The proliferation of Internet of Things (IoT) devices has been paralleled by a surge in sophisticated malware threats, posing significant challenges to traditional security mechanisms. Conventional malware classification models often depend on extensive labeled datasets and exhibit limited generalization capabilities, particularly when encountering novel or crossarchitecture malware variants. In this study, we introduce TOMNet, a transductive meta-learning framework that synergistically integrates few-shot learning with graph-based inference to facilitate efficient IoT malware classification under datascarce conditions. TOM-Net employs a hierarchical GraphSAGE encoder to extract both structural and semantic features from function call graphs, augmented with adaptive similarity kernels for transductive label propagation and entropy-regularized decision boundaries to enhance open-set recognition. Empirical evaluations demonstrate that TOM-Net achieves a classification accuracy of $92.64 \%$ in the 5 -way 10 -shot setting under the closedset condition, and an area under the curve (AUC) of $93.59 \%$ in the open-set setting, significantly outperforming state-of-the-art baselines in detecting previously unseen threats. These results underscore the practical applicability of TOM-Net for robust IoT malware defense in scenarios characterized by limited labeled data.

The rapid expansion of the Internet of Things (IoT) has significantly increased the prevalence of malware targeting IoT devices. Although machine learning models offer promising solutions for automatic malware detection, they are increasingly vulnerable to adversarial attacks. These attacks exploit the model’s feedback loop to iteratively refine malware, producing adversarial samples that evade detection. As such, enhancing the robustness of these models is of paramount importance. Our research introduces a novel approach to bolster malware detection by retaining additional semantic information within the execution order analysis of malware programs. The method significantly improves the resilience of detection models against adversarial samples and implements two adversarial attack methods to rigorously test our model’s robustness by generating authentic adversarial examples for validation. We highlight the critical impact of preserving semantic integrity in malware detection and present a solution to counteract the growing threat of adversarial attacks in IoT environments.

Coexistence and interference management issues for machine-to-machine (M2M) and human-to-human (H2H) communications are crucial for the Internet of Things (IoT). This article considers beamforming and power allocation for M2M/H2H coexistence networks adopting the dynamic time division duplex (TDD) spectrum sharing scheme and energy harvesting (EH). The design objective is total system power minimization with device Quality-of-Service (QoS) constraints as well as EH constraints. Since the dynamic TDD introduces new types of interference, i.e., uplink/downlink cross-interference, the considered problem is a challenging nonconvex coupled problem. We first consider a simplified problem without the EH considerations. We propose a novel low-complexity algorithm based on uplink-downlink duality (UDD) and alternating optimization (AO) to tackle this problem. Then, we propose a second-order cone programming (SOCP) relaxation-based AO low-complexity algorithm to deal with the general problem. In the simulation, we study the performance of the QoS, the number of antennas, the number of users, and the power splitting ratio. Finally, the performance of the proposed algorithms have low-complexity than the classical convex optimization method.

In the era of rapid network development, IoT devices are being deployed more and more widely, and various kinds of malware programs are gradually appearing at the deployment level. As a widely adopted static analysis approach, structure based analysis such as graph embedding can capture the semantic features of malware binaries and has received much research attention. In this paper, to further improve the robustness of the graph embedding approaches to IoT malware detection, we propose a novel method that incorporates both local and global characterizing features extracted from Function-Call Graphs (FCG) to perform the detection. The caller-callee relationship represents the local semantic features, and the global statistic feature represents the graph’s structural characteristics. The performance of the proposed method is evaluated on a largescale dataset consisting of 112K malware and 89k benignware samples collected from seven CPU architectures. It shows a 99% accuracy on IoT malware detection, outperforming existing graph embedding solutions. Moreover, when CPU architecture is taken into consideration, the proposed method combined with support vector machine and multilayer perception classifier can yield even higher performance.

This paper investigates beamforming and power allocation problems in the dynamic time division duplex (TDD) cellular networks. Based on the dynamic TDD coexistence scheme, the network comprises uplink and downlink networks serving machine-type devices (MTDs) and human-type devices (HTDs), respectively. The design goal is to minimize the total system power consumption by optimizing the transmit/receive beamforming and uplink power under MTD and HTD quality-of-service (QoS) constraints. The resulting optimization problem is challenging to solve because the variables to be designed are tightly coupled in the constraints. By using the uplink-downlink duality (UDD) and alternating optimization (AO) algorithm, we propose a novel algorithm to overcome the difficulty in this work. Numerical results demonstrate the superiority of the proposed algorithm.

