Motivated by the distributed nature of many network applications, this paper proposes a new online learning problem called multi-stage bandits. In multi-stage bandits, a job needs to go through multiple stages, each managed by a different agent, before generating an outcome. Each agent can only control its own action and learn the final outcome of the job. It has neither knowledge nor control on actions taken by agents in the next stage. The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments. The setting of this paper significantly expands the traditional multi-armed bandit problem, which considers only one agent and one stage. In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. To solve this newly introduced exploration-exploitation-education trilemma, we propose a simple distributed online learning algorithm, ϵ−EXP3. We theoretically prove that the ϵ−EXP3 algorithm is a no-regret policy that achieves sublinear regret. We also show that the regret of ϵ−EXP3 is close to the regret lower bound under a class of time-homogeneous oracle policies. Finally, we conduct simulation studies on a variety of network applications. Simulation results show that the ϵ−EXP3 algorithm significantly outperforms existing no-regret online learning algorithms for the traditional multi-armed bandit problem.

This paper characterizes the fundamental trade-off between throughput and Age of Information (AoI) in wireless networks where multiple devices transmit status updates to a central base station over unreliable channels. To address the complexity introduced by stochastic transmission successes, we propose the throughput-AoI capacity region, which defines all feasible throughput-AoI pairs achievable under any scheduling policy. Using a second-order approximation that incorporates both mean and temporal variance, we derive an outer bound and a tight inner bound for the throughput-AoI capacity region. Furthermore, we propose a simple and low complexity scheduling policy and prove that it achieves every interior point within the tight inner bound. This establishes a systematic and theoretically grounded framework for the joint optimization of throughput and information freshness in practical wireless communication scenarios.

In this paper, we study the moments of the Age of Information (AoI) for both active and passive users in a random access network. In this network, active users broadcast sensing data, while passive users detect in-band radio activities from out-of-network devices, such as jammers. Collisions occur when multiple active users transmit simultaneously. Passive users can detect radio activities only when no active user transmits. Each active user’s transmission behavior follows a Markov process. We aim to minimize the weighted sum of any moments of AoI for both user types. To achieve this, we employ a second-order analysis of system behavior. Specifically, we characterize an active user’s transmission Markov process using its mean and temporal variance. We show that any moment of the AoI can be approximated by a function of these two parameters. This insight enables us to analyze and optimize the transmission Markov process for active users. We apply this strategy to two different random access models. Simulation results show that policies derived from this strategy outperform other baseline policies.

This paper studies multi-stage systems with end-to-end bandit feedback. In such systems, each job needs to go through multiple stages, each managed by a different agent, before generating an outcome. Each agent can only control its own action and learn the final outcome of the job. It has neither knowledge nor control on actions taken by agents in the next stage. The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments.

Scheduling in multi-channel wireless communication system presents formidable challenges in effectively allocating resources. To address these challenges, we investigate a multi-resource restless matching bandit (MR-RMB) model for heterogeneous resource systems with an objective of maximizing long-term discounted total rewards while respecting resource constraints. We have also generalized to applications beyond multi-channel wireless. We discuss the Max-Weight Index Matching algorithm, which optimizes resource allocation based on learned partial indexes. We have derived the policy gradient theorem for index learning. Our main contribution is the introduction of a new Deep Index Policy (DIP), an online learning algorithm tailored for MR-RMB. DIP learns the partial index by leveraging the policy gradient theorem for restless arms with convoluted and unknown transition kernels of heterogeneous resources. We demonstrate the utility of DIP by evaluating its performance for three different MR-RMB problems. Our simulation results show that DIP indeed learns the partial indexes efficiently.

This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling Markovian fading wireless channels and emerging network performance metrics such as age-of-information (AoI) and timely-throughput. Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it to an unsolved network optimization problem where some clients wish to minimize AoI while others wish to maximize timely-throughput. We show that this framework accurately characterizes AoI and timely-throughput. Moreover, it leads to a tractable scheduling policy that outperforms other existing work.

In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network infers a time-varying target (e.g., the locations of vehicles and pedestrians) based on features (e.g., video frames) observed at a sensing node (e.g., a camera). One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain, whereas it is not true if the data sequence is far from being Markovian. Hence, the inference error is a function of Age of Information (AoI), where the function could be non-monotonic. To minimize the inference error in real-time, we propose a new “selection-from-buffer” model for sending the features, which is more general than the “generate-at-will” model used in earlier studies. In addition, we design low-complexity scheduling policies to improve inference performance. For single-source, single-channel systems, we provide an optimal scheduling policy. In multi-source, multi-channel systems, the scheduling problem becomes a multi-action restless multi-armed bandit problem. For this setting, we design a new scheduling policy by integrating Whittle index-based source selection and duality-based feature selection-from-buffer algorithms. This new scheduling policy is proven to be asymptotically optimal. These scheduling results hold for minimizing general AoI functions (monotonic or non-monotonic). Data-driven evaluations demonstrate the significant advantages of our proposed scheduling policies.

This paper studies an online service caching problem, where an edge server, equipped with a prediction window of future service request arrivals, needs to decide which services to host locally subject to limited storage capacity. The edge server aims to minimize the sum of a request forwarding cost (i.e., the cost of forwarding requests to remote data centers to process) and a service instantiating cost (i.e., that of retrieving and setting up a service). Considering request patterns are usually non-stationary in practice, the performance of the edge server is measured by dynamic regret, which compares the total cost with that of the dynamic optimal offline solution. To solve the problem, we propose a randomized online algorithm with low complexity and theoretically derive an upper bound on its expected dynamic regret. Simulation results show that our algorithm significantly outperforms other state-of-the-art policies in terms of the runtime and expected total cost.

