Large language models (LLMs) are increasingly used as evaluators in natural language generation tasks, offering advantages in scalability and interpretability over traditional evaluation methods.However, existing LLMbased evaluations often suffer from biases and misalignment, particularly in domain-specific tasks, due to limited functional understanding and knowledge gaps.To address these challenges, we first investigate the relationship between an LLM-based evaluator's familiarity with the target task and its evaluation performance.We then introduce the Co-Eval framework, which leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLMbased evaluation.Experimental results on both general and domain-specific tasks demonstrate that Co-Eval reduces biases, achieving up to a 0.4903 reduction in self-preference bias, and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation.

Language-guided object recognition in remote sensing imagery is crucial for large-scale mapping and automated data annotation. However, existing open-vocabulary and visual grounding methods rely on explicit category cues, limiting their ability to handle complex or implicit queries that require advanced reasoning. To address this issue, we introduce a new suite of tasks, including Instruction-Oriented Object Counting, Detection, and Segmentation (InstructCDS), covering open-vocabulary, open-ended, and open-subclass scenarios. We further present EarthInstruct, the first InstructCDS benchmark for earth observation. It is constructed from two diverse remote sensing datasets with varying spatial resolutions and annotation rules across 20 categories, necessitating models to interpret dataset-specific instructions. Given the scarcity of semantically rich labeled data in remote sensing, we propose InstructSAM, a training-free framework for instruction-driven object recognition. InstructSAM leverages large vision-language models to interpret user instructions and estimate object counts, employs SAM2 for mask proposal, and formulates mask-label assignment as a binary integer programming problem. By integrating semantic similarity with counting constraints, InstructSAM efficiently assigns categories to predicted masks without relying on confidence thresholds. Experiments demonstrate that InstructSAM matches or surpasses specialized baselines across multiple tasks while maintaining near-constant inference time regardless of object count, reducing output tokens by 89\% and overall runtime by over 32\% compared to direct generation approaches. We believe the contributions of the proposed tasks, benchmark, and effective approach will advance future research in developing versatile object recognition systems. The code is available at https://VoyagerXvoyagerx.github.io/InstructSAM.

ABSTRACT In the current context of the extensive use of deep neural networks, it has been observed that neural network models are vulnerable to adversarial perturbations, which may lead to unexpected results. In this paper, we introduce an Adversarial Purification Model rooted in latent representation compression, aimed at enhancing the robustness of deep learning models. Initially, we employ an encoder‐decoder architecture inspired by the U‐net to extract features from input samples. Subsequently, these features undergo a process of information compression to remove adversarial perturbations from the latent space. To counteract the model's tendency to overly focus on fine‐grained details of input samples, resulting in ineffective adversarial sample purification, an early freezing mechanism is introduced during the encoder training process. We tested our model's ability to purify adversarial samples generated from the CIFAR‐10, CIFAR‐100, and ImageNet datasets using various methods. These samples were then used to test ResNet, an image recognition classifiers. Our experiments covered different resolutions and attack types to fully assess LRCM's effectiveness against adversarial attacks. We also compared LRCM with other defence strategies, demonstrating its strong defensive capabilities.

Most researches on indoor target trajectory tracking using millimeter-wave radar have faced challenges such as interference from multipath effects, which led to corrupted point clouds, and large tracking errors in multi-target scenarios. This study aims to improve the accuracy of human multiple-object tracking, addressing the practical challenges of target trajectory tracking in indoor environments. We proposed an extended Distributed Radar Multi-Target Tracking (DRMTrack) framework that enabled the fusion of point clouds from multiple radar nodes. Additionally, by exploiting the spatial distribution characteristics of the target point clouds for clustering, tracking filtering and integrating historical data for tracking association, the framework enhances multiple-object trajectory tracking performance. Experimental results demonstrate that for various trajectory paths, the minimum position tracking error for multi-target is 5.2 cm. In the five-target tracking scenario, the 90th percentile tracking error is 18.8 cm, representing a 27.7% improvement in accuracy compared to single-radar tracking. The DRMTrack system effectively reduces interference from clutter point clouds, enhances multiple-object tracking precision, and supports real-time computation. This system is suitable for motion monitoring in indoor environments such as homes and hospital rooms, effectively fulfilling the needs of real-time, non-contact health management.