Coexistence and interference management issues for human-to-human (H2H) and machine-to-machine (M2M) communications are crucial for the Internet of Things (IoT). This paper considers beamforming and power allocation for H2H/M2M coexistence networks with dynamic time division duplex (TDD) spectrum sharing and energy harvesting. The design objective is total system power minimization with device quality-of-service (QoS) constraints as well as energy harvesting constraints. The resulting optimization problem is nonconvex and challenging to solve due to the new interference sources introduced by dynamic TDD spectrum management, which are nonexistent in conventional spectrum usage systems. To tackle this problem with tightly coupled design parameters in the constraints, we propose a second-order cone programming (SOCP) relaxation-based alternating optimization (AO) algorithm. Numerical results demonstrate the performance of the proposed algorithm from various perspectives.

In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous mal ware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and linkable format when the training and test are done on the same platform. Meanwhile, it also achieves a 96% accuracy when training is carried out on a few popular IoT platforms but test is done on different platforms. Efficient malware prevention and mitigation solutions can be enabled based on the proposed method to prevent and mitigate IoT malware damages across different platforms.

Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting. However, when attacking a deployed machine learning service, one can only acquire the input-output correspondences of the target model; this is the so-called black-box attack setting. The major drawback of existing black-box attacks is the need for excessive model queries, which may give a false sense of model robustness due to inefficient query designs. To bridge this gap, we propose a generic framework for query-efficient blackbox attacks. Our framework, AutoZOOM, which is short for Autoencoder-based Zeroth Order Optimization Method, has two novel building blocks towards efficient black-box attacks: (i) an adaptive random gradient estimation strategy to balance query counts and distortion, and (ii) an autoencoder that is either trained offline with unlabeled data or a bilinear resizing operation for attack acceleration. Experimental results suggest that, by applying AutoZOOM to a state-of-the-art black-box attack (ZOO), a significant reduction in model queries can be achieved without sacrificing the attack success rate and the visual quality of the resulting adversarial examples. In particular, when compared to the standard ZOO method, AutoZOOM can consistently reduce the mean query counts in finding successful adversarial examples (or reaching the same distortion level) by at least 93% on MNIST, CIFAR-10 and ImageNet datasets, leading to novel insights on adversarial robustness.

Long Term Evolution (LTE) is governing mobile communication technology in recent years, and its security issue always receives lots of attention. Due to the popularity of software defined radio (SDR), low-cost development hardware, and open source LTE project, rouge Base Station (BS) attack can be achieved with much lower price in recent years. However, the current rouge BS attack suffers from limited by the non-commercial antennas, which results in a very small attack range. In this paper, we encapsulate the functions of LTE into different Docker containers located in different physical network entities following the concept of network function virtualization (NFV). By networking those Docker containers using the recent innovation, overlay network, a cooperative and large-scale rouge BS attack can be achieved. Under the coordination of the proposed controller, multiple victims supported by multiple telecommunication operators (and the multiple spectra), using multiple types of equipment (and thus the chipset), or located in multiple locations are targeted in a simultaneous fashion. We verify the feasibility of the proposed attack using the proposed experimental platform.

This paper considers the precoder design for energy-efficient data transmissions in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid multiple-input multiple-output (MIMO) cellular networks. We aim to maximize the energy efficiency (EE) of the network, defined as the ratio of the system sum rate to the system power consumption, under EH and transmit power constraints for both cellular and D2D users. The considered problem is nonconvex due to the concave-convex and fractional form of the objective. We propose to apply the concave-convex procedure (CCCP) and the Dinkelbach method to find tractable, approximate solutions. Numerical results demonstrate the performance of the proposed method from various perspectives.