In this paper, we address the optimization problem of moments of Age of Information (AoI) for active and passive users in a random access network. In this network, active users broadcast sensing data while passive users only receive signals. Collisions occur when multiple active users transmit simultaneously, and passive users are unable to receive signals while any active user is transmitting. Each active user follows a Markov process for their transmissions. We aim to minimize the weighted sum of any moments of AoI for both active and passive users in this network. To achieve this, we employ a second-order analysis to analyze the system. Specifically, we characterize an active user’s transmission Markov process by its mean and temporal process. We show that any moment of the AoI can be expressed a function of the mean and temporal variance, which, in turn, enables us to derive the optimal transmission Markov process. Our simulation results demonstrate that this proposed strategy outperforms other baseline policies that use different active user transmission models.

This paper studies a problem of jointly optimizing two important operations in mobile edge computing without knowing future requests, namely service caching, which deter-mines which services to be hosted at the edge, and service routing, which determines which requests to be processed locally at the edge. We aim to address several practical challenges, including limited storage and computation capacities of edge servers and unknown future request arrival patterns. To this end, we formulate the problem as an online optimization problem, in which the objective function includes costs of forwarding requests, processing requests, and reconfiguring edge servers. By leveraging a natural timescale separation between service routing and service caching, namely, the former happens faster than the latter, we propose an online two-stage algorithm and its randomized variant. Both algorithms have low complexity, and our fractional solution achieves sublinear regret. Simulation results show that our algorithms significantly outperform other state-of-the-art online policies.

This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling stateful fading wireless channels and emerging network performance metrics such as age-of-information (AoI). Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it for an important open problem: the optimization of AoI over Gilbert-Elliott channels. We show that this framework provides a very accurate characterization of AoI. Moreover, it leads to a tractable scheduling policy that outperforms other existing work.

We consider the problem of learning the optimal threshold policy for control problems. Threshold policies make control decisions by evaluating whether an element of the system state exceeds a certain threshold, whose value is determined by other elements of the system state. By leveraging the monotone property of threshold policies, we prove that their policy gradients have a surprisingly simple expression. We use this simple expression to build an off-policy actor-critic algorithm for learning the optimal threshold policy. Simulation results show that our policy significantly outperforms other reinforcement learning algorithms due to its ability to exploit the monotone property.In addition, we show that the Whittle index, a powerful tool for restless multi-armed bandit problems, is equivalent to the optimal threshold policy for an alternative problem. This observation leads to a simple algorithm that finds the Whittle index by learning the optimal threshold policy in the alternative problem. Simulation results show that our algorithm learns the Whittle index much faster than several recent studies that learn the Whittle index through indirect means.

This paper studies a remote sensing system where multiple wireless sensors generate possibly noisy information updates of various surveillance fields and delivering these updates to a control center over a wireless network. The control center needs a sufficient number of recently generated information updates to have an accurate estimate of the current system status, which is critical for the control center to make appropriate control decisions. The goal of this work is then to design the optimal policy for scheduling the transmissions of information updates. Through Brownian approximation, we demonstrate that the control center's ability to make accurate real-time estimates depends on the averages and temporal variances of the delivery processes. We then formulate a constrained optimization problem to find the optimal means and variances. We also develop a simple online scheduling policy that employs the optimal means and variances to achieve the optimal system-wide performance. Simulation results show that our scheduling policy enjoys fast convergence speed and better performance when compared to other state-of-the-art policies.

Whittle index policy is a powerful tool to obtain asymptotically optimal solutions for the notoriously intractable problem of restless bandits. However, finding the Whittle indices remains a difficult problem for many practical restless bandits with convoluted transition kernels. This paper proposes NeurWIN, a neural Whittle index network that seeks to learn the Whittle indices for any restless bandits by leveraging mathematical properties of the Whittle indices. We show that a neural network that produces the Whittle index is also one that produces the optimal control for a set of Markov decision problems. This property motivates using deep reinforcement learning for the training of NeurWIN. We demonstrate the utility of NeurWIN by evaluating its performance for three recently studied restless bandit problems.Our experiment results show that the performance of NeurWIN is significantly better than other RL algorithms.

This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end-user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the Confidence-in-Estimation by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a Loss-of-Confidence (LoC) if this number is insufficient for the end user to make a reliable estimation with small confidence intervals. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has a different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of the temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involve the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies. The practical value of this work is further evaluated by a case study of the real-time estimation problem of linear Gaussian processes, where we show that, under the optimal estimate algorithm, our scheduling policy results in better estimate accuracy, both in terms of the average mean square error and 95-percentile of mean square error, than other policies, including one that aims to optimize Age-of-Information, another performance metric for the application of real-time estimation.

The index coding problem includes a server, a group of clients, and a set of data chunks. While each client wants a subset of the data chunks and already has another subset as its side information, the server transmits some (uncoded or coded) chunks to the clients over a noiseless broadcast channel. The objective of the problem is to satisfy the demands of all clients with the minimum number of transmissions. This paper investigates the index coding setting from a game-theoretical perspective. We consider selfish clients, where each selfish client has private side information and a private valuation of each data chunk it wants. In this context, our objectives are following: 1) to motivate each selfish client to reveal the correct side information and true valuation of each data chunk it wants; 2) to maximize the social welfare, i.e., the total valuation of the data chunks recovered by the clients minus the total cost incurred by the transmissions from the server. Our main contribution is to jointly develop coding and incentive schemes for achieving the first objective perfectly and achieving the second objective optimally or approximately with guaranteed approximation ratios (potentially within some restricted sets of coding matrices).

A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases. In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.