New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge.Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty with 14,041 questions and Xiezhi-Interdiscipline with 10,746 questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. All the evaluation code and data are open sourced in https://github.com/MikeGu721/XiezhiBenchmark

Segment Anything Model (SAM) has revolutionized the way of segmentation due to its remarkable capacity for generalized segmentation. However, SAM’s performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM’s performance on sonar images. Specifically, we evaluate SAM with various settings on sonar images. Moreover, we fine-tune SAM for sonar images using effective methods both with prompts and for semantic segmentation. The experimental results reveal a substantial enhancement in the performance of the fine-tuned SAM, increasing from 0.24 to 0.75 in mIoU. This underscores the promising potential of SAM for sonar image segmentation applications. Additionally, even when only 2 out of the 11 categories are utilized for training, the model with box prompt sustains an mIoU of 0.69, showcasing its outstanding capability for general segmentation in sonar images. The code is available at https://github.com/wangsssky/SonarSAM.

Ternary Content Addressable Memory (TCAM) enables fast lookup and is widely used by routers and switches to support policy-based forwarding. Due to high cost and small capacity, only a small subset of important rules can be cached in TCAM, so determining it is critical to increasing the hit ratio. This is more challenging than traditional caching problems because of complicated rule dependency relationships. Existing works are based on heuristics and they don’t work well under all practical scenarios. Worse still, the lack of fundamental understanding of the design space, complexity, and optimality makes all explorations in mystery. In this paper, we use a modeling-based method to formulate the problem, prove its complexity, and propose DROPS, a dynamic rule caching framework with a much higher hit ratio. In particular, we deduce the rule selection problem into a multi-dimensional rule space transformation problem. Thus, we are no longer limited by using the intrinsic rules; rather, we can transform original rules into “new rules” equivalently without rule dependency. We design non-trivial rule placement and update algorithms and implement them in programmable switches. In the experimental evaluation, we show that our method outperforms all existing methods.

Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video. This technology could be beneficial in multiple medical fields, such as diagnosis and privacy protection. Previous studies on face animation often relied on a single source image to generate an output video. With a significant pose difference between the source image and the driving frame, the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map. In this study, we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues. We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video. We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame. The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods. It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.

Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis. However, its computational efficiency is heavily limited by theslow convergencerate (a.k.a. long burn-in period). To address this issue, we propose a common neighbor aware random walk framework called CNARW, which leverages weighted walking by differentiating the next-hop candidate nodes to speed up the convergence. Specifically, CNARW takes into consideration the common neighbors between previously visited nodes and next-hop candidate nodes in each walking step. Based on CNARW, we further develop two efficient “unbiased sampling” schemes, and we also design two variant algorithms which can reduce sampling cost and speed up the convergence. Experimental results on real-world network datasets show that our approach converges remarkably faster than the state-of-the-art random walk sampling algorithms; and to achieve the same estimation accuracy, our approach reduces the query cost significantly. Last, we use two case studies to demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.

As applications are often allocated more memory than they actually used to cope with peak memory demand due to the workload dynamics in virtualized systems running multiple virtual machines, memory adjustment by reclaiming inactive memory in virtual machines is an effective way to enable memory overcommitment so as to reduce the cost. However, existing schemes are designed based on one-shot adjustment, which may reclaim a large size in a single operation, and they are unaware of both memory access dynamics and memory sensitivity of different applications, so they usually result in excessive reclamation and lead to severe performance slowdown. To address this issue, we propose PMA, a progressive memory adjustment scheme that takes into consideration both memory access dynamics and memory sensitivity and leverages virtual machine performance feedback to progressively reclaim inactive memory to avoid performance slowdown. Besides, PMA is designed based on ballooning (i.e., the balloon driver) so it preserves the good isolation between host and virtual machines for full virtualization systems. We also implement a prototype in host user space, and experiments show that PMA effectively limits the performance drop of memory overcommitment (e.g., the performance drop of every virtual machine is limited within 10% for up to 33% memory overcommitment), which is already very close to the optimal case of having enough memory, so PMA is efficient to enable memory overcommitment with performance guarantee in full virtualization systems.

For NFV systems, the key design space includes the function chaining for network requests and the resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of real-time decision making with limited information. Furthermore, the benefits of predictive scheduling to NFV systems still remain unexplored. In this article, we propose POSCARS, an efficient predictive and online service chaining and resource scheduling scheme that achieves tunable trade-offs among various system metrics with stability guarantee. Through a careful choice of granularity in system modeling, we acquire a better understanding of the trade-offs in our design space. By a non-trivial transformation, we decouple the complex optimization problem into a series of online sub-problems to achieve the optimality with only limited information. By employing randomized load balancing techniques, we propose three variants of POSCARS to reduce the overheads of decision making. Theoretical analysis and simulations show that POSCARS and its variants require only mild-value of future information to achieve near-optimal system cost with an ultra-low request response time.

