Search

Current communication networks are facing more and more threats from large-scale regional damages, such as natural disasters (e.g., earthquake or tornado) and physical attacks (e.g., electromagnetic pulse attack or dragging anchors). Recently, several region failure models have been proposed to evaluate the impact of such geographically correlated failures on communication networks. These works mainly adopt a kind of “deterministic” models, where network components within the affected area would fail simultaneously. Such failure models simplify the analysis but may fail to reflect some important behaviors of attacks and thus cause significant over- or under-estimation of region failures. To emulate the impact of realistic catastrophe events such as earthquake and tornado, this paper introduces two probabilistic failure models: 1) a concentric circle model and 2) a line segment model. In the probabilistic models, both the location and effect of the damage are treated as random events. The failure may randomly incur on the entire network plane, and the failure probability of a device depends on many factors, e.g., link length and distance to the damage center. We develop an efficient grid partition-based scheme to estimate the network vulnerability. Based on the grid partition scheme, we further develop a sampling scheme to significantly reduce the computation cost. The probability model helps us more deeply understand the network behaviors under region failure and facilitates the design and maintenance of future highly survivable mission critical networks. [ABSTRACT FROM AUTHOR]

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing. [ABSTRACT FROM AUTHOR]

The limited battery capacity of sensor nodes has become one of the most critical impediments that stunt the deployment of wireless sensor networks (WSNs). Recent breakthroughs in wireless energy transfer and rechargeable lithium batteries provide a promising alternative to power WSNs: mobile vehicles/robots carrying high volume batteries serve as mobile chargers to periodically deliver energy to sensor nodes. In this paper, we consider how to schedule multiple mobile chargers to optimize energy usage effectiveness, such that every sensor will not run out of energy. We introduce a novel charging paradigm, collaborative mobile charging, where mobile chargers are allowed to intentionally transfer energy between themselves. To provide some intuitive insights into the problem structure, we first consider a scenario that satisfies three conditions, and propose a scheduling algorithm, PushWait, which is proven to be optimal and can cover a one-dimensional WSN of infinite length. Then, we remove the conditions one by one, investigating chargers’ scheduling in a series of scenarios ranging from the most restricted one to a general 2D WSN. Through theoretical analysis and simulations, we demonstrate the advantages of the proposed algorithms in energy usage effectiveness and charging coverage. [ABSTRACT FROM AUTHOR]

While mobility is the inherent cause of resource poverty and low connectivity in wireless environment, it also enables the opportunity for surrounding sensing, therefor providing mobilityaugmented cloud service becomes the key challenge for the emerging paradigm of Mobile Cloud Computing (MCC). This article provides an overview of the mechanisms and open issues for mobility-augmented service provisioning in MCC. We first outline the concept, system architecture, and taxonomy of research issues. Then we introduce three key mechanisms with respect to mobility augmentation, heterogeneous network convergence and mobile service provisioning. Moreover, we discuss the open challenges to reveal the future direction of MCC. [ABSTRACT FROM PUBLISHER]

In the multi-access edge computing environment, app vendors deploy their services and applications at the network edges, and edge users offload their computation tasks to edge servers. We study the user-perceived delay-aware service placement and user-allocation problem in edge environment. We model the MEC-enabled network, where the user-perceived delay consists of computing delay and transmission delay. The total cost in the offloading system is defined as the sum of service placement, edge server usage and energy consumption cost, and we need to minimize the total cost by determining the overall service-placing decision and user-allocation decision, while guaranteeing that the user-perceived delay requirement of each user is fulfilled. Our considered problem is formulated as a Mixed Integer Linear Programming problem, and we prove its NP-hardness. Due to the intractability of the considered problem, we propose a LOCal-search based algorithm for USer-perceived delay-aware service placement and user-allocation in edge environment, named LOCUS, which starts with a feasible solution and then repeatedly reduces the total cost by performing local-search steps. After that, we analyze the time complexity of LOCUS and prove that it achieves provable guaranteed performance. Finally, we compare LOCUS with other existing methods and show its good performance through experiments. [ABSTRACT FROM AUTHOR]

