Peer-to-peer (P2P) electricity markets enable prosumers to minimize their costs, which has been extensively studied in recent research. However, there are several challenges with P2P trading when physical network constraints are also included. Moreover, most studies use fixed prices for grid power prices without considering dynamic grid pricing, and equity for all participants. This policy may negatively affect the long-term development of the market if prosumers with low demand are not treated fairly. An initial step towards addressing these problems is the design of a new decentralized P2P electricity market with two dynamic grid pricing schemes that are determined by consumer demand. Furthermore, we consider a decentralized system with physical constraints for optimizing power flow in networks without compromising privacy. We propose a dynamic congestion price to effectively address congestion and then prove the convergence and global optimality of the proposed method. Our experiments show that P2P energy trade decreases generation cost of main grid by 56.9% compared with previous works. Consumers reduce grid trading by 57.3% while the social welfare of consumers is barely affected by the increase of grid price.

In this article, we propose FedAND, a unified federated learning optimization algorithm, to tackle client drift and server drift issues under partial client participation. Federated learning is gaining popularity due to privacy concerns and mobile computing, but it still faces challenges due to heterogeneous and distributed data. FedAND leverages consensus alternating direction method of multipliers (ADMM) and resolves the server drift caused by the server state in the global update. Under partial participation, we prove that FedAND preserves the strong convergence properties of ADMM while suppressing the server drift, which in turn reduces the client drift and thus achieves better convergence. Our empirical results demonstrate superior performance compared to other methods such as FedProx, FedADMM, FedPD, and FedDyn in diverse scenarios of statistical and system heterogeneity under partial client participation.

Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods have been proposed to run SGD with biased gradient estimates obtained from a Markov chain. This paper provides the first non-asymptotic convergence analysis of these methods by establishing their mixing rate and gradient variance. To do this, we demonstrate that these methods—which we collectively refer to as Markov chain score ascent (MCSA) methods—can be cast as special cases of the Markov chain gradient descent framework. Furthermore, by leveraging this new understanding, we develop a novel MCSA scheme, parallel MCSA (pMCSA), that achieves a tighter bound on the gradient variance. We demonstrate that this improved theoretical result translates to superior empirical performance.

For smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and “robotic embodiment of learned assembly tasks in the form of skills.” In the former, robots observe human assembly operations to learn a sequence of assembly tasks, which is formalized into a human assembly script. The latter transforms the human assembly script into a robot assembly script in which a sequence of robot-executable assembly tasks are defined based on action planning supported by workspace modeling and simulated retargeting. The assembly tasks, in the form of the robot assembly script, are then implemented via pretrained robot skills. These skills aim to enable robots to execute difficult tasks that involve inherent uncertainties and variations. We validate the proposed approach by building a prototype of the automated robotic assembly system for a power breaker and an electronic set-top box. The results verify that the proposed automated robotic assembly system is not only feasible but also viable, as it is associated with a dramatic reduction in the human effort required for automating robotic assembly.

More network operators have recently provided content delivery network (CDN) services, where traffic engineering techniques, such as the base station (BS) association, are jointly employed with content delivery. This deployment attempts to reduce the network operating cost and enhance the quality of service (QoS) of end users. Toward this end, we study the BS association and file caching problem considering the spatial diversity of the file popularity and the realistic time-scale separation between the file caching and the BS association decisions in this paper. Our design aims to minimize the file delivery latency and operating power consumption in the cellular networks, where the tradeoff between these conflicting objectives is controlled by a single parameter. The short time-scale BS association problem is solved by using the convex optimization technique for a given file caching solution. However, the long time-scale file caching problem considering the varying BS association decisions taken at the short time-scale is difficult to tackle. To solve this file caching problem, we prove and leverage the submodularity property of the underlying objective function to develop a greedy content caching algorithm that guarantees a constant approximation ratio of the optimal objective value. Via simulations using real-world datasets, we show that the proposed algorithms outperform file caching and BS association algorithms that do not consider the spatial diversity of the file popularity in terms of the power consumption and delay performance in the geographically heterogeneous file popularity scenario.