For NFV systems, the key design space includes the function chaining for network requests and resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of decision making with limited information. Besides, the limits and benefits of predictive scheduling to NFV systems still remain unexplored. In this paper, we propose POSCARS, an efficient, distributed, and online algorithm that achieves a tunable trade-off between various system metrics with stability guarantee, while exploiting the power of predictive scheduling. Using randomized load balancing techniques, we propose three variants of POSCARS to further reduce sampling overheads. Theoretical analysis and trace-driven simulations show that POSCARS and its variants require only mild-value of future information to achieve a near- optimal average system cost while effectively shortening the average request response time.

Various differentiated pricing schemes have been proposed for the Internet market. Aiming at replacing the traditional single-class pricing for better welfare, yet, researchers have shown that existing schemes can bring only marginal profit gain for the ISPs. In this article, we point out that a proper form of differentiated pricing for the Internet should not only consider congestion, but more importantly, it should provide application specific treatment to data delivery. Formally, we propose an “application-driven pricing” approach, where an ISP offers a number of service classes in terms of a guaranteed quality of service and announces a unit usage price for each class, and content providers are free to choose which class to use depending on the requirement of their applications. Unlike previous studies, we point out that the revenue gain of multi-class pricing under our scheme can be significant. This is because we capture important aspects of application heterogeneity and take the quality of service and price as control knobs. We identify key factors that impact the revenue gain and reveal fundamental understandings on when and why an application-driven multi-class pricing can significantly increase the revenue of ISPs.

In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items in the bundle. In this work, our aim is to understand the underlying dynamics of bundling, particularly what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effects . We formulate mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and provide an optimization framework to determine the optimal sale strategy for a service provider. We analyze the impact of various factors on the profitability of bundling, including the network effects, operating costs, and variance and correlation of customers’ valuations toward these services. We show that bundling is more profitable when the variance of customers’ valuations and the operational cost of the services are small. In addition, a positive network effect and a negative correlation among customers’ valuation on services increase the profitability of bundling, whereas the heterogeneity of services and the asymmetry of operating costs reduce its advantage.

Network function virtualization (NFV) can significantly reduce the operation cost and speed up the deployment for network services to markets. Under NFV, a network service is composed by a chain of ordered virtual functions, or we call a "network function chain." A fundamental question is when given a number of network function chains, on which servers should we place these functions and how should we form a chain on these functions? This is challenging due to the intricate dependency relationship of functions and the intrinsic complex nature of the optimization. In this paper, we formulate the function placement and chaining problem as an integer optimization, where each variable is an indicator whether one service chain can be deployed on a configuration (or a possible function placement of a service chain). While this problem is generally NP-hard, our contribution is to show that it can be mapped to an exponential number of min-cost flow problems. Instead of solving all the min-cost problems, one can select a small number of mapped min-cost problems, which are likely to have a low cost. To achieve this, we relax the integer problem into a fractional linear problem, and theoretically prove that the fractional solutions possess some desirable properties, i.e., the number and the utilization of selected configurations can be upper and lower bounded, respectively. Based on such properties, we determine some "good" configurations selected from the fractional solution and determine the mapped min-cost flow problem, and this helps us to develop efficient algorithms for network function placement and chaining. Via extensive simulations, we show that our algorithms significantly outperform state-of-art algorithms and achieve near-optimal performance.

The current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide end-to-end (E2E) quality-of-service (QoS) guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first hop ASes via service level agreements (SLAs). But beyond the first hop, QoS along the path from source to destination AS is not within the source AS's control regime. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as "brokers" to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with B-dominating path Guarantee (MCBG) problem, which we prove to be NP-hard. To address this problem, we design a (1-e-1/4)-approximation algorithm and also an efficient heuristic algorithm when considering additional constraints (e.g., path length). Based on the current Internet topology, we discover a "3540-alliance" subset (accounting only 6.8%) of 52,079 ASes/IXPs, which can provide high QoS guarantees for 99.29% E2E connections.