In cognitive radio networks (CRNs), a wireless user with primary access right on a channel (called primary user) has prioritized access to the channel and the user with secondary access right (called secondary user) can use the channel when the primary user is idle. Spectrum auction has emerged as a promising approach to address the access allocation problem in CRNs. A significant challenge in designing such auction is providing truthfulness to avoid market manipulation. In most previous work, the primary access rights on channels are pre-determined before the auction and bidders can only compete for the secondary access rights. However, a user's requirement on spectrum access rights relies on their QoS demands. Therefore, it is much desirable to allocate spectrum access rights on the basis of QoS demands as well as to exploit the resulting spatial spectrum reuse opportunities. To solve this problem, we propose TRUMP, a truthful spectrum auction mechanism, by taking into consideration both QoS demands and spectrum spatial reuse, which can drastically improve spectrum utilization. The theoretical analysis proves that TRUMP achieves truthfulness and individual rationality with polynomial-time complexity. Our extensive simulation results show that our proposals outperform previous work in terms of both social welfare and spectrum utilization. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of RFID technologies, RFID has been introduced to the applications like safety inspection and warehouse management. Conventionally a number of deployment rules are specified for these applications. This paper studies a practically important problem of rule checking over RFID tags, i.e., checking whether the specified rules are satisfied according to the RFID tags within the monitoring area. This rule checking function may need to be executed frequently over a large number of tags and therefore should be made efficient in terms of execution time. Aiming to achieve time efficiency, we respectively propose two protocols, CRCP and ECRCP. CRCP works based on collision detection, while ECRCP combines the collision detection and the logical features of the rules. Simulation results indicate that our protocols achieve much better performance than other solutions in terms of time efficiency. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 (1 + ξ) ln (d + 1) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm. [Copyright &y& Elsevier]

In recent years, OFDMA relay networks have become a key component in the 4G standards (e.g., IEEE 802.16j, 3GPP LTE-Advanced) for broadband wireless access. When numerous bidirectional flows pass through the relay stations in an OFDMA relay network that supports various interactive applications, plenty of network coding opportunities arise and can be leveraged to enhance the throughput. In this paper, we study the proportional-fair scheduling problem in the presence of network coding in OFDMA relay networks. Considering the tradeoff between performance and overhead, we propose two models, global approach (GA) and local approach (LA), under which the corresponding problems are shown both NP-hard. For the GA model, we show that it cannot be approximated within some constant factor. Hence, we propose a heuristic algorithm with low time complexity. For the LA model, we propose a theoretical polynomial time approximation scheme (PTAS), and also present a practical greedy algorithm with approximation factor of (1\over 2). Simulation results show that our algorithms can achieve significant throughput improvement over a state-of-the-art noncoding scheme. [ABSTRACT FROM AUTHOR]

With the rapid development of machine-to-machine (M2M) mobile smart terminals, M2M services can be used in a wide range of industries, including such as tele-medicine, remote meter reading and public security. Since different industries and enterprise users have different requirements for M2M specific applications, the security identity authentication of M2M mobile terminals is particularly worthy of attention. Existing methods can effectively solve the unsustainable problem of one-time verification, however they cannot address the dynamic relevance characteristics of user behavior sufficiently. Thus, the accuracy of user identity authentication needs to be further improved. In this paper, we propose a terminal identity authentication technology based on user association behavior analysis. In order to identify the abnormal login during each behavior process of authenticated user, we take largest coincident part of the user behavior sequence and short coincide into consideration. In addition, we propose Behavior Common Subsequence Sequence Similarity Algorithm based on the traditional sequence pattern of Behavior Common Subsequence(BCS). The experimental results demonstrate that the proposed method can effectively improve the accuracy of user's behavioral sequence, and prove the uniqueness of different sequences on the other hand. [ABSTRACT FROM AUTHOR]