We present a framework called Hierarchically Interacting Logs (HIL) for constructing Flash Translation Layers (FTLs). The main goal of the HIL framework is to heal the Achilles heel —the crash recovery—of FTLs (hence, its name). Nonetheless, the framework itself is general enough to encompass not only block-mapped and page-mapped FTLs but also many of their variants, including hybrid ones, because of its compositional nature. Crash recovery within the HIL framework proceeds in two phases: structural recovery and functional recovery. During the structural recovery, residual effects due to program operations ongoing at the time of the crash are eliminated in an atomic manner using shadow paging. During the functional recovery, operations that would have been performed if there had been no crash are replayed in a redo-only fashion. Both phases operate in an idempotent manner, preventing repeated crashes during recovery from causing any additional problems. We demonstrate the practicality of the proposed HIL framework by implementing a prototype and showing that its performance during normal execution and also during crash recovery is at least as good as those of state-of-the-art SSDs.

In this paper, we propose an ON/OFF reporting mechanism for cooperative sensing of cognitive radio so that reporting overhead from secondary sensing users to a fusion center can be significantly reduced. The significant reduction in reporting overhead of ON/OFF reporting contributes to power saving, which in turn realizes robust reporting to fusion center by overcoming channel fading. Furthermore, ON/OFF reporting enables graceful degradation when sensing nodes fail during operation. Instead of using AND rule, we propose an iNOR rule that can achieve O(1) reporting overhead in total, irrespective of the number of sensing nodes. The asymptotic overhead value is given by P(ℋ1) log (1/Pd), where P(ℋ1) is the probability of primary user's presence and P̅dis the target detection probability. We also show that OR rule has its asymptotically negligible reporting overhead given by P(ℋ1)log (1/1-Pd). Simulation results show that with the proposed technique fusion center can make a reliable decision to avoid harmful interference to primary user.

Flash memory is used as a main data storage medium in increasingly large areas of applications, rapidly replacing hard disk drives because of its low power consumption, fast random access, and high shock resistance. Such flash-based storage devices generally incorporate multiple flash memory chips to meet the ever growing capacity demands. Using multiple chips in a single storage device, at the same time, opens an opportunity to boost the performance based on multi-unit parallelism. However, parallel execution of multiple flash operations introduces complications when bad blocks occur, which is unavoidable due to flash memory’s physical characteristics. The situation gets even worse when bad block occurrences are accompanied by sudden power failures. We propose a bad block management scheme called P-BMS that can fully utilize flash-level parallelism, while guaranteeing provably correct block replacement. Experiments show that our P-BMS achieves a throughput that is more than 95% of the maximum bandwidth of the flash controller, even with bad block occurrences far heavier than in real flash memory.

In this paper, we propose an on/offreporting mechanism for cooperative sensing of cognitive radio so that reporting overhead from secondary sensing users to a fusion center can be significantly reduced. A key advantage of our scheme is robust and energy-efficient cooperative sensing over imperfect reporting channels. Furthermore, ON/OFF reporting enables graceful degradation when sensing nodes fail during operation. We focus on hard decision of cooperative sensing with many energy detectors based on the Neyman-Pearson criterion. Instead of using the conventional and rule, we propose an iNOR rule that can achieve O(1) reporting overhead in total, irrespective of the number of sensing nodes. The asymptotic overhead value is given by P(H1) log (1/P̅d), where P(H1) is the probability of primary user's presence and P̅dis the target detection probability. In addition, we show that the or rule also has its asymptotically negligible reporting overhead given by P(H1) log (1/1-Pd). Significant reduction in reporting overhead of ON/OFF reporting contributes to power saving, which in turn realizes robust reporting to fusion center by overcoming channel fading. Our simulation results show that with the proposed technique fusion center can make a reliable decision to avoid harmful interference to the primary user.

