to an untrusted cloud results in privacy leakage, such as trajectories tracking and home address exposal, threatening the personal security. Obfuscation and cryptography based schemes are two main solutions to protect the location privacy. However, these schemes may either degrade the accuracy of task allocation or rely on some strong assumptions. Thus, how to protect location privacy without strong assumptions while remaining high accuracy in task allocation is challenging. In this paper, we propose a secure protocol for edge-assisted mobile crowdsensing, which removes the assumption that the cloud cannot collude with mobile nodes. Specifically, we deploy homomorphic encryption among service requestor, cloud server and edge nodes in a collaborative manner. Benefiting from the additive property of the cryptosystem, the cloud is able to securely calculate the mobile node’s travel distance while knowing nothing about the mobile mode’s location and task location. Based on the protocol, two types of location-dependent task allocation, travel distance based task allocation and spatial distribution based task allocation, can be implemented with location privacy preservation. Experimental results show the effectiveness of our work in task allocation. In addition, comprehensive privacy discussion indicates that the proposed protocol is secure from the collusion between cloud and mobile nodes, while preserving the task location and location privacy of mobile nodes.

With the emergence of computation-intensive vehicular applications, computation offloading based on mobile-edge computing (MEC) has become a promising paradigm in resource-constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that in this article, a blockchain-based secure computation offloading scheduling scheme is proposed. It embraces the blockchain-based trust management paradigm and smart contract-enabled deep reinforcement learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, roadside unit (RSU) blockchain, and cloud blockchain. Furthermore, a smart contract-enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

The Internet of Things (IoT) is evolving into the Internet of Video Things (IoVT) that supports massive smart devices and multiple video applications. However, how to effectively control massive devices and transmit large-volume video data have become challenges in the current IoVT. Inspired by device-to-device (D2D) communications and coalitional game, this article constructs a self-organized D2D collaborative video content sharing framework for the IoVT. Specifically, we first propose a collaboration mechanism by introducing the social attributes of IoVT devices and their owners. In this mechanism, D2D collaborative coalitions are automatically formed among IoVT devices and video content is shared in the coalitions through D2D links. In this way, the burden of controlling massive IoVT devices and video data traffic are offloaded. Then, by integrating the scalability of the scalable-high-efficiency-video-coding (SHVC) streams and the flexibility of D2D networking, a collaborative video streaming strategy is developed. It takes advantage of provider set arrangement and transmission scheduling to reduce the impact of network instability on video services. Simulation results verify the effectiveness of the proposed mechanism and strategy.

Distributed learning is proposed as a promising technique to reduce heavy data transmissions in centralized machine learning. By allowing the participants training the model locally, raw data is unnecessarily uploaded to the centralized cloud server, reducing the risks of privacy leakage as well. However, the existing studies have shown that an adversary is able to derive the raw data by analyzing the obtained machine learning models. To tackle this challenge, the state-of-the-art solutions mainly depend on differential privacy and encryption techniques (e.g., homomorphic encryption). Whereas, differential privacy degrades data utility and leads to inaccurate learning, while encryption based approaches are not effective to all machine learning algorithms due to the limited operations and excessive computation cost. In this work, we propose a novel scheme to resolve the privacy issues from the anonymous authentication approach. Different from the two types of existing solutions, this approach is generalized to all machine learning algorithms without reducing data utility, while guaranteeing privacy preservation. In addition, it can be integrated with detection schemes against data poisoning attacks and free-rider attacks, being more practical for distributed learning. To this end, we first design a pairing-based certificateless signature scheme. Based on the signature scheme, we further propose an anonymous and efficient authentication protocol which supports dynamic batch verification. The proposed protocol guarantees the desired security properties while being computationally efficient. Formal security proof and analysis have been provided to demonstrate the achieved security properties, including confidentiality, anonymity, mutual authentication, unlinkability, unforgeability, forward security, backward security, and non-repudiation. In addition, the performance analysis reveals that our proposed protocol significantly reduces the time consumption in batch verification, achieving high computational efficiency.

Supporting non-orthogonal multiple access (NOMA) in non-coherent joint transmission (NCJT) systems is beneficial for improving spectral efficiency (SE), but the interference coordination, user scheduling, and resource allocation problems in this new scenario have not been well studied. In this paper, a NOMA-enabled NCJT system is considered in which the connected users are jointly served by two multi-antenna transmitting-receiving points (TRPs) with non-ideal backhauls. Each user has two independent receiving (RX) chains that can work with orthogonal multiple access (OMA) or NOMA mode. A joint resource allocation and hybrid OMA/NOMA mode selection is proposed to maximize the throughput. The primal non-convex and NP-hard problem is decomposed into the following three subproblems, i.e., power allocation (PA) of a single TRP, hybrid mode selection (HMS) of a single TRP, and cross-TRP interference optimization (CIO). Firstly, a successive convex approximation (SCA) method is proposed to solve the non-convex PA subproblem, which achieves a local maximum solution. Secondly, the combinatorial HMS subproblem is transformed into finding the maximum matching of bipartite graphs. By constructing two weighted bipartite graphs for the OMA/near UEs and far UEs, a suboptimal solution is found. Thirdly, an alternating optimization is proposed to solve the CIO subproblem by iteratively performing PA and HMS of the two TRPs. Finally, simulation results demonstrate the superiority of throughput improvement of the proposed method, and the sum rate of the NOMA-enabled NCJT system can approach the sum rate of individual TRPs without interference.

Integrated sensing, communication, and computation (ISCC) will become a key enabler for automation applications. However, since the performance region of ISCC has a higher dimension than those of traditional wireless networks, existing schedulers typically fail to simultaneously meet the heterogeneous requests in ISCC. In this work, we propose a novel wireless scheduling architecture to explore the coordination gains of sensing, communication, and computation from a perspective of joint optimization. Specifically, we first construct an implementation framework of ISCC by combining the mobile edge computing paradigm with the integrated sensing and communication technology, where the inherent tradeoff between sensing, communication, and computation performance is characterized. Next, a joint device association and subchannel assignment problem is formulated to capture the network externalities induced by resource competition among mobile devices with multi-functional requirements. Due to its intractability, we then reformulate it in the matching theoretical manner. To obtain a mutually satisfactory solution under externalities, an iterative matching algorithm is developed by introducing pairwise stability and proved to be convergent and stable. The extensive simulations elucidate the significant superiority of our proposed scheme over those externality-unaware wireless schedulers.

As a popular scenario of mobile crowdsensing, edge computing assisted vehicular crowdsensing (EVCS) encourages vehicles to participate in sensing data with the equipped devices. Due to the vehicular mobility, vehicles may dynamically enter and leave the coverage area of an edge node, leading to recurrent task allocations that consume excessive communication and computational resources. How to avoid recurring recruitment in task allocation is challenging. In this paper, we propose an optimization framework to facilitate task allocation by utilizing the cooperation between edge nodes. The proposed framework avoids complicated recruitment procedures while maximizing the connection time between the recruited vehicles and the edge node. Due to the NP-hardness of the formulated optimization problem, we design a reinforcement learning based algorithm to solve the problem with high accuracy and efficiency. Simulation results show the effectiveness of our proposed framework.

Coordinated beamforming (CoBF) for multi-user massive multiple-input and multiple-output (MIMO) heterogeneous networks (HetNets) promises for capacity enhancement. However, challenges of energy efficiency (EE) and ultra-low latency are yet to be addressed due to the circuit power and calculation latency heavily depend on the number of transmit antennas. To solve these problems, a maximizing EE algorithm named coordinated beamforming based on convolutional neural networks (CoBFCNN) is proposed in which the advantages of convolutional neural networks and deep learning are fully exploited. Basing on the results of this study, an optimization problem of maximizing EE with lower complexity and lower calculation latency for the different constraints is formulated and exploited for multi-user massive MIMO HetNets. Simulation and analysis show that the proposed CoBFCNN algorithm can significantly satisfy the performance of maximizing EE for the multi-user massive MIMO HetNets with significantly lower complexity and ultra-low calculation latency, especially when the number of antennas is large.

In recent years, computation offloading has been considered as a promising technology to support computation- intensive vehicular applications. In this paper, we mainly focus on computation offloading in a vehicular cloud network (VCN), in which both vehicles and infrastructures with resource availability are defined as resource providers. However, due to the dynamically changing on-board resource distribution and the uncoordinated offloading strategies among vehicles, the computation offloading problem in a VCN is very challenging. In this paper we first model the problem as a multi-agent multi- armed bandit problem. We then propose a reshaped upper confidence bound (UCB) algorithm to estimate the on-board resource distribution with the reward estimation. We further utilize a novel multi-agent reinforcement learning algorithm to manage the computation offloading in a VCN. Simulation results demonstrate the performance gains by using the proposed algorithm.

The development of artificial intelligence, wireless communication and smart sensor platform facilities the emergence of multitude of novel vehicular applications in recent years. These new vehicular applications usually are realized with BIG models, which are normally delay sensitive and computation intensive. To alleviate the heavy pressure on the resource-constrained vehicles, computation offloading has been regarded as a promising approach to circumvent this challenge. In this paper, we take the deep learning model as an BIG model example to investigate the computation offloading in a vehicular cloud network. Specifically, task division technology is utilized to decomposed the BIG model into several components, in which there are dependencies between multiple components. To satisfy the requirements of delay-sensitive vehicular applications, it is crucial to propose an efficient computation offloading scheme. To solve it, a novel deep reinforcement learning algorithm is proposed, wherein a common deep learning model is maintained by all agents. The reward mechanism is elaborately designed to combine the long-term reward and short-term reward. In the final, the proposed algorithm’s effectiveness is verified by the experimental simulations.

With the advancement of Internet of Things (IoT) and computing paradigms, massive data are collected and processed to enhance intelligent applications. However, by deliberately sending some queries, an attacker may be able to derive the sensitive information of IoT data owners. To prevent privacy leakage during IoT data query, differential privacy (DP) hides private information by introducing noise to the query results. As DP introduces randomized noise that will affect query accuracy (data utility), the tradeoff between privacy preservation and data utility is a challenge. In this article, we first propose a novel optimization framework for single query to minimize the privacy cost, while satisfying both personalized DP and customized data utility. We design a reinforcement learning-based algorithm for single query optimization framework (SQOF_RL) to solve the optimization problem efficiently. Then, we propose a SQOF_RL and SVT-based batch query optimization mechanism (S2BQOM) to answer more queries privately. The performance evaluation shows that SQOF_RL and S2BQOM can effectively optimize single query and batch queries in terms of privacy cost, data utility, personalized privacy, and query satisfaction. Finally, the performance analysis reveals that our work can be applied to multiple linear/nonlinear query functions instead of one particular query function.

To a large extent, the deployment of edge computing (EC) can reduce the burden of the explosive growth of the Internet of Things. As a powerful hub between the Internet of Things and cloud servers, edge devices make the transmission of cloud to things no longer complicated. However, edge nodes are faced with a series of problems, such as a large number, a wide range of distribution, and complex environment, the security of EC should not be underestimated. Based on this, we propose a tactic to improve the safety of EC by virtualizing edge nodes. In detail, first of all, we propose a strategy of edge node partition, virtualize the edge nodes dealing with different types of things into various virtual networks, which are deployed between the edge nodes and the cloud server. Second, considering that different information transmission has different security requirement, we propose a security tactic based on security level measurement. Finally, through simulation experiments, we compare with the existing advanced algorithms which are committed to virtual network security, and prove that the model proposed in this article has definite progressiveness in enhancing the security of edge computing.

Nowadays the privacy issue arising in data mining applications has attracted much attention. In the context of distributed data mining, a major concern of the participant is that its privacy may be disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally causes a negative effect on the mining result. Thus there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. We propose a game theoretical approach to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.

Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based onS-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.

With the development of intelligent transportation system (ITS), a multitude of novel vehicular applications have been emerging. There is an urgent need for simultaneously supporting multi-tasks across a group of vehicles in a vehicular network, forming a typical multi-task multi-agent (MTMA) environment. Deep Reinforcement Learning (DRL) is deemed a promising approach to solving the highly complicated MTMA problem. However, owing to the extraordinarily growing computational complexity as well as the explosively increasing dimension of state and action spaces in the MTMA environment, the value functions in the DRL are usually bulky and could be difficult to be learned efficiently. In this way, by virtue of the correlations among multiple vehicular tasks, we adopt the value-decomposition mechanism (VDM) to decompose the complicated value function into several small pieces and then compute each sub-function separately. The proposed paradigm can yield great speed-up in learning and help substantially with a smaller state and action space but without degrading the performance. In this work, we consider an MTMA environment with three vehicular tasks to demonstrate the effectiveness of the proposed mechanism with simulation results.

With the explosive growth of the computation intensive vehicular applications, the demand for computational resource in vehicular networks has increased dramatically. However some vehicular networks may be deployed in an environment that lack resource-rich facilities to support computationally expensive vehicular applications. In this work we propose a new scheme that enables computational resource sharing among vehicles in vehicular cloud network (VCN), which can be formulated as a complex multi-knapsack problem. In order to solve it, a deep reinforcement learning (DRL) algorithm is developed. Considering the non-stationary behavior brought in by the parallel learning and exploring processes among vehicles, computational resource sharing in such a vehicular network is a typical multiagent problem, therefore we model the problem with a Markov game problem. In addition, to tackle the heterogeneity property of the computational resources, a multi-hot encoding scheme is designed to standardize the action space in DRL. Furthermore, we propose a centralized training and decentralized execution framework that can be solved by a multi-agent deep deterministic policy gradient (MADDPG) algorithm. The numerical simulation results demonstrate the effectiveness of the proposed scheme.

Energy-efficient design and secure communications are of crucial importance in the future wireless communication networks. However, the energy efficiency when using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied in this paper. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix. An alternative optimization algorithm is proposed based on semidefinite programing (SDP) relaxation for solving the challenging non-convex fractional optimization problem. Simulation results demonstrate that our proposed design outperforms the benchmark schemes in term of energy efficiency. The study sheds light on the tradeoff between energy efficiency and the secrecy rate in the IRS-assisted MISO network.

Fairness among different users and system robustness are key issues in the future communication network design. A robust max-min fairness energy efficiency (EE) maximization problem in a downlink non-orthogonal multiple access (NOMA) heterogeneous network is studied when channel state information and interference power are uncertain. A worst-case EE of the small cell is maximized by jointly optimizing the transmit power and cell association under the bounded channel uncertainty model, subject to constraints on the cross-tier interference power, maximum transmit power, and the minimum rate requirement of each small-cell user. The formulated robust max-min fairness EE problem is a mixed-integer and non-convex programming problem with infinite inequality constraints. An iterative resource allocation algorithm is designed based on the proposed power allocation and cell association scheme. Simulation results demonstrate the effectiveness of the proposed robust resource allocation scheme and its improvement over existing schemes.

Vehicular networks are expected to significantly improve the efficiency and safety of transportation system. The number of connected vehicles is estimated to exceed 200 millions by 2020. With the rapid developed technologies such as the fifth generation cellular networks, various real-time network applications will be applied in vehicular networks. Considering the limited capability of devices installed on vehicles, cloud computing can be used to provide sufficient storage and computational power. However, the numerous vehicles with massive applications will generate tremendous volume of data and exhaust finite bandwidth. It is difficult for the conventional cloud computing paradigm to satisfy stringent quality of service requirements of vehicular networks, especially for delay-sensitive applications. Then vehicular fog computing, in which servers are allocated close to vehicles, has been proposed to reduce the latency. Vehicular fog computing is still in its early stage. How to provide reliable and secure fog service to client vehicles has not been well addressed. In this paper, we propose a scalable and efficient security provision scheme based on Chinese Remainder Theory. Simulation results demonstrate that the proposed scheme significantly reduces the authentication delay.

The cloud-based Internet-of-Things (IoT) has been applied to support ubiquitous data collection and centralized data processing among various applications. Equipped with powerful resources, a semi-trusted cloud is able to deduce private information by launching inference attack. Homomorphic Encryption (HE) has been proposed as an effective way to preserve privacy from inference attack while allowing certain computation over ciphertext. However, HE leads to longer latency due to additional communication and computation overheads. In this paper, we propose an optimization framework in privacy-preserving access control under cloud-fog computing systems. The optimization goal is to maximize the average user satisfaction in the system, where cost and latency serve as key metrics measuring user satisfaction. Due to the NP-hardness of the formulated problem, we propose a low-complexity suboptimal algorithm to solve it, where the access offloading decision making, user cooperation, and resource allocation are considered. Simulation results are presented to show the advantages of our proposed algorithm in terms of the average USI (User Satisfaction Index) and the number of users with zero USI.