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current job alone is insufficient in a long-term scope since some datasets would be used multiple times. Instead, optimizing the data distribution is much more efficient and could directly benefit forthcoming jobs, although it may postpone the execution of current one. Unfortunately, due to the throwaway feature of data fetcher, existing data analytics systems fail to re-distribute corresponding data out of hotspot edges after the execution of data analytics. In order to minimize the overall completion time for a sequence of jobs as well as to guarantee the performance of current one, we propose to re-distribute the data along with task offloading, and formulate corresponding $\varepsilon$ ɛ -bounded data-driven task scheduling problem over wide area network under the consideration of edge heterogeneity. We design an online schema $run$ r u n Data, which offloads proper tasks and related data via piggybacking to the datacenter based on delicately calculated probabilities. Through rigorous theoretical analysis, $run$ r u n Data is proved concentrated on its optimum with high probability. We implement $run$ r u n Data based on Spark and HDFS. Both testbed results and trace-driven simulations show that $run$ r u n Data re-distributes proper data via piggybacking and achieves up to 37 percent reduction on average response time compared with state-of-the-art schemas. [ABSTRACT FROM AUTHOR]

The booming of Convolutional Neural Networks (CNNs) has empowered lots of computer-vision applications. Due to its stringent requirement for computing resources, substantial research has been conducted on how to optimize its deployment and execution on resource-constrained devices. However, previous works have several weaknesses, including limited support for various CNN structures, fixed scheduling strategies, overlapped computations, high synchronization overheads, etc. In this article, we present DeepSlicing, a collaborative and adaptive inference system that adapts to various CNNs and supports customized flexible fine-grained scheduling. As a built-in functionality, DeepSlicing has supported typical CNNs including GoogLeNet, ResNet, etc. By partitioning both model and data, we also design an efficient scheduler, Proportional Synchronized Scheduler (PSS), which achieves the trade-off between computation and synchronization. Based on PyTorch, we have implemented DeepSlicing on the testbed with real-world edge settings that consists of 8 heterogeneous Raspberry Pi's. The results indicate that DeepSlicing with PSS outperforms the existing systems dramatically, e.g., the inference latency and memory footprint are reduced up to 5.79× and 14.72×, respectively. [ABSTRACT FROM AUTHOR]

Instead of relying on remote clouds, today’s Augmented Reality (AR) applications usually send videos to nearby edge servers for analysis (such as objection detection) so as to optimize the user’s quality of experience (QoE), which is often determined by not only detection latency but also detection accuracy, playback fluency, etc. Therefore, many studies have been conducted to help adaptively choose best video configuration, e.g., resolution and frame per second (fps), based on network bandwidth to further improve QoE. However, we notice that the video content itself has significant impacts on the configuration selection, e.g., the videos with high-speed objects must be encoded with a high fps to meet the user’s fluency requirement. In this article, we aim to adaptively select configurations that match the time-varying network condition as well as the video content. We design Cuttlefish, a system that generates video configuration decisions using reinforcement learning (RL). Cuttlefish trains a neural network model that picks a configuration for the next encoding slot based on observations collected by AR devices. Cuttlefish does not rely on any pre-programmed models or specific assumptions on the environments. Instead, it learns to make configuration decisions solely through observations of the resulting performance of historical decisions. Cuttlefish automatically learns the adaptive configuration policy for diverse AR video streams and obtains a gratifying QoE. We compared Cuttlefish to several state-of-the-art bandwidth-based and velocity-based methods using trace-driven and real world experiments. The results show that Cuttlefish achieves a 18.4-25.8 percent higher QoE than the others. [ABSTRACT FROM AUTHOR]