In this paper, we develop a self-organizing mechanism for spectrum breathing and user association in cellular networks employing frequency reuse patterns. Specifically, our focus is on flow-level cell load balancing under spatially inhomogeneous traffic distributions. Our work adaptively changes the spectrum bandwidth of each base station (BS) so that spectrums of BSs breathe in and out in order to balance the loads of BSs. Spectrum breathing is further combined with delay-optimal user association for better load balancing. Our problem is challenging because the problem is not a convex optimization. To tackle the difficulty, we decouple spectrum breathing and user association and propose an iterative algorithm that always converges to a fixed point, which is possibly an optimal solution. We show that spectrum breathing dominates a family of α-optimal user association in cell load balancing. Surprisingly, the flow-level delay performance under spectrum breathing gets even better as spatial traffic distribution becomes unbalanced, which is not the case of α-optimal user association. Our extensive simulations confirm that spectrum breathing significantly improves the system performances: decreasing the delay more than 10 times or increasing the admittable traffic load by more than 125%. Furthermore, spectrum breathing outperforms full frequency reuse when spatial traffic distribution is inhomogeneous.

In this paper, we address the problem of associating mobile stations (MSs) with base stations (BSs) in an energy-efficient manner. We take a population game approach, which allows tractable analysis of many selfish mobiles without growing mathematical complexity. From our game-theoretical analysis, we prove that a simple power-dependent pricing by operators leads a Nash equilibrium to be equal to the optimal solution of a social optimization problem (i.e., no price-of-anarchy). We study three evolution dynamics of associating MSs, each expressed as a differential equation, all of which provably and/or numerically converge to the Nash equilibrium. Based on several considerations regarding implementation of association algorithms in practice, we found that asynchronicity and fast load tracking are the key components to practical algorithms. Motivated by this, we propose a practical energy-efficient user association mechanism, named BRUTE. To evaluate the performance of BRUTE, we implement a cellular network simulator using an event-driven simulator, SimPy, and perform extensive simulations under various scenarios including a real BS topology in U.K. Our simulation results show that BRUTE outperforms other conventional user association techniques.

In this paper, we introduce the basic principles of wireless power transfer using magnetic field resonance and describe techniques for the design of a resonant magnetic coil, the formation of a magnetic field distribution, and electromagnetic field (EMF) noise suppression methods. The experimental results of wireless power transfer systems in consumer electronics applications are discussed in terms of issues related to their efficiency and EMF noise. Furthermore, we present a passive shielding method and a magnetic field cancellation method using a reactive resonant current loop and the utilization of these methods in an online electric vehicle (OLEV) system, in which an OLEV green transportation bus system absorbs wireless power from power cables underneath the road surface with only a minimal battery capacity.

This paper investigates pricing-based spectrum access control in cognitive radio networks, where primary users (PUs) sell the temporarily unused spectrum and secondary users (SUs) compete via random access for such spectrum opportunities. Compared to existing market-based approaches with centralized scheduling, pricing-based spectrum management with random access provides a platform for SUs contending for spectrum access and is amenable to decentralized implementation due to its low complexity. We focus on two market models, one with a monopoly PU market and the other with a multiple-PU market. For the monopoly PU market model, we devise decentralized pricing-based spectrum access mechanisms that enable SUs to contend for channel usage. Specifically, we first consider SUs contending via slotted Aloha. Since the revenue maximization problem therein is nonconvex, we characterize the corresponding Pareto-optimal region and obtain a Pareto-optimal solution that maximizes the SUs' throughput subject to their budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, which results in more efficient spectrum utilization and higher revenue. We then study the tradeoff between the PU's utility and its revenue when the PU's salable spectrum is controllable. Next, for the multiple-PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We explore the existence and the uniqueness of Nash equilibrium, in terms of access prices and the spectrum offered to SUs, and develop an iterative algorithm for strategy adaptation to achieve the Nash equilibrium. Our findings reveal that there exists a unique Nash equilibrium when the number of PUs is less than a threshold determined by the budgets and elasticity of SUs.