To fully empower sensor networks with cognitive Internet of Things (IoT) technology, efficient medium access control protocols that enable the coexistence of cognitive sensor networks with current wireless infrastructure are as essential as the cognitive power in data fusion and processing due to shared wireless spectrum. Cognitive radio (CR) is introduced to increase spectrum efficiency and support such an endeavor, which thereby becomes a promising building block toward facilitating cognitive IoT. In this paper, primary users (PUs) refer to devices in existing wireless infrastructure, and secondary users (SUs) refer to cognitive sensors. For interference control between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, which inevitably leads to increasing latency and poses new challenges on the reliability of IoT communications. To guarantee operations of primary systems while simultaneously optimizing system performance in CR ad hoc networks (CRAHNs), this paper proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHNs in terms of end-to-end transmission reliability and buffer occupancy. This paper sheds new light on analysis of recovery-assisted flooding schemes in CRAHNs and provides performance evaluation of cognitive IoT services built upon CRAHNs.

To achieve end-to-end delivery in intermittently connected mobile Internet of Vehicles (IoV) networks, epidemic routing is proposed for data dissemination at the price of excessive buffer occupancy due to its store-and-forward nature. Typically, epidemic routing should be controlled to reduce system resource usage (e.g., buffer occupancy) while simultaneously providing data delivery with differentiated level of statistical guarantee. With the aid of social connectivity among vehicles, the control of data dissemination could be benefited from the property of instant end-to-end communication in Social IoV (SIoV). In particular, social links are leveraged to deliver control message for balancing the tradeoffs between buffer occupancy and data delivery reliability for supporting data dissemination in SIoV. In this paper, we proposed two representative schemes: the global timeout scheme and the antipacket dissemination scheme, respectively, for lossy and lossless data delivery, where control messages are delivered in social-based end-to-end and local-based ad-hoc fashions. For lossy data delivery, our investigation shows that with the suggested global timeout value, the per-node buffer occupancy only depends on the maximum tolerable packet loss rate and pairwise meeting rate, providing principles toward mission-critical protocols. For lossless data delivery, our analytical results show that the buffer occupancy can be significantly reduced via fully antipacket dissemination, providing efficient end-to-end communication. The developed tools therefore offer new insights for epidemic routing protocol designs and performance evaluations for SIoV.

In the heterogeneous cloud radio access network (H-CRAN), which consists of multiple access points (APs) providing smaller coverage and a high power node (HPN) providing ubiquitous coverage, the mobile machines can connect to multiple APs and HPN by coordinated multi-point transmission (CoMP) concurrently to achieve ultra-reliable and low-latency communication. However, the current network association (or priorly known as handovers), which only focuses on switching between two base stations, may not be an efficient scheme in H-CRAN. In this paper, we innovate a proactive network association mechanism by taking CoMP into consideration under the H-CRAN architecture. We consider two scenarios under the H-CRAN architecture: with and without the assistance of HPN in the network. By regarding APs/HPN in H-CRAN as resources that allocated to mobile machines, a novel proactive network association concept is proposed, and then generalized from one-to-one to multiple-to-multiple case. With the assistance of Lyapunov optimization theory, effective bandwidth, and capacity theory, we can prove that this proactive network association scheme can guarantee that the queueing delay performance and the delay violation probability can be both smaller than a corresponding upper bound. That is, both low-latency and ultra-reliable communication can be guaranteed. We also conduct experiments by using real trace from taxis movement data to verify the analytical results. Our results suggest the guidelines to design the proactive network association scheme in H-CRAN.

The conflagration of fire is still a serious problem caused by humans, and houses are at a high risk of fire. Recently, people have used smoke alarms which only have one sensor to detect fire. Smoke is emitted in several forms in daily life. A single sensor is not a reliable way to detect fire. With the rapid advancement in Internet technology, people can monitor their houses remotely to determine the current condition of the house. This paper introduces an intelligent smoke alarm system that uses ZigBee transmission technology to build a wireless network, uses random forest to identify smoke, and uses E‐charts for data visualization. By combining the real‐time dynamic changes of various environmental factors, compared to the traditional smoke alarm, the accuracy and controllability of the fire warning are increased, and the visualization of the data enables users to monitor the room environment more intuitively. The proposed system consists of a smoke detection module, a wireless communication module, and intelligent identification and data visualization module. At present, the collected environmental data can be classified into four statuses, that is, normal air, water mist, kitchen cooking, and fire smoke. Reducing the frequency of miscalculations also means improving the safety of the person and property of the user.