Popular data analytics systems including Apache Hadoop, Dryad, and Apache Spark abstract jobs as directed acyclic graphs (DAGs). Speeding up completions for DAG jobs matter in practice in order to support real-time decisions. State-of-the-art works propose clairvoyant schedulers to optimize these goals, however, they assume complete job information as a prior knowledge which includes the precise DAG structure, and fine-grained resource requirement and duration time of each task. This assumption limits their applicability. In this paper, to be more practical, we relax the complete prior knowledge assumption and rely solely on partial prior information, based on which, we design a semi-clairvoyant task scheduler Cobra operating within each job. When managing resources for a job, Cobra adaptively adjusts its resource desires in a multiplicative-increase multiplicative-decrease manner on the basis of the its current resource utilization and the presence of current waiting tasks. When assigning tasks to run on the allocated resources, Cobra strives to satisfy task locality preference by tolerating each task waiting for some time that is bounded by a parameterized threshold. Even with the partial prior job information, when a set of jobs in which each employing Cobra as its task scheduler, run on a cluster that employs the fair job scheduler, we theoretically prove the produced makespan and average job response time are O(1)-competitive in different settings. We implement our design in Spark on YARN system, and use experiments from both real deployments and simulations on Google's trace to verify the performance promotion and sensitivity of Cobra. [ABSTRACT FROM AUTHOR]

While modern wireless devices are capable of using multiple WiFi interfaces, the Multipath TCP (MPTCP) protocol has been employed to make full use of the capacity of many radios by enabling multiple path communication simultaneously. To provide exceptional mobility support in wireless networks, a key question is to determine the best handoff strategy to switch between access points or among WiFi/3G interfaces during roaming. In this paper, we propose SERO, a novel model-driven SEamless ROaming framework to optimize layer-2 handoff and vertical handoff for multihomed devices using MPTCP. The proposed framework adopts a measurement-based method to derive the TCP throughput model for wireless communication during handoff. Based on the throughput model, we propose a hybrid handoff strategy that uses multiple WiFi interfaces for data transmission and employs 3G augmentation to bridge the network interruption caused by handoff and to guarantee the total throughput above a predefined threshold for roaming devices. We implement the SERO framework in a real-deployed wireless mesh network testbed, and evaluate its performance by extensive experiments, which shows that SERO achieves performance gain of 26%–180% compared with several existing handoff strategies. [ABSTRACT FROM AUTHOR]

Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the initial allocation of virtual machines based on the CPU, memory and network bandwidth requirements. However, when tasks finished or lease expired, related virtual machines would be deleted from the system which would generate resource fragments. Such fragments lead to unbalanced resource utilization and decline of communication performance. This paper investigates the network influence on typical applications in data centers and proposed a self-adaptive network-aware virtual machine clustering and consolidation algorithm to maintain an optimal system-wide status. Our consolidation algorithm periodically checks whether consolidation is necessary and then clusters and consolidates virtual machines to lower communication cost with an online heuristic. We used two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also built a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments showed that, our algorithm greatly shortened the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through aggregate switches and improved the communication ability among virtual machines. [ABSTRACT FROM AUTHOR]

Existing data center transport protocols are usually based on the Processor Sharing (PS) policy and/or the Shortest Remaining Processing Time (SRPT) policy. PS divides link bandwidth equally between competing flows, thus it fails to achieve optimal average flow completion time (FCT). SRPT prioritizes flows that have the shortest remaining processing time and provides near-optimal average FCT, but it may cause long flows to suffer unfair delays, or even starve them. In fact, these two types of policies represent two directions in the design space: PS prefers fairness (in terms of starvation freedom) while SRPT favors efficiency (in terms of average FCT). In this paper, we propose a novel metric, expansion ratio, which enables us to strike a balance between SRPT and PS. We design MERP that achieves efficient flow scheduling without starvation. MERP takes care of both average and tail FCTs by minimizing the expansion ratio of competing flows in a lexicographically manner. MERP controls the sending rate of competing flows via synchronized virtual deadlines and routes flows in a downstream-aware manner that reacts quickly to link failures. We evaluate MERP using extensive NS2-based simulations. Results show that, under various traffic loads, MERP reduces the tail FCT significantly with a negligible increase of average FCT compared with pFabric, and MERP reduces the average FCT notably compared with ECMP and CONGA when link failures occur. [ABSTRACT FROM PUBLISHER]