Smart Grid applications are imposing challenging requirements of security and reliability on the N-way communication infrastructure being designed to support multiple grid applications. These challenges stem from the increasing incorporation of distributed renewable energy sources on to the grid, the rising deployment of electric vehicles, and active consumer participation into power grid operations, all of which communicate with the utility control center with varying degrees of priority and security. To address these challenging requirements, we propose SeDAX, a SEcure Data-centric Application eXtensible platform for Smart Grid applications. SeDAX implements scalable, resilient and secure data delivery and data sharing in a wide area network. The platform can scalably handle high volumes of data generated by both applications and sensors. The SeDAX architecture has as its basis a Delaunay Triangulation (DT) network. The properties of the DT graph are leveraged to scalably support secure data-centric (or information-centric) group communication. The primary goals of this platform are to support communication resilience and data availability. The key functional blocks of the SeDAX platform are: (1) a geographic hash forwarding algorithm that operates over the DT graph (DT-GHF), and (2) a DT-based data replication scheme. The forwarding and replication schemes are scalable and cost effective in terms of communication overhead and memory. We describe the design details of the SeDAX platform and present empirical results on the performance of SeDAX as compared with other geometric-based alternatives such as Geographic Hash Table (GHT) forwarding and Content Addressable Networking (CAN). The operation of SeDAX is illustrated in the context of implementing demand response, a known Smart Grid application.

In this paper, we develop a framework for user association in infrastructure-based wireless networks, specifically focused on flow-level cell load balancing under spatially inhomogeneous traffic distributions. Our work encompasses several different user association policies: rate-optimal, throughput-optimal, delay-optimal, and load-equalizing, which we collectively denote α-optimal user association. We prove that the optimal load vector ρ*that minimizes a generalized system performance function is the fixed point of a certain mapping. Based on this mapping, we propose and analyze an iterative distributed user association policy that adapts to spatial traffic loads and converges to a globally optimal allocation. We then address admission control policies for the case where the system is overloaded. For an appropriate system-level cost function, the optimal admission control policy blocks all flows at cells edges. However, providing a minimum level of connectivity to all spatial locations might be desirable. To this end, a location-dependent random blocking and user association policy are proposed.

In this paper, we study the revenue sharing and rate allocation for Internet Service Providers (ISPs) that jointly provide network connectivity between content providers and end-users. Without colluding, each ISP may selfishly set a high transit-price to cover its cost and maximize its own profit, which inevitably results in a loss in social profit. We model this noncooperative interaction between an “eyeball” ISP and a “content” ISP as a Stackelberg game and quantify the resulting loss in social profit. To recover the profit loss, we propose a revenue sharing contract between ISPs by modeling them as a supply chain to deliver traffic in a two-sided market. Parameterized by the profit division factor, the sharing contract coordinates ISPs' objectives such that they aim to maximize the social profit self-incentively. We further propose a Nash bargaining process to determine the profit division factor such that all ISPs are simultaneously better off compared to the noncooperative equilibrium.

Market-based mechanisms offer promising approaches for spectrum access in cognitive radio networks. In this paper, we focus on two market models, one with a monopoly primary user (PU) market and the other with a multiple PU market, where each PU sells its temporarily unused spectrum to secondary users (SUs). We propose a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling. For the monopoly PU market model, we first consider SUs contending via slotted Aloha. The revenue maximization problems here are nonconvex. We first characterize the Pareto optimal region, and then obtain a Pareto optimal solution that maximizes the SUs' throughput subject to the SUs' budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, and show that spectrum utilization is enhanced, resulting in higher revenue. When the PU's unused spectrum is a control parameter, we study further the tradeoff between the PU's utility and its revenue. For the multiple PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We characterize the Nash equilibria, in terms of access prices and the spectrum offered to SUs. Our findings reveal that the number of equilibria exhibits a phase transition phenomenon, in the sense that when the number of PUs is greater than a threshold, there exist infinitely many equilibria; otherwise, there exists a unique Nash equilibrium, where the access prices and spectrum opportunities are determined by the budgets/elasticity of SUs and the utility level of PUs.

