Next generation communications demand better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, we introduce a model-driven deep unfolding (DU) algorithm in this paper to address the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is further refined using projection gradient descent (PGD). DU is employed to further accelerate convergence, thereby improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, and adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with only 1.1% violation rate for the five-layer DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly from lower-frequency bands due to severe free space path loss, minimal diffraction and specular reflection, and prominent scattering, making conventional channel modeling and pilot-based estimation approaches inefficient. In this work, we investigate the feasibility of applying radio radiance field (RRF) framework to the THz band. This method reconstructs a continuous RRF using visual-based geometry and sparse THz RF measurements, enabling efficient spatial channel state information (Spatial-CSI) modeling without dense sampling. We first build a fine simulated THz scenario, then we reconstruct the RRF and evaluate the performance in terms of both reconstruction quality and effectiveness in THz communication, showing that the reconstructed RRF captures key propagation paths with sparse training samples. Our findings demonstrate that RRF modeling remains effective in the THz regime and provides a promising direction for scalable, low-cost spatial channel reconstruction in future 6G networks.

Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-ofdistribution (OOD) data.

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.

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.

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.

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.

Application offloading plays a crucial role in application deployment in edge-cloud computing. However, finding the optimal solution for application offloading is challenging due to the heterogeneous resources, computing dependency of tasks, and complex network. Existing research on application offloading problems often assumes that the communication delay between the edge and the cloud (inter-side) is symmetrical or that within the cloud or edge (intra-side) can be omitted. However, this assumption is not practical considering the distinct features of the clouds and the edge clusters. Therefore, we study application offloading in the heterogeneous edge-cloud environment by considering both intra-side communication delay between tasks assigned to the same side and asymmetry inter-side communication delay between edge and cloud sides. We first focus on the specific circumstances with a boundary condition that lead to an optimal offloading solution in a heterogeneous edge-cloud environment. Then we study the general case by designing an iterative algorithm with maximum gain technique to solve it. Furthermore, considering various bandwidths within each side and resource capacities of physical nodes, we develop two efficient algorithms by combining minimum cut and maximum gain approaches. Both simulations and real trace-based evaluations are conducted to validate that the proposed algorithms outperform existing solutions.

Network traffic classification plays an essential role in network measurement and management. Emerging Artificial Intelligence (AI) algorithms have become a viable solution to encrypted network traffic classification. Nonetheless, the classification performance of existing AI-based traffic classifiers is restricted to a limited number of network applications depending on the coverage of the knowledge database. Such AI-based traffic classifiers cannot maintain high performance to provide accurate traffic classification when dealing with updated or new network applications. To tackle the issues, we present an autonomous model update mechanism to sustain the high performance of AI-based traffic classifiers. Specifically, an instability check algorithm is derived to evaluate if the current classifier requires an update. A filtering algorithm is proposed to extract unknown traffic and build a new knowledge database based on a new metric, i.e., familiarity, defined based on the prediction confidence and instability. Extensive experiment results demonstrate that our proposed updating mechanism can provide prompt model updates and establish a proper new knowledge base to maintain high accuracy in various experimental scenarios. Moreover, the comparison is conducted and the results show the proposed familiarity-based filtering algorithm can filter about 7 and 3 times more true positive packets in the two considered scenarios, respectively.

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.

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. The proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

Recently, many Artificial Intelligence (AI) based schemes have been proposed to support network measurement and management, such as network traffic classification, intrusion detection, traffic prediction, etc. These AI schemes have demonstrated promising performance in supporting networking. However, the development of these AI schemes requires a massive amount of fresh databases. The scarcity and futility of public datasets are straining the development of the networking AI models. Not to mention that most available datasets are not up-to-date. Collecting new datasets can be time-consuming and restricted by networking capabilities. To address the issues, we have introduced a real-application enabled network traffic generator. In this work, we further enhance the network traffic generator with more functionalities. In particular, a traffic flow segment scheme is proposed for the quick establishment of traffic flow databases. An intelligent generator is implemented to simulate point-to-point communications, including network multiplexing, and network duplexing. The evaluation results demonstrate that the improved intelligent traffic generator can generate a large amount of diverse network traffic with practical settings more efficiently than collecting data in real life. Moreover, a case study is given to demonstrate the quality of the generated traffic data.