The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program characterization, a process that numerically or structurally quantifies a target program. While great achievements have been made in identifying key features to characterize programs, choosing a correct set of features for a specific compiler task remains an ad hoc procedure. In order to guarantee a comprehensive coverage of features, compiler engineers usually need to select excessive number of features. This, unfortunately, would potentially lead to a selection of multiple similar features, which in turn could create a new problem of bias that emphasizes certain aspects of a program's characteristics, hence reducing the accuracy and performance of the target compiler task. In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool. Specifically, FEAST utilizes widely used statistics and machine-learning tools, including LASSO, sequential forward and backward selection, for automatic feature selection, and can in general be applied to any numerical feature set. This paper further proposes an automated approach to compiler parameter assignment for assessing the performance of FEAST. Intensive experimental results demonstrate that, under the compiler parameter assignment task, FEAST can achieve comparable results with about 18% of features that are automatically selected from the entire feature pool. We also inspect these selected features and discuss their roles in program execution.

This paper investigates the beamforming design for sum-rate maximization in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid cellular networks. In the considered system, each receiving cellular or D2D user performs EH while decoding information from the base station (BS) or the paired transmitting D2D user. The objective is to derive optimal beamforming strategies at the BS and power allocations at transmitting D2D users, such that the network sum rate is maximized under EH and transmit power constraints. The original nonconvex problem is convexified by the semidefinite relaxation technique and a reformulation of the objective function with first-order approximation in each algorithm iteration, and solved by an iterative algorithm, based on the concept of the Frank-Wolfe algorithm. Simulation provides numerical validation of the proposed method from various perspectives.

Device-to-Device (D2D) communications enable a wider set of applications and use cases in the Internet of Things (IoT) paradigm. Without the presence of Access Points (APs), Wi- Fi Direct, also known as Wi-Fi Peer-to-Peer (P2P), becomes an intrinsic facilitator for IoT applications due to its popularity. In particular, the P2P group owner (GO) plays the role of the AP and is responsible for making connections with P2P group client (GC). In IoT paradigm, power control should be carefully examined to prevent significant power drop on both Wi-Fi P2P GO and GC. By leveraging the information of connections such as received signal strength and retry count, this paper proposes two novel power control mechanisms for Wi-Fi P2P connections in IoT paradigm. We first design a threshold-based mechanism, which limits the maximum number of connection retries to eliminate unnecessary power consumption. However, considering the dynamic change of wireless condition, an adaptive power control mechanism is proposed to further reduce the waste of energy. We establish an intensive experiment by practically implementing both mechanisms in Wi-Fi P2P devices. The experiment results show that the proposed mechanisms can significantly reduce the power consumption and thus makes Wi-Fi P2P connection more efficient for IoT applications.

Nowadays, the design of a secure access authentication protocol in heterogeneous networks achieving seamless roaming across radio access technologies for mobile users (MUs) is a major technical challenge. This paper proposes a Distributed Anonymous Authentication (DAA) protocol to resolve the problems of heavy signaling overheads and long signaling delay when authentication is executed in a centralized manner. By applying MUs and point of attachments (PoAs) as group members, the adopted group signature algorithms provide identity verification directly without sharing secrets in advance, which significantly reduces signaling overheads. Moreover, MUs sign messages on behalf of the group, so that anonymity and unlinkability against PoAs are provided and thus privacy is preserved. Performance analysis confirm the advantages of DAA over existing solutions.

The cognitive capability of secondary users in multichannel wireless networks enables the functionalities of system parameter estimation and learning, so that a more intelligent channel access without interfering the primary users is possible. This paper proposes a novel cognitive channel access algorithm with threshold policy on the basis of a continuous-time Markov chain built by the estimated parameters. The secondary users could access the channel in a more intelligent fashion and thus the better quality of service can be achieved. The numerical results show that cognitive channel access can significantly increase the total utility of system while keeping blocking probability of primary users' requests under a predefined constraint.

In the last few years, the growing popularity of mobile devices with rich wireless communication capabilities has made them attractive to digital viruses and malicious contents. The user mobility and novel proximity-based communication technologies on one hand facilitate the information sharing and dissemination among people, on the other hand they increase the possibility of spreading malware. Understanding the propagation characteristics of malware could aid in planning protection strategies, but the most of the current models discuss it from the view of whole network. In this paper, we establish the model from the perspective of individuals by using Markov chain. Our model investigates the incubation period and remaining life time of a mobile smartphone when it is infected by malware. The numerical results provide comprehensive and intuitive explanations which are more realistic in real world.