In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs have to be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstiness-aware server consolidation problem from the perspective of resource reservation, i.e., reserving a certain amount of extra resources on each PM to avoid live migrations, and propose a novel server consolidation algorithm,  \sf QUEUE . We first model the resource requirement pattern of each VM as a two-state Markov chain to capture burstiness, then we design a resource reservation strategy for each PM based on the stationary distribution of a Markov chain. Finally, we present \sf QUEUE , a complete server consolidation algorithm with a reasonable time complexity. We also show how to cope with heterogenous spikes and provide remarks on several extensions. Simulation and testbed results show that, \sf QUEUE improves the consolidation ratio by up to 45 percent with large spike size and around 30 percent with normal spike size compared with the strategy that provisions for peak workload, and achieves a better balance between performance and energy consumption in comparison with other commonly-used consolidation algorithms. [ABSTRACT FROM PUBLISHER]

With the rapid proliferation of RFID technologies, RFID has been introduced into applications such as inventory and sampling inspection. Conventionally, in RFID systems, the reader usually identifies all the RFID tags in the interrogation region with the maximum power. However, some applications may only need to identify the tags in a specified area, which is usually smaller than the reader’s default interrogation region. An example could be identifying the tags in a box, while ignoring the tags out of the box. In this paper, we respectively present two solutions to identify the tags in the specified area. The principle of the solutions can be compared to the picture-taking process of an auto-focus camera, which firstly focuses on the target automatically and then takes the picture. Similarly, our solutions first focus on the specified area and then shoot the tags. The design of the two solutions is based on the extensive empirical study on RFID tags. Realistic experiment results show that our solutions can reduce the execution time by $44$ percent compared to the baseline solution, which identifies the tags with maximum power. Furthermore, we improve the proposed solutions to make them work well in more complex environments. [ABSTRACT FROM AUTHOR]

As a variation of random linear network coding, segmented network coding (SNC) has attracted great interest in data dissemination over lossy networks due to its low computational cost. In order to guarantee the success of decoding, SNC can adopt a feedbackless forward error correction (FEC) approach by applying a linear block code to the input packets before segmentation at the source node. In particular, if the empirical rank distribution of transfer matrices of segments is known in advance, several classes of coded SNC can achieve close-to-optimal decoding performance. However, the empirical rank distribution in the absence of feedback has been little investigated yet, making the whole performance of the FEC approach unknown. To close this gap, in this paper, we present the first comprehensive study on the transmission scheduling issue for the FEC approach, aiming at optimizing the rank distribution of transfer matrices with little control overhead. We propose an efficient adaptive scheduling framework for coded SNC in lossy unicast networks. This framework is one-sided (i.e., each network node forwards the segments adaptively only according to its own state) and scalable (i.e., its buffer cost will not keep on growing when the number of input packets goes to infinity). The performance of the framework is further optimized based on a linear programming approach. Extensive numerical results show that our framework performs near-optimally with respect to the empirical rank distribution. [ABSTRACT FROM AUTHOR]