Internet-of-Things (IoT) applications have been rapidly deployed into pervasive environment, where both challenges and opportunities abound. On the one hand, a large number of IoT devices and their rich functions contribute significant volumes of data, which has brought tremendous convenience to the daily lives of end users. On the other hand, the heterogeneous IoT devices and a large amount of private information transmitted through networks also bring serious security and privacy issues. It is a big challenge to model IoT systems and trust relationships between different entities with a large number of heterogeneous IoT devices. In this article, we study a general IoT system architecture with consideration of heterogeneous IoT devices. Different trust models are proposed and analyzed based on the trust relationships between different entities in the IoT system. We propose a flexible and efficient authentication scheme with a consideration of heterogeneous IoT devices based on the least trust-required model. The proposed scheme provides security and privacy to resource-limited IoT devices flexibly and efficiently by utilizing IoT devices with better storage and computational ability. Moreover, secure data transmission is presented with contextual privacy and data integrity services. The proposed scheme achieves not only the mutual authentication, initial session key agreement, and data integrity but also anonymity, contextual privacy, forward security, end-to-end security, and key escrow resilience. Security analysis is presented to provide verification of the proposed protocol and security objectives. Moreover, performance evaluation is presented with comparison to the other schemes in terms of security features, computational overhead, and communication overhead. The performance comparisons show that our proposed scheme provides flexible and efficient security by consideration of heterogeneous IoT devices. With the higher proportion of resource-limited IoT devices, our proposed scheme outperforms other similar schemes.

The ever-growing demand of the Internet of Things (IoT) imposes great challenges in the existing cellular systems and calls for novel approaches for the wireless network design. In this article, we develop a joint energy and computation optimization paradigm in an IoT network. The tasks collected at local IoT devices can be computed at hierarchical mobile-edge computing facilities. Both nonorthogonal multiple access (NOMA) and frequency-division multiple access (FDMA) are used for computation offloading. The system model considers both long-term and short-term system behaviors and makes the best decision for energy consumption and computation efficiency. The long short-term memory (LSTM) network is applied to predict the long-term workload, based on which the number of active process units in the edge layer is optimized. In the short-term model, a resource optimization problem is formulated. Due to the dynamic arrival workload and nonconvex features of the problem, the Lyapunov optimization approach and successive convex approximation for the low-complexity method are applied to solve this problem. The simulation results show that the proposed scheme can significantly improve the delay and energy consumption performance.

The emergence of a massive Internet-of-Things (IoT) ecosystem is changing the human lifestyle. In several practical scenarios, IoT still faces significant challenges with reliance on human assistance and unacceptable response time for the treatment of big data. Therefore, it is very urgent to establish a new framework and algorithm to solve problems specific to this kind of fast autonomous IoT. Traditional reinforcement learning and deep reinforcement learning (DRL) approaches have abilities of autonomous decision making, but time-consuming modeling and training procedures limit their applications. To get over this dilemma, this article proposes the broad reinforcement learning (BRL) approach that fits fast autonomous IoT as it combines the broad learning system (BLS) with a reinforcement learning paradigm to improve the agent's efficiency and accuracy of modeling and decision making. Specifically, a BRL framework is first constructed. Then, the associated learning algorithm, containing training pool introduction, training sample preparation, and incremental learning for BLS, is carefully designed. Finally, as a case study of fast autonomous IoT, the proposed BRL approach is applied to traffic light control, aiming to alleviate traffic congestion in the intersections of smart cities. The experimental results show that the proposed BRL approach can learn better action policy at a shorter execution time when compared with competing approaches.

To efficiently support real-time control applications, networked control systems operating with ultrareliable and low-latency communications (URLLCs) become a fundamental technology for the future Internet of Things (IoT). However, the design of control, sensing, and communications is generally isolated at present. In this article, we investigate the joint optimization of control cost and energy consumption for a centralized wireless networked control system. Specifically, with the “sensing-then-control” protocol, we first develop an optimization framework that jointly takes control, sensing, and communications into account. In this framework, we derive the spectral efficiency, linear quadratic regulator cost, and energy consumption. Then, a novel performance metric called the energy-to-control efficiency (ECE) is proposed for the IoT control system. In addition, we optimize the ECE while guaranteeing the requirements of URLLCs, thereupon a general and complex max-min joint optimization problem is formulated for the IoT control system. To optimally solve the formulated problem by reasonable complexity, we propose two radio resource allocation algorithms. Finally, simulation results show that our proposed algorithms can significantly improve the ECE for the IoT control system with URLLCs.

Combination of unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) is deemed as an promising solution to achieving massive connectivity in future wireless networks. In this paper, a UAV-aided NOMA scheme is proposed to achieve simultaneous wireless information and power transfer (SWIPT) and guarantee the secure transmission for ground passive receivers (PRs), in which the nonlinear energy harvesting model is applied. Each time frame is divided into two phases. In the first phase, the received power at each PR is maximized to achieve rapid charging. In the second phase, SWIPT is performed via NOMA with the remaining energy at each PR, and artificial jamming is generated at UAV together with the NOMA information to guarantee the security. The throughput of PRs is maximized, with the highest received jamming power cancelled at each PR via successive interference cancellation (SIC). This disrupts the eavesdropping effectively by jamming without affecting the legitimate transmission. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and then propose iterative algorithms to solve them. Simulation results are presented to show the effectiveness of the proposed scheme.

Device-to-device (D2D) based packet caching technologies recently attract increasing attention, thanks to their great potentials to facilitate network traffic offloading. Despite the many popular issues arising in the D2D caching area, one of the new perspectives, namely the competition due to the packet request diversity originating from various D2D user equipment (UE) groups is not sufficiently investigated. In this work, we analyze several key aspects of the competition for packet allocation among diverse packet requests. Firstly, we study the impact from diverse group proportions on the system throughput and the packet allocation fairness. Particularly, the novel group separation index (GSI) is introduced, which helps to reflect the packet allocation fairness. We derive and analyze both the upper and lower bounds of GSI. Secondly, we investigate how the concentration levels of diverse packet requests may affect the system performance, such as the impact from the caching size limit on packet allocation. Thirdly, we derive the average energy consumption metric using the binomial point process based network model, which facilitates a comprehensive evaluation of the competition among UE groups. Finally, simulations validate our proposed analysis method, which may provide important design hints for improving existing D2D caching schemes.

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity.

Relying on the computation offloading technology, edge computing has shown potential in countless tasks processing in the industrial Internet of Things (IIoT), which is composed of multiple edge clouds and multiple IIoT devices. Nevertheless, with increasing demands for computation service, how to design reliable transmission mechanism and allocate proper computation resource has become bottlenecks. In this article, we propose a computation offloading mechanism based on two-stage Stackelberg game to analyze the interaction between multiple edge clouds and multiple IIoT devices. To be specific, the edge clouds are denoted as leaders who set the appropriate price for their computation resource. Besides considering the payment cost, the IIoT devices which are termed as the followers formulate their utility function by considering the social interaction information from the potential IIoT devices. The existence and uniqueness of the Stackelberg equilibrium are analyzed considering two possible cases, i.e., complete information and incomplete information. Moreover, two dynamic iterative algorithms are invoked for solving both problem models, respectively. Finally, experimental results show that our proposed scheme is conducive to seeking the appropriate price and computation requirement. Besides, social interaction information plays an important role in achieving a reasonable computation requirement for IIoT devices.

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed Q-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed Q-learning based power allocation algorithm.

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ultra-reliable and low-latency communications (URLLCs). However, the frame size is significantly shortened in V2V URLLCs because of the rigorous latency requirements, and thus the overhead is no longer negligible compared with the payload information from the perspective of size. In this paper, we investigate the frame design and resource allocation for an urban V2V URLLC system in which the uplink cellular resources are reused at the underlay mode. Specifically, we first analyze the lower bounds of performance for V2V pairs and cellular users based on the regular pilot scheme and superimposed pilot scheme. Then, we propose a frame design algorithm and a semi-persistent scheduling algorithm to achieve the optimal frame design and resource allocation with the reasonable complexity. Finally, our simulation results show that the proposed frame design and resource allocation scheme can greatly satisfy the URLLC requirements of V2V pairs and guarantee the communication quality of cellular users.

Cellular based vehicle-to-everything (V2X) communications have recently gained more interest from both academia and industry. However, there exist many challenges in cellular-based V2X communications in which resource allocation is one of the main challenges. In this paper, we propose a graph and deep reinforcement learning-based resource allocations in which channels for vehicular communications are assigned in a centralized manner by the base station whereas vehicular user equipment uses deep reinforcement learning for distributed power control. Graph-based channel allocation includes a weighted bipartite matching and clustering scheme and relies on strictly limited channel state information (CSI). Whereas, power selection is performed using deep reinforcement learning where each agent selects the transmission power to maximize the aggregated V2V data rate. Our proposed scheme relies on realistic channel assumption with minimum transmission overhead. In addition, we have also performed simulations and have shown that our scheme is better compared to previous schemes in terms of sum V2V and sum V2I capacity.

With the emerging applications of vehicular networks, how to provide sufficient communication and computation supports are the two most important challenges for vehicular communication systems. Cloud-based vehicular networks and mobile edge computing frameworks have been proposed to relieve the computing burden of vehicles. However, for time-sensitive and computation-intensive applications with large input data size, e.g. image aided navigation, the data transmission process occupies large bandwidth, which may degrade the quality of service of all network users, especially in high density scenarios. Thus, in this paper we formulate a computation offloading problem for these time-sensitive and computation-intensive applications to minimize the data transmitted to the server. We propose two approaches to solve the formulated problem, i.e. a graph theory based method and a heuristic algorithm. Simulation results demonstrate that both algorithms can achieve near optimal solutions and greatly reduce the data volume transmitted to the server.

Vehicular networks are susceptible to variety of attacks such as denial of service (DoS) attack, sybil attack and false alert generation attack. Different cryptographic methods have been proposed to protect vehicular networks from these kind of attacks. However, cryptographic methods have been found to be less effective to protect from insider attacks which are generated within the vehicular network system. Misbehavior detection system is found to be more effective to detect and prevent insider attacks. In this paper, we propose a machine learning based misbehavior detection system which is trained using datasets generated through extensive simulation based on realistic vehicular network environment. The simulation results demonstrate that our proposed scheme outperforms previous methods in terms of accurately identifying various misbehavior.

Machine learning plays a vital role in the detection of network anomalies. In this paper, we first briefly examine the different categories of machine learning models, regarding to the acquisition of data label. With the support of fog computing, we then propose data-driven network intelligence for anomaly detection. The proposed framework includes fog enabled infrastructure and fog assisted artificial intelligence (AI) engine. Fog enabled infrastructure provides efficient computing resources for the selection of optimal learning model and sampling ratio. Fog assisted AI engine trains effective and robust semi-supervised learning models for detecting anomalies. We demonstrate that the optimal learning model achieves high detection accuracy and effective computational performance, with the close cooperation between infrastructure and AI engine in a fog computing environment.

Energy-efficient computation is of crucial importance in mobile edge computing (MEC) networks. However, few investigations have studied resource allocation strategies for maximizing the computation efficiency. A computation efficiency maximization framework is established in wireless-powered MEC networks relying on non-orthogonal multiple access (NOMA) under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. The energy harvesting time, the local computing frequency, the operation mode selection, the offloading time and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. An iterative algorithm and an alternative optimization algorithm are proposed to solve the formulated challenging non-convex problems. Simulation results show that our proposed resource allocation schemes outperform the benchmark schemes in terms of computation efficiency. Moreover, a tradeoff is elucidated between the computation efficiency and the computation throughput.

In this paper we consider the behavioral trust to be user-centric rather than device-centric. We propose that optimal Device-to-Device (D2D) association in mobile or vehicular networks can be attained through trust calculation after determining socially central users that are behaviorally motivated to communicate. The proposed algorithm considers user or device association as temporal and spatial human interactions to calculate trust for optimal association. Our findings show that the network performance is significantly affected by both the device-centric approaches, which have always considered social-aware models for associations, and the user-centric sporadic behavioral transition from selfishness to altruism or vice- versa. Therefore, we have considered assigning a selfishness probability to nodes based on the Shape Index metric and proposed selfish disassociation before network reconfiguration. We have shown that with this new approach it is possible to obtain a performance gain of over 300% when compared to random association or social-aware algorithms.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

The increasing maritime activities with the exclusive economic zone lead to increasing demands for wideband communications in recent years. This paper presents a maritime communication network architecture where the onshore high-tower base station provides wireless backhaul for the shipborne base stations, while the shipborne base stations serve as the mobile access points for the user ships. Under this architecture, considering the out-of-band full-duplex (OBFD) backhauling mode, we propose a joint backhaul and access link resource management scheme in respect to the wireless backhaul power allocation, access link power allocation and user association, for the sake of maximizing the network capacity. Specially, this scheme takes the inter-cell interference and the multiuser association into account in order to model the realistic maritime communication scenarios. The optimization problem is solved by the successive convex approximation (SCA) method and the numerical simulation results show the effectiveness of the algorithm in terms of the network capacity and the iteration convergence.

In 5G wireless networks, the highly growing concern of data privacy from end users drives security and privacy fundamental and strong requirements for information services. End users will always expect and constantly demand efficient and effective privacy-preserving based security services in 5G communications. Those services should not only adjust the levels of security protection, but also optimize the entire secure data communication process from the users to an untrusted cloud, via multiple fog nodes. In this new paradigm, a list of options should be open at the side of fog nodes, so that they can adjust the desired level of enhanced security protection for users' data intelligently and dynamically. In this way, the load of computational overhead for enhanced security protection at the user side will be greatly reduced. The requested options of those security services will be provided based on learning the contributed attributes, and further be enforced by fog nodes, where the data will be subsequently adjusted in a suitable way. The idea of Quality of Protection (QoP) can be applied at the fog nodes in 5G networks, so that fog nodes can supply different levels of security protection to different data protection demands. In this paper, we propose a privacy-preserving data preprocessing scheme for fog computing in 5G network security. Specifically, this work is conducted in the perspective of QoP, aiming to preserve the security service option learned from attributes and to enable fog nodes to supply different levels of privacy protection services with different security demands from users.

In this paper, we propose a heterogeneous network (HetNet) system with a cloud control center to dynamically manage small base stations (SBSs) based on traffic load. The cloud can provide a user equipment (UE) association mechanism to balance both traffic load and spectrum allocation of SBSs and the macro base station (MBS) with throughput requirements of uplink and downlink. Our proposed association mechanism and SBS management mechanism can optimize the energy efficiency (EE) of the network and UE by considering EE of both uplink and downlink. Device-to-device communications are adopted under service request probability of UE and distance limitation. The EE optimization problem is solved in two steps in this paper. First, a decoupled association for UE over uplink and downlink is adopted. Least path loss criterion is used for uplink association. And priority SBS under signal-to-interference-plus-noise rate threshold and data rate requirement is applied in downlink association. After association, the SBSs management is implemented iteratively for adjusting the operation of SBSs to maximize the EE of both the network and UE. Simulation results show that our proposed method can improve the EE of the system with better performance on offloading traffic from the MBS to SBSs.

Public safety communications provide effective communications amongst the first responders and victims in public safety scenarios. Device-to-device (D2D) communication is a technique that can be used to enhance network coverage in cellular networks. In this paper, we propose a novel D2D clustering scheme to expand cellular coverage for public safety communications. In the proposed scheme, cluster heads are selected from a group of public safety user equipment based on different metrics such as remaining battery power, SINR, number of discovered out of coverage devices and mobility. Each cluster head provides synchronization, radio resource management information and coverage to its cluster members. The simulation results demonstrate that our proposed scheme outperforms previous methods in terms of coverage percentage and energy consumption.

Public safety communication aims to provide efficient mission critical and first responder communication scenarios. Device-to-device (D2D) proximity services are designed to offload massive traffic from base stations and extend the coverage area. Utilizing relay to provide network services for user equipment (UE) that out of coverage is one of the most important attributes of proximity services. Existing works mainly focus on the transmission rate and energy efficiency for relay selection. In this paper, we propose a relay selection scheme that targets to extend the connection time in public safety communications. In particular, the proposed scheme takes into consideration the remaining battery capacity and communication capability of each UE. The system level simulation results show that the proposed scheme can prolong the connection time for the UE that is out of coverage.

Sensors are significant in electric machine applications, such as electric vehicles, to maintain the safe operation of a system. The sensor reliability must be ensured to help avoid unexpected system failure caused by faulty sensor signals. To mitigate the machine current sensor fault impact on permanent magnet synchronous machines (PMSMs), this paper proposes a novel PMSM current sensor fault detection and isolation (FDI) method based on DC link current estimation. The fault detection is achieved by comparing the residual between measured and estimated DC link currents with a threshold value; the fault isolation is achieved based on the phase signals estimation and residuals examination. The proposed method is easy to implement without complicated modeling, not influenced by system imbalance and capable of distinguishing between machine current sensor and non-sensor faults. As compared to the existing PMSM systems without the proposed FDI method, the system failure risk under a machine current sensor fault is reduced when the proposed method is integrated into the PMSM controller. The effectiveness of the proposed FDI method is validated by simulation results in MATLAB.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

This paper studies the joint optimal resource allocation and probabilistic caching design for device-to-device (D2D) communications in a wireless heterogeneous network with full-duplex (FD) relays. In particular, popular contents can be cached at user devices as well as at relays that are located close to users. A user can request contents from another user via D2D communications and also from a nearby relay equipped with FD radios. In the case that there is a caching miss (i.e., the requested contents are not found at the other users/relays within the coverage range), users can connect to the base station via a relay by using the FD communication technology. Subsequently, we develop mathematical models to analyze the throughput performance with edge caching where both cochannel system level interference and FD self-interference are considered. Due to the high complexity of stochastic optimization, we develop low-complexity optimization formulation by decomposing the original problem into three simple subproblems that can be efficiently solved. Finally, numerical results are presented to illustrate developed theoretical findings in the paper and significant performance gains of the throughput performance.