Product bundling is widely adopted for information goods and online services because it can increase profit for companies. For example, cable companies often bundle Internet access and video streaming services together. However, it is challenging to obtain an optimal bundling strategy, not only because it is computationally expensive, but also that customers’ private information (e.g., valuations for products) is needed for the decision, and we need to infer it from accessible datasets. As customers’ purchasing data are getting richer due to the popularity of online shopping, doors are open for us to infer this information. This paper aims to address: (1) How to infer customers’ valuations from the purchasing data? (2) How to determine the optimal product bundle to maximize the profit? We first formulate a profit maximization framework to select the optimal bundle set. We show that finding the optimal bundle set is NPhard. We then identify key factors that impact the profitability of product bundling. These findings give us insights to develop a computationally efficient algorithm to approximate the optimal product bundle with a provable performance guarantee. To obtain the input of the bundling algorithm, we infer the distribution of customers’ valuations from their purchasing data, based on which we run our bundling algorithm and conduct experiments on an Amazon co-purchasing dataset. We extensively evaluate the accuracy of our inference and the bundling algorithm. Our results reveal conditions under which bundling is highly profitable and provide insights to guide the deployment of product bundling.

A fundamental class of matrix optimization problems that arise in many areas of science and engineering is that of quadratic optimization with orthogonality constraints. Such problems can be solved using line-search methods on the Stiefel manifold, which are known to converge globally under mild conditions. To determine the convergence rates of these methods, we give an explicit estimate of the exponent in a Lojasiewicz inequality for the (non-convex) set of critical points of the aforementioned class of problems. This not only allows us to establish the linear convergence of a large class of line-search methods but also answers an important and intriguing problem in mathematical analysis and numerical optimization. A key step in our proof is to establish a local error bound for the set of critical points, which may be of independent interest.

Mobile data demand is increasing tremendously, and thus new pricing models are in urgent need. One promising new pricing scheme is the “sponsored data plan”, i.e., end users may enjoy free access to contents from certain content providers, while these content providers will pay ISPs for corresponding traffic consumed by end users. Proven a number of advantages, the sponsored data plan is still in its infancy. In this paper, we explore some potential of further development of this plan. We extend the design space and propose the idea of time-dependent, sponsoring, i.e, content providers can decide when to sponsor how much fractions of traffic. The key intuition is by migrating some traffic consumption from peak to valley times, bandwidth resources can be better utilized. We formulate a game model to study the interactions between the ISP, CPs and users, and derive the optimal sponsoring fractions over various times under this new plan. We show that all parties involved can benefit from this plan, and social welfare increases. We believe our proposal, i.e., time-dependent sponsoring, provides important insights to potential development of the sponsored data plan.

In this paper, we investigate the impact of information-theoretic secrecy constraint on the capacity and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume n legitimate nodes move according to the fast i.i.d. mobility pattern and each desires to communicate with one randomly selected destination node. There are also nνstatic eavesdroppers located uniformly in the network and we assume the number of eavesdroppers is much larger than that of legitimate nodes, i.e., ν > 1. We propose a novel simple secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model under a few technical assumptions. Based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint D, we find that the optimal secrecy throughput capacity is ~Θ(W((D/n))(2/3)), where W is the data rate of each link. We observe that: 1) the capacity-delay tradeoff is independent of the number of eavesdroppers, which indicates that adding more eavesdroppers will not degenerate the performance of the legitimate network as long as ν > 1; 2) the capacity-delay tradeoff of our paper outperforms the previous result Θ((1/nψe)) in , where ψe=nν-1=ω(1) is the density of the eavesdroppers. Throughout this paper, for functions f(n) and g(n), we denote f(n)=o(g(n)) if limn→∞(f(n)/g(n))=0; f(n)=ω(g(n)) if g(n)=o(f(n)); f(n)=O(g(n)) if there is a positive constant c such that f(n) ≤ cg(n) for sufficiently large n; f(n)=Ω(g(n)) if g(n)=O(f(n)); f(n)=Θ(g(n)) if both f(n)=O(g(n)) and f(n)=Ω(g(n)) hold. Besides, the order notation ~Θ omits the polylogarithmic factors for better readability.

This paper investigates the multicast capacity of static wireless social networks. We adopt the two-layer network model, which includes the social layer and the networking layer. In the social layer, the social group size of each source node is modeled as power-law distribution. Moreover, the rank-based model is utilized to describe the relation between source and destinations in the networking layer. Based on the two-layer network model, the probability density function (PDF) of the destination positions is analyzed and verified by numerical simulation, which is different from the traditional ad hoc networks. According to the PDF, the bound of the network capacity is derived, and we propose a Euclidean minimum-spanning-tree-based transmission scheme, which is proved to achieve the order of capacity bound for most cases. Finally, the capacity of social networks is compared to the traditional multicast ad hoc networks, which indicates that the capacity scaling performs better in social networks than traditional ones. To our best knowledge, this is the first work of analyzing the impact on the capacity of social relation and group size in multicast ad hoc networks for the rank-based model.