This paper presents a Brownian-approximation framework to optimize the quality of experience (QoE) for real-time video streaming in wireless networks. In real-time video streaming, one major challenge is to tackle the natural tension between the two most critical QoE metrics: playback latency and video interruption. To study this trade-off, we first propose an analytical model that precisely captures all aspects of the playback process of a real-time video stream, including playback latency, video interruptions, and packet dropping. Built on this model, we show that the playback process of a real-time video can be approximated by a two-sided reflected Brownian motion. Through such Brownian approximation, we are able to study the fundamental limits of the two QoE metrics and characterize a necessary and sufficient condition for a set of QoE performance requirements to be feasible. We propose a scheduling policy that satisfies any feasible set of QoE performance requirements and then obtain simple rules on the trade-off between playback latency and the video interrupt rates, in both heavy-traffic and under-loaded regimes. Finally, simulation results verify the accuracy of the proposed approximation and show that the proposed policy outperforms other popular baseline policies.

Mixed-criticality system is a popular model for reducing pessimism in real-time scheduling while providing guarantee for critical tasks in presence of unexpected overrun. However, it is controversial due to some drawbacks. First, a single high-criticality job overrun leads to the pessimistic mode for all high-criticality tasks and consequently resource utilization becomes inefficient. Second, all low-criticality tasks are dropped in high-criticality mode, although they are still needed. These two issues have been addressed in several recent works, which are mostly focused on uniprocessor scheduling. In this work, we attempt to tackle these two issues in multiprocessor scheduling for dual-criticality systems. A deferred switching protocol is introduced so that the chance of switching to high-criticality mode is significantly reduced. Moreover, a service preserving technique is developed such that all low-criticality tasks can continue to execute in high-criticality mode. Further, the two techniques are unified into a single framework. Schedulability of these methods is studied so that the Quality-of-Service is improved with guarantee of satisfying all deadline constraints. The effectiveness of the proposed techniques is confirmed through simulations.

This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the credibility of an information flow by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a loss-of-credibility (LoC) if this number is insufficient for the end user to make an accurate estimation. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involves the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies.

We study the problem of broadcasting realtime flows in multi-hop wireless networks. We assume that each packet has a stringent deadline, and each node in the network obtains some utility based on the number of packets delivered to it on time for each flow. We propose a distributed protocol called the delegated-set routing (DSR) that incurs virtually no overhead of coordination among nodes. We also develop distributed algorithms that aim to maximize the total timely utility under DSR. The utility of the DSR protocol and distributed algorithms are demonstrated by both theoretical analysis and simulation results. We show that our algorithms achieve higher timely throughput even when compared against centralized throughput optimal policies that do not consider deadline constraints.

Wireless edge networks are promising to provide better video streaming services to mobile users by provisioning computing and storage resources at the edge of wireless network. However, due to the diversity of user interests, user devices, video versions or resolutions, cache sizes, network conditions, etc., it is challenging to decide where to place the video contents, and which cache and video version a mobile user device should select. In this paper, we study the joint optimization of cache-version selection and content placement for adaptive video streaming in wireless edge networks. We propose practical distributed algorithms that operate at each user device and each network cache to maximize the overall network utility. In addition to proving the optimality of our algorithms, we implement our algorithms as well as several baseline algorithms on ndnSIM, an ns-3 based Named Data Networking simulator. Simulation evaluations demonstrate that our algorithms significantly outperform conventional heuristic solutions.

Ultra-high reliability and low latency have not been in the mainstream in most wireless networks. Mobile networks have been driven so far by human-centric communications, delay-tolerant content, and non-critical services. The main target have been to boosting data rate and increasing coverage, adopting a rather best-effort networking approach. As wireless connectivity starts to get the status of a commodity, there is an increasing focus on support of services that rely critically on wireless links and therefore the reliability of the wireless connections. Next generation wireless systems, mainly 5G and beyond, are designed to provide wireless connectivity for massive machine-type communications (mMTC) and to support ultra-reliable, low latency communication (URLLC) for mission-critical services. URLLC scenarios impose stringent requirements in terms of latency (ranging from 1 ms and below to few milliseconds end-to-end latency depending on the use cases) and reliability (higher than 99.9999%). This does not mean that there are interesting applications where reliability is of paramount importance, while latency can be in the order of seconds, as in e.g. certain remote healthcare applications. Nevertheless, the coupling of low-latency networking and reliable communication is mainly driven by the need to push the technology boundaries and address a plethora of socially useful services and business domains that could benefit greatly from it. Some of the most challenging use cases are factory automation and industrial control, automated driving/flying, haptic communications, and real-time remote healthcare. URLLC is also expected to revolutionize processes in the areas of smart cities, smart farming, smart grid, remote manufacturing, and algorithmic trading. Although the URLLC constraints and the nature of real-time mission-critical applications imply the predominance of short packets and low-rate transmissions, future evolution of URLLC may also consider rate requirements. The emergence of immersive services, such as augmented and virtual reality (AR/VR), high-definition entertainment and gaming, and consumer robotics, calls for real-time, high-fidelity, broadband networks operating at latencies of few milliseconds.

It has been shown that it is impossible to achieve stringent timely delivery guarantees in a large network without having complete information of all future packet arrivals. In order to maintain desirable performance in the presence of uncertainty of future, a viable approach is to add redundancy by increasing link capacities. This paper studies the amount of capacity needed to provide stringent timely delivery guarantees. We propose a low-complexity online algorithm and prove that it only requires a small amount of redundancy to guarantee the timely delivery of most packets. Furthermore, we show that in large networks with very high timely delivery requirements, the redundancy needed by our policy is at most twice as large as the theoretical lower bound. For practical implementation, we propose a distributed protocol based on this centralized policy. Without adding redundancy, we further propose a low-complexity order-optimal online policy for the network. The simulation results show that our policies achieve much better performance than the other state-of-the-art policies.