Nowadays, manufacturers want to send the data of their products to the cloud so that they can conduct analysis and improve their operation, maintenance, and services. Manufacturers are looking for a self-contained solution. This is because their products are deployed in a large number of different buildings, and it is neither feasible for a vendor to negotiate with each building to use the building’s network (e.g., WiFi) nor practical to establish its own network infrastructure. The vendor can rent a dedicated channel from an ISP to act as a thing-to-cloud communication (TCC) link for each of its IoT devices. The readily available choices, e.g., 3G, is over costly for most IoT devices. ISPs are developing cheaper choices for TCC links, yet we expect that the number of choices for TCC links will be small as compared to hundreds or thousands of requirements on different costs and data rates from IoT applications. We address this issue by proposing a communication sharing architecture sTube+, sharing tube . The objective of sTube+ is to organize a greater number of IoT devices, with heterogeneous data communication and cost requirements, to efficiently share fewer choices of TCC links and transmit their data to the cloud. We take a design of centralized price optimization and distributed network control. More specifically, we architect a layered architecture for data delivery, develop algorithms to optimize the overall monetary cost, and prototype a fully functioning system of sTube+. We evaluate sTube+ by both experiments and simulations. In addition, we develop a case study on smart maintenance of chillers and pumps, using sTube+ as the underlying network architecture.

The Internet of Things (IoT) increasingly demonstrates its role in smart services, such as smart home, smart grid, smart transportation, etc. However, due to lack of standards among different vendors, existing networked IoT devices (NoTs) can hardly provide enough security. Moreover, it is impractical to apply advanced cryptographic solutions to many NoTs due to limited computing capability and power supply. Inspired by recent advances in IoT demand, in this paper, we develop an IoT security architecture that can protect NoTs in different IoT scenarios. Specifically, the security architecture consists of an auditing module and two network-level security controllers. The auditing module is designed to have a stand-alone intrusion detection system for threat detection in a NoT network cluster. The two network-level security controllers are designed to provide security services from either network resource management or cryptographic schemes regardless of the NoT security capability. We also demonstrate the proposed IoT security architecture with a network based one-hop confidentiality scheme and a cryptography-based secure link mechanism.

Terrestrial-satellite networks (TSNs) play a significant role in achieving 100\% geographic coverage in the next generation of wireless networks. In TSNs, multimedia transmission is an important application scenario, where efficient content delivery solutions are required for effectively alleviating network congestion. In this paper, we study multicast beamforming problems in TSNs by reusing the entire bandwidth for providing efficient solutions for content delivery. In order to mitigate the interference imposed by multicast transmission of the satellite, we formulate the cooperative multicast beamforming problems for the TSN under the quality of service (QoS) constraints. Then, according to different considerations, the semidefinite relaxation (SDR) method is applied for the beamforming design of the satellite, whilst the recently developed feasible point pursuit successive convex approximation (FPP-SCA) approach is adopted for tackling the beamforming design problem of the base station. The system performance is studied by simulation results. Our investigations show that our TSN is capable of attaining a sufficient high rate for supporting multimedia transmission.

Heterogeneous cellular network has become an important network architecture to improve network spectral efficiency. This paper works on a theoretical framework for analyzing outage probability and spectral efficiency in a two-tier heterogeneous network with joint processing cooperation. The heterogeneous network consists of high power nodes (HPNs) and low power nodes (LPNs). A range expansion association scheme is used to extend the coverage range of LPNs and to help achieve load balancing. Cooperation is applied to LPN cell edge users that are subject to strong interference from HPNs. Joint processing cooperation is formed between the serving LPN and nearest HPN and data is transmitted to the user simultaneously from these two cooperative nodes by taking radio resources from both. Study shows that cooperation can greatly improve LPN cell edge outage performance. When range expansion area increases, the average per user spectral efficiency of the whole system with cooperation remains steady while the average per user spectral efficiency without cooperation decreases.

This paper investigates a multimedia multicast beamforming scheme in the integrated terrestrial-satellite networks, where base stations (BSs) and the satellite work cooperatively provide ubiquitous services for ground users. Due to the contents diversity of multimedia services, users that request the same contents can be served as a group using multicasting. By utilizing multiple transmission antennas, multicast beamforming is performed among groups while reusing the entire bandwidth, which, however, can inevitably cause the co-channel interference among users. Taking both system performance and user fairness into account, we optimize the total system capacity performance under the satellite capacity constraint and derive the optimal power allocation schemes. Numerical results are presented in the end to evaluate the effectiveness of the proposed scheme compared with the greedy and suboptimal searching strategies.

In this paper two different non-orthogonal multiple access (NOMA) relay schemes are analyzed, namely NOMA cooperative scheme and NOMA TDMA scheme. Both schemes apply NOMA in the first stage by sending NOMA superimposed signals to relays. Relays then forward the messages to two UEs in the second stage. In the second stage of NOMA cooperative scheme, two relays form a cooperative pair to simultaneously transmit the decoded signals to their respective recipients. Dirty paper coding is used as precoding to cancel out inter-user interference. The second stage of NOMA TDMA scheme uses TDMA to send to two users in two separate time slots. The outage probability is analyzed for both schemes and the impact of error propagation in the NOMA successive interference cancellation is analyzed and evaluated. Performance study shows that the theoretical analysis matches the simulation results very well. NOMA cooperative scheme achieves an overall better outage performance than NOMA TDMA scheme.

"Cloud" is a common metaphor for an Internet accessible infrastructure (e.g., data storage and computing hardware) that is hidden from users. Cloud computing makes data truly mobile and a user can simply access a chosen cloud with any internet accessible device. In cloud computing, IT-related capabilities are provided as services, accessible without requiring detailed knowledge of the underlying technology. Thus, many mature technologies are used as components in cloud computing, but still there are many unresolved and open problems. This special issue includes articles addressing the state-of-the-art in strengthening performance and security and cloud computing. Eight representative research articles were carefully selected based on the original presentations at the 2016 International Conference on Cloud Computing and Big Data (CloudCom-Asia'16). The objective of this conference is to bring together researchers who work on cloud computing and related technologies. According to whether its research theme relates more to performance or security, the accepted papers are briefly described in the remaining part of this section. Task scheduling is critical for guaranteeing cloud performance. In the past years, more and more business-to-consumer and enterprise applications start running in the heterogeneous cloud. Such cloud bag-of-tasks (BoT) applications are usually budget-constrained and their scheduling is an essential problem for cloud provider. The problem is even more complex and challenging when the accurate knowledge about task execution time is unknown in advance. Focusing on these challenges, Tang et al1 build a cloud resource management architecture and stochastic task model, which divides cloud task into two execution parts. Then they deduce BoT applications schedule length and total cost according to heterogenous clouds online feedback information. They further formulate this stochastic scheduling problem as a linear programming problem and propose a time and cost multi-objective stochastic task scheduling genetic algorithm, which can find Pareto-optimal schedules for stochastic cloud task that meet its budget constraint. With the rapid development of Internet and cloud computing, the high performance requirements for data center networks (DCNs) are increasing for meeting the need of users. A large number of data need to be processed and shared among servers in a data center. Recently, multicast traffic in DCNs has attracted much attention from academia due to the fact that multicast traffics have the dominating advantages for group communications in DCNs. Therefore, the appropriate multicast traffic scheduling in data center networks cannot only improve network efficiency but also save network resources. Li et al2 propose a multicast scheduling algorithm to appropriately schedule flows to achieve traffic load balance so that network blocking can be avoided. In order to reduce the network blocking, they propose an efficient blocking cost-driven multicast scheduling algorithm in fat-tree DCNs. The paper establishes the blocking model of multicast network based on multicast network state and presents the blocking probability at the next time-slot, which can reduce the scheduling delay of multicast traffic. Furthermore, the paper presents also an optimal selection mechanism of feasible links based on the given link blocking probability at the next time-slot. In order to study cloud performance from a more comprehensive perspective, Wang et al3 investigate how to decide key parameters in service-oriented cloud computing systems to improve the system's performance and maximize the service provider's profit. The paper proposes a multiple game model to formulate the critical parameters decision process. For games among different participators, different rules are used to estimate corresponding key parameters. The proposed MG model can achieve Pareto-optimal equilibrium point, and its efficiency in dynamically deciding key parameters in CCSs are demonstrated by simulation results. Underlying infrastructure plays also a vital role for cloud performance. Newly emerging networking paradigms like Software-Defined Networking (SDN) promises more advances like flexibility and efficiency to cloud management. He et al4 propose NetCore-M language, a high abstraction level programming language for SDN. It can support for packet drop and conflicts detection. NetCore-M language provides a more abstract programming language for network configuration in data center. Specifically, the paper describes in detail the syntax, semanteme, and implementation of NetCore-M language as well as network policy conflict. Besides, this paper verifies that the modified multi-policies combination algorithm can effectively detect policy conflicts based on the implementation of the Pyretic project. Security of applications in multi-cloud collaborative environments is a major concern in today's distributed computing environment. Multi-cloud collaborative environments are highly heterogeneous. The security issues in such environments most commonly arise due to the use of ineffective access control mechanisms. The primary goal of Attribute-based Access Control (ABAC) as an access control model is to fulfill the requirements of highly heterogeneous environments such as multi-cloud environment. There are two major challenges for a system employing ABAC. The first is to determine suitable attributes for users and resources in the system. Formation of the correct set of ABAC rules is another major challenge. John et al5 investigate the development of two alternative approaches for deriving the minimum number of ABAC rules in a multi-cloud environment. In the first approach, they consider forming a minimal set of positive authorizations only. The second approach shows the advantage of developing negative authorizations along with positive authorizations. Together, these two contributions extend the current state-of-the-art in cloud security. With the advent of cloud computing, more and more consumers prefer to use the cloud services with the pay-as-you-consume mode. The cloud storage brings about great convenience to users, who store data in cloud and access to it using the smart devices anytime and anywhere. Consumers' information should be encrypted to guarantee the data privacy. Flexible searching on ciphertext is a critical challenge to be solved for effective data utilization. Yang et al6 propose a novel semantic keyword searchable proxy re-encryption scheme for secure cloud storage. The scheme is quantum attack resistant, while most of the available searchable encryption schemes are not. It not only supports exact keyword search, but also synonym keyword search. Moreover, the data owner is capable to delegate his search right to another user using the proxy re-encryption mechanism. In the generation process of re-encryption key, the delegator and delegate do not need to interactive with each other. The scheme is also collusion resistant. Under the learning with errors hardness problem, this scheme is proved secure in standard model. Wu et al7 investigates how to prohibit massive Twitter spams from cloud. This paper leveraged the massive posts information from social network platforms especially Twitter. Learning from millions of text-based tweets (Twitter messages), algorithms were generated to detect social spammers who propagate suspicious information. The authors developed an innovative spam detection method in Twitter using deep learning techniques, which may contribute to the field in terms of protecting cyber security. They put forward a new Twitter spam detection method based on deep learning to address the problems of existing methods. A series of empirical and theoretical analysis have been adopted to prove the outperformance of the proposed method. These studies will contribute to future analysis and optimization on Twitter spam detection. Meanwhile, more and more client applications for cloud are based on mobile devices. Thus, the security of mobile operating systems is crucial for securing the cloud applications. Qiang et al8 embark on solving the covert channel issues in smartphone operating systems, which may lead to furtive data transmission between applications with different permissions that might threaten users' privacy. The authors propose a general method that can detect covert channel attacks at runtime without impacting the accessibility of shared resources in the system. The method allows users to describe and audit the target covert channels in the application layer as well as the OS layer, by making use of Java hooks and kernel audit tool auditd. The main idea of the method is to track and audit the use of system resources known as potential covert channel variables and impose interferences on those channels to reduce their capacity once violations are detected. They implement a prototype framework to audit and interfere covert communication in both the application layer and the native layer of Android. The experimental results demonstrate that the proposed method can effectively reduce the data rate of user-defined covert channels while the overhead is negligible. The papers presented in this special issue provide research articles related to recent advances in cloud computing. In particular, these research articles aim to strengthen cloud performance and security, from various aspects including task scheduling and underlying SDN infrastructure. We hope that the readers of this special issue will benefit from the research ideas and concepts presented in these research articles. The guest editors of this special issue would like to express their special thanks to all of the authors who submitted their papers to this special issue and to all reviewers who contribute to the paper selection process. We would also like to deeply thank Professor Geoffrey C. Fox, the Editor-in-Chief, for providing the opportunity to publish this special issue and for offering continuous support, encouragement, and guidance throughout this publishing project.

Cognitive radio (CR) is an emerging technique to improve the efficiency of spectrum resource utilization. In CR networks, the selfish behavior of secondary users (SUs) can considerably affect the performance of primary users (PUs). Accordingly, game theory, which considers the game players' selfish behavior, has been applied to the design of CR networks. Most of the existing studies focus on the network design only from the network perspective to improve system performance, such as utility and throughput. However, the users' experience to the service, which cannot simply be reflected by quality of service, has been largely ignored. The user-perceived multimedia quality and service can be different from the actual received multimedia quality, and thus is very important to consider the network design. To better serve the network users, quality of experience (QoE) is adopted to measure the network service from the users' perspective and help improve the users' satisfaction to the CR network service. As CR networks require a lot of data storage and computation for spectrum sensing, spectrum sharing, and algorithm design, cloud computation comes as a convenient solution, because it can provide massive storage and fast computation. In this paper, we propose to design a user-oriented CR cloud network for multimedia applications, where the user's satisfaction is reflected in the CR cloud network design. In the proposed framework, the PU and SU game is formulated as Stackelberg game. In particular, a refunding term is defined in the users' utility function to effectively consider and to reflect the network users' QoE requirement. Our contributions are twofold: 1) a game-based CR cloud network design for multimedia transmission is proposed, and the network user's QoE requirement is satisfied in the design and 2) the existence and the uniqueness of the Stackelberg Nash equilibrium are proved, and the design is optimal. Our simulation results demonstrate the effectiveness of the game user-oriented CR cloud network design.

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.

To achieve a significant boost on capacity performance in the next generation (5G) cellular network, novel radio access technologies (RAT) are demanded to make the system more spectrum efficient. As a promising multiple access scheme for 5G cellular network, non-orthogonal multiple access (NOMA) has attracted extensive research attention recently. Existing works show that NOMA posses the potential to further improve system spectrum efficiency compared with the orthogonal multiple access (OMA), which is predominantly adopted by existing wireless networks. In this paper, we develop the analytical framework on system coverage and average user achievable rate in a downlink NOMA system. We explicitly consider the inter-cell interference in the study, which is a capacity limiting factor in most wireless networks but less addressed in most existing analytical work for NOMA. Additional to NOMA, the analysis on an OMA access scheme, i.e., orthogonal frequency division multiple access (OFDMA), is also conducted for comparison. Owing to the tractability of Poisson Point Process (PPP) model used in this work, all the analytical results are derived and expressed in a pseudo-closed form or a succinct closed form. The analytical results are validated by simulations and demonstrate that NOMA can bring considerable performance gain compared to OMA when success interference cancellation (SIC) error is low.

This work focuses on the analysis for multicast services in fifth-generation (5G) wireless communication system. We investigate the physical- layer wireless multicast technology in massive multi-input multi-output (MIMO) mutual coupling channel model, and proposed the hybrid unicast/multicast transmission system. The mutual coupling channel model is adopted to describe the channel characteristics under the linear antenna array scenario and the rectangular antenna array scenario. The proposed hybrid transmission scheme adopts multicast beamforming in the multicast groups as well as multi-user MIMO (MU-MIMO) linear precoding in the unicast group to increase system throughput. The null-space method based interference cancellation is further performed between each group to eliminate signal leakage generated from each group. Comparisons between two types of antenna array configurations, different channel models, linear precoding as well as multicast beamforming, and user grouping strategies for multicast services are presented and analyzed by simulation.

In this paper, we considers the resource allocation problems for video transmission in space based information networks. The queueing system analyzed in this work is composed of multiple users and a single server. To minimize both of the time average cost and delay of the system, and subject to the constraint that the queues in the system must be stable, we introduce a predictive backpressure algorithm into the consideration of resource allocation to make decision on which packets to be served first. Meanwhile, a multi-resolution wavelet decomposition based backpropagation neural network for the prediction of video traffic is designed in this paper. Performances of the proposed video traffic prediction system and resource allocation scheme are analyzed in the simulations. Results indicate that the prediction accuracy for the video traffic is improved according to the proposed prediction system, and the delay of the queueing system can be reduced through this prediction based resource allocation.