In this paper, we propose a data partitioning technique for the memory subsystem that consists of a multi-ported scratchpad memory (SPM) unit and a single-ported data cache in coarse-grained reconfigurable arrays (CGRA) architecture. The embedded reconfigurable processor executes programs by switching between the Non-VLIW and VLIW modes depending on the type of the code region to achieve high performance. The VLIW mode exploits code regions with high ILP that require high memory bandwidth and the Non-VLIW mode exploits those with low ILP that require low memory latency. Our data partitioning technique between the SPM and the data cache is based on data interference graph reduction and profiling information. Given an SPM size, it finds the optimal data partitions by taking the VLIW instruction schedule into consideration. We evaluate our data partitioning technique for the CGRA architecture with three representative multimedia applications.

In this study, the authors consider the benefits of mobile terminals (MTs) with different access points (APs) for uplink and downlink transmission to conserve MTs’ energy. In traditional cellular networks, an MT is typically associated with a single AP. However, as wireless networks evolve, heterogeneous and/or overlay deployment scenario become viable and an MT can be associated with different APs for uplink and downlink transmission. The authors call this ‘dual APs association’. The authors show that allowing dual APs association provides a significant gain on the uplink system capacity and/or the uplink transmit power savings. As a specific example of the use of dual APs, the authors focus on relay networks and show that considering the relay cost further increases the benefits of this approach. Based on extensive simulations using IEEE 802.16m relay network Evaluation Methodology, the authors demonstrate that dual APs can improve the uplink harmonic capacity by 350% or reduce the uplink transmit power by 7 dB. The authors note, however, that there exists a signalling cost in implementing dual APs association, which needs be overcome to achieve these substantial performance improvements.

Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on/off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption.

This paper studies the problem of energy conservation of mobile terminals in a multi-cell TDMA network supporting real-time sessions. The corresponding optimization problem involves joint scheduling, rate control, and power control, which is often highly complex to solve. To reduce the solution complexity, we decompose the overall problem into two sub-problems: intra-cell energy optimization and inter-cell interference control. The solution of the two subproblems results in a "win-win" situation: both the energy consumptions and inter-cell interference are reduced simultaneously. We simulate our decomposition method with the typical parameters in WiMAX system, and the simulation results show that our decomposition method can achieve an energy reduction of more than 70% compared with the simplistic maximum transmit power policy. Furthermore, the inter-cell interference power can be reduced by more than 35% compared with the maximum transmit power policy. We find that the interference power stays largely constant throughout a TDMA frame in our decomposition method. Based on this premise, we derive an interesting decoupling property: if the idle power consumption of terminals is no less than their circuit power consumption, or when both are negligible, then the energy-optimal transmission rates of the users are independent of the inter-cell interference power.

In the use of a wearable GPS and cellular tracker for applications such as elderly tracking, device power consumption is an important consideration. To save power, assisted GPS (AGPS) location fixes should not be performed frequently. On the other hand, we also do not want to lose important information about the user's mobility patterns and routines. To solve this dilemma, in this paper, we present the design of a system that intelligently schedules on-line AGPS location fixes only when necessary based on information extracted from user's historical mobility data, and then reconstruct the user path based on these sparsely taken on-line location fixes. Experimental results show that our on-line algorithm can significantly reduce the number of AGPS fixes needed and the reconstruction method works well without a priori knowledge of a map and streets information.