This paper proposes a feasibility-optimal decentralized algorithm for real-time wireless ad hoc networks, where a strict deadline is imposed for each packet. While centralized scheduling algorithms provide provably optimal theoretical guarantees, they may not be practical in many settings, such as industrial networked control systems. Therefore, it is of great importance to design an algorithm that achieves feasibility optimality in a decentralized manner. To design a decentralized algorithm, we leverage two widely-used functions of wireless devices: carrier sensing and backoff timers. Different from the conventional approach, the proposed algorithm utilizes a collision-free backoff scheme to enforce the transmission priority of different links. This design obviates the capacity loss due to collision with quantifiably small backoff overhead. The algorithm is fully decentralized in the sense that every link only needs to know its own priority, and links contend for priorities only through carrier sensing. We prove that the proposed algorithm is feasibility-optimal. NS-3 simulation results show that the proposed algorithm indeed performs as well as the feasibility-optimal centralized algorithm. Moreover, the results also show that the proposed algorithm converges to optimality very fast.

An increasing number of applications that will be supported by next generation wireless networks require packets to arrive before a certain deadline for the system to have the desired performance. While many time-sensitive scheduling protocols have been proposed, few have been experimentally evaluated to establish realistic performance. Furthermore, some of these protocols involve high complexity algorithms that need to be performed on a per-packet basis. Experimental evaluation of these protocols requires a flexible platform that is readily capable of implementing and experimenting with these protocols.

Ultra-low per-packet latency has become an essential system requirement as well as a critical challenge for wireless networks. While there is a rich literature on real-time wireless scheduling, it is still unclear what the minimum achievable latency is and what level of throughput can be obtained in practice. This demo presents PULS, a processor-supported software-defined wireless testbed that supports ultra-low-latency scheduling protocols. We will demonstrate that PULS provides strict per-packet latency guarantees as low as 1 millisecond with realistic throughput for wireless networks.

Edge servers, which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile Internet applications by moving cloud services to the edge of the network. Due to limited capacity of edge servers and dynamic request arrival, proper service caching at the edge is essential to guarantee good performance. This paper proposes a tractable online algorithm called retrospective download with least-requested deletion that caches services dynamically without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio of our policy, which quantifies the worst case performance in comparison to an optimal offline policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge server. We also show that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. Moreover, we prove that our policy is scalable, in the sense that it only needs doubled capacity to achieve a constant competitive ratio. The utility of our online policy is further evaluated on real-world traces. These trace-based simulations demonstrate that our policy has better, or similar, performance compared with many intelligent offline policies.

The emerging connected-vehicle technology provides a new dimension for developing more intelligent traffic control algorithms for signalized intersections. An important challenge for scheduling in networked transportation systems is the switchover delay caused by the guard time before any traffic signal change. The switch-over delay can result in significant loss of system capacity and hence needs to be accommodated in the scheduling design. To tackle this challenge, we propose a distributed online scheduling policy that extends the well-known Max-Pressure policy to address switch-over delay by introducing a bias factor favoring the current schedule. We prove that the proposed policy is throughput-optimal with switch-over delay. Furthermore, the proposed policy remains optimal when there are both connected signalized intersections and conventional fixed-time ones in the system. With connected-vehicle technology, the proposed policy can be easily incorporated into the current transportation systems without additional infrastructure. Through extensive simulation in VISSIM, we show that our policy indeed outperforms the existing popular policies.

This paper proposes a practical non-monetary mechanism that induces the efficient solution to the optimal rate control problem, where each client optimizes its request arrival rate to maximize its own net utility individually, and at the Nash Equilibrium the total net utility of the system is also maximized. Existing mechanisms typically rely on monetary exchange which requires additional infrastructure that is not always available. Instead, the proposed protocol is based on efficient delay allocation, where the server controls the delay experienced by each client through an intelligent scheduling policy. Specifically, we present an efficient delay allocation rule for the server to determine the target delay of each client. Then we propose a simple scheduling policy to achieve such delay allocation. Furthermore, we design a distributed rate control protocol for the system to converge to the Nash Equilibrium. The optimality of our mechanism is validated via extensive simulations on two representative systems against a baseline mechanism with FIFO scheduling and centralized rate control.

WiFi offloading, where mobile users opportunistically obtain data through WiFi rather than cellular networks, is a promising technique for greatly improving spectrum efficiency and reduce cellular network congestion. We consider a system where the service provider deploys multiple WiFi hotspots to offload mobile traffic, and study the scheduling policy to maximize the amount of offloaded data. Since users' movements are unpredictable, we focus on online scheduling policy, where APs have no knowledge of users' mobility patterns. We study the performance of online policies by comparing them against the optimal offline policy. We prove that any work-conserving policy is able to offload at least half as much data as the offline policy, and then propose an online policy such that when the requested data by each user is very large, the policy can offload $(e-1)/e$ as much data as the offline policy, where $e$ is Euler's constant. We further study the case where the service provider can increase the capacity of WiFi so as to provide some guarantees on the amount of offloaded data. We derive a lower-bound on the trade-off between capacity and the amount of offloaded data, and propose a simple online policy that achieves this lower bound. In addition, we show that our policy only needs half as much capacity as current mechanisms to provide the same performance guarantee.

This paper studies the problem of using minimum power to provide satisfactory performance for real-time applications over unreliable and fading wireless channels. We demonstrate that this problem can be formulated as a linear programming problem. However, this formulation involves exponentially many constraints, and many parameters are either unavailable or difficult to compute, which makes it infeasible to employ standard techniques to solve the linear programming problem. Instead, we propose a simple online algorithm for this problem. We prove that our algorithm provides satisfactory performance to all real-time applications, and the total power consumption can be made arbitrarily close to the theoretical lower bound. Furthermore, our algorithm has very low complexity and does not require knowledge of many parameters in the linear programming problem, including the distributions of channel qualities. We further extend our algorithm to address systems where real-time applications and non-real-time ones coexist. We demonstrate that our algorithm achieves both low total power consumption and high utility for each non-real-time client while satisfying the performance requirements of real-time clients. Simulation results further provide some insights in setting important parameters of our algorithms, and demonstrate that our algorithm indeed achieves a significant reduction in power consumption.