Comparing with the unicast technology, multiple flows from the same source in multicast scenario can be aggregated even if their destinations are different. This paper evaluates such distinction by the multicast gain on per-node capacity and delay, which are defined as the per-node capacity and delay ratios between multi-unicast and multicast ( m destinations for each multicast session). Particularly, the restricted mobility model is proposed, which is a representative mobility model characterizing a class of mobility models with different average moving speeds. The theoretical analysis of this model indicates that the mobility significantly decreases the multicast gain on per-node capacity and delay, though the per-node capacity of both unicast and multicast can be enhanced by mobility. This finding suggests that mobility weakens the distinction between multicast and unicast. Finally, a general framework of multicast study is constituted by analyzing the upper-bound ( Θ(m)), the lower-bound ( Θ(1)) and the main determinants of the multicast gain on both per-node capacity and delay regardless of mobility model.

In this paper, we derive the critical transmission range, i.e., the smallest transmission distance of nodes such that wireless network can be connected, in large-scale clustered wireless networks. Contrary to most previous literature on independent and homogeneous mobility of nodes, we consider general settings with inhomogeneous node distribution and correlated mobility. In particular, we consider three network states based on the degree of correlation among nodes, i.e., cluster-sparse state (strong correlations), cluster-dense state (weak correlations), and cluster-transitional state (medium correlations). Under each state, we focus on the following problems: 1) how to place cluster-head nodes to minimize the critical transmission range and 2) what is the corresponding minimum critical transmission range. We derive the optimal distribution of cluster-head nodes that minimizes the critical transmission range, and show that the inhomogeneous distribution of mobile nodes leads to a smaller critical transmission range.

This paper studies the impacts of social relationships and inhomogeneous node distribution on wireless network performance. Motivated by the social characteristic that makes nodes more likely to communicate with nearby nodes, we present a model that captures the small-world property, power-law distribution, and multi-clustering topology of wireless networks. We compute the average traffic distance to derive the optimal per-node capacity, which is then analyzed from the social perspective. Based on the communication pattern, we introduce the quasi-strong ties and quasi-weak ties in order to discover how system parameters control the amount of network flows with different strengths of social relationships. Moreover, we propose an indicator of information propagation speed (IIPS), and show that there is a tradeoff between the IIPS and the per-node capacity of overall nodes.

Abstract In this paper, we focus on maximizing the influence of online social networks (OSNs). Particularly, we try to answer how to select proper information initiators such that information can propagate as widely as possible. We stress our attention on the susceptible‐infected model, a type of epidemic models, to describe the process of information diffusion. In general, OSNs can be classified into two categories, Facebook‐like OSNs and Twitter‐like OSNs. The former ones require bidirectional connections, while the latter do not, so we use the undirected unweighted graph and directed unweighted graph to describe them, respectively. We also pay additional attention to the nonidentity of the link probability on information transmission and build the weight graph, which can also cover both the two types of OSNs. In order to determine values of weight graph's weights, we introduce a learning method to obtain useful factors from raw data for assessing the true link probability on information transmission. Based on spectral analysis within the three graphs, our investigations on the information diffusion show that the spectral radius of the graph adjacency matrix can reflect the capability of information propagation, according to which we could determine effective initiators. We conduct our simulations on real OSNs. Experimental results show that our approach could effectively discover the initiators that spread information widely. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Device‐to‐device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D‐enabled cellular networks, interference among spectrum‐sharing links is severer than that in traditional cellular networks, which motivates the adoption of interference cancelation (IC) techniques at the receivers. However, to date, how IC can affect the performance of D2D‐enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of two IC methods, that is, unconditional IC and successive IC, in large‐scale D2D‐enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose an approach of stochastic equivalence of the interference, which converts the two‐tier interference (interference from the cellular tier and D2D tier) to an equivalent single‐tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of both cellular uplinks and D2D links in the networks where unconditional IC and successive IC are respectively applied. We demonstrate how these IC methods affect the network performance using both analytical and numerical results. Copyright © 2016 John Wiley & Sons, Ltd.