The rapid growth of Internet-of-things (IoT) devices modernizes our society and lives. Such a growth leads to the pro-liferation of the wireless technologies, most of which occupy the unlicensed/open industrial, scientific and medical (ISM) bands. Since each of these wireless technologies adheres to different standards, they give rise to challenges like cross technology interference, incompatible security protocols and high energy consumption. In this work, we envision to achieve an energy-efficient communication on a seamless IoT platform. In specific, a game theoretical approach is proposed to formulate a multi-device IoT communication system. The existence and uniqueness of the Nash equilibrium is proven to achieve the optimal transmit power for the IoT devices. Moreover, a distributed scheme is developed for practical implementations depending on receiving signal strength indicators. Simulation results demonstrate that optimal transmit power can be achieved through the proposed scheme. We further implement and demonstrate the distributed approach on a universal software radio peripheral (USRP) testbed.

Network measurement and management are more challenging in the next generation network systems due to the increasing demand for communications and complex network infrastructure. Recently, artificial intelligence (AI) algorithms have attracted much attention in networking systems, such as AI-based network traffic classification, traffic prediction, intrusion detection systems, etc. The development and maintenance of networking AI models usually require a large amount of traffic data samples from real applications. However, the publicly available datasets for network development are limited and rarely updated. In this paper, we develop a real application enabled traffic generator for AI model development in networking. In particular, a data loader is provided to establish two databases. One is a payload database that consists of packets from real applications. The other one is a traffic database that consists of network traffic flow statistics. The traffic generator allows a user to simulate data traffic flows that mimic one or more real applications. Moreover, two networking AI models are implemented to validate the simulated traffic flows. Evaluation results demonstrate that the developed traffic generator can help with networking AI model development.

Network traffic classification (NTC) plays an important role in network measurement and management. Recently, Artificial Intelligence (AI) based NTC has been widely considered a good candidate because of its accuracy in processing both clear and encrypted data traffic. However, those AI-based NTC schemes usually apply full-length packets, e.g., through padding. Such lengthy inputs can lead to complex designs of NTC models, which can be challenging to network devices. To tackle this issue, we propose a byte importance distillation scheme to extract the packet payload bytes that contribute the most to traffic classification results. In the proposed scheme, a Bayesian Multilayer Perception (BMLP) is first initialized based on full-size data packets. The byte importance is then defined as the corresponding mean absolute weights in the first layer of a BMLP model with K-integration. By choosing the important bytes, input data packets can be reduced dramatically to a few bytes that contribute the most to the classification output. Two popular AI-based NTC models, i.e., MLP based and CNN based, are implemented to evaluate the proposed byte importance distillation scheme. The results demonstrate that the data packets optimized from the proposed scheme can speed up the MLP and CNN based NTC models by one to two magnitudes, depending on the implementation platforms, while maintaining high classification accuracy. In comparison, packets that are reduced to the same size through traditional dimension reduction approaches such as principal component analysis and convolutional block attention module cannot maintain high classification accuracy of the AI-based NTCs.

With the popularity of Deep Learning (DL), researchers have begun to apply DL to tackle with encrypted traffic classification problems. Although these methods can automatically extract traffic features to improve the ability of feature engineering of traditional methods like DPI, a large amount of data is still needed to learn the characteristics of various types of traffic. Therefore, the performance of classification model always significantly depends on the quality of datasets. Nonetheless, the building of datasets is a time-consuming and costly task, especially encrypted traffic. Apparently, it is often more difficult to collect a large amount of traffic samples of those unpopular applications than well-known ones, which often leads to the problem of class imbalance between major and minor encrypted applications in datasets. In this paper, we proposed a novel traffic data augmenting method called PacketCGAN using Conditional GAN, which can control the modes of data to be generated. PacketCGAN exploit the benefit of CGAN to generate specified samples with the input of applications’ types as conditional and thereby achieve data balancing. As a proof of concept, three classical DL models including CNN were adopted to classify four types of encrypted traffic datasets augmented by Random Over Sampling (ROS), SMOTE(Synthetic Minority Over-sampling Techinique), vanilla GAN and PacketCGAN respectively using public datasets. The experimental evaluation results demonstrate that DL based encrypted traffic classifier over our new dataset augmented by PacketCGAN can achieve better performance than the other three in terms of encrypted traffic classification.

Network intrusion detection is the fundamental of the Cybersecurity which plays an important role in preventing the systems away from malicious network traffic. Recent Artificial Intelligence (AI) based intrusion detection systems provide simple and accurate intrusion detection compared with the conventional intrusion detection schemes, however, the detection performance may not be reliable because the models in the AI algorithms must output a prediction result for each incoming instance even when the models are not confident. To tackle the issue, we propose to adopt Bayesian Deep Learning, specifically, Bayesian Convolutional Neural Network, to build intrusion detection models. Moreover, an ensemble-based detection scheme is further proposed to enhance the detection performance. Two open datasets (i.e., NSL-KDD and UNSW-NB15) are used to evaluate the proposed schemes. In comparison, Convolutional Neural Network and Support Vector Machine are implemented as baseline IDS (i.e., CNN-IDS and SVM-IDS). The evaluation results demonstrate that the proposed BCNN-IDS can significantly boost the detection accuracy and reduce the false alarm rate by adopting the proposed T-ensemble detection scheme.

Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has attracted much attention from the researchers due to its effectiveness even in encrypted network traffic without compromising neither user privacy nor network security. However, most of the existing models are created from closed-world datasets, thus they can only classify those existing classes previously sampled and labeled. In this case, unknown classes cannot be correctly classified. To tackle this issue, an autonomous learning framework is proposed to effectively update DL-based traffic classification models during active operations. The core of the proposed framework consists of a DL-based classifier, a self-learned discriminator, and an autonomous self-labeling model. The discriminator and self-labeling process can generate new dataset during active operations to support classifier update. Evaluation of the proposed framework is performed on an open dataset, i.e., ISCX VPN-nonVPN, and independently collected data packets. The results demonstrate that the proposed autonomous learning framework can filter packets from unknown classes and provide accurate labels. Thus, corresponding DL-based classification models can be updated successfully with the autonomously generated dataset.

Network traffic classification is essential in access network for end-to-end network management and measurement such as network intrusion detection, network resource allocation. State-of- the-art Deep Learning based classifiers have high accuracy even when processing encrypted data packets. Such classifiers would need to be updated when a new application is in the network traffic. However, it is challenging to build and label a dataset of the unknown application from active network traffic. In this paper, we propose an autonomous model update scheme to (i) filter the data packets of a new application from active network traffic and build a corresponding training dataset; and (ii) update the current network traffic classifier with transfer learning. In particular, the core of the proposed scheme is a discriminator that consists of a statistical filter and a convolutional neural network based binary classifier to filter and build a dataset of new application packets from active network traffic. Evaluation is conducted based on an open dataset (i.e., ISCX VPN-nonVPN dataset). The results demonstrated that our proposed autonomous classifier update scheme can successfully filter packets of a new application from network traffic and build a corresponding training dataset. Moreover, the packet classifier can be effectively updated through transfer learning. The proposed update scheme can contribute significantly in the access network for further end-to-end network measurement and management.

For applications of the communication networks and big data, E-Health has emerged and become a very popular paradigm to not only monitor people's health conditions continuously, but also provide precise medical treatment accordingly and instantly. However, recent E-Health applications have raised serious concerns on the anomalous behaviors of a person's sensitive medical records, that the private health conditions are being maliciously compromised and modified by evil third parties. Existing research literatures have investigated such an issue by incorporating multiple cryptographic schemes to guarantee the privacy of a user's sensitive medical records. In this paper, we propose a predicate encryption scheme for anomaly detection in communication networks applied to E-Health applications. Specifically, the major novelty and contribution in this work is the use of session key as a message in the encryption operation of predicate encryption system, for the purpose of achieving both information privacy and efficient cryptographic computations. We then introduce the system and security model in an E-Health network, and provide the detailed descriptions on the design of predicates and anomaly detection procedures. Finally, we present the simulation results in terms of computational and communication overhead, and future studies of our work.

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.

In urban area, fast growth in the number of vehicles has led to a series of traffic problems, including traffic jams,high traffic accident rates, etc. Efficient traffic signal control methods has been shown to be an essential way to significantly mitigate traffic problems. In this poster, different from previous online methods, we propose a frame-based model and an efficient algorithm to solve the drawbacks of online algorithm by scheduling the vehicles that have accumulated at the intersection over a period of time. Preliminary experiments exhibit that the proposed algorithm could greatly improve the throughput of the intersection.

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.

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.

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.

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 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.

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.

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 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.

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.

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 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.

Cognitive Radio has emerged as a new technology to improve spectrum utilization and to alleviate spectrum scarcity by allowing unlicensed users to sense and opportunistically access the under-utilized spectrum. This paper proposes a system framework for the Cognitive Radio enabled TD-LTE system that opportunistically accesses and utilizes the TV White Space spectrum. The system performance of both TV broadcasting network and TD-LTE network is evaluated and analyzed. The simulation studies show that the proposed framework can significantly improve the performance of both TV and TD-LTE systems when they are co-operating within the same TV spectrum with TV as the primary user and TD-LTE as the secondary user. Both TV spectrum utilization and TD-LTE coverage are greatly enhanced.