This paper studies wireless networks with multiple real-time flows that have stringent requirements on both per-packet delay and long-term average delivery ratio. Each flow dynamically adjusts its traffic load based on its observation of network status. When the requirements of per-packet delay and delivery ratio are satisfied, each flow obtains some utility based on its traffic load. We aim to design joint rate control and scheduling policies that maximize the total utility in the system. We first show that the problem of maximizing total utility can be formulated as a submodular optimization problem with exponentially many constraints. We then propose two simple distributed policies that require almost no coordination between different entities in the network. The total utilities under these two policies can be made arbitrarily close to the theoretical upper-bound. Extensive simulations also show that they achieve much better performance than state-of-the-art policies.

The problem of allocating jobs to appropriate servers in cloud computing is studied in this paper. We consider that jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We propose two online job allocation policies with low complexity. We prove that, given a hard allocation ratio requirement, these two policies can achieve the requirement with the least capacity. We also derive a closed-from expression for the amount of capacity needed to achieve any given requirement. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two when jobs arrive according to some random process.

This paper studies wireless networks consisting of multiple real-time flows that impose hard delay bounds for all packets. In contrast to most current studies that focus on the long-term average rate of timely deliveries, we aim to model and optimize the short-term performance of each real-time flow, which is vital for most safety-critical applications. We propose to define the instantaneous performance of a flow by a moving average within a short window in the past. Each flow incurs some penalty if its moving average is below some specified requirement, and we aim to minimize the overall penalty of the system. We approximate the system by Brownian motions, and formulate an optimization problem for minimizing the overall penalty. While the optimization problem is not convex, we establish a low-complexity algorithm for optimally solving it by leveraging inherent structures of real-time wireless networks. We also propose a simple online packet scheduling policy and prove that it achieves the minimum overall penalty. Simulation results show that Brownian approximations are accurate in capturing short-term performance, and our policies achieve much better performance than other policies. Moreover, they also demonstrate that policies with optimal long-term average delivery rates can actually have poor short-term performance.

This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.

"Edge-clouds," which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile applications by moving cloud services closer to users at the edge. Due to their limited storage capacity, proper configurations of edge-clouds have a significant impact on their performance. This paper proposes a tractable online algorithm that configures edge-clouds dynamically solely based on past system history without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio, which quantifies the worst-case performance in comparison to an optimal offline policy, of our policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge-cloud. Moreover, we also prove that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. The utility of our online policy is further evaluated by traces from real-world data centers. These trace-based simulations demonstrate that our policy has better, or similar, performance compared to many intelligent offline policies that have complete knowledge of all future arrivals.

We present a novel scheduling policy, regularity-aware proportional fair (R-PF) that enhances service regularity for the legacy proportional fair (PF) scheduling policy. R-PF addresses the challenge that clients may have different preferences between service regularity and long-term average throughput. R-PF allows clients to specify their own preferences, and then provides services tailored to clients' specifications. R-PF preserves the advantages of PF, including high spectrum efficiency, fairness among clients, minimal coordination between the base station and clients, and low complexity. We analytically study the performance of R-PF under an i.i.d. channel model. We prove that each client achieves its optimal tradeoff between service regularity and throughput by reporting its true preference. We also prove that the total throughput under R-PF is almost the same as that under PF. Furthermore, we compare the performance of R-PF against other state-of-the-art scheduling policies using trace-based simulations. Simulation results demonstrate that our policy provides the optimal performance for all kinds of clients with different preferences between service regularity and throughput. Simulation results also show that R-PF is backward compatible in that it enhances service regularity for clients who prefer high regularity without sacrificing the throughput for those who prefer high throughput.

WiFi offloading, where mobile users opportunistically obtain data through WiFi rather than through cellular networks, is a promising technique to greatly improve spectrum efficiency and reduce cellular network congestion. We consider a system where the service provider deploys multiple WiFi hotspots to offload mobile traffic, and study the scheduling policy to maximize the amount of offloaded data. Since the movements of users are unpredictable, we focus on online scheduling policy where APs do not have any knowledge about the users' mobility patterns. We study performance of online policies by comparing against the optimal offline policy. We prove that any work-conserving policy is able to offload at least half as much data as the offline policy, and then propose an online policy that can offload (e-1)/e as much data as the offline policy. We further study the case where the service provider can increase the capacity of WiFi so as to provide guarantees on the amount of offloaded data. We propose a simple online policy and prove that our policy only needs half as much capacity as current mechanism to provide the same performance guarantee.

Video streaming is anticipated to dominate wireless traffic in the near future. We study wireless systems where an access point delivers video streams to multiple clients over unreliable wireless channels. The performance of each client is measured by the amount of time that its video playback halts due to buffer underflow, which has been shown to have the most impact on client's perceived quality of experience (QoE).

We consider ad hoc wireless networks with real-time traffic, and study the capacity requirement of a low-complexity scheduling policy, called largest-deficit-first (LDF), for achieving the same quality of service (QoS) as the optimal policy in networks with unit capacity. We derive theoretical upper and lower bounds for general traffic. The bounds depend on the interference degree of the network and the max/min delay bound ratio. The performance of LDF is further evaluated using simulations and compared with two other algorithms. The simulation results show that LDF significantly outperforms the other two algorithms.