Multiple Radio Access Technologies (Multi-RAT) make it possible to exploit the advantages of Heterogeneous networks (HetNets) resulting from a joint consideration of the networks as a whole. Users in HetNets can be served with a proper RAT to maximize the system-level utility. Especially, when user dynamics are considered, they can stay in a RAT or handover to another RAT with a transition probability depending on system configuration. By formulating these dynamics as a Markov chain model, the system-level utility is defined as a combinatorial object function. However, the combinatorial optimization is NP-hard, thus we can only use exhaustive search to obtain the optimum solution, which comes up with high computational complexity and is not practical. To this end, we use Markov approximation to obtain the approximate utility and transition probability. In addition, we propose a Count Down and Select (CDS) algorithm to implement the RAT selection. Numerical results validate the convergence of Markov approximation and the effectiveness of the CDS algorithm.

The minimum-length multicast tree can achieve efficient multicast transmissions and can be formulated as a Steiner Tree. Its construction is non-trivial and has been proven to be NP-hard. In this paper, we combine the design wisdoms in the minimum spanning tree and the shortest path, and propose a new distributed algorithm for constructing an approximate Steiner Tree, particularly applicable to dynamic wireless networks without centralized control. We rigorously prove the performance bounds of our algorithm in terms of tree length, running time and energy consumption. Let m be the multicast group size and n be the network size. We theoretically show that the ratio of our tree length to the minimum value is upper bounded by 2(1 + δ)/√3 (where δ can be any positive value). Simulation results show that this ratio is in fact very close to 1. We also prove that the running time is O( √(mn log m log3n)). The energy consumption is evaluated in terms of message complexity, and is upper bounded by O(n logm). In all, our algorithm achieves the near-optimal tree length, as well as the shortest running time and the lowest message complexity among all solutions we are aware of. We believe our algorithm provides a significant improvement in designing practical routing policies in wireless networks.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, the interference among spectrum-sharing links is more severer than that in traditional cellular networks, which motivates the adoption of interference cancellation techniques such as successive interference cancellation (SIC) at the receivers. However, to date, how SIC can affect the performance of D2D-enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of SIC in large-scale D2D-enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose the approach of stochastic equivalence of the interference, which converts the two-tier interference (interference from both the cellular tier and D2D tier) to an equivalent single-tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of cellular uplinks and D2D links with infinite and finite SIC capabilities respectively. We demonstrate how SIC affects the performance of large-scale D2D-enabled cellular networks by both analytical and numerical results.

With the pervasion of mobile devices, crowdsourcing based received signal strength (RSS) fingerprint collection method has drawn much attention to facilitate the indoor localization since it is effective and requires no pre-deployment. However, in large open indoor environment like museums and exhibition centres, RSS measurement points cannot be collocated densely, which degrades localization accuracy. This paper focuses on measurement point collocation in different cases and their effects on localization accuracy. We first study two simple preliminary cases under assumption that users are uniformly distributed: when measurement points are collocated regularly, we propose a collocation pattern which is most beneficial to localization accuracy; when measurement points are collocated randomly, we prove that localization accuracy is limited by a tight bound. Under the general case that users are distributed asymmetrically, we show the best allocation scheme of measurement points: measurement point density ρ is proportional to (cμ)2/3in every part of the region, where μ is user density and c is a constant determined by the collocation pattern. We also give some guidelines on collocation choice and perform extensive simulations to validate our assumptions and results.

Multicast tree is a key structure for data dissemination from one source to multiple receivers in wireless networks. Minimum length multicast tree can be modeled as the Steiner Tree Problem, and is proven to be NP-hard. In this paper, we explore how to efficiently generate minimum length multicast trees in wireless sensor networks (WSNs), where only limited knowledge of network topology is available at each node. We design and analyze a simple and distributed algorithm, which we call Toward Source Tree (TST), to build multicast trees in WSNs. We show three metrics of TST algorithm, i.e., running time, tree length and energy efficiency. We prove that its running time is O(√nlog n), the best among all existing solutions to our best knowledge. We prove that TST tree length is in the same order as Steiner tree, give a theoretical upper bound and use simulations to show the ratio between them is only 1.114 when nodes are uniformly distributed. We evaluate energy efficiency in terms of the number of forwarding nodes in multicast trees, and prove that it is order-optimal. We give an efficient way to construct multicast tree in support of transmission of voluminous data.

Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between Internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when accessing contents from a particular CP, end users do not need to pay for that volume of traffic consumed, but the CP will sponsor for this data consumption. In this paper, our goal is to understand the rationale behind this new pricing model, as well as its impacts to the wireless data market, in particular, who will benefit and who will be hurt from this scheme. We build a two-class service model to analyze the consumers' traffic demand under the sponsored data plan with consideration of QoS. We use a two-stage Stackelberg game to characterize the interaction between CPs and the ISP and reveal a number of important findings. Our conclusions include: 1) When the ISP's capacity is sufficient, the sponsored data plan benefits consumers and CPs in the short run, but the ISP does not have incentives to further improve its service in the long run. 2) When ISP's capacity is insufficient, the ISP and end users may achieve a win- win trade, while the ISP and CPs always compete for the revenue. 3) The sponsored data plan may enlarge the un- balance in revenue distribution between different CPs; CPs with higher unit income and poorer technology support are more likely to prefer the sponsored data plan.

In this paper, we study the throughput and delay scaling laws for cognitive radio network with static primary users and heterogeneous mobile secondary users coexist in the unit planar region for both without base-station case and with base-station case. The primary network consists of $n$ randomly and uniformly distributed static primary users with higher priority to access the spectrum. The secondary network consists of $m=(h+1)n^{1+\epsilon}$ heterogeneous mobile secondary users with $h=O(\log n)$ We use standard asymptotic notations in our paper. Consider two nonnegative function $f(\cdot)$ and $g(\cdot)$ : (1) $f(n)=o(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/\break g(n)= 0$ . (2) $f(n)=O(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) . (3) $f(n)=\omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n)=\infty$ . (4) $f(n)=\Omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) > 0$ . (5) $f(n)=\Theta(g(n))$ means $f(n)=O(g(n))$ and $g(n)=O(f(n))$ . (6) $f(n)=\mathtilde{\Theta}(g(n))$ means that $f(n)=\Theta(g(n))$ when poly-logarithmic factors are ignored.

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

Online Social Network becomes the most prevalent platform for exchanging information between users, maintaining friendships online. As is well-known to us, however, some friendships even those intimate ones might vanish. Therefore, precisely modeling and predicting state of each online relationship is worthwhile in many respects. For social communication services such modeling permits new and novel online services. In addition, constructing this model might enlighten us in exploiting information spreading pattern in online social network. In this paper, we propose a model in determining a probability distribution which describes the ‘surviving time’ of each friendships by applying one commonly used method in sociology, survival analysis. We discuss a series of social explanatory variables that highly affect this probability distribution. Moreover, methods in the moving average process are devoted to determining the appropriate parameter in survival model. Furthermore, to avoid the high computational complexity in kernel learning we impose sparsity in our model. Finally, with the experiments on real data, the proposed survival model is proven to be of high accuracy, and thus of great potential for further applications.

Auction is often applied in cognitive radio networks due to its efficiency and fairness properties. An important issue in designing an auction mechanism is how to utilize the limited spectrum resource in an efficient manner. In order to achieve this goal, we propose a predictive double spectrum auction model in this paper. Our auction model first obtains the bidding range from statistical analysis, and then separates the interval into independent states and employees a Markovian prediction based algorithm to generate guidelines for the bidding range of primary and secondary users, respectively. Comparing with existing approaches, our proposed auction model is more efficient in spectrum utilization and satisfies the economic properties. Extensive simulation results show that our work achieves an utilization ratio up to 91%.

In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of its products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items. In this paper, our goal is to understand the underlying dynamics of bundling, in particular, what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effect. We provide mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and formulate an optimization framework to determine the optimal sale strategy for the service provider. We analyze the impact of the key factors, including the network effects and operating costs, on the profitability of bundling. We show that bundling is more profitable than separate sale in most cases; however, the heterogeneity of services and the asymmetry of operating costs reduce the advantage of bundling. These findings provide important insights in designing proper sale strategies for online services.

With the advances of bandwidth-intensive mobile devices, we see severe congestion problems in wireless data networks. Recently, research emerges to solve this problem from a pricing point of view. Time dependent pricing has been introduced, and initial investigations have shown its advantages over the conventional time independent pricing. Nevertheless, much is unknown in how a practical and effective time dependent pricing scheme can be designed. In this paper, we explore the design space of time dependent pricing. In particular, we focus on a number of schemes, e.g., the usage-based scheme, the flat-rate scheme, and a mixture of them which we called a cap scheme. Our findings include: 1) the ISP obtains a higher profit with usage-based (or flat-rate) scheme if the capacity is insufficient (or sufficient); 2) the usage-based scheme usually achieves a higher consumer surplus and more efficient traffic utilization than the flat-rate scheme; and 3) the cap scheme is strongly preferred by the ISP to further increase its revenue. We believe our findings provide important insights for ISPs to design effective pricing schemes.