Collecting histograms over RFID tags is an essential premise for effective aggregate queries and analysis in large-scale RFID-based applications. In this paper we consider an efficient collection of histograms from the massive number of RFID tags, without the need to read all tag data. In order to achieve time efficiency, we propose a novel, ensemble sampling-based method to simultaneously estimate the tag size for a number of categories. We first consider the problem of basic histogram collection, and propose an efficient algorithm based on the idea of ensemble sampling. We further consider the problems of advanced histogram collection, respectively, with an iceberg query and a top-$k$ query. Efficient algorithms are proposed to tackle the above problems such that the qualified/unqualified categories can be quickly identified. This ensemble sampling-based framework is very flexible and compatible to current tag-counting estimators, which can be efficiently leveraged to estimate the tag size for each category. Experiment results indicate that our ensemble sampling-based solutions can achieve a much better performance than the basic estimation/identification schemes. [ABSTRACT FROM AUTHOR]

In wireless networks, a recent trend is to make spectrum access dynamic for efficient utilization of spectrum. In such a scenario, the spectrum is periodically allocated to wireless users using an auction-based market mechanism. A critical property required for designing such a mechanism is truthfulness, which could avoid market manipulation. Such mechanism design typically involves solving NP-hard problems; hence, approximation algorithms are always resorted to in real systems. However, recent results have suggested that it is impossible to implement reasonable approximations without losing robustness to manipulation. In this paper, we solve the problem in a novel perspective by relaxing the constraints of ensuring strong truthfulness. We discuss the concepts of approximate truthfulness and provide approximately truthful mechanisms to improve efficiency (in terms of social welfare and spectrum utilization). We first develop a computationally efficient mechanism that achieves truthful in expectation. This mechanism is based on the assumption that bidders are risk neutral. Following that, we break the assumption by proposing a hard-to-manipulate auction (HMA), which makes it hard to manipulate the auction for profit gains. Our extensive simulation results show that our mechanisms can achieve significant improvement over the state-of-the-art mechanisms. [ABSTRACT FROM PUBLISHER]

Inferences easily incur computation overload at the edge of the network, since they often consume plenty of resources and are often implemented by using deep neural networks (DNNs). Traditional approach via offloading those inference tasks to remote cloud is unsuitable, since the round-trip time is often a burden. As a result, offloading by using nearby idle edges is promising, which sacrifices the task replication overhead for speeding up the edge inference. Unfortunately, due to stochastic changes on both edge networks and edge inference, it is hard to determine the best suitable targets for replication, especially when those DNNs consist of multiple kernels for inference, the replication decision involves multiple edge candidates as the destinations, and the edges are further heterogeneous. In this paper, we propose to optimize the inference replication at edges, under the consideration of stochastic changes. We formulate related problem and design an online algorithm via combinatorial multi-armed bandit for the inference with minimum response time, which decides multiple destinations simultaneously for replication, upon both revealed feedback after the deployment and the offline profile. By rigorous proof, the sublinear regret is ensured, which measures the gap between our online decision and the offline optimum. Through extensive trace-driven experiments with Huawei Atlas and NVIDIA Jetson, the improvement earned by inference replication is confirmed, compared with other alternatives. [ABSTRACT FROM AUTHOR]

Many studies have presented the order sense results of information transmission capacity and packet delivery delay in mobile ad hoc networks (MANETs). To achieve the fundamental understanding of MANETs, we focus on deriving the closed-form expressions of the network capacity and end-to-end delay. A MANET with the generalized correlated mobility model is considered in this paper, where the mobility of nodes clustered in one group is confined within a specified area, and multiple groups move uniformly across the network. We also leverage limited packet redundancy to speed up the packet transmission, i.e., each source node is allowed to distribute at most f copies of each packet in its delivery process. Specifically, we first propose an effective multi-hop scheduling-routing scheme under the correlated mobility model, and then develop the closed-form expressions of both per node throughput capacity and expected end-to-end delay. We further explore the tradeoff between throughput capacity and packet delay by using packet redundancy f. The simulation studies validate our theoretical results. [ABSTRACT FROM AUTHOR]