Heterogeneous cellular network has become an important network architecture to improve network capacity and spectral efficiency. This paper works on a theoretical framework for analyzing the system spectral efficiency in a heterogeneous network with different association schemes including best power and range expansion associations. Furthermore, a fractional frequency reuse scheme is applied to mitigate the inter-tier interference in a heterogeneous network. Proportional fair bandwidth allocation is used to balance the system spectral efficiency and user fairness. Numerical results show that range expansion with fractional frequency reuse can improve the system spectral efficiency significantly.

Device-to-Device (D2D) communication is a promising technology in the next generation (5G) cellular network as it can significantly improve the system performance by leveraging the proximity of communications and reusing cellular frequency resources. However, this benefit may not be fully exploited if the co-channel interference among D2D users (DUE) and cellular users (CUE) is not properly tackled. In this paper we propose a distance based power control scheme for D2D communication underlaying uplink cellular network to achieve the expected performance gain without generating evident interference to primary CUEs. We analyze the coverage performance of both CUEs and DUEs. Stochastic geometry model, more specifically, Poisson Point Process (PPP) model, is applied to get the tractable analysis results. The analytical results are validated by simulation.

Facing a huge volume of data that quickly generated from big data applications, it is crucial for the information and communication technology (ICT) infrastructure being able to process, aggregate, store, manage and analyze with massive data. When an ICT conducts a centralized data learning task, the information privacy of each local dataset might be violated. In this paper, we consider a secure data learning scheme in which multiple parties would find the predictive models from their overall data, while not revealing its own private data to one another at the same time. Instead of deploying the centralized data learning process which may jeopardize the information privacy, we distribute the centralized data learning tasks to each local learning party as their own local data learning tasks to learn the value from local data. Besides that, we propose an associated secure scheme to show the guarantee of privacy of learning results during the information reassemble and value response process. Evaluation work is provided to verify the privacy of training dataset, as well as the accuracy of learning weights. A case study is presented based on an open metering data analysis.

Good Quality of Experience is critical to the success of IPTV business development and promotion. To this end, the paper combines status data from the set-top box with the data of user's complaints and then selects the appropriate model to predict user's QoE. Firstly, we clean and conduct some statistical analysis for the dataset. Then, random under-sampling and synthetic over-sampling are applied to the dataset after these procedures. In order to get better performance, this paper improves the Synthetic Minority Over-sampling Technique (SMOTE) algorithm. In addition, we compare the decision tree model and k-Nearest Neighbors (k-NN) model in user's complaint dataset. Through rigorous modeling and prediction, extensive experimental results show that k-NN model performs better than the decision tree in terms of predicting the user's complaint.

Providing encrypted communications among power grid components is expected to be a basic requirement of smart grid systems in the future. Here, we propose a key management architecture and associated protocols tailored to support encrypted smart grid communications. The architecture consists of two levels structured around the grid control system hierarchy. At the top level, which consist of control centers and regional coordinators, a bottom-up key structure is adopted using hash chaining and a logical key hierarchy. The lower level of the architecture consists of the regional coordinators (i.e., substations and distribution systems) and remote ends (e.g., meters and pole-top sensors) and utilizes a top-down key management approach built on an inverse element method. The proposed key management schema supports the hierarchical structure of the smart grid control mechanisms, and it takes the resource and electronic/physical security differences of the control levels into account. We define a set of protocols utilizing the architecture to provide secure unicast, multicast, and broadcast communications. Furthermore, we illustrate how the architecture is flexible enough to easily handle power grid nodes joining and leaving the system at the different levels. Lastly, we compare the proposed schema with existing ones and show that our architecture can achieve efficient key management to provide secure communications. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data‐driven, cloud‐based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity‐based schemes are proposed especially because of their advantage in key management. Specifically, an identity‐based signcryption (IBSC) scheme is proposed to provide confidentiality, non‐repudiation, and data integrity. For practical purposes, an identity‐based signature scheme is relaxed from the proposed IBSC to provide non‐repudiation only. Moreover, identity‐based schemes are also proposed to achieve signature delegation within the ICT framework. Security of the proposed IBSC scheme is rigorously analyzed in this work. Efficiency of the proposed IBSC scheme is demonstrated with an implementation using modified Weil pairing over an elliptic curve. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we propose a new security protocol for a wireless sensor network, which is designed for monitoring long range power transmission lines in smart grid. Part of the monitoring network is composed of optical fiber composite over head ground wire (OPGW), thus it can be secured with conventional security protocol. However, the wireless sensor network between two neighboring OPGW gateways remains vulnerable. Our proposed security protocol focuses on the wireless sensor network part, it provides mutual authentication, data integrity, and data confidentiality for both uplink and downlink transmissions between the sensor nodes and the OPGW gateway. Besides, our proposed protocol is adaptive to the dynamic node changes of the monitoring sensor network; for example, new sensors are added to the network, or some of the sensors are malfunctioning. We further propose a self‐healing process using an “i‐neighboring nodes” public key structure and an asymmetric algorithm. We also conduct energy consumption analysis for both general and extreme conditions to show that our security protocol improves the availability of the monitoring sensor network. Copyright © 2015 John Wiley & Sons, Ltd.

The staggering mobile growth is shaping to be the biggest shift in technology since the advent of the Internet, paving the way for unprecedented and yet to be thought of video services and applications. The proliferation of these mobile devices and applications, however, requires new paradigms to satisfy the increasing demands for capacity and energy, and achieve a good video quality. In this paper, we propose a video quality-based framework for spectrum and energy efficient mobile association and resource allocation in heterogeneous wireless networks. The basic tenets of the framework are (1) two novel performance metrics, namely QSE and QEE, to capture spectrum usage and energy consumption from video quality's perspective; and (2) a computationally efficient optimization model to derive mobile association and resource allocation for video connections in heterogeneous wireless networks. To this end, we first study the fundamental tradeoff between QSE and QEE in a PtP Rayleigh fading wireless channel. We then study QSE and QEE at the system level and develop a mobile association and resource allocation scheme that aims to jointly optimize system level QSE and QEE. The problem is formulated as a mixed-integer nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way. The simulation results evaluate the performance tradeoff between QSE and QEE, and show that the system performance, including PSNR distribution and maximum QSE/QEE values, greatly depends on bandwidth and power decaying factors.

The United States is deploying the broadband wireless network for public safety and emergency response. While this will provide a foundational network when disasters or other emergencies appear, the network capacity could still be insufficient when large scale emergency events, such as earthquakes or flooding, happen. On-demand broadcast is a possible solution to address this situation, but its effectiveness remains unknown. In this paper, we aim to uncover the effectiveness of on-demand broadcast at different situations. Specifically, we study the scaling of average response time on the information requests with regard to the indicators of the content diversity - the number of distinct content files and the content popularity distribution. To achieve this goal, we first derive the lower bound of the average response time. We further investigate broadcast for streaming videos and uncover that parallel broadcast could have the same lower bound as the sequential broadcast under certain conditions. Based on the lower bound we derived, we evaluate the scaling of response time with regard to the number of files under different distributions. We further provide numerical results to demonstrate the accuracy of the approximation and the value of the lower bound on evaluating the optimality of a basic heuristic on-demand broadcast scheme.

This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.

The continuous growth of media-rich content calls for more efficient and secure methods for content delivery. In this paper, we will address the optimallinear network coding(LNC) design forsecure unicast streamingagainst passive attacks, under the requirement ofinformation theoretical security. The objectives include 1) satisfying the information theoretical security requirement, 2) maximizing the transmission rate of a unicast stream, 3) minimizing the number of additional random symbols, and 4) minimizing the total bandwidth cost of content delivery. To fulfill the first three objectives, we formulate aninformation theoretically secure unicast streaming(ITSUS) problem, and then solve it by transforming it to a maximum network flow problem with node-capacity constraints. Based on the solution of the ITSUS problem, we develop an efficient algorithm that can find the optimal transmission topology with minimum bandwidth cost in a polynomial amount of time. With the optimal transmission topology, we investigate the design of bothdeterministicLNC and random LNC. For thedeterministicLNC design, we not only prove that it achieves the four objectives but also analyze the size of required finite field. Moreover, for the random LNC design, we analyze the probability that a random LNC scheme satisfies the information theoretical security requirement. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

In this paper, we study a real-time information based demand-side management (DSM) system with advanced communication networks in smart grid. DSM can smooth peak-to-average ratio (PAR) of power usage in the grid, which in turn reduces the waste of fuel and the emission of greenhouse gas. We first target to minimize PAR with a centralized scheme. To motivate power suppliers, we further propose another centralized scheme targeting minimum power generation cost. However, customers may not be motivated by a centralized scheme since such a scheme requires total control and privacy from them. A centralized scheme also requires too much real-time data exchange for frequent DSM deployment. To tackle these issues, we propose game theoretical approaches so that most of the computation is performed locally. In the proposed game, all the customers are motivated by extra savings if participating. Moreover, we prove that all parties benefit from the DSM system to the same level because both the centralized schemes and the game theoretical approach minimize global PAR. Such an analysis is further demonstrated by the simulation results and discussions. Additionally, we evaluate the performance of several (partially) distributed approaches in order to find the best way to deploy DSM system.

Abstract For the problem of spectrum scarcity and wastage, cognitive radio (CR) technology provides a solution to utilizing the vacant spectrum more efficiently. As one of the most promising techniques to obtain the cognitive information in TV white spaces, geo‐location database approach has attracted a lot of recent attentions, with its goal of enhancing the efficiency of spectrum usage and avoiding the interference to TV receivers. However, existing works mainly focus on the construction and applications of geo‐location database, and seldom consider how to deliver the cognitive information from the database to TV band devices. In this paper, we investigate the tradeoff between increasing the accuracy of cognitive information delivery and reducing the overhead. We design two mesh fusion algorithms to reduce the redundancy of cognitive information and improve the efficiency of cognitive information delivery. Finally, we verify our analysis and evaluate the efficiency of the proposed mesh fusion algorithms through numerical studies. Copyright © 2016 John Wiley & Sons, Ltd.

ABSTRACT Satisfying the different requirements of applications is important in multi‐hop cognitive radio networks, because the spectrum resources change dynamically and the requirements for various applications could be very different. In this paper, we propose new schemes of channel allocation and route selection for real‐time and non‐real‐time applications to tackle this challenge. Our scheme is flexible so that it can adapt to the different application requirements, and it can provide enough throughput while maintaining the packet loss rate and transmission rate requirements. First, we give the network model in a cognitive radio network environment, and show how to calculate the capacity of the route in multi‐hop cognitive radio networks. Second, we formulate optimization problems to fulfill the rate requirements of different applications for each unicast session. We also consider the primary user activities, channel availability, interface and interference constraint. We propose the corresponding routing and channel allocation schemes for different application scenarios. Third, we propose an admission control scheme to study the impact of application requirements for multiple sessions in cognitive radio networks. Finally, we implement simulations to show the performance of our schemes and compare them with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract In multi‐hop cognitive radio networks, it is a challenge to improve the energy efficiency of the radio nodes. To address this challenge, in this paper, we propose a two‐level Stackelberg game model, where the primary users and the secondary users act as the leaders and the followers, respectively. Based on the game model, our proposed scheme not only considers the power allocation problem for secondary users but also takes into account the price of spectrum. First, we give the cognitive radio network model, and show how to set up the game theoretic model in multi‐hop cognitive radio networks. We then analyze this problem and show the existence and uniqueness of the Nash equilibrium point for the game. We also study the impact of the spectrum price of the primary users in the cognitive radio network and study how to select the best price for the primary users to maximize their own profit. Finally, we implement simulations to show the performance of our schemes. Our work gives an insight on how to improve the energy efficiency and allocate spectrum resources in multi‐hop cognitive radio networks. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In order to timely and precisely locate a problem over the power lines, the control center needs to monitor the status of the transmissionlines and the towers. In this paper, we design such a monitoring network by taking advantage of the existing optical fiber composite overhead ground wire (OPGW) alongside the transmission lines. Because it is not cost‐effective to have gateway access to the OPGW for every transmission tower, we propose to deploy a multi‐hop wireless sensor network in between two sparsely deployed neighboring OPGW gateways. We mainly study the power allocation for the data transmission of the wireless sensors because of the assumption that such sensors are powered by green energy and a battery with limited capacity for easy deployment and maintenance. Specifically, we propose several centralized schemes with different objectives, for example, the minimum power usage and fast computation. We also propose a distributed scheme so that the sensors can be even more energy efficient dealing with dynamic traffic in field operations. Moreover, we analyze the centralized schemes to study their pros and cons. We also conduct a case study for the distributed scheme to demonstrate its feasibility in field operations. Copyright © 2015 John Wiley & Sons, Ltd.

Future heterogeneous networks (HetNets) will have to face a great challenge of overwhelming demand of spectrum resource, due to the exponential increase in mobile internet traffic driven by a new generation of wireless devices. In this paper, we propose a spectrum sensing and scheduling scheme for circular array, in order to make better use of the spectrum resource and improve the performance of multi-user MIMO (MU-MIMO) in HetNets. The proposed scheme can effectively detect the users and frequency use based on angles, and schedule the users with optimized codebook. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and users' data rate, with significantly reduced system complexity and increased efficiency.

Cognitive radio (CR) networks have been developed to fully utilize spectrum resources. In the CR networks, imperfect sensing and selfish behavior of secondary users (SU) can significantly degrade the performance of primary users (PU). Game theory, which considers the game players' (PU/SU) selfish behavior, has been extensively investigated in the design of CR network. However, most related works focus on the network protocol design from the network perspective to improve video transmission system (i.e., PU) performance such as utility and throughput. However, a user's satisfaction such as user's quality of experience (QoE) has been largely neglected in existing works. In this paper, we propose to design a game user-oriented CR network for video applications, aiming to improve the QoE of users. In the proposed framework, the primary user games and the secondary user games are formulated as Stackelberg game. Specifically, a refunding term is defined in the user's utility function to effectively consider and to reflect the QoE requirement of video streaming users. Our contributions include two folds: (1) A game based CR network design for video transmission is proposed, and the network user's QoE requirement is satisfied in the design; (2) The existence and uniqueness of the Stackelberg game's Nash equilibrium is proved, and the design is optimal. Our theoretical analysis and simulation results demonstrate the effectiveness of the game-based user-oriented CR network design.

In order to address the ever increasing high capacity demands, next generation wireless networks are required to revolutionize the infrastructure design and air interface technologies. In this paper, we introduce a cooperative non-orthogonal multiple access (NOMA) technique with successive interference cancellation (SIC) in wireless heterogeneous networks. Aiming to improve the system capacity, the cooperative NOMA scheme exploits both NOMA and dirty paper coding (DPC), based on which a resource scheduling optimization problem is formulated. The optimization problem is a combinatorial mixed-integer non-linear problem. A genetic algorithm is used to solve the problem with a low computational complexity. Simulation results show that the proposed cooperative NOMA scheme can significantly improve the network capacity.

In this paper, a big data driven, cloud based information and communication technology (ICT) framework for smart grid is proposed. The proposed ICT framework is to provide price forecast to customers and energy forecast to utility company. Cloud computing and big data analytics are introduced to assist local control centers dealing with large amount of data. However, public cloud and transmission over internet may be vulnerable in security, especially privacy preserving and authentication. To secure the proposed framework, we propose an identity-based signcryption (IBSC) security scheme. The proposed IBSC scheme provides confidentiality and non-repudiation since it performs simultaneously the functions of encryption and digital signature. Moreover, data integrity is also provided in the IBSC scheme. Identity-based signature and key distribution are presented as extended applications from the IBSC scheme. The security and performance of the proposed IBSC scheme are analyzed. Efficiency of the proposed IBSC scheme is demonstrated with an implementation using modified Weil pairing over an elliptic curve.

Underlaying Device-to-Device (D2D) communications in next generation (5G) cellular networks is a promising technology because D2D can exploit the proximity of communication pairs and reuse existing cellular frequency resources. Although D2D communication has a great potential to improve the capacity and spectral efficiency of the overall system, the interference between D2D user equipment (DUE) and cellular user equipment (CUE) needs to be properly tackled in order to achieve the target performance gains. Fractional frequency reuse (FFR) has been demonstrated as an effective scheme to mitigate uplink interference. Further, uplink power control in a cellular network is a widely employed mechanism to manage interference across the uplink network while improving spectral efficiency. Existing uplink fractional power control (FPC) in LTE trades off reasonably well the cell-edge UE performance with the overall system performance. In this paper, we study the performance of D2D communications underlaying an uplink LTE network which utilizes both FPC and FFR. FPC in such a network is slightly different than the existing FPC mechanism in that it is only applied within certain geographical areas. In such a system, geometry information is exploited to allocate resources so that significant interference reduction can be attained. Coverage analysis is conducted for both CUEs and DUEs by following a mathematically trackable Poisson Point Process (PPP) model. A spectral efficiency study is also presented along with the impact of various uplink parameters on the system performance.