Increasingly, more spectrum bands are utilized for unlicensed use in wireless cognitive networks. It is important to study how information-theoretic secrecy capacity is affected in large-scale cognitive networks. We consider two scenarios: (1) non-colluding case, where eavesdroppers decode messages individually. In this case, we propose a new secure protocol model to analyze the transmission opportunities of secondary nodes. We show that the secrecy capacity of the primary network is not affected, while the secondary network can achieve the same performance as a standalone network in the order sense. Since our analysis is general as we only make a few relaxed assumptions on both networks, the conclusions hold when both networks are classic static networks, networks with i.i.d mobility, multicast networks etc. (2) colluding case where eavesdroppers can collude to decode a message. In that case, we show that the lower bound of per-node secrecy capacity of the primary network is Ω(1/√n φe-2/α-1(n)) when the eavesdropper density is φe(n)=Ω(log2n). Interestingly the existence of secondary nodes increases the secrecy capacity of the primary network.

Peer-to-peer (P2P) systems rely on peers' cooperation to provide a more robust and scalable service as compared to the traditional client-server architecture. However, the peers might be selfish in nature-they would like to receive services from others, but would not like to contribute their own resources by default. To conquer this problem, proper incentive schemes are needed so as to stimulate the peers' contributions. In particular, in P2P video-on-demand (VoD) systems, peers need to distributively cache the proper videos so as to mutually upload and help each other to acquire the required data. Content providers of P2P-VoD services want to incentivize peers to do so and alleviate the workload of the content server. In this paper, we design a practical mechanism to incentivize distributed caching in such systems, under which the peers are rewarded based on the popularity of the video they cache. We characterize the impact of this incentive scheme on peers' caching behaviors. In particular, we formulate an optimization framework to decide the optimal reward price for each video so as to keep enough replicas and minimize the content provider's operational cost. We first derive close form solutions in an asymptotic system, and then extend our results to be adaptive to various practical issues. Via extensive simulations, we validate the effectiveness and efficiency of our incentive scheme.

No abstract available.

P2P-Video-on-Demand (P2P-VoD) is a popular Internet service which aims to provide a scalable and high-quality service to users. At the same time, content providers of P2P-VoD services also need to make sure that the service is operated with a manageable operating cost. Given the volume-based charging model by ISPs, P2P-VoD content providers would like to reduce peers' access to the content server so as to reduce the operating cost. In this paper, we address an important open problem: what is the “optimal replication ratio” in a P2P-VoD system such that peers will receive service from each other and at the same time, reduce the access to the content server? We address two fundamental issues: 1) what is the optimal replication ratio of a movie if we know its popularity, and 2) how to achieve these optimal ratios in a distributed and dynamic fashion. We first formally show how movie popularities can impact server's workload, and formulate the video replication as an optimization problem. We show that the conventional wisdom of using the proportional replication strategy is “suboptimal,” and expand the design space to both “passive replacement policy” and “active push policy” to achieve the optimal replication ratios. We consider practical implementation issues, evaluate the performance of P2P-VoD systems and show how to greatly reduce server's workload and improve streaming quality via our distributed algorithms.

Content providers of P2P-Video-on-Demand (P2P-VoD) services aim to provide a high quality, scalable service to users, and at the same time, operate the system with a manageable operating cost. Given the volume-based charging model by ISPs, it is to the best interest of the P2P-VoD content providers to reduce peers' access to the content server so as to reduce the operating cost. In this paper, we address an important open problem: what is the “optimal replication ratio” in a P2P-VoD system such that peers will receive service from each other and at the same time, reduce the traffic to the content server. We address two fundamental problems: (1) what is the optimal replication ratio of a movie given its popularity, and (2) how to achieve the optimal ratios in a distributed and dynamic fashion. We formally show how movie popularities can impact server's workload, and formulate the video replication as an optimization problem. We show that the conventional wisdom of using the proportional replication strategy is non-optimal, and expand the design space to both passive replacement policy and active push policy to achieve the optimal replication ratios. We consider practical implementation issues, evaluate the performance of P2P-VoD systems and show that our algorithms can greatly reduce server's workload and improve streaming quality.