Motivated by various applications in mobile ad-hoc networks (MANETs) that require nodes to share their individual information to each other, we study the multi-message dissemination problem in a MANET, which is to distribute multiple messages to all mobile nodes in the network in parallel. The objective is to minimize the stopping time, i.e., the time taking for all nodes to receive a copy of the whole messages. We consider an intrinsically one-sided protocol based on random linear network coding (RLNC), where all packets forwarded are in the form of random linear combinations of packets received so far. Its supreme performance is demonstrated theoretically for two cases, low mobility and high mobility, according to the node velocity. In particular, we show that, under general settings, our derived upper bounds of the stopping time match the established lower bound in both cases, although the effects of mobility in the two cases are significantly different. Thus, we conclude that RLNC achieves order optimality for fast information dissemination in MANETs. [ABSTRACT FROM AUTHOR]

Network virtualization has emerged as a promising approach to overcome the ossification of the Internet. A major challenge in network virtualization is the so-called virtual network embedding problem, which deals with the efficient embedding of virtual networks with resource constraints into a shared substrate network. A number of heuristics have been proposed to cope with the NP-hardness of this problem; however, all of the existing proposals reserve fixed resources throughout the entire lifetime of a virtual network. In this paper, we re-examine this problem with the position that time-varying resource requirements of virtual networks should be taken into consideration, and we present an opportunistic resource sharing-based mapping framework, ORS, where substrate resources are opportunistically shared among multiple virtual networks. We formulate the time slot assignment as an optimization problem; then, we prove the decision version of the problem to be NP-hard in the strong sense. Observing the resemblance between our problem and the bin packing problem, we adopt the core idea of first-fit and propose two practical solutions: first-fit by collision probability (CFF) and first-fit by expectation of indicators' sum (EFF). Simulation results show that ORS provides a more efficient utilization of substrate resources than two state-of-the-art fixed-resource embedding schemes. [ABSTRACT FROM AUTHOR]

Many emerging online stream processing services require the consideration of quality of service (QoS), which is highly dependent on the placement of services at various hosts. This paper investigates the QoS-aware placement problems of stream processing services under different contexts. On condition that the client demands are stable, the QoS-aware placement problem aiming to minimize the cost when servers are CPU-uncapacitated, is equivalent to the set cover problem, and can be solved by a greedy algorithm with approximation factor O(log n), where n is the number of clients. However, when CPU capacity constraints on servers are taken into account, the QoS-aware placement problem cannot be approximated unless P=NP. Therefore, we propose two heuristic algorithms: (1) ISCA (Iterated Set Cover-based Algorithm) and (2) KBA (Knapsack-Based Algorithm). We also consider the placement problem of client demands increasing over time. Two objectives, called extension factor and system lifetime, are proposed for demand increment-blind and increment-aware models respectively. Both of them can be solved by extending ISCA and KBA. The experimental results show that ISCA and KBA have distinct effects on different demand sizes. ISCA is more efficient when client demands are relatively small, while KBA performs better for larger demands. [ABSTRACT FROM AUTHOR]

Data caching is widely used to facilitate information access in wireless mobile networks. Caching frequently used content on mobile clients brings benefits by reducing bandwidth usage, saving energy, and leveraging server workload. Maintaining cache consistency is an important issue in mobile caching and has received much attention by researchers. However, most existing studies on cache consistency strategies are limited to cellular wireless networks in which the mobile terminals can communicate with the base stations directly. In multi-hop wireless network environment, mobile terminals communicate with data server through multiple unreliable links with a high probability of disconnection from the network, which makes it more challenging for the maintenance of cache data consistency. In this paper, we investigate three cache consistency strategies for multi-hop wireless networks: the pull-based strategy POD ( pull on demand), the push-based strategy MAT ( modified amnesic terminals) and the store and forward strategy PIR ( pull-based invalidation report). Extensive theoretical analysis is provided to compare the cache hit ratio, query delay and communication overhead of these methods, and the impact of system parameters on performance is studied as well. Simulation experiments are used to examine the performance of these three schemes, and it is shown that PIR provides the best overall performance. [ABSTRACT FROM AUTHOR]