In this paper, we propose a multi-objective optimization framework to address the joint mobile association and resource allocation problem in a video transmitted wireless heterogeneous network. We consider user quality of experience (QoE) as one of the design objectives together with two other performance metrics to characterize the design tradeoffs among perceived video quality, power consumption, and network resource consumption. In order to find the Pareto optimal solutions for a multi-objective optimization problem, we first apply weighted Tchebycheff approach to aggregate multiple objectives and minimize the Tchebycheff distance between the optimal solution and utopia solution. Dual decomposition technique is then introduced to decompose the above mentioned problem into a series of similar subproblems. By using linear relaxation and variable transformation, standard convex optimization method is applied to solve these subproblems efficiently and the optimal mobile association and resource allocation are obtained. System level simulation studies numerically demonstrate the tradeoffs among three design objectives and provide an insightful understanding on the performance compromise under multi-objective conditions.

Delay issue is a challenge in multi-hop cognitive radio networking because of the dynamic change of channels available to the cognitive radio nodes. To address this challenge, we propose a channel allocation and rate allocation scheme with low complexity in this paper. Our scheme is flexible so that it can react to the dynamic change of channel availability, and it can minimize the delay by considering the primary user activities. First, we model the primary users' activity, channel availability and the interference among the cognitive radio nodes in a cognitive radio network environment, and show how to implement a channel allocation algorithm and a rate allocation scheme in multi-hop cognitive radio networks. Second, we formulate an optimization problem to minimize the end-to-end delay of the network. We consider the channel availability constraint and the primary user activities jointly, and analyze the end-to-end delay in multi-hop cognitive radio networks. In order to reduce the computing complexity, we propose a graph coloring based channel allocation and rate selection algorithm using gradient descent method. Furthermore, we show the performance of our schemes and compare them with existing schemes for different scenarios of channel availability and number of channels through simulations. Our work brings insights on how to make rate selection and channel allocation in multi-hop cognitive radio networks.

Abstract Multiple‐input multiple‐output orthogonal frequency division multiplexing has become an attractive air‐interface solution for the next generation wireless networks because of its high spectrum efficiency. This paper addresses the security concern and proposes to achieve covert orthogonal frequency division multiplexing transmission using cyclic delay diversity featured multiple‐input multiple‐output technology. Particularly, our physical layer security scheme takes the advantage of cyclic delay diversity formed periodical frequency selective channel and utilizes uneven comb pilots to confuse unauthorized receivers and benefit authorized receivers. We conduct simulation study to evaluate the impact of different cyclic delay, antenna number, and interpolation algorithms on our scheme. Numerical results show that our scheme can provide authorized users with significant advantage over eavesdroppers without complicated upper‐layer encryption and decryption. Moreover, the scheme has flexible choice of parameters and thus can be easily deployed in a variety of wireless networks with different requirements. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract The advanced metering infrastructure (AMI) is the key to demand‐side management system in smart grid. The communication over AMI consists of plenty important data, which have different security requirements. In this paper, we propose a hierarchical identity‐based signature scheme (HIBaSS) to enhance the sender authentication for downlink transmission of AMI. The downlink in AMI mainly distributes control messages such as price and tariff information to the smart meters. Unlike the metering data in the uplink transmission that requires confidentiality, most of the control messages in downlink only require data integrity and sender authentication. Moreover, since most of the control messages are valid for a relatively long time period (e.g., an hour), more complicated but stronger cryptographic schemes can be applied in downlink AMI. In our proposed HIBaSS, each smart meter does not trust a signature from a single data aggregate point (DAP) although it includes the original signature from the authentication server (AS), because a smart meter has no way to verify a message or a DAP directly with the AS. Instead, a smart meter receives a group signature created by all the DAPs with certificates from the AS that prevents the messages from forgery, manipulation and repudiation. The performance evaluation also shows that HIBaSS is efficient enough to be applied in the AMI downlink transmission. Copyright © 2015 John Wiley & Sons, Ltd.

In this work, we propose a dynamic distributed resource sharing scheme which jointly considers mode selection, resource allocation, and power control in a unified framework for general D2D communications. First, we model the joint issue of mode selection and resource allocation as a hedonic coalition formation game, while accounting for the tradeoff between the benefits in terms of available rate and the costs in terms of the mutual interference. Moreover, we develop a coalition formation process based on the switch rule, through which each cellular user makes an individual and distributed decision to form a Nash-stable partition. Second, we view the members of each coalition as a whole, and formulate a power control problem to share the aim of maximizing the sum-rate of cellular links in this coalition. To solve this NP-hard problem with online operation, we present a power control process, which employs the local piecewise-linear approach to take a locally and separately approximate optimal outcome. Finally, we present a dynamic resource sharing algorithm, which iteratively operates the coalition formation and power control processes.

Device-to-Device (D2D) communication operating as an underlay to the cellular network has been lately exploited to facilitate proximity-aware services and data traffic offloading. While D2D communication has great potentials to improve wireless network spectral efficiency and energy efficiency due to the proximity of communication parties and a higher spectrum reuse gain, how to guarantee the coverage and capacity for both cellular users and D2D users when dense D2D communications are carried underlay cellar networks still remains as a big challenge. This paper provides the coverage study by deriving the uplink and downlink SINR distributions for both cellular users and dense D2D users based on statistical user distribution and channel information. We model the spacial distribution of the D2D pairs as a homogeneous Poisson Point Process (PPP) and D2D users can either use cellular uplink resources or downlink resources. The simulation results match closely with the analytical studies. The analytical tools can be conveniently extended to evaluate other key D2D wireless network performance metrics including network capacity and outage probability, and provide great insights on the critical network design issues such as power control, interference management, and resource allocations.

This paper proposes a distributed resource allocation and power control scheme based on stackelberg game framework to improve network capacity in Device-to-Device (D2D) communications networks. D2D users are supported in an underlay mode by sharing radio resources with the cellular downlink communications. The system aims to maximize the number of underlay D2D users while guaranteeing Quality of Service (QoS) of the prioritized cellular users. We formulate the problem through joint optimization on the D2D power control and resource allocation. The global optimization problem is a complicated task to tackle mathematically and has a high computational complexity. Instead we formulate the optimization problem with a distributed stackelberg game theoretical model and decompose it into two steps to approach the game equilibrium. The simulation results show that our proposed distributed algorithm converges very fast and the system capacity of the D2D underlay network is significantly improved.

This paper presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC) for LTE-A heterogeneous networks (HetNets). Particularly, we take advantage of the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungary algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

In this paper, we propose a security protocol for advanced metering infrastructure (AMI) in smart grid. AMI is one of the important components in smart grid and it suffers from various vulnerabilities due to its uniqueness compared with wired networks and traditional wireless mesh networks. Our proposed security protocol for AMI includes initial authentication, secure uplink data aggregation/recovery, and secure downlink data transmission. Compared with existing researches in such area, our proposed security protocol let the customers be treated fairly, the privacy of customers be protected, and the control messages from the service provider be delivered safely and timely.

Neighborhood area network (NAN) is one of the most important sections in smart grid communications. It connects residential customers as part of a two-way communication infrastructure responsible for transmitting power grid sensing and measuring status as well as the control messages. In this paper, we propose a cost-effective, flexible, and sustainable NAN design using wireless technologies such as IEEE 802.11s and IEEE 802.16, as well as renewable energy such as solar power. We provide analysis to select the optimum number of gateways in a NAN. We also discuss the general way to compute real time power usage for a NAN gateway. In addition, we set the boundary of the gateway power usage under two extreme scenarios to ensure the NAN can be self-sustaining while meeting the critical transmission criteria.

Long Term Evolution uses a new location update (LU) scheme, called a tracking area list (TAL)-based scheme, to overcome the defects of the LU scheme used in 2G and 3G cellular networks. Under the TAL-based LU scheme, each time a user equipment (UE) performs an LU, it is allocated a group of tracking areas, referred to as a TAL, within which the UE can move freely without any LU. The UE performs an LU when moving out of the TAL. The performance of the TAL-based LU scheme depends on the allocated TAL. In this paper we develop a mathematical model to analyze the signaling overhead of the TAL-based LU scheme for local UEs whose mobility exhibits strong regularity. We derive formulas for the LU cost and the paging cost of a TAL allocation strategy. With these formulas we can find an optimal TAL allocation strategy to minimize the signaling cost of the TAL-based LU scheme.

In this paper, we introduce a security protocol for wireless sensor network which is designed for monitoring long range power transmission lines in smart grid. The proposed security protocol provides authentications to the sensor nodes and the information data, and the encryption for uplink and downlink information of the power line monitoring sensor network. Different from the existing protocol, our proposed one has an auto-correction process to keep the network operating even with malfunctioning nodes. We also conduct energy consumption analysis and select more energy efficient authentication and encryption methods. In addition, the results of energy consumption analysis help to determine the transmitting power for each node so that the sensor network can meet the delay requirement at the same time.

Smart grid is a modernized power grid that uses information and communication technology to gather and act on information, to use the information to provide automatic control to improve the efficiency, reliability and sustainability of the grid. In this paper, we study a wireless sensor network for monitoring long range power transmission line in smart grid. The energy efficiency is the major concern in this paper since the monitoring network is powered by renewable energy. Taking delay in consideration, we first get the optimal energy efficiency. To provide better quality-of-service (QoS) of the sensor network, we propose schemes for weighted average energy efficiency and average delay. Then, we propose a sequential control scheme, which achieves higher weighted average energy efficiency by increasing signal-to-interference-plus-noise ratio. Moreover, with the combination of transmitting power, sequential control and delay, we conduct numerical study to demonstrate that the proposed sequential control is a practical method in improving weighted average energy efficiency. We also study the practical application of the sequential control when delay is taken into consideration since monitoring data in smart grid is delay-sensitive.

Currently, a rapidly increasing numbers of sensors are being used on vehicles and in surface transportation and this trend is expected to continue. In support of these sensor networks several energy management schemes, including hierarchical clustering methods, have been proposed in the literature to reduce energy usage. However, the operating conditions for wireless sensors in vehicular environments change continuously. Sensor networks must thus be able to adapt to the environment to maximize performance and reliability. In this paper, we discuss the necessity for splitting a cluster. In particular, we explore the changes in the energy usage profile when a cluster is split. Even though our algorithm is designed for specific applications relevant to automation and control of railroad operations we believe that our discussions on energy consumption is a universal issue relevant to any form of clustered networks in vehicular environments.

ABSTRACT In this paper, we address the energy‐efficient connectivity problem of awireless sensor network(WSN) that consists of (1) staticsensor nodesthat have a short communication range and limited energy level, and (2)relay nodesthat have a long communication range and unlimited power supply, and that can be added or relocated arbitrarily. For such a WSN, existing studies have been focused on the design of efficient approximation algorithms to minimize the number of relay nodes. By contrast, we propose a unified backbone construction framework that can be performed in a centralized manner with two objectives: (1) to minimize the number of nodes in the backbone and (2) to maximize the lifetime of the network. To solve such a challenging problem, we formulate three subproblems: (1)partial dominating set with energy threshold(PDSET); (2)partial dominating set with largest residual energy(PDSLE); and (3)minimum relay node placement(MRNP). For these three subproblems, we develop polynomial‐time algorithms. We also prove that our algorithm for PDSLE is optimal, and our algorithm for the PDSET and MRNP problems have small approximation ratios. Numerical results show that the proposed framework can significantly improve energy efficiency and reduce backbone size. Copyright © 2012 John Wiley & Sons, Ltd.

This paper considers a two-phase two-way relay network (TWRN) with two sources and multiple amplify-and-forward (AF) relays, where the direct link between the sources exists and one best relay is chosen for data communication to maximize the minimum signal-to-noise ratio (SNR) of bidirectional communication. To efficiently exploit the direct link within two phases, a switch-and-stay combining (SSC) protocol is employed. In SSC, one branch out of the relay and direct branches is activated for data communication, and the branch switching occurs when the end-to-end SNRs fall below the given thresholds. We analyze the system performances over the independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels, by deriving lower bounds and asymptotic expressions with high SNR for the outage probability and bit error rate (BER). It is shown that SSC can preserve the same spectral efficiency as analog network coding (ANC), while can concurrently achieve the full diversity order as the optimal selection (OS) with less implementation complexity. Numerical and simulation results verify the proposed studies.

Asynchronous system provides great performance improvement for wireless ad hoc networks, such as anti-jamming, collision reduction and device simplification. Nevertheless, new media access and routing protocols are required to assist the asynchronous system, e.g., a neighbor discovery algorithm, which is the first step in the initialization of wireless ad hoc networks. In the past few years, a number of algorithms have been proposed for neighbor discovery. However, most of them only consider synchronous system and cannot work efficiently in asynchronous system. In this paper, firstly, we propose an analytical model for an 1-way asynchronous system in wireless ad hoc networks with directional antennas. Then, we compare the time-slot consumption in asynchronous system to complete the neighbor discovery process with that in synchronous system. Finally, in order to improve the performance of the neighbor discovery process, we extend the 1-way asynchronous discovery algorithms to a 2-way asynchronous discovery algorithm. To the best of our knowledge, this is the first practical analytical model of 2-way asynchronous neighbor discovery algorithm with directional antennas.

Wide deployment of communication devices on vehicles is on the horizon due to the development of intelligent transportation systems. Although these communication devices are originally designed for highway efficiency and safety applications, the mobile communication ability on vehicles also enables a lot of other emerging applications. In this paper we study an application scenario on utilizing vehicles to carry messages for a set of sensor collectors, which are deployed at places without other communication infrastructure. A core problem that we study is on how to schedule messages so that the message delay is minimized. To achieve this goal, we propose three different strategies: the first feasible vehicle strategy, the optimal bound strategy, and the multiple attempt strategy. We further provide mathematical analysis on these strategies that are also verified by simulations, and compare the performance of these three strategies in simulations.We draw the conclusion that the optimal bound strategy is an effective strategy as it achieves low delay of the messages. At the same time it avoids the disadvantages such as high energy consumption and large storage size as in the multiple attempt strategy.

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing the “energy efficiency” aspect of mobile cellular networks. Cognitive heterogeneous mobile networks are considered as important techniques to improve the energy efficiency. However, most existing works do not consider the power grid, which provides electricity to cellular networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient, since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study cognitive heterogeneous mobile networks in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management are performed. We formulate the problems of electricity price decision, energy-efficient power allocation and interference management as a three-level Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO2emissions in cognitive heterogeneous mobile networks.

In mobile ad hoc networks (MANETs), it is important to understand the throughput-delay trade-off (TD trade-off) problem in large-scale scenarios. In the literature, the TD tradeoff problem has been studied extensively and many of them are based on the independent and identically distributed (i.i.d.) mobility model, in which each node can randomly move to any place in the network, after every time slot. Although the i.i.d. model has been widely used, it cannot fully represent MANETs in which nodes change positions less frequently. To characterize such MANETs, in this paper, we propose a generalized i.i.d. (g.i.i.d.) mobility model, in which each node moves once after every 1/f (0 < f ≤ 1) time slots, and remains static between two moves. To investigate the TD trade-off under the g.i.i.d. model, we develop a novel multi-relay multi-hop (MRMH) scheme that exploits the opportunities of multi-hop transmissions when the network is static. Furthermore, to enable the multi-hop transmissions, we construct a new percolation highway system, which has not been used in the TD trade-off analysis for MANETs. Using the proposed MRMH scheme, we develop and prove constructive bounds for throughput and delay in MANETs with different scales of f. Our constructive bound is asymptotically optimal for f = 1 (i.e., the i.i.d. model).

In this paper, we have investigated Covert Channels in 3GPP LTE-A (Long Term Evolution-Advanced) for identifying headers and extension fields where covert data can be hidden in covert communications. We also discuss the covert channel capacity for LTE-A where the specific fields in the headers of the MAC (Medium Access Control), RLC (Radio Link Control) and PDCP (Packet Data Convergence Protocol) layers are analyzed. We review different scenarios of channel coding rate, modulation schemes and channel bandwidths and present our results.

The North American railroad industry, with the objective of improving safety and security of their operations, have been exploring the possibilities of real-time monitoring and control of trains and wayside equipment. Wireless Sensor Networks (WSNs) have emerged as the de-facto solution for most measurement and monitoring operations. However, widely used commercial WSN solutions, like ZigBee, exhibit significant delay and traffic congestion problems due to their limited range, throughput and overall capabilities. To overcome this problem we proposed a multi-tier multi-hop heterogeneous network technology, called Hybrid Technology Networking (HTN). HTN is comprised of sensors equipped with multiple complementary radio technologies in which the sensors form a cluster and communicate within their own cluster using low-power communication. Each cluster's gateway then utilizes WiFi or similar longer range technologies for inter-cluster communication. In this paper, we present the design of our HTN node representation in OPNET. Furthermore, we present a theoretical framework that accurately models the delay in the network and verify the model with simulation results. We also present relevant results that demonstrate its full functionality, and also explore the efficacy of such hybrid multi-tier multi-hop implementations.