Robust network design against attacks is one of the most fundamental issues in Internet of Things (IoT) architecture as IoT operations highly rely on the support of the underlaying communication infrastructures. In this paper, the vulnerability of IoT infrastructure under intentional attacks is investigated by relating the network resilience to the percolation-based connectivity. Intentional attacks impose severe threats on the network operations as it can effectively disrupt a network by paralyzing a small fraction of nodes, and therefore deteriorating IoT operations. A fusion-based defense mechanism is proposed to mitigate the damage caused by such attacks, where each node feedbacks minimum (one-bit) local decision to the fusion center for attack inference. By formulating the attack and defense strategy as a zero-sum game, the outcome of the game equilibrium is used to evaluate the effectiveness of the proposed mechanism. The robustness of the Internet-oriented and the cyber-physical system (CPS)-oriented networks are specifically analyzed to illustrate the foundation of future IoT infrastructure. Both analytical and empirical results show that the proposed mechanism greatly enhances the robustness of IoT, even in the weak local detection capability and fragile network structure regime.

Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.

This paper analyzes the delay of multi-hop transmissions in cognitive radio ad hoc networks (CRNs) to investigate the performance of routings in CRN. Compared to routing in ad hoc networks, additional medium access delay is introduced in CRN since opportunistic transmissions of secondary users (SUs) should not violate the interference constraints at primary receivers. Moreover, additional retransmission delay occurs in CRN because received signals at SUs are interfered with by both primary transmitters and concurrent secondary transmitters. We propose an analytically tractable model to investigate routings in CRN considering medium access delay, retransmission delay and the hop count of the end-to-end route. Through optimizing the number of hops of the end-to-end route, a lower bound on the end-to-end packet transmission delay is developed, which facilitates delay QoS provisioning and rate-delay trade-off in CRN. Consequently, this research serves as the valid framework for baseline performance analysis and offers novel avenues to routing design in CRN.

The recent deployment of Cyber-Physical Systems (CPS) has emerged as the most promising approach to provide an extensive computational capability for processing and controlling physical entities, which relies on reliable data exchanges among machines in CPS. However, for CPS exploiting public network infrastructures, links in CPS may suffer from a variety of vulnerabilities to harm real-time data exchanges. Providing network resilience for real-time communications consequently becomes the most critical requirement in CPS. Considering the support of multiple communication paths in state-of-the-art network infrastructures, in this paper, we develop a mathematical resource-optimal network resilience design for CPS. In our design, duplicates of timing sensitive data are simultaneously forwarded via multiple communication paths. Therefore, timing constraints are violated only if all communication paths fail to forward data to the destination on time. By analyzing the relationship among the probability of timing constraint violation, the time domain resource allocation, and the number of communication paths (the spatial domain resource allocation), our design leads to the minimum resource usage to support real-time data exchanges in CPS. Our mathematical resource-optimal design solves the most challenging issue of unreliable data exchanges in CPS in the most efficient fashion, to consequently support the maximum number of machines in CPS.

Two-tier macro/femto heterogeneous cellular networks (HCNs) have received considerable attention due to substantial improvements in high quality in-building coverage and system capacity. Distributed self-configured femtocells can be realized to mitigate inter-tier interference between macro cells and femtocells without heavy operating costs by incorporating broadcasting mechanism of macro cell. With the aid of the macro cell, who provides critical global information, femtocells can configure related parameters to achieve interference mitigation. A tractable stochastic geometry-based analytical model is proposed to evaluate of proposed self-configured scheme in terms of coverage probability. We also conduct simulation experiments according to data from OpenCellID to prove the effectiveness of the proposed self-configured scheme in the realistic two-tier HCNs.

In cognitive radio networks, the signal reception quality of a secondary user degrades due to the interference from multiple heterogeneous primary networks, and also the transmission activity of a secondary user is constrained by its interference to the primary networks. It is difficult to ensure the connectivity of the secondary network. However, since there may exist multiple heterogeneous secondary networks with different radio access technologies, such secondary networks may be treated as one secondary network via proper cooperation, to improve connectivity. In this paper, we investigate the connectivity of such a cooperative secondary network from a percolation-based perspective, in which each secondary network's user may have other secondary networks' users acting as relays. The connectivity of this cooperative secondary network is characterized in terms of percolation threshold, from which the benefit of cooperation is justified. For example, while a noncooperative secondary network does not percolate, percolation may occur in the cooperative secondary network; or when a noncooperative secondary network percolates, less power would be required to sustain the same level of connectivity in the cooperative secondary network.