Abstract: Current research on UHF RFID system security mainly focus on protecting communication safety and information privacy between a pair of specific tags and its corresponding interrogation reader. However, instead of stealing detailed private information of tags, adversaries may just want to estimate the cardinality of tags, which is named counting attack. Unfortunately, most existing protocols are vulnerable to counting attack. To defend against this attack, in this paper we propose ACSP, a novel Anti-Counting Security Protocol. ACSP employs session identifier and provides a corresponding authentication metric to verify the commands sent by the reader. To handle counting attack, ACSP periodically updates the session identifier, and securely identifies tags with encryption. We evaluate the performance of ACSP through theoretical analysis and qualitative comparison. Results show that ACSP can efficiently withstand counting attack as well as defending against regular security threats as existing protocols. [Copyright &y& Elsevier]

Location Dependent Information Services (LDISs), through which mobile clients can access location sensitive data such as weather information, traffic reports, and local news, are gaining increasing popularity in recent years. Due to limited client power and intermittent connectivity, caching is an important approach to improve the performance of LDISs. In this paper, we propose a cache replacement policy called Location Dependent Cooperative Caching (LDCC). Unlike existing location dependent cache replacement policies, the LDCC strategy applies a prediction model to approximate client movement behavior and a probabilistic transition model to analyze the communication cost. These models are used in the design of a cache replacement policy to improve system overall performance. Simulation results demonstrate that the proposed strategy significantly outperforms existing caching policies in providing LDIS in mobile ad hoc networks. [ABSTRACT FROM AUTHOR]

Multipath data transmission techniques have been proposed to aggregate the resource of multiple heterogeneous access networks to enhance the robustness and throughput of multi-homed mobile devices. The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of conventional TCP, which enables establishing several subflows over multiple interfaces for simultaneous data transmission. However, current implementation of MPTCP only supports static subflow management strategies, which cannot adapt to the changing to network environment. In this paper, we focus on the issues of dynamic subflow management for MPTCP. We present theoretical analysis to the overall throughput of establishing multiple subflows with MPTCP, based on which we propose an adaptive subflow allocation algorithm to determine the proper number of subflows for each network interface to optimize the performance gain. We implement the proposed algorithm, integrate it into the MPTCP Linux Kernel implementation, and evaluate its performance on a wireless network testbed. Extensive experiments show that the proposed allocation algorithm significantly outperforms the existing subflow management strategies with 57% improvement. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are key for various applications that involve long-term and low-cost monitoring and actuating. In these applications, sensor nodes use batteries as the sole energy source. Therefore, energy efficiency becomes critical. We observe that many WSN applications require redundant sensor nodes to achieve fault tolerance and Quality of Service (QoS) of the sensing. However, the same redundancy may not be necessary for multihop communication because of the light traffic load and the stable wireless links. In this paper, we present a novel sleep-scheduling technique called Virtual Backbone Scheduling (VBS). VBS is designed for WSNs has redundant sensor nodes. VBS forms multiple overlapped backbones which work alternatively to prolong the network lifetime. In VBS, traffic is only forwarded by backbone sensor nodes, and the rest of the sensor nodes turn off their radios to save energy. The rotation of multiple backbones makes sure that the energy consumption of all sensor nodes is balanced, which fully utilizes the energy and achieves a longer network lifetime compared to the existing techniques. The scheduling problem of VBS is formulated as the Maximum Lifetime Backbone Scheduling (MLBS) problem. Since the MLBS problem is NP-hard, we propose approximation algorithms based on the Schedule Transition Graph (STG) and Virtual Scheduling Graph (VSG). We also present an Iterative Local Replacement (ILR) scheme as a distributed implementation. Theoretical analyses and simulation studies verify that VBS is superior to the existing techniques. [ABSTRACT FROM AUTHOR]

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions. [ABSTRACT FROM AUTHOR]