In this paper, we propose a joint channel allocation and multicast routing scheme for a multihop cognitive radio network. Our scheme can react to the dynamic change of channel availability. It can maximize the multicast throughput by jointly performing channel allocation and multicast routing. We first model the activities of the primary users, channel availability and the interference among the secondary users in a cognitive radio network, and show a basic scenario of multicast for a multihop cognitive radio network. Second, we formulate an optimization problem to maximize the multicast throughput of the network. We consider the channel availability constraints and the interference constraints jointly, and make use of the transmission of the hyper-links. Furthermore, we compare the performance of our scheme with existing schemes for different scenarios of channel availability and the number of channels through simulations. Our work brings insights on how to make route selection and channel allocation for multicast in multihop cognitive radio networks.

Wireless heterogeneous networks have emerged as a new paradigm to meet the fast increasing wireless capacity and coverage demands. Coordinated Multipoint Processing (CoMP) and Precoding are two promising techniques to further improve the network capacity and spectral efficiency. This paper presents an optimal intra-cell CoMP resource allocation scheme in a wireless heterogeneous network and explores the Tomlinson-Harashima Precoding (THP) in the physical layer to reduce the inter-user interference. The objective is to maximize the aggregate proportional rates in the system. We derive an asymptotically optimal solution for resource allocation by using a gradient descent based scheduling and KKT conditions for optimality. Simulation results demonstrate the system proportional fairness capacity gain of proposed resource allocation scheme and this resource management framework provides a guideline for future radio resource management in wireless heterogeneous networks.

Cognitive radio is an efficient technique to relieve the tense of wireless spectrum scarcity by allowing unlicensed secondary users (SUs) to access the licensed band opportunistically without causing interference to primary users (PUs). Although Federal Communications Commission (FCC) recently ruled that the data of PU activity schedule is accessible to SUs 24 hours ahead, which relieves SUs from heavy sensing or interruption by sudden PU activity, however, multi-hop wireless cognitive radio networks (MWCRN) suffers a unique problem caused by the fact that the spectrum resources are not unified in different areas affected by different PUs. In other words, an SU origin-destination (OD) pair transmission would meet the bottleneck in bandwidth when crossing areas with different available spectrum resources. To solve this problem, we formulate an optimization problem to maximize the number of connection bridges to cross different areas. Moreover, we introduce channel bonding technique into the MWCRN for network performance improvement. We also propose a distributed algorithm for practical application. Simulation results verifies the better performance of our proposed scheme.

In this paper we investigate the scalability of communication architectures to provide traffic information services in a vehicular network based on the peer-to-peer (P2P) network technology. We study a general scenario in a metropolitan area where there are a huge number of data sources disseminating traffic data accessible to the vehicles through a multitude of roadside units. The large quantities of both data sources and customers post paramount challenges to the network scalability design. The existing work in P2P multicast, including the clustering based architecture, can only solve the scalability problem for applications with a limited number of data sources. We study the scenario that has a large number of data sources and prove that the clustering based architecture does not scale well with the traffic load at each node. Therefore, we propose a proxy based scalable architecture, in which data download traffic on each node is proved to keep constant even when the network size grows. We further verify the accuracy of the analysis using simulations.

In this work, we investigate the properties of energy-efficiency (EE) and spectrum-efficiency (SE) for video streaming over mobile ad hoc networks by developing an energy-spectrum-aware scheduling (ESAS) scheme. To describe a practical mobile scenario, we use a random walk mobility model, in which each node can choose its mobility direction and velocity randomly and independently. Through rigorous analysis and extensive simulations, we demonstrate that the node mobility is beneficial to EE but not to SE. The contributions of this work are twofold: 1) We propose an ESAS scheme with a dynamic transmission range, which significantly outperforms the previous minimum-distortion video scheduling in terms of joint EE and SE performance; 2) We derive an achievable EE-SE tradeoff range and a tight upper/lower bound with respect to energy-spectrum efficiency index for various node velocities. We believe that this work helps to shed insights on the fundamental design guidelines on building an energy and spectrum efficient mobile video transmission system.

A type of disruption/delay tolerant networks, known as pigeon networks, utilizes controllable special purpose vehicles called pigeons to convey messages among segregated areas. This paper aims to provide optimization methods for the challenging problem that how a pigeon should design its route to minimize the average delay of messages. For small scale networks, we construct an exact optimization algorithm that shows significant reduction in delay over the existing heuristic algorithms. For large scale networks, we present both the lower bound and upper bound of the average delay, and devise partitioning-based optimization algorithms that perform close to the lower bound. Both theoretical analysis and numerical results show that the delay could be reduced by as much as 50% compared to the existing algorithm.

Traditional α -fairness resource allocation in wireless multimedia communications assumes that the quality of experience (QoE) model (or utility function) of each user is available to the base station (BS), which may not be valid in many practical cases. In this paper, we consider a blind scenario where the BS has no knowledge of the underlying QoE model. Generally, this consideration raises two fundamental questions. Is it possible to set the fairness parameter α in a precisely mathematical specific α -fairness resource allocation schememanner? If so, is it possible to implement a specific α -fairness resource allocation scheme online? In this work, we will give positive answers to both questions. First, we characterize the tradeoff between the performance and fairness by providing an upper bound of the performance loss resulting from employing α -fairness scheme. Then, we decompose the α-fairness problem into two subproblems that describe the behaviors of the users and BS and design a bidding game for the reconciliation between the two subproblems. We demonstrate that, although all users behave selfishly, the equilibrium point of the game can realize the α-fairness efficiently, and the convergence time is reasonably short. Furthermore, we present numerical simulation results that confirm the validity of the analytical results.

Heterogeneous wireless networks have emerged as a new paradigm to meet the fast growing wireless network capacity and coverage demands. Due to the co-deployment of high power and low power nodes in the same network using the same spectrum, more advanced interference coordination and radio resource management schemes are required than in the traditional cellular network in order to achieve a high network capacity and good user experience. In this paper, we propose an optimal fractional frequency reuse and power control scheme that can effectively coordinate the interference among high power and low power nodes. The scheme can be optimized to maximize the sum of the long term log-scale throughput among all the user equipments (UEs). Towards that end, the Lagrange dual function is first derived for the proposed optimization problem. Gradient descent method is then used to search the optimal solution for the convex dual problem. Due to the strong duality condition, the optimal solution for the dual problem is also the optimal solution for the primal problem. Simulation results show that the proposed scheme can greatly improve the wireless heterogeneous network performance on system capacity and user experience.

ABSTRACT In this paper, we consider the applications of delay‐tolerant networks (DTNs), where the nodes in a network are located in separated areas, and in each separated area, there exists (at least) an anchor node that provides regional network coverage for the nearby nodes. The anchor nodes are responsible for collecting and distributing messages for the nodes in the vicinity. This work proposes to use a set of messengers (named pigeons) that move around the network to deliver messages among multiple anchor nodes. Each source node (anchor node or Internet access point) owns multiple dedicated pigeons, and each pigeon takes a round trip starting from its home (i.e., the source) through the destination anchor nodes and then returns home, disseminating the messages on its way. We named this as a homing‐pigeon‐based messaging (HoPM) scheme. The HoPM scheme is different from the prior schemes in that each messenger is completely dedicated to its home node for providing messaging service. We obtained the average message delay of HoPM scheme in DTN through theoretical analysis with three different pigeon scheduling schemes. The analytical model was validated by simulations. We also studied the effects of several key parameters on the system performance and compared the results with previous solutions. The results allowed us to better understand the impacts of different scheduling schemes on the system performance of HoPM and demonstrated that our proposed scheme outperforms the previous ones. Copyright © 2011 John Wiley & Sons, Ltd.

As a key technology in 4G-LTE, heterogeneous networks can effectively extend the coverage and capacity of wireless networks by deploying multiple micro-nodes on top of the conventional macro base stations (BS). The deployed micro-nodes differ in transmission power and processing capabilities, leading to new challenges in interference management, mobile association, and radio resource management (RRM). In this paper, we consider RRM for heterogeneous networks with relays (RN) where the RNs have full RRM capabilities and can be viewed as micro BSs. A radio resource allocation framework is proposed with the objective to ensure proportional fairness among the UEs. An asymptotically optimal solution is derived by applying the gradient-based scheduling scheme and the Karush-Kuhn-Tucker (KKT) conditions for optimality. To implement RRM in networks with RNs, the resource consumption in the backhaul links, which depends on the demand of the UEs associated with the RN, should be counted at both the BS and the RN. The derived resource allocation scheme gives insight on the optimal radio resource allocation for heterogeneous networks with RNs.

In this paper, we investigate a network coding-aware channel allocation and routing scheme for multi-hop cognitive radio networks. We consider network coding and channel availability in cognitive radio networks and maximize the throughput by allocating the channel and link rate appropriately. First, we model the activities of the primary users and the interference among the secondary users in a cognitive radio network and show how to implement network coding in the multi-hop cognitive radio network. Second, we formulate an optimization problem to maximize the throughput of the network. It takes advantage of the network coding opportunities and considers the channel availability constraint. By solving this optimization problem, we can determine how to allocate channels and rates of links in different channel availability scenarios. Furthermore, we compare the performance of our scheme with the coding oblivious routing for different scenarios of channel availability and maximum number of channels in a random wireless network. Our work brings insights on the benefits of network coding-aware routing in multi-hop cognitive radio networks.

In this paper, we propose a backbone construction scheme for multi-hop wireless cognitive radio networks (MWCRN) with a channel bonding technique. In our proposed scheme, the backbone is established purely in licensed bands using cognitive radio (CR) technology. We introduce a channel bonding technique to get higher performance of the network. To get higher reliability of MWCRN, we use backup channels in our proposed scheme. The proposed scheme includes two major steps. The first step is to formulate a backbone network; and the second step is to assign operating channels and backup channels to each backbone link. Simulation results show that the proposed scheme achieves higher network performance of multi-hop wireless cognitive radio networks.

In this paper, we study the communication architecture of smart grid for rural areas that employ cognitive radio technique. Since the communication in a cognitive radio network is normally unreliable, it is a great challenge to support applications which have stringent delay requirements, for instance, the transmission line monitoring application. To this end, we propose to reroute the data traffic with stringent delay requirement through the neighboring cells that work properly. This leads to a challenging scheduling problem, with the goal of maximizing the total throughput of the network while preserving the priority of real-time traffic in the local cell. To solve this problem, we present a scheduling algorithm that outperforms the earliest deadline scheduling and priority queue scheduling. The simulation results verify the effectiveness of our algorithm in achieving the design goals.

Smart grid uses machine-to-machine (M2M) communication infrastructures and advanced control techniques for improved power distributions and management. Different types of communication schemes could possibly be used for smart grid applications, but need to be further explored in smart grid application scenarios. In this paper, we present the utilization of a zero correlation zone (ZCZ) CDMA based scheme in M2M communications for the advanced metering infrastructure (AMI) in smart grid. We design the ZCZ code generation and distribution through mutual authentication in the initialization procedure before data transmission. We examine the security and efficiency aspects of the proposed ZCZ CDMA based scheme. The paper concludes with the comparison of the proposed scheme and the legacy approach for the system security and performance in smart grid M2M communications.

In a disruption/delay tolerant network, message carriers are often used to act as relays between segregated nodes and gateways. In this paper, we study a special routing problem for message carriers in delay tolerant networks, the visiting frequency problem. Specifically, we present a detailed analysis on a near-optimal visiting frequency on the nodes in a subregion of the network, to ensure low average message delay, using a partitioning-based optimization approach. Then we propose a multiple visit algorithm and apply the above near-optimal visiting frequency to our multiple visit algorithm, to guarantee the low average message delay for the network. Simulation results demonstrate the superiority of our new algorithm comparing with the existing single visit algorithms.

A special type of disruption/delay tolerant networks, known as pigeon networks, utilizes controllable vehicles called pigeons to convey messages among segregated hosts. This paper investigates the optimal assignment of multiple pigeons to serve hosts in a pigeon network such that the average message delay is minimized. To achieve this goal, first we provide an asymptotic analysis on the effectiveness of geographical partitioning-based method, which establishes the theoretical foundation for partitioning-based pigeon assignment. Second, we derive a near-optimal assignment of pigeons for a given partitioning through analysis. Finally, we design a pigeon assignment algorithm based on the theoretical analysis we have provided. Simulation results show that our algorithm can achieve lower delay compared to the existing schemes in delay tolerant networks.

This paper considers a cellular network with multiple low-power relay nodes (RN) deployed in each cell, helping the downlink transmission between the base stations (BS) and the user equipments (UE). The difference in transmission power between the BSs and the RNs makes this network heterogeneous. With conventional mobile association schemes, traffic load may concentrate on the BSs due to their high transmit power, leading to a highly unbalanced traffic load distribution and inefficient utilization of the RNs. To improve the capacity and the overall spectrum efficiency of the network, it is beneficial to let the RNs share a larger amount of the traffic burden. Towards this end, in associating the UEs to the RNs, the spectrum consumption on both UE to RN and RN to BS links should be cautiously considered. Also the interference from the high power BSs to the low power RNs need to be carefully mitigated. A systematic mobile association scheme considering load-balancing and overall spectrum efficiency is essential to ensure a fair and efficient usage of network resources. In this paper, we propose a load-balancing based mobile association framework under both full frequency reuse and partial frequency reuse and find the pseudo-optimal solutions using gradient descent method. By comparing with other mobile association schemes, a significant improvement in overall network capacity and RN utilization can be observed for the proposed mobile association framework. To enable dynamic and timely mobile association for the incoming UEs, we also develop an online mobile association algorithm based on the gradient descent method. Simulation results show that the proposed online algorithm achieves a good tradeoff between association delay and network capacity.

In most existing wireless networks, end users obtain data content from the wired network, typically, the Internet. In this manner, virtually all of their traffic must go through a few access points, which implies that the capacity of wireless network is limited by the aggregated transmission data rate of these access points. To fully exploit the capability of wireless network, we envision that future wireless networks shall be able to provide data content within themselves. In this paper, we address the behavior of such networks from a theoretical perspective. Specifically, we consider that multicast is used for distributed content delivery, and we investigate the asymptotic upper bound of the throughput capacity for distributed content delivery in large-scale wireless ad hoc networks (DCD-WANET). Our analysis shows how the upper bound of throughput capacity is affected by the geometric size of the network, the number of data items, the popularity of the data content, and the number of storage nodes that contain those data items. In particular, our theoretical results show that, if the number of storage nodes exceed a critical threshold, the upper bound grows with the number of storage nodes, according to a power-law where the scaling exponent depends on the popularity of data items. We also provide the data item placement strategy to achieve the upper bound of throughput capacity for DCD-WANET.

In this paper, we investigate the scalability of three communication architectures for advanced metering infrastructure (AMI) in smart grid. AMI in smart grid is a typical cyber-physical system (CPS) example, in which large amount of data from hundreds of thousands of smart meters are collected and processed through an AMI communication infrastructure. Scalability is one of the most important issues for the AMI deployment in smart grid. In this study, we introduce a new performance metric, accumulated bandwidth-distance product (ABDP), to represent the total communication resource usages. For each distributed communication architecture, we formulate an optimization problem and obtain the solutions for minimizing the total cost of the system that considers both the ABDP and the deployment cost of the meter data management system (MDMS). The simulation results indicate the significant benefits of the distributed communication architectures over the traditional centralized one. More importantly, we analyze the scalability of the total cost of the communication system (including MDMS) with regard to the traffic load on the smart meters for both the centralized and the distributed communication architectures. Through the closed form expressions obtained in our analysis, we demonstrate that the total cost for the centralized architecture scales linearly as O(\lambda N), with N being the number of smart meters, and \lambda being the average traffic rate on a smart meter. In contrast, the total cost for the fully distributed communication architecture is O(\lambda^{2\over 3} N^{2\over 3} ), which is significantly lower.

In this paper, we investigate the throughput capacity of large-scale wireless networks, in which three network-assisted coding schemes are considered: (1) multi-point-to-point coding (MPPC); (2) MPPC based network coding (NC); and (3) MPPC based physical-layer network coding (PLNC). This study is based on the generalized physical model, in which the transmission rate depends on the signal to noise and interference ratio (SINR). Such a model has not been used to analyze the behaviors of large-scale wireless networks with the aforementioned coding schemes. To understand the capacity gains of these schemes, we develop constructive lower bounds for one-dimensional (1D) and two-dimensional (2D) networks with size factor w, in which we construct novel wireless highway systems. This study shows that, compared to point-to-point coding (PPC), MPPC can improve the scaling law of network capacity when w exceeds a certain scale. In addition, this study reveals that MPPC based NC and PLNC can improve the capacity by constant factors. Specifically, NC can always obtain a gain of 2 in both 1D and 2D networks. On the other hand, the gain of PLNC can be larger than 2 in 1D networks, and can be up to 2 in 2D networks, depending on w, transmission power, noise, and path-loss of propagation.

Heterogeneous cellular networks have emerged as a new paradigm in the wireless network to increase cellular capacity and coverage. Future heterogeneous networks will have a mixed deployment of high power and low power nodes. This creates complicated interference scenarios that require more tight resource coordination among neighbouring nodes than in the traditional cellular networks. In this paper we consider a heterogeneous LTE relay network and investigate two downlink resource coordination schemes in the time/frequency/power domains in such a network. We perform detailed simulation study on the two proposed schemes, and compare them with a third scheme that does not have frequency or power coordination. Our study shows that by allowing tight time/frequency/power coordination, the system can achieve much higher system capacity and better received downlink signal quality.