The recent deployment of Cyber-Physical Systems (CPS) has emerged as a promising approach to provide extensive interaction between computational and physical worlds. For a large-scale distributed CPS comprising of numerous machines, sharing radio resource efficiently with the existing wireless networks while maintaining sufficient quality of service (QoS) for machine-to-machine (M2M) communications becomes an essential and challenging requirement. By clustering CPS machines as a swarm with the cluster head managing radio resources inside the swarm, spectrum sharing among numerous machines can be achieved in a distributed and scalable fashion. Specifically, we apply the recent innovation, cognitive radio, and a special mode in cognitive radio, interweave coexistence, to leverage machines to collect radio resource usage information for autonomous and interference-free radio resource management in the CPS. To reduce the communication overheads of channel sensing feed backing from machines, we apply compressive sensing to construct a spectrum map indicating the radio resource availability on any given locations within the CPS coverage. Such spectrum map resource management (SMRM) only utilizes a small portion of machines to perform channel sensing but enables distributed cluster-based spectrum sharing in an efficient way. Through the concept of effective capacity, the SMRM controls available resources to guarantee the QoS for communications of CPS. By evaluating the performance of the proposed SMRM in the most promising realization of CPS based on LTE-Advanced machine-type communications coexisting with LTE-Advanced Macrocells to utilize identical spectrum, the simulation results show effective QoS guarantees of CPS by SMRM in the realistic environments.

To increase spectrum utilization, cognitive radio allows concurrent secondary and primary transmissions as long as interference to primary users is constrained under a threshold. This research proposes an enhanced opportunistic interference mitigation scheme utilizing both successfully and unsuccessfully decoded primary packets to improve data rate of secondary transmission. Moreover, we propose an analytical model to investigate characteristic changes of the spectrum usage affected by the coexisting secondary transmission in terms of overall spectral efficiency. The interference mitigation scheme can be applied to realistic two-tier femtocell networks to enable robust communication against cross-tier interference thereby obtaining a substantial spectrum reuse gain.

Characterizing the topology and therefore fundamental limits is a must to establish effective end-to-end cognitive radio networking (CRN). However, there lacks complete understanding of the relationship among connectivity, interference, latency and other system parameters of the CRN. To clarify this complication, by employing tools from both percolation theory and stochastic geometry, we thus provide a novel parametrization of underlay secondary ad hoc CRN wherein the secondary network is regarded as an operating point in the phase space. Coexisting with a primary ad hoc network, the secondary network undergoes a phase transition due to avoiding interference to primary receivers, while being interfered by primary transmitters. Furthermore, transmit power allocation of secondary users is represented by a Pareto contour in the phase space, and the impact of interference on connectivity is captured by the latency-to- percolate. Finally, with the cognitive capability of CR, performance improvement of importing an SU- avoidance region around primary receivers is analyzed, and CRNs can be therefore successfully supplied.

Cognitive radio (CR) technology has been considered promising to enhance spectrum efficiency via opportunistic transmission at link level. To make CR useful, networking CRs to form a cognitive radio network (CRN) is able to support end-to-end transmission from CR source to CR destination. However, the opportunistic nature of CR link for interference avoidance to primary users degrades the quality-of-service (QoS) of end-to-end CR transmission and challenges the CRN toward a completely successful operation. Through queueing analysis, we propose a statistical control mechanism to deal with such opportunistic links in CRN by cooperative relaying the same packet flows into several opportunistic paths simultaneously. The availability and reliability of redundant transmission over the end-to-end paths in the same group is enhanced. By maximizing the number of groups with bounded statistical delay, the spectrum efficiency is enhanced. This optimized grouping problem is mathematically equivalent to the bin covering problem with NP-hard complexity. By the proposed Round-Robin algorithm, simulation results show that the optimal performance can be achieved in the case that the statistical availabilities of all opportunistic links are the same. This work therefore provides an essential viewpoint via cooperative relay among CRs in CRN, the QoS (i.e., average delay) can be guaranteed and spectrum can be efficiently utilized.