This demo presents WiMAC, a general-purpose wireless testbed for researchers to quickly prototype a wide variety of real-time MAC protocols for wireless networks. As the interface between the link layer and the physical layer, MAC protocols are often tightly coupled with the underlying physical layer, and need to have extremely small latencies. Implementing a new MAC requires a long time. In fact, very few MACs have ever been implemented, even though dozens of new MAC protocols have been proposed. To enable quick prototyping, we employ the mechanism vs. policy separation to decompose the functionality in the MAC layer and the PHY layer. Built on the separation framework, WiMAC achieves the independence of the software from the hardware, offering a high degree of function reuse and design flexibility. Hence, our platform not only supports easy cross-layer design but also allows protocol changes on the fly. Following the 802.11-like reference design, we demonstrate that deploying a new MAC protocol is quick and simple on the proposed platform through the implementation of the CSMA/CA and CHAIN protocols.

There is increasing demand for using wireless networks for applications that generate packets with strict per-packet delay constraints. In addition to delay constraints, such applications also have various traffic patterns and require guarantees on throughputs of packets that are delivered within their delay constraints. Furthermore, a mechanism for serving delay-constrained traffic needs to specifically consider the unreliable nature of wireless links, which may differ from link to link. Also, as it is usually infeasible to gather feedback information from all clients after each transmission, broadcasting delay-constrained traffic requires addressing the challenge of the lack of feedback information. We study a model that jointly considers the application requirements on traffic patterns, delay constraints, and throughput requirements, as well as wireless limitations, including the unreliable wireless links and the lack of feedback information. Based on this model, we develop a general framework for designing feasibility-optimal broadcasting policies that applies to systems with various network coding mechanisms. We demonstrate the usage of this framework by designing policies for three different kinds of systems: one that does not use network coding, one that employs XOR coding, and the last that allows the usage of linear coding.

We consider the problem of real-time surveillance where remote sensors transmit data periodically to a control center through multihop transmissions. All data need to be delivered within a common deadline, which is the time that the control center makes control decisions. We propose a model that jointly considers the end-to-end delay constraints and delivery ratio requirements of flows, the need for multihop transmissions, and the unreliable nature of wireless transmissions. We develop a framework for designing feasibility-optimal policies. We then demonstrate the utility of this framework by considering two types of systems: one where sensors can transmit and receive packets simultaneously, possibly on different channels, and the other where sensors cannot. For the first type of systems, we propose an online distributed scheduling policy and prove that the policy is feasibility optimal. We also provide a heuristic for the second type of systems. We show that this heuristic is still feasibility optimal for some topologies. Simulation results show that both policies outperform state-of-the-art policies by large margins.

Hou et al. have analyzed wireless networks where clients served by an access point require a timely-throughput of packets to be delivered by hard per-packet deadlines and also proved the timely-throughput optimality of certain debt-based policies. However, this is a weak notion of optimality; there might be long time intervals in which a client does not receive any packets, undesirable for real-time applications. Motivated by this, the authors, in an earlier work, introduced a pathwise cost function based on the law of the iterated logarithm, studied in fluctuation theory, which captures the deviation from a steady stream of packet deliveries and showed that a debt-based policy is optimal if the frame length is one. This work extends the analysis of debt-based policies to general frame lengths greater than one, as is important for general applications.

We develop a general approach for designing scheduling policies for real-time traffic over wireless channels. We extend prior work, which characterizes a real-time flow by its traffic pattern, delay bound, timely throughput requirement, and channel reliability, to allow clients to have different deadlines and allow a variety of channel models. In particular, our extended model consider scenarios where channel qualities are time-varying, the access point may not have explicit information on channel qualities, and the access point may or may not employ rate adaptation. Thus, our model allows the treatment of more realistic fading channels as well as scenarios with mobile nodes and the usage of more general transmission strategies. We derive a sufficient condition for a scheduling policy to be feasibility optimal, and thereby establish a class of feasibility optimal policies. We demonstrate the utility of the identified class by deriving a feasibility optimal policy for the scenario with rate adaptation, time-varying channels, and heterogeneous delay bounds. When rate adaptation is not available, we also derive feasibility optimal policies for both scenarios where the access point may or may not have explicit knowledge on channel qualities. For the scenario where rate adaptation is not available but clients have different delay bounds, we describe a heuristic. Simulation results are also presented, which indicate the usefulness of the scheduling policies for more realistic and complex scenarios.

A challenging problem in multiband multicell self-organized wireless systems, such as femtocells/picocells in cellular networks, multichannel Wi-Fi networks, and more recent wireless networks over TV white spaces, is of distributed resource allocation. This in general involves four components: channel selection, client association, channel access, and client scheduling. In this paper, we present a unified framework for jointly addressing the four components with the global system objective of maximizing the clients throughput in a proportionally fair manner. Our formulation allows a natural dissociation of the problem into two subparts. We show that the first part, involving channel access and client scheduling, is convex and derive a distributed adaptation procedure for achieving a Pareto-optimal solution. For the second part, involving channel selection and client association, we develop a Gibbs-sampler-based approach for local adaptation to achieve the global objective, as well as derive fast greedy algorithms from it that achieve good solutions often.

Wireless sensor networks have been increasingly used for real-time surveillance over large areas. In such applications, it is important to support end-to-end delay constraints for packet deliveries even when the corresponding flows require multi-hop transmissions. In addition to delay constraints, each flow of real-time surveillance may require some guarantees on throughput of packets that meet the delay constraints. Further, as wireless sensor networks are usually deployed in challenging environments, it is important to specifically consider the effects of unreliable wireless transmissions. In this paper, we study the problem of providing end-to-end delay guarantees for multi-hop wireless networks. We propose a model that jointly considers the end-to-end delay constraints and throughput requirements of flows, the need for multi-hop transmissions, and the unreliable nature of wireless transmissions. We develop scheduling policies for two types of systems and prove that they are optimal. We also conduct extensive simulations to show that our policies outperform other policies by large margins.