In this paper we develop a framework for user association in infrastructure-based wireless networks, specifically focused on flow-level cell load balancing under spatially inhomogeneous traffic distributions. Our work encompasses several different user association policies: rate-optimal, throughput- optimal, delay-optimal, and load-equalizing, which we collectively denote α-optimal user association. We prove that the optimal load vector ρ∗ that minimizes a generalized system performance function is the fixed point of a certain mapping. Based on this mapping we propose and analyze an iterative distributed user association policy that adapts to spatial traffic loads and converges to a globally optimal allocation.

Usage-based pricing has been recognized as a network congestion management tool. Internet Service Providers (ISPs), however, have limited ability to set time-adaptive usage-price to manage congestion arising from time-varying consumer utility for data. To achieve the maximum revenue, ISP can set its time-invariant usage-price low enough to aggressively encourage consumer's traffic demand. The downside is that ISP has to drop consumer's excessive traffic demand through congestion management (i.e., packet dropping), which may degrade Quality of Service (QoS) of consumer's traffic. Alternatively, to protect consumer's QoS, ISP can set its time-invariant usage-price high enough to reduce consumer's traffic demand, thus minimizing the need for congestion management through packet dropping. The downside is that ISP suffers a revenue loss due to the inefficient usage of its network. The tradeoff between ISP's revenue maximization and consumer's QoS protection motivates us to study ISP's revenue maximization subject to QoS constraint in terms of the number of packets dropped. We investigate two different QoS measures: short-term per-slot packet dropping constraint and long-term packet dropping constraint. The short-term constraint can be interpreted as a more transparent congestion management practice compared to the long-term constraint. We analyze ISP's optimal time-invariant pricing for both constraints, and develop an upper bound for the optimal revenue by considering the specified packet dropping threshold. We quantify the impact of consumer's price elasticity on ISP's optimal revenue and show that ISP should carry out a differentiated QoS protection strategy based on consumer's price elasticity in order to mitigate the revenue loss1.

Flash memory solid-state disks (SSDs) are replacing hard disk drives (HDDs) in mobile computing systems because of their lower power consumption, faster random access, and greater shock resistance. We describe Hydra, a high-performance flash memory SSD architecture that translates the parallelism inherent in multiple flash memory chips into improved performance, by means of both bus-level and chip-level interleaving. Hydra has a prioritized structure of memory controllers, consisting of a single high-priority foreground unit, to deal with read requests, and multiple background units, all capable of autonomous execution of sequences of high-level flash memory operations. Hydra also employs an aggressive write buffering mechanism based on block mapping to ensure that multiple flash memory chips are used effectively, and also to expedite the processing of write requests. Performance evaluation of an FPGA implementation of the Hydra SSD architecture shows that its performance is more than 80 percent better than the best of the comparable HDDs and SSDs that we considered.

In this paper, we study several ways in which mobile terminals can backoff on their uplink transmit power in order to extend battery lifetimes. This is particularly effective when a wireless system is underloaded as the degradation in user's perceived quality of service can be negligible. The challenge, however, is developing a mechanism that achieves a good tradeoff among transmit power, idling/circuit power, and the performance customers will see. We consider systems with flow-level dynamics supporting either real-time or best effort (e.g., file transfers) sessions. The energy-optimal transmission strategy for real-time sessions is determined by solving a convex optimization. An iterative approach exhibiting superlinear convergence achieves substantial amount energy savings, e.g., more than 50% when the session blocking probability is 0.1% or less. The case of file transfers is more subtle because power backoff changes the system dynamics. We study energy-efficient transmission strategies that realize energy-delay tradeoff. The proposed mechanism achieves a 35%-75% in energy savings depending on the load and file transfer target throughput. A key insight, relative to previous work focusing onstaticscenarios, is that idling power has a significant impact on energy-efficiency, while circuit power has limited impact as the load increases.