In two-tier networks consisting of a macrocell overlaid with femtocells in co-channel deployment and closed-access policy, spatial reuse is achieved at the price of severe cross-tier interference from concurrent transmissions. The lack of direct coordination between the macro and femtocells makes interference control as a challenging issue. Cognitive radio (CR) becomes a promising solution, where femtocells with cognitive information accomplish concurrent transmissions while meeting a per-tier outage constraint. Several interference-aware allocation approaches are proposed to enhance spatial reuse according to cognitive capabilities of femtocell. By employing stochastic geometry model, bounds on the distribution of aggregated interference from two-tier spatial point processes are successfully analyzed. The maximum number of simultaneously transmitting femtocells and overall downlink capacity of two-tier networks meeting a per-tier outage requirement in each approach are theoretically derived. This paper proves that with stronger cognitive capability (i.e., more knowledge interpreted) at femtocell, more spatial reuse gain can be found.

The Authentication, Authorization, Accounting (AAA) infrastructure in Mobile IP network is designed to distribute keys to network entities for signaling message protection. In Mobile IP network, Regional Registration is employed to migrate the high signaling delay when a mobile user moves between network agents within the same visited domain. How to distribute keys in Mobile IP Regional Registration is still an open issue and adopting AAA infrastructure may be a suitable solution. However, in the literature, no work has a sound analytical study on Regional Registration in Mobile IP network with AAA infrastructure. In this paper, we develop a complete analytical model to investigate handover delay of Mobile IP network with and without Regional Registration. The accuracy of this model is validated by the developed simulations. From the proposed model, this paper precisely justifies performance improvement of Regional Registration in Mobile IP network.

3GPP 33.246 proposes key management mechanism (KMM) to distribute security keys for universal mobile telecommunications system (UMTS) multimedia broadcast and multicast service (MBMS). KMM introduces extra communication overhead to UMTS. The previous study, key-tree scheme (KTS), resolves this issue for the IP multicast network. However, this scheme may not be so efficient while applied in UMTS MBMS due to lots of storage space and heavy multicast traffic introduced, which may decrease the QoS of UMTS MBMS. In this paper, we propose a more efficient scheme, hash function scheme (HFS), to release both storage and communication overhead for KMM in UMTS MBMS. We first modify the KTS applied in the UMTS MBMS and then detail the execution of HFS, which is proven to be correct. We conduct an analytical model and simulation experiments to compare the performance between the UMTS KMM with KTS and with HFS. Our study shows that the proposed HFS can reduce both communication and storage overhead without damaging QoS of UMTS MBMS.

3GPP 33.246 proposes key management mechanism (KMM) to distribute security keys for universal mobile telecommunications system (UMTS) multimedia broadcast and multicast service (MBMS). KMM introduces extra communication overhead to UMTS. The previous study, key-tree scheme (KTS), resolves this issue for the IP multicast network. However, this scheme may not be so efficient while applied in UMTS MBMS due to lots of storage space and heavy multicast traffic introduced, which may decrease the QoS of UMTS MBMS. In this paper, we propose a more efficient scheme, Hash function scheme (HFS), to release both storage and communication overhead for KMM in UMTS MBMS. In this paper, we first modify the KTS to be applied in UMTS MBMS. Then we detail HFS. We prove the correctness of HFS. Our study shows that the proposed HFS can reduce both communication and storage overhead without damaging QoS of UMTS MBMS.

The IEEE 802.16 standard proposes the Media Access Control (MAC) protocol for the Wireless Metropolitan Area Network (WMAN). Two transmission modes are defined in the IEEE 802.16, including Point-to-Multipoint (PMP) mode and mesh mode. In the 802.16 mesh mode, allocation of minislots can be handled by the centralized and distributed scheduling mechanisms. This paper proposes the Combined Distributed and Centralized (CDC) scheme to combine the distributed scheduling and centralized scheduling mechanisms so that the minislot allocation can be more flexible, and the utilization is increased. Two scheduling algorithms, Round Robin (RR) and Greedy, are proposed as the baseline algorithms for the centralized scheduling mechanism. We conduct simulation experiments to investigate the performance of the CDC scheme with the RR and Greedy algorithms. Our study indicates that with CDC scheme, the minislot utilization can be significantly increased.