The Index Coding problem has recently attracted a significant interest from the research community. In this problem, a server needs to deliver a set of packets to a group of wireless clients over a noiseless broadcast channel. Each client requests a subset of packets and has another subset given to it as side information. The objective is to satisfy the demands of all clients with the minimum number of transmissions. In this paper, we study the Index Coding problem from the game-theoretic perspective. We assume that each client is selfish and has a hidden private value for each packet it requests. The objective of the server is to maximize the value of social welfare that captures the trade-off between values of the transmitted packets and the transmission cost incurred by the server. The transmission process is decided through an auction in which the clients are required to submit bids to the server. Our goal is to design a truthful auction scheme that provides an incentive for each client to bid the true value of the packets and maximizes the value of the social welfare. The key challenge in this context is to determine the encoding functions of the transmitted packets. Since finding an optimal encoding function is an NP-hard problem, we propose efficient algorithms that identify the encoding functions as well as a payment scheme that provide an approximate solution and guarantee truthfulness.

This paper studies the problem of serving multiple live video streams to several different clients from a single access point over unreliable wireless links, which is expected to be major a consumer of future wireless capacity. This problem involves two characteristics. On the streaming side, different video streams may generate variable-bit-rate traffic with different traffic patterns. On the network side, the wireless transmissions are unreliable, and the link qualities differ from client to client. In order to alleviate the above stochastic aspects of both video streams and link unreliability, each client typically buffers incoming packets before playing the video. The quality of the video playback subscribed to by each flow depends, among other factors, on both the delay of packets as well as their throughput. In this paper we address how to schedule packets at the access point to satisfy the joint per-packet-delay-throughput performance measure. We test the designed policy on the traces of three movies. From our tests, it appears to outperform other policies by a large margin.

This paper studies the problem of self-organizing heterogeneous LTE systems. We propose a model that jointly considers several important characteristics of heterogeneous LTE system, including the usage of orthogonal frequency division multiple access (OFDMA), the frequency-selective fading for each link, the interference among different links, and the different transmission capabilities of different types of base stations. We also consider the cost of energy by taking into account the power consumption, including that for wireless transmission and that for operation, of base stations and the price of energy. Based on this model, we aim to propose a distributed protocol that improves the spectrum efficiency of the system, which is measured in terms of the weighted proportional fairness among the throughputs of clients, and reduces the cost of energy. We identify that there are several important components involved in this problem. We propose distributed strategies for each of these components. Each of the proposed strategies requires small computational and communicational overheads. Moreover, the interactions between components are also considered in the proposed strategies. Hence, these strategies result in a solution that jointly considers all factors of heterogeneous LTE systems. Simulation results also show that our proposed strategies achieve much better performance than existing ones.

We consider the problem of LTE network self organization and optimization of resource allocation. One particular challenge for LTE systems is that, by applying OFDMA, a transmission may use multiple resource blocks scheduled over the frequency and time. There are three key components involved in the resource allocation and network optimization: resource block scheduling, power control, and client association. We propose a distributed protocol that aims to achieve weighted proportional fairness (WPF) among clients by jointly consider them. The cross-layer design includes: (i) an optimal online policy for resource block scheduling, (ii) a heuristic for transmit power control, and (iii) a selfish strategy for client association. The proposed scheme only requires limited local information exchange and thus can be easily implemented for large networks. Simulation results have shown its effectiveness in both the system throughput and user fairness.

A challenging problem in multi-band multi-cell self-organized wireless systems, such as multi-channel Wi-Fi networks, femto/pico cells in 3G/4G cellular networks, and more recent wireless networks over TV white spaces, is of distributed resource allocation. This involves four components: channel selection, client association, channel access, and client scheduling. In this paper, we present a unified framework for jointly addressing the four components with the global system objective of maximizing the clients throughput in a proportionally fair manner. Our formulation allows a natural dissociation of the problem into two sub-parts. We show that the first part, involving channel access and client scheduling, is convex and derive a distributed adaptation procedure for achieving Pareto-optimal solution. For the second part, involving channel selection and client association, we develop a Gibbs-sampler based approach for local adaptation to achieve the global objective, as well as derive fast greedy algorithms from it that achieve good solutions.

There is increasing demand for using wireless networks for applications that generate packets with strict per-packet delay constraints. In addition to delay constraints, such applications also have various traffic patterns and require guarantees on throughputs of packets that are delivered within their delay constraints. Furthermore, a mechanism for serving delay-constrained traffic needs to specifically consider the unreliable nature of wireless links, which may differ from link to link. Also, as it is usually infeasible to gather feedback information from all clients after each transmission, broadcasting delay-constrained traffic requires addressing the challenge of the lack of feedback information.

We study the problem of scheduling periodic real-time tasks which have individual minimum reward requirements. We consider situations where tasks generate jobs that can be provided arbitrary service times before their deadlines, and obtain rewards based on the service times received by the jobs of the task. We show that this model is compatible with the imprecise computation models and the increasing reward with increasing service models. In contrast to previous work on these models, which mainly focus on maximizing the total reward in the system, we additionally aim to fulfill different reward requirements by different tasks. This provides better fairness and also allows fine-grained tradeoff between tasks. We first derive a necessary and sufficient condition for a system with reward requirements of tasks to be feasible. We next obtain an off-line feasibility optimal scheduling policy. We then study a sufficient condition for a policy to be feasibility optimal or achieve some approximation bound. This condition serves as a guideline for designing on-line scheduling policy and we obtain a greedy policy based on it. We prove that the on-line policy is feasibility optimal when all tasks have the same periods, and also obtain an approximation bound for the policy under general cases. We test our policies in comparative simulations.