In this paper, we investigate secrecy capacity of a cognitive radio network based on stochastic geometry distributions. We consider the Poisson process of both the secondary users and the eavesdroppers, and analyze how the stochastic interference from the secondary users can influence the secrecy capacity of the primary users. First, we describe a network model with primary users, secondary users and eavesdroppers in a cognitive radio communication network environment, and obtain the expression of secrecy capacity in an additive white Gaussian noise channel. Then, we study the outage probability of secrecy capacity of a primary node from a secure communication graph point of view. Furthermore, we present numerical results of the cumulative distribution function (c.d.f.) of the secrecy capacity between a primary transmitter and a primary receiver. Our analysis brings the insights on secure communications in terms of spatially Poisson distributions of primary users, secondary users and eavesdroppers.

With the proliferation of wireless networks for practical deployments in recent years, secure fast handoff has become significantly important to provide secured access while reduce the latency caused by handoff procedure. Channel probing/scanning delay has been a major contribution to the overall latency of handoff in IEEE 802.11 based wireless networks. In this paper, we present a novel secure fast handoff scheme that adopts network-assisted radio statement (NACS) to eliminate lengthy probing/scanning delay by taking advantage of the knowledge of network topology of neighboring nodes in the network. We apply an opportunistic mechanism to retrieve the channel condition from neighboring nodes close enough to reduce the scanning/probing delay while providing secure wireless access for the handoff candidate node. In this manner, it can reduce the channel probing/scanning delay and achieve secure handoff. We show analysis and simulation results to verify the performance of the proposed secure fast handoff scheme.

Cyber security for smart grid communication systems is one of the most critical requirements need to be assured before smart grid can be operationally ready for the market. Privacy is one of very important security consideration. The customer information privacy in smart grid need to be protected. Smart grid data privacy encompasses confidentiality and anonymity of the information extracted from smart device metering transmission in smart grid communication system. In this paper, we consider a home area network as a basic reading data aggregation and dispatch unit in smart grid systems. Then, we propose a secure in-network data aggregation and dispatch scheme to keep the confidentiality and anonymity for collecting power usage information of home smart devices to the household smart meter and the reverse control message distributing procedure. Specifically, we introduce an orthogonal chip code to spread reading-data of different home smart devices into spread code, followed by a circuit shifting operation to coupling neighboring smart devices tightly. We adopt an in-network mechanism to further mask it with its spread data with its forwarding data. Finally, we analyze the cyber security protection levels using an information theoretic quantity, residual uncertainty. Simulation studies are conducted to test the performance on different metering datasets for the proposed scheme. This paper sets the ground for further research on optimizing of home power management systems with regarding to the privacy of customer power usage behaviors.

Fitting noisy measurements to a circle is a classic statistical estimation problem. In this paper, we make two contributions to the study of this problem. First, we propose a novel formulation of the maximum likelihood (ML) estimator for identifying the center and radius of the circle from noisy measurements. This new estimator uses the unknown true values of the measurement points as the nuisance parameter to obtain an exact ML formulation. We then examine the Karush-Kuhn-Tucker (KKT) conditions for the optimum solution to the ML estimator. We show analytically that this new estimator is in fact equivalent to the well-known least squares (LS) form of the circle fitting problem. Second, from the insights gained in deriving the optimum solution, a computationally simple circle fitting algorithm based on greedy search is proposed. Performance results are given to illustrate the performance of the proposed algorithm.

In this paper, we study the optimal design of linear network coding (LNC) for secure unicast against passive attacks, under the requirement of information theoretical security (ITS). The objectives of our optimal LNC design include (1) satisfying the ITS requirement, (2) maximizing the transmission rate of a unicast stream, and (3) minimizing the number of additional random symbols. We first formulate the problem that maximizes the secure transmission rate under the requirement of ITS, which is then transformed to a constrained maximum network flow problem.We devise an efficient algorithm that can find the optimal transmission topology. Based on the transmission topology, we then design a deterministic LNC which satisfies the aforementioned objectives and provide a constructive upper bound of the size of the finite field. In addition, we also study the potential of random LNC and derive the low bound of the probability that a random LNC is information theoretically secure.

We consider various security vulnerabilities of deploying Advanced Metering Infrastructure (AMI) in smart grid, and explore the issues related to confidentiality for customer privacy and customer behavior as well as message authentication for meter reading and control messages. There are only a very few research work on AMI authentications, and no work exists on confidentiality for user privacy and user behavior, from the best of our knowledge. In this paper, we propose an in-network collaborative scheme to provide secure and reliable AMI communications in smart grid, with smart meters interconnected through a multihop wireless network. In this approach, an AMI system can provide trust services, data privacy and integrity by mutual authentications whenever a new smart meter initiates and joins the smart grid AMI network. Data integrity and confidentiality are fulfilled through message authentication and encryption services respectively using the corresponding keys established in the mutual authentications. A transmission scheme is proposed to facilitate the data collection and management message delivery between smart meters and a local collector for AMI communications. Simulation results show that the proposed method has a better end-to-end delay and packet losses comparing with a basic security method, and the proposed method can provide secure and reliable communications for AMI in smart grid systems.

Dynamic spectrum access has become a promising technique to fully utilize the scarce spectrum resources. However, spectrum allocation schemes with high efficiency and Quality of Service (QoS) guarantee for the primary users have yet to be designed. In this paper, two novel dynamic spectrum access schemes are proposed. The proposed schemes are based on continuous-time Markov chains (CTMC), through which the interactions between primary and secondary users are explicitly modeled. The effects of sensing errors (i.e. miss-detection and false alarm) are taken into consideration. Since miss-detection may lead to collision between primary and secondary users and false alarm will leave spectrum opportunities unused, we derive the optimal access probabilities for each secondary user, so that the QoS of primary user in terms of collision probability constraint is guaranteed, and the missing spectrum opportunities caused by false alarm can be utilized by secondary users. Simulation results show that the proposed schemes can guarantee primary user''s QoS effectively. Moreover, the scheme with buffer can improve the channel occupancy remarkably.

Network coding has been proved as an effective way to enhance the throughput of the multihop wireless ad hoc networks in both unicast and multicast traffics. However, in a random network topology with non-coding-aware routing protocols, the performance enhancement may be limited because the packet routing scheme does not take advantage of the possibility of network coding. In this paper, we propose a Backbone ROuting with Network Coding (BRONC) scheme over a multihop wireless network, which combines the benefits of both backbone routing and network coding techniques. With backbone-based routing, all packets are forced to be transmitted over a constructed backbone. Because of the characteristics of bi-directional traffic flow and pre-specified routes in backbone routing, the possibility of coding packets at intermediate nodes can be substantially increased, and thus the benefit of network coding is fully exploited. Simulation results show that BRONC improves the coding opportunity significantly, and it outperforms the existing opportunistic coding schemes in terms of throughput, packet delivery ratio and transmission overhead.

The Wireless Broadband (Wibro) specified by IEEE 802.16e has gained great popularity in the next generation mobile telecommunication networks. However, as mobility is supported, handover procedure especially the authentication mechanism becomes a critical part in system performance. In this paper, we present an analytical evaluation for MAC layer handover procedure with RSA-based authentication which is a security standard in IEEE 802.16e specification. Our analysis consists of differentiated considerations between mobile subscribers (MS) and base stations (BS) in signaling message transmission, queueing model and computing process for the authentication and key establishment process during handover procedure. We have observed that the major portion of the handover delay is the authentication process due to heavy computing operations. The analysis provides deep insights into the system performance of the handover procedure in a Wibro network.

In this paper, we develop a performance modeling technique for analyzing the time varying network layer queueing behavior of multihop wireless networks with constant bit rate traffic. Our approach is a hybrid of fluid flow queueing modeling and a time varying connectivity matrix. Network queues are modeled using fluid-flow based differential equation models which are solved using numerical methods, while node mobility is modeled using deterministic or stochastic modeling of adjacency matrix elements. Numerical and simulation experiments show that the new approach can provide reasonably accurate results with significant improvements in the computation time compared to standard simulation tools.

In the past few years, wireless mesh networks (WMNs) have drawn significant attention from academia and industry as a fast, easy, and inexpensive solution for broadband wireless access. In WMNs, it is important to support video communications in an efficient way. To address this issue, this paper studies the multipath routing for multiple description (MD) video delivery over IEEE 802.11 based WMN. Specifically, we first design a framework to transmit MD video over WMNs through multiple paths; we then investigate the technical challenges encountered. In our proposed framework, multipath routing relies on the maximally disjoint paths to achieve good traffic engineering performance. However, video applications usually have strict delay requirements, which make it difficult to find multiple qualified paths with the least joints. To overcome this problem, we develop an enhanced version of Guaranteed-Rate (GR) packet scheduling algorithm, namely virtual reserved rate GR (VRR-GR), to shorten the packet delay of video communications in multiservice network environment. Simulation study shows that our proposed approach can reduce the latency of video delivery and achieve desirable traffic engineering performance in multipath routing environment.

Network coding is a promising technology that can effectively improve the efficiency and capacity of multihop wireless networks by exploiting the broadcast nature of the wireless medium. However, current packet routing schemes do not take advantage of the network coding, and the benefits of network coding have not been fully utilized. To improve the performance gain of network coding, in this paper, we apply network coding over a wireless backbone and investigate the performance of this approach from a theoretical perspective. Our analysis shows that, compared to network coding over ad hoc networks with traditional routing schemes, network coding over the backbone structure exhibits significant advantages. This is because all packets are transmitted over the constructed backbone with pre-specified routes, and consequently the opportunity for coding packets at intermediate nodes can be substantially improved. To further enhance the performance, we also present an optimized link scheduling protocol for network coding over a wireless backbone. The performance results show that with proposed approach, the coding gain can achieve the theoretical bound in some scenarios.

While original network coding is proposed over the data link layer, recent work suggests that it can also be implemented on the physical layer. In fact it is more natural for wireless networks because of its omnidirectional transmission. In this paper, we investigate the symbol-error-rate (SER) for binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK), but the approaches can be generalized to other constellation schemes. The closed-form SER results are derived for physical layer network coding over AWGN channels. The theoretical analysis is also validated by numerical simulation.

In this paper, we investigate how Multiple Description Coding (MDC) can improve the user's satisfaction for a group of heterogeneous destinations for video multicast over wireless ad hoc networks. Specifically, we explore the independent-description property of MDC with multiple paths to improve the user's satisfaction. In our approach, the assignment of multiple description (MD) video is randomly done by a multicast source node. Based on the assignment of MD video, the multicast source constructs multiple multicast trees accordingly. Simulation results demonstrate that our approach can achieve higher user's satisfaction regardless of the multicast group size.

In this paper, we analyze a new type of delay tolerant networks (DTN) where each node owns multiple dedicated messengers, called pigeons. The only form of inter- node communication is for a pigeon to periodically carry a batch of messages originated at the home node, deliver to the corresponding destination nodes and return home. We name this as homing-pigeon (HoP) based routing mechanism, which is an effective way to overcome end-to-end disconnection in DTN. In this work, we model the HoP mechanism analytically with a periodic pigeon scheduling algorithm. Our analysis focuses on understanding the pigeon departure frequency from the home node while keeping the message delivery ratio above a given threshold. Simulation results are given to validate our analysis.

In this paper, we propose a backbone construction scheme over heterogeneous ad hoc networks, where the network nodes have different characteristics such as communication capacity, processing power and energy resource. Most of the wireless backbone construction techniques focus on minimizing the number of backbone nodes. In our proposed scheme, we not only minimize the backbone size, but also take the characteristics of nodes into account when building a backbone. In the scheme, the more capable nodes have higher probability to serve as backbone nodes and provide a wireless highway over which end-to-end communication can take place. The proposed scheme includes two major steps, which can be solved by formulating as a Dominating Set (DS) problem and a Steiner Tree Problem with Minimum Number of Sterner Points (STP-MSP) respectively. We focus on the two subproblems and present a number of polynomial time approximation algorithms. Simulation results show that the proposed scheme achieves higher average backbone node performance, while has approximately the same backbone size comparing with other schemes.

Physical-layer network coding (PLNC) is a novel wireless communication technology, in which multiple transmitters can send signals on the same channel to the same receiver at the same time. Our previous studies have revealed that PLNC can substantially improve the throughput performance of the whole network. In this paper, we address the security performance of PLNC. In particular, we investigate the symbol error performance of a potential eavesdropper in the PLNC system. Extensive simulation studies show that PLNC can provide security means against passive eavesdroppers.

Throughput capacity of a random wireless network has been studied extensively in the literature. Most existing studies were based on the assumption that each transmission involves only one transmitter in order to avoid interference. However, recent studies on physical-layer network coding (PLNC) have shown that such an assumption can be relaxed to improve throughput performance of a wireless network. In PLNC, signals from different senders can be transmitted to the same receiver in the same channel simultaneously. In this paper, we investigate the impact of PLNC on throughput capacity of a random wireless network. Our study reveals that, although PLNC scheme does not change the scaling law, it can improve throughput capacity by a fixed factor. Specifically, for a one-dimensional network, we observe that PLNC can eliminate the effect of interference in some scenarios. A tighter capacity bound is derived for a two-dimensional network. In addition, we also show achievable lower bounds for random wireless networks with network coding and PLNC.

This paper presents a new collaborative peer-to-peer (P2P) streaming framework for heterogeneous bandwidth capacity of clients. The proposed architecture aims for two primary goals: (1) providing higher streaming quality by making best use of the extra available bandwidth that might exist among heterogeneous clients and (2) providing robustness and resilience to high churn rate of peers by introducing redundancy, both in network paths (multisender overlay) and data (multilayered video content). For achieving these goals, we employ a gossip-based data-driven scheme for partnership formation and layered video coding for bandwidth adaptivity. To solve multilayer bitstream allocation problem, we proposed two algorithms, namely optimized transmission policy and graceful degradation scheme algorithms. The proposed system has a complete self-regulation and in a decentralized fashion. Extensive simulations show that the proposed system achieves higher quality of service through peer-assisted streaming and layered video coding. The results also show that the system outperforms some previous schemes in system overhead and reliability for dynamic node behavior.

In this paper, we address the topology control issue for future wireless mesh networks (WMNs). In particular, we propose a novel topology control scheme that attempts to maximize the overall throughput in the network with random unicast traffic demands. The main idea of the scheme is to establish multiple semi-permanent wireless highways, each of which can convey the traffic for nodes along the highways. To evaluate the performance of the proposed scheme, we conduct theoretical analysis, which demonstrates that viable solutions for highways do exist with high probability. The theoretical analysis also proves the optimality. Within the new topology control framework, we demonstrate that advanced technologies, including network coding and physical- layer network coding (PLNC), can be applied to substantially improve the throughput capacity of the network.

Vehicular ad hoc networking is an important component of Intelligent Transportation Systems. The main benefit of vehicular ad hoc network (VANET) communication is seen in active safety systems that increase passenger safety by exchanging warning messages between vehicles. Other applications and private services are also permitted in order to lower the cost and to encourage VANET deployment and adoption. Dedicated Short Range Communications (DSRC) is a key enabling technology for VANET applications and services. There are many challenges that must be addressed before VANETs can be successfully deployed. Among these challenges is designing of security mechanisms to secure VANETs against abuse, and designing of efficient medium access control (MAC) protocols so that safety related and other application messages can be timely and reliably disseminated through VANETs. In this paper we propose a secure MAC protocol for VANETs, with different message priorities for different types of applications to access DSRC channels. Our simulations and analysis show that the proposed MAC protocol can provide secure communications while guarantee the reliability and latency requirements of safety related DSRC applications for VANETs.

Since the pioneer work by Gupta and Kumar, the throughput capacity of random wireless networks has been studied extensively in the literature. Nevertheless, most existing studies are based on the assumption that each node can receive at most one transmission at a time. However, several recent studies have shown that such a constraint can be relaxed. Particularly, with physical-layer network coding, one node can receive more than one transmission from different transmitters simultaneously. In this paper, we investigate the impact of physical-layer network coding on the throughput capacity of random wireless networks. Our analysis show that the physical-layer network coding scheme can improve the throughput capacity but cannot change the scaling law. Specifically, for one-dimensional random wireless network, our analysis provides the capacity of network with physical-layer network coding. For two-dimensional random wireless networks, we derive tighter capacity bounds for existing transmission schemes, as well as the bounds for physical-layer network coding.

In wireless mesh network (WMN), it is important to provide an efficient handoff scheme, due to the frequent user mobility. To address this issue, we propose a mobile agent (MA) based handoff approach, where each mesh client has a MA residing on its registered mesh router. To guarantee quality of service (QoS) and achieve differentiated priorities during the handoff, we develop a proportional threshold structured optimal effective bandwidth policy for call admission control (CAC) on the mesh router. Simulation study shows that our proposed CAC scheme can obtain satisfying tradeoff between differentiated priorities and statistical effective bandwidth in WMN handoff environment.