In this paper, we propose a mechanism to switch between multiple-input multiple-output (MIMO) with two transmit antennas and single-input multiple-output (SIMO) to conserve mobile terminals' energy. We focus on saving uplink RF transmission energy of mobile terminals in cellular systems supporting best effort traffic. The key idea is to judiciously slow down transmission rates when a base station is underutilized. We show that there exists a crossover point on the transmission rate below which SIMO consumes less power than MIMO when circuit power is included. The crossover point is an increasing function of the circuit power, the number of receive antennas and channel correlation, all of which increase the potential energy savings resulting from mode switching. We propose an adaptive mode switching algorithm combined with rate selection to maintain a user's target throughput while achieving energy efficiency. Extensive flow-level simulations under dynamic loads confirm that the proposed technique can reduce the transmission energy by more than 50% and enables an effective tradeoff between file transfer delay and energy conservation.

In this paper, we explain the basics of flash memory technology in general and solid state disks in particular, and describe development platforms specifically designed for flash memory solid state disks.

In this paper we evaluate interactions among flow-level performance metrics when integrating QoS and best effort flows in a wireless system using opportunistic scheduling. We introduce a simple flow-level model capturing the salient features of bandwidth sharing for an opportunistic scheduler which ensures a mean throughput to each QoS stream for every time slot. We then explore the flow-level performance showing that integration of QoS and best effort flows results in loss in opportunism, which in turn results in a reduction of the stability region, degradation in system throughput, and increased file transfer delay. These losses are shown to be proportional to opportunistic gains, the guaranteed bandwidth and number of QoS flows, but inversely proportional to SNR under a Rayleigh fading channel model. In an integrated system exploiting opportunism, local instability appears to be more severe than in wired networks and average delays experienced by best effort flows are prolonged. We suggest that a form of admission control for best effort flows is necessary to avoid local instability, and ensure adequate performance.

This paper presents a novel dynamic bandwidth allocation (DBA) scheme, called SLICT, for service level agreeable EPON. To guarantee the upper bound of maximum polling interval, we introduce sliding cycle time constraint. A new concept of shared time and remnant time is used to calculate extended bandwidth allocation. For service level agreeable EPON, we propose a design method to support multiple services such as fixed, guaranteed and extended bandwidth service. We show that simple remnant time management based on greedy contention guarantees steady state fairness. Simulation results show that proposed scheme outperforms existing schemes in aspects of throughput, delay, loss, and average queue size under self similar traffic.

In this paper, a coordinated scheduling strategy is proposed to reduce end-to-end response time in distributed control systems based on the CAN (controller area network). For the strategy, the analysis of practical end-to-end response time in the worst case is performed priory with considering real-time multi-task operating systems. The end-to-end response time is composed of response time in real-time multi-task scheduling of the operating systems, response time in the CAN communications, and delay caused by asynchronous timing between the operating systems and the CAN communications. The coordinated scheduling strategy is focused on the reduction of the delay caused by asynchronous timing between operating systems and network communications. An online deadline assignment strategy is proposed for the coordination. In the strategy, periods are defined as hard deadlines, and the deadlines of the DMS (deadline monotonic scheduling) are defined as soft deadlines with online assignments for the coordination. The performance enhancement with the coordinated scheduling strategy is validated through simulation tests, by performance comparison between a system with the proposed coordinated scheduling strategy and the system without the coordinated scheduling strategy.

Digital watermarking is a means of copyright protection by embedding an imperceptible signal into the multimedia data. Nearly all watermarking schemes developed basically employ a spread spectrum technique, and the extraction of embedded watermark depends on the correlation method. In this paper, we propose a stochastic model based public audio watermark. Based on the stochastic model and detection theory, the watermark shaping effect on detection is well explained. We also provide the theoretical whitening filter SNR gain and show that the detection performance can be significantly improved with the whitening filter. As regards robust detection with the whitening filter, we propose a robust detection parameter and demonstrate the reliability of the proposed estimated whitening filter to various kinds of audio degradation and distortions, e.g., MPEG compression, filtering, requantization, resampling, etc.