This paper studies the problem of utility maximization for clients with delay based QoS requirements in wireless networks. We adopt a model used in a previous work that characterizes the QoS requirements of clients by their delay constraints, channel reliabilities, and timely throughput requirements. In this work, we assume that the utility of a client is a function of the timely throughput it obtains. We treat the timely throughput for a client as a tunable parameter by the access point (AP), instead of a given value as in the previous work. We then study how the AP should assign timely throughputs to clients so that the total utility of all clients is maximized. We apply the techniques introduced in two previous papers to decompose the utility maximization problem into two simpler problems, a CLIENT problem and an ACCESS-POINT problem. We show that this decomposition actually describes a bidding game, where clients bid for the service time from the AP. We prove that although all clients behave selfishly in this game, the resulting equilibrium point of the game maximizes the total utility. In addition, we also establish an efficient scheduling policy for the AP to reach the optimal point of the ACCESS-POINT problem. We prove that the policy not only approaches the optimal point but also achieves some forms of fairness among clients. Finally, simulation results show that our proposed policy does achieve higher utility than all other compared policies.

We develop a general approach for designing scheduling policies for real-time traffic over wireless channels. We extend prior work, which characterizes a real-time flow by its traffic pattern, delay bound, timely-throughput requirement, and channel reliability, to allow time-varying channels, allow clients to have different deadlines, and allow for the optional employment of rate adaptation. Thus, our model allow the treatment of more realistic fading channels as well as scenarios with mobile nodes, and the usage of more general transmission strategies. We derive a sufficient condition for a scheduling policy to be feasibility optimal, and thereby establish a class of feasibility optimal policies. We demonstrate the utility of the identified class by deriving a feasibility optimal policy for the scenario with rate adaptation, time-varying channels, and heterogeneous delay bounds. When rate adaptation is not available, we also derive a feasibility optimal policy for time-varying channels. For the scenario where rate adaptation is not available but clients have different delay bounds, we describe a heuristic. Simulation results are also presented which indicate the usefulness of the scheduling policies for more realistic and complex scenarios.

Clients in wireless networks may have per-packet delay con-straints on their traffic. Further, in contrast to wireline net-works, the wireless medium is subject to fading. In such a time-varying environment, we consider the system problem of maximizing the total utility of clients, where the utilities are determined by their long-term average rates of being served within their delay constraints. We also allow for the addi-tional fairness requirement that each client may require a cer-tain minimum service rate. This overall model can be applied to a wide range of applications, including delay-constrained networks, mobile cellular networks, and dynamic spectrum allocation. We address this problem through convex programming. We propose an on-line scheduling policy and prove that it is utility-optimal. Surprisingly, this policy does not need to know the probability distribution of system states. We also design an auction mechanism where clients are scheduled and charged according to their bids. We prove that the auction mechanism restricts any selfish client from improving its utility by faking its utility function. We also show that the auction mechanism schedules clients in the same way as that done by the on-line scheduling policy. Thus, the auction mechanism is both truth-ful and utility-optimal. Finally, we design specific algorithms that implement the auction mechanism for a variety of appli-cations.

Wireless networks are increasingly used to carry applications with QoS constraints. Two problems arise when dealing with traffic with QoS constraints. One is admission control, which consists of determining whether it is possible to fulfill the demands of a set of clients. The other is finding an optimal scheduling policy to meet the demands of all clients. In this paper, we propose a framework for jointly addressing three QoS criteria: delay, delivery ratio, and channel reliability. We analytically prove the necessary and sufficient condition for a set of clients to be feasible with respect to the above three criteria. We then establish an efficient algorithm for admission control to decide whether a set of clients is feasible. We further propose two scheduling policies and prove that they are feasibility optimal in the sense that they can meet the demands of every feasible set of clients. In addition, we show that these policies are easily implementable on the IEEE 802.11 mechanisms. We also present the results of simulation studies that appear to confirm the theoretical studies and suggest that the proposed policies outperform others tested under a variety of settings.

We consider the placement of sensors to detect propagative sources where the sensing area of each sensor is anisotropic and arbitrarily-shaped due to the terrain and meteorological conditions. The propagation and detection times are non-negligible due to the propagation of source effects through space at a slow speed. We formulate the problem as placing the minimum number of sensors to ensure a detection time T and the coverage utility C. Both the sensing areas of sensors and the utility function U(ldr) are chosen to capture the environmental factors and the population distribution. We show this problem to be NP-hard, and present heuristic algorithms for 1-coverage and fc-coverage by adopting exiting methods. We evaluate the proposed algorithms in the realistic setting of Port of Memphis where the objective is to protect the population against chemical leaks or attacks. We utilize the SCIPUFF dispersion model to determine the sensing areas by accounting for the terrain and meteorological conditions, and use the real-life population distribution as the utility function. Based on empirical study, we make several important observations.

Providing differentiated Quality of Service (QoS) over unreliable wireless channels is an important challenge for supporting several future applications. We analyze a model that has been proposed to describe the QoS requirements by four criteria: traffic pattern, channel reliability, delay bound, and throughput bound. We study this mathematical model and extend it to handle variable bit rate applications. We then obtain a sharp characterization of schedulability vis-a-vis latencies and timely throughput. Our results extend the results so that they are general enough to be applied on a wide range of wireless applications, including MPEG Variable-Bit-Rate (VBR) video streaming, VoIP with differentiated quality, and wireless sensor networks (WSN).

Code updates, such as those for debugging purposes, are frequent and expensive in the early development stages of wireless sensor network applications. We propose AdapCode, a reliable data dissemination protocol that uses adaptive network coding to reduce broadcast traffic in the process of code updates. Packets on every node are coded by linear combination and decoded by Gaussian elimination. The core idea in AdapCode is to adaptively change the coding scheme according to the link quality. Our evaluation shows that AdapCode uses up to 40% less packets than Deluge in large networks. In addition, AdapCode performs much better in terms of load balancing, which prolongs the system lifetime, and has a slightly shorter propagation delay. Finally, we show that network coding is doable on sensor networks in that (i) it imposes only a 3 byte header overhead, (ii) it is easy to find linearly independent packets, and (3) Gaussian elimination needs only 1 KB of memory.

No abstract available.