Radio resources in third generation (3G) wireless cellular networks (WCNs) such as the universal mobile telecommunications system (UMTS) network are limited in terms of soft capacity. The quality of a signaling service transmission depends on various factors (i.e., a user's location, speed, and data rate requirement), and has an impact on the quality of user data communications where the opposite is also true. In this paper, we provide the first step to evaluate the impact that various signaling service types have on call blocking and ongoing call dropping in UMTS systems. The radio resource's acquisition time for various signaling services is calculated according to the specifications in UMTS standards. The maximum number of sessions that a signaling service type can transmit simultaneously is estimated along with the impact when other signaling service types are transmitted. Our analysis reduces the computational complexity in the call admission control (CAC) and allows the preservation of classes of services. An example traffic scenario is given illustrating the benefit of our study.

Abstract In this paper, we investigate optimal key management design for secure and survivable heterogeneous wireless sensor networks (HWSNs). In particular, we formulate the key management problem as a multi‐objective optimization problem, in which the cost of the sensor network, and the security and survivability metrics of the sensor network are taken into account. To solve the multi‐objective optimization model, we develop a genetic algorithm (GA)‐based approach that can efficiently obtain near‐optimal solutions. We show the performance of our scheme through extensive numerical results. With a small amount of powerful sensor nodes in HWSN, we can balance the cost of the sensor network and the resilience of the sensor network with the required security constraint in different hostile environment. Copyright © 2008 John Wiley & Sons, Ltd.

Key management has become a challenging issue in the design and deployment of secure wireless sensor networks. A common assumption in most existing distributed key management schemes is that all sensor nodes have the same capability. However, recent research works have suggested that connectivity and lifetime of a sensor network can be substantially improved if some nodes are given greater power and transmission capability. Therefore, how to exploit those heterogeneity features in design of a good distributed key management scheme has become an important issue. This paper proposes a unified framework for distributed key management schemes in heterogeneous wireless sensor networks. Analytical models are developed to evaluate its performance in terms of connectivity, reliability, and resilience. Extensive simulation results show that, even with a small number of heterogeneous nodes, the performance of a wireless sensor network can be improved substantially. It is also shown that our analytical models can be used to accurately predict the performance of wireless sensor networks under varying conditions.

Wireless mesh network (WMN) has recently emerged as a promising technology for next-generation wireless networking. In WMNs, many important applications, such as mobile TV and video/audio conferencing, require the support of multicast communication with quality-of-service (QoS) guarantee. In this paper, we address the QoS multicast routing issue in WMNs. Specifically, we propose a novel network graph preprocessing approach to enable traffic engineering and enhance the performance of QoS multicast routing algorithms. In this approach, we employ prioritized admission control scheme and develop a utility-constrained optimal priority gain policy. Extensive simulation results show that our approach can significantly improve the performance of QoS multicast routing in WMNs.

In wireless ad-hoc networks, a major challenge is how to provide robust and efficient communication. To achieve this goal, cooperative communication and network coding have been proven to be effective. In the literature, most existing studies focus on the performance of the two schemes separately. In our study, we will investigate the performance of system that combines them tightly together. In particular, we will propose a unified two-way traffic model that can characterize the features of both of them. Based on this model, we develop a new cooperative network coding scheme to further improve the system throughput. Both decode-and-forward and amplify-and-forward techniques are discussed for the two-way traffic model. Numerical results show that the new scheme can significantly improve the performance over traditional schemes.

In this paper, we propose a novel retransmission scheme that can substantially increase the throughput of wireless local area networks (WLANs). The main idea of the scheme is to enable cooperative communication in the medium access control (MAC) layer such that one node can retransmit messages for its neighboring nodes if the delivery of those messages failed previously due to transmission error. In addition, we also redesign the transmission and acknowledgement policy of the existing IEEE 802.11 protocol in that 1) an upper layer packet will be partitioned into blocks, and 2) the receiver of a message can acknowledge individual blocks through negative acknowledgement (NACK). To evaluate the performance of the proposed scheme, we conduct extensive simulation study under various conditions, such as number of nodes, size of packet, and bit error rate. Simulation results show that, the proposed scheme can significantly increase the throughput performance of WLANs under these conditions, and an optimal block size can lead to the highest throughput in each specific scenario.

In this paper, we study the design of future wireless ad hoc networks. Particularly, we consider that the future wireless ad hoc networks shall be able to efficiently provide connectivity and support a variety of quality of service requirements, such as bandwidth and delay requirements. To address these issues, we propose a novel framework for the design of future wireless ad hoc networks. The key idea of this framework is to establish an overlay hypernetwork, which consists of multiple hyperchannels, each of which can provide connectivity for multiple nodes in the hyperchannel. By comparison, a typical tunnel in existing overlay network schemes can only provide connectivity for two end nodes. Within the framework, we also develop advanced network coding design that can further improve the performance of the network in terms of throughput and delay.

With the advances in wireless communication technologies, wireless sensor networks (WSNs) are becoming more and more attractive because they can provide services that are not possible or not feasible before. In this paper, we address the design issues of an important type of WSN, i.e., WSNs that enable environmental monitoring applications. We first provide an overview and analysis for our ongoing research project about the WSN for coastal area acoustic monitoring. Based on the analysis, we then propose a novel framework that can be used to guide the design of future WSNs that provide environmental monitoring services. The focus of the framework is the network layer design. In our framework, we consider that 1) the future WSN shall be heterogeneous, 2) the network layer design shall better meet the requirements of applications and services, 3) the network layer design shall be able to utilize advanced wireless communication technologies, and 4) the network layer can provide the monitoring functionality.

In this paper, we investigate optimal key management design for secure and survivable heterogeneous wireless sensor networks. In particular, we formulate the key management problem as a multi-objective optimization problem, in which the cost of the sensor network, and the security and survivability metrics of the sensor network are taken into account. To solve the multi-objective optimization model, we develop a genetic algorithm (GA) based approach that can efficiently obtain near- optimal solutions. We show the performance of our scheme through extensive numerical results. With a small amount of powerful sensor nodes in heterogeneous wireless sensor network, we can balance the cost of the sensor network and the resilience of the sensor network with the required security constraint in different hostile environment.

The current trends in service consolidation over Internet Protocol (IP) also stimulates the integration of the industrial automation system with the information technology (IT) infrastructure for more efficient information access and more cost-effective production and management. Field buses have been the de facto communication standard in industrial automation, but mostly based on manufacture-specific protocols. Thus, the interoperability between the manufacturer-specific field bus systems and the external operating environment is the critical factor in enabling the networked industrial automation systems. However, most of the existing field bus integration solutions lack either flexibility or scalability. In this paper, we propose a service-oriented architecture (SOA) based field bus integration architecture (SOAFBIA), where each field bus system is encapsulated with optional interface, manageability interface, and semantic descriptions in a standard format to facilitate interoperability. Moreover, a resource agent is proposed as an enhanced service broker, which implements not only the standard service registry functionality in SOA, but also the resource management functions including admission control, service scheduling, and load balancing.

WiMAX (Worldwide Interoperability for Microwave Access) is a promising technology for last-mile broadband Internet access. In multiservice WiMAX networks, call admission control (CAC) plays a critical role. In this paper, we address CAC problem from the perspectives of both WiMAX service providers and subscribers. Specifically, we formulate CAC as an optimization problem, in which the demands of service providers and subscribers are taken into account. To solve the optimization problem, we develop a utility and fairness constrained greedy revenue algorithm. Simulation results show that the proposed approach has satisfying performance.

In this paper, we present a comprehensive study on the design of secure and survivable wireless sensor networks (WSNs). Our goal is to develop a framework that provides both security and survivability features that are crucial to applications in WSNs, which are vulnerable to physical and network based security attacks, accidents, and failures. To achieve such a goal, we first examine the security requirements and survivability requirements. We then propose an architecture for security and survivability in WSNs with heterogeneous sensor nodes. To understand the interactions between survivability and security, we also design and analyze a key management scheme. The experiment results show that 1) a good design can improve both security and survivability of WSNs; and 2) in some situation, there is a trade-off between security and survivability.

In this paper, we propose a novel downlink resource management framework for multiservice WiMAX (worldwide interoperability for microwave access) networks. Our framework consists of two major components: adaptive power allocation (APA) and call admission control (CAC). We formulate each of them as an optimization problem, where the demands of both WiMAX service providers and subscribers are taken into account. To solve the optimization problems, we develop a fairness-constrained greedy revenue algorithm for downlink APA optimization and a utility-constrained greedy approximation algorithm for downlink CAC optimization. Our simulation results show that, when combining the APA and CAC optimization methods together, the proposed resource management framework can meet the expectations of both service providers and subscribers

In this paper, we propose a novel scheme to improve the performance of wireless local area networks (WLANs) in error-prone environment. The key idea of the scheme is to partition a frame into several blocks, and then to retransmit only the blocks that encountered errors in the previous transmission. To evaluate the performance of the scheme, we also develop an analytical model to analyze the saturated throughput performance. Extensive simulation and analysis results show that, 1) the proposed scheme can significantly improve the throughput performance of WLANs in error-prone conditions; 2) an optimum block size can lead to the maximum saturated throughput; 3) with an appropriate block size, the throughput can increase with the increase of the size of a frame, even in error-prone environment; and 4) the analytical results are highly accurate.

For deploying wireless group-oriented applications in an adversarial environment such as battlefield or disaster rescue cases, it is necessary to provide support for secure multicast communication. In this paper, pyramidal security model is proposed to safeguard a special multicast scenario of multi-security- level information broadcast in an information sharing domain of mobile ad hoc network. In order to give an efficient key management solution to the pyramidal security model, we propose an integrated tree key graph scheme. Performance comparison proves that this scheme possesses many advantages over its counterparts.

In the past few years, wireless local area networks (WLANs) have been widely deployed, where the most important standard is IEEE 802.11. To support high data rate applications in the next generation WLANs, a common approach is to aggregate multiple upper layer packets into one large frame in the MAC layer. However, with the increase of frame size, the frame error rate will also be increased in error-prone wireless environments. Therefore, existing frame retransmission scheme may not be efficient since the entire frame will be retransmitted. In this paper, we study a retransmission scheme that is suitable for the aggregation-based MAC protocol, in which only the packets that encounter transmission errors will be retransmitted. The main contribution of our study is to develop an analytical model for evaluating the saturated throughput performance of the MAC protocol. Extensive simulation and analytical results show that, our model is highly accurate and the proposed retransmission scheme can significantly improve the throughput performance in error-prone wireless channels.

This paper proposes a new survivable traffic grooming wavelength retuning (STGWR) scheme in an all-optical wavelength division multiplexing (WDM) network. In a dynamic WDM network, a connection may require bandwidth less than a wavelength capacity. Also, a connection should be protected against any network failures. Survivable traffic grooming (STG) can protect connections at subwavelength granularities. Wavelength retuning is a promising approach in an all-optical WDM network, where a signal must remain on the same wavelength from its source to the destination, to alleviate the wavelength continuity constraint and reduce the connection blocking probability. While both STG and wavelength retuning have attracted extensive research attentions nowadays, no effort has been made to combine these two promising approaches in one network. In this paper, we propose a wavelength retuning scheme with no service interruption in an all-optical network with survivable traffic grooming capability. The scheme allocates two routes, one for active path and one for backup path, in a shared mesh restoration way to each incoming connection request and conducts wavelength retuning only on the backup path. Both wavelength retuning and mesh protection are done at the connection level instead of at the lightpath level. The simulation results of the proposed schemes are also presented

Key management is a major challenge in the design and deployment of secure wireless sensor networks. A common assumption in most distributed key management schemes is that all sensor nodes have the same capability. However, recent research work has shown that the connectivity and lifetime of the sensor network can be substantially improved if a small number of sensor nodes have more energy capacity and transmission capability. Therefore, how to utilize these heterogeneity features to design a good distributed key management scheme has become an important issue and needs to be explored. In this paper, we propose a framework for key management schemes in distributed wireless sensor networks with heterogeneous sensor nodes. We show by simulations analysis that, with a small number of heterogeneous nodes, the wireless sensor network can achieve higher key connectivity and higher resilience

In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in a heterogeneous WLAN. Quality of Service in a DCF based heterogeneous WLAN is a challenging task due to the lack of centralized scheduling capability. The proposed scheme can maximize the total channel throughput and provide the service differentiation among different traffic stations. This is achieved by updating the transmission probability for each station in a timely manner based on the real-time network measurements. The simulation results show that the scheme can quickly adapt the station transmission probabilities to the desirable values in order to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment.

With the explosively increasing demand of multimedia applications in wireless local area networks (WLAN), Quality of Service (QoS) provisioning has become an important issue. IEEE 802.11 WLAN is the most popular WLAN technology today. In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in WLAN. The proposed scheme can maximize the total channel throughput and also can provide the service differentiation to multiple traffic classes. This is achieved by updating the transmission probability for each station in a timely manner based on the network measurements. The simulation results show the scheme can quickly adapt the station transmission probabilities to the desirable values to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment. The simulation results also match well with the theoretical analysis.

In this paper we present a heuristic scheduling algorithm for core optical routers with heterogeneous traffic. We contrast two versions of the algorithm, the 'frozen algorithm' and the 'nonfrozen algorithm.' The nonfrozen algorithm deals effectively with the hot spot scenarios by allowing more flexibility in the wave slot assignments. Performance evaluation results indicate that the nonfrozen algorithm produces a 'higher valued' schedule than the frozen algorithm at a marginal incremental increase cost in computation time.

Information assurance techniques employed in wired networks have limited direct applicability in wireless networks because of the unique aspects of wireless networks (e.g., user mobility, wireless communication channel, power conservation, limited computational power in mobile nodes, security at the link layer, etc.). The interaction between the components of information assurance, namely availability and security in a wireless network environment poses new challenges. In this article, we present a framework for understanding survivability and security in wireless network and also discuss the issues related to the interaction between survivability and security in hybrid wireless access networks.

In this paper we develop a performance modeling technique for analyzing the time varying performance of mobile ad hoc networks. Our approach is a novel hybrid of discrete event simulation and numerical method based queueing analysis. Network queues are modeled using fluid-flow based differential equation models which are solved using numerical methods, while node mobility is modeled using an adjacency matrix topology representation whose values are determined via discrete event simulation techniques. Numerical results are given illustrating the approach.

There are many system proposals for satellite-based multimedia communications that promise high capacity and ease of access. Many of these proposals require advanced switching technology and signal processing on-board satellites that will directly impact their cost, performance, availability, and time-to-market. Given the amount of commercial and technical risk involved in such complex systems, satellite operators have been looking for solutions that are simpler, yet flexible. One solution is based on a geosynchronous (GEO) satellite system equipped with simple on-board processing and switching. The satellite network is ATM-based and carries heterogeneous traffic. An important feature of this system is allowing for a maximum number of simultaneous users, hence, requiring effective connection admission control (CAC) and bandwidth on demand (BOD) algorithms. Nortel Networks has already developed an innovative CAC and BOD algorithm for the system. We present the BOD performance evaluations of the integrated algorithm. By detailed simulations, we show that the BOD scheme is able to efficiently utilize all available bandwidth and to gain high throughput. We also find that the end-to-end delays for voice traffic in the system falls well within the ITU's QoS specification for GEO-based satellite systems. The application buffer sizes observed in the simulations can serve as a guideline for ground station and satellite on-board memory design.

We investigate the impact of uplink multiple access schemes on traffic reshaping for a meshed broadband VSAT network over geostationary (GEO) satellite. The system includes a bent-pipe satellite with a number of broadband VSATs carrying multimedia traffic. An integrated CAC and BOD algorithm is used for the uplink M-TDMA access. An OPNET(TM) simulator is developed to investigate the affects of the CAC/BOD algorithm on the different applications in the satellite networks. Source application traffic is modeled using one of the following ATM transfer capabilities; CBR, rt-VBR, nrt-VBR, ABR, and UBR. We perform detailed traffic studies to compare the source traffic with the traffic out of the uplink multiple access, We show by simulation and analysis that all traffic contracts for the CBR, rt-VBR, and nrt-VBR source traffic are violated after uplink access. The results provide guidelines on the design of a corrective traffic reshaping function that counter-acts the traffic reshaping caused by the CAC/BOD uplink multiple access of the satellite networks.

We present a comparative analysis of bandwidth access control schemes under nonstationary traffic conditions for heterogeneous circuit-mode traffic offered to a B-ISDN network link. A unified model for analysing the behavior of several bandwidth access control schemes is developed using a Markov process model with acceptance functions. Numerical techniques are used to solve the associated Chapman-Kolmogorov equations and determine the nonstationary behavior. Performance results are given for several common bandwidth access control schemes, namely: complete sharing, complete partitioning, partial sharing, classical trunk reservation, and the probabilistic reservation policy. It is shown that the use of the average arrival rate to estimate the average connection blocking rate may be an underestimate for a system with a time varying arrival process. Also, it is found that a nonstationarity in one traffic class arrival process can impact other stationary traffic classes and the degree of variability depends on the particular access control scheme.