This paper presents a novel federated learning solution, QHetFed, suitable for Internet of Things deployments, addressing the challenges of clustered geographic distribution, communication resource limitation, and data heterogeneity. QHetFed is based on hierarchical federated learning over multiple device clusters, where the learning process and learning parameters take the necessary data quantization and the data heterogeneity into consideration to achieve high accuracy and fast convergence. Unlike conventional hierarchical federated learning algorithms, the proposed approach combines gradient aggregation in intra-cluster iterations with model aggregation in inter-cluster iterations. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate, and give a closed form expression for the optimal learning parameters under a deadline, that accounts for communication and computation times. Our findings reveal that QHetFed consistently achieves high learning accuracy and significantly outperforms other hierarchical algorithms, particularly under heterogeneous data distributions.

We consider the problem of non-coherent over-the-air computation (AirComp), where$n$devices carry highdimensional data vectors$\mathrm{x}_{i} \in \mathbb{R}^{d}$of sparsity$\left\vert\mathrm{x}_{i}\right\vert_{0} \leq k$and the sum of these data vectors has to be computed at a receiver. Previous results on non-coherent AirComp require more than$d$channel uses to compute functions of$\mathrm{x}_{i}$, where the extra redundancy is used to combat non-coherent signal aggregation. However, if the data vectors are sparse, sparsity can be exploited to offer significantly cheaper communication. In this paper, we propose to use random transforms to transmit lower-dimensional projections$s_{i} \in \mathbb{R}^{T}$of the data vectors. These projected vectors are communicated to the receiver using a majority vote (MV)AirComp scheme, which estimates the bit-vector corresponding to the signs of the aggregated projections, i.e.,$\mathbf{y}=\text{sign}\left(\sum_{i} \mathbf{s}_{i}\right)$. By leveraging 1-bit compressed sensing (1bCS) at the receiver, the real-valued and high-dimensional aggregate$\sum_{i} \mathrm{x}_{i}$can be recovered from$y$. We prove analytically that the proposed MVCS scheme estimates the aggregate data vector$\sum_{i} \mathrm{x}_{i}$with$\ell_{2}$-norm error$\epsilon$in$T=\mathcal{O}\left(k n \log (d) / \epsilon^{2}\right)$channel uses. We consider distributed histogram estimation, a canonical building block for federated analytics, as an aplication for MVCS where the data vectors$\mathrm{x}_{i}$are inherently 1 -sparse. Our numerical evaluations demonstrate that our scheme achieves the same order of communication cost as state-of-the-art methods while avoiding the complexity and overhead of additional cryptographic tools.

We consider the problem of secure histogram es-timation, where$n$users hold private items xifrom a size-d domain and a server aims to estimate the histogram of the user items. Previous results utilizing orthogonal communication schemes have shown that this problem can be solved securely with a total communication cost of O(n2log(d)) bits by hiding each item xiwith a mask. In this paper, we offer a different approach to achieving secure aggregation. Instead of masking the data, our scheme protects individuals by aggregating their messages via a multiple-access channel. A naive communication scheme over the multiple-access channel requires$d$channel uses, which is generally worse than the O(n21og(d)) bits communication cost of the prior art in the most relevant regime$d$>>$n$. Instead, we propose a new scheme that we call Over-the-Air Group Testing (AirG T) which uses group testing codes to solve the histogram estimation problem in O(n log(d)) channel uses. AirGT reconstructs the histogram exactly with a vanishing probability of error Perror= O(d-T) that drops exponentially in the number of channel uses$T$.

When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a learning method designed to address these challenges, along with a scalable transmission scheme that efficiently uses a single wireless resource through over-the-air computation. To provide resistance against data heterogeneity, we employ gradient aggregations. Meanwhile, the impact of interference is minimized through optimized receiver normalizing factors. For this, we model a multi-cluster wireless network using stochastic geometry, and characterize the mean squared error of the aggregation estimations as a function of the network parameters. We show that despite the interference and the data heterogeneity, the proposed scheme achieves high learning accuracy and can significantly outperform the conventional hierarchical algorithm.

When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a new two-level learning method designed to address these challenges, along with a scalable over-the-air aggregation scheme for the uplink and a bandwidth-limited broadcast scheme for the downlink that efficiently use a single wireless resource. To provide resistance against data heterogeneity, we employ gradient aggregations. Meanwhile, the impact of uplink and downlink interference is minimized through optimized receiver normalizing factors. We present a comprehensive mathematical approach to derive the convergence bound for the proposed algorithm, applicable to a multi-cluster wireless network encompassing any count of collaborating clusters, and provide special cases and design remarks. As a key step to enable a tractable analysis, we develop a spatial model for the setup by modeling devices as a Poisson cluster process over the edge servers and rigorously quantify uplink and downlink error terms due to the interference. Finally, we show that despite the interference and data heterogeneity, the proposed algorithm not only achieves high learning accuracy for a variety of parameters but also significantly outperforms the conventional hierarchical learning algorithm.

Edge intelligence is an emerging technology where the base stations located at the edge of the network are equipped with computing units that provide machine learning services to the end users. To provide high-quality services in a cost-efficient way, the wireless and computing resources need to be dimensioned carefully. In this paper, we address the problem of resource dimensioning in a single-cell system that supports edge video analytics under latency and accuracy constraints. We show that the resource-dimensioning problem can be transformed into a convex optimization problem, and we provide numerical results that give insights into the trade-offs between the wireless and computing resources for varying cell sizes and for varying intensity of incoming tasks. Overall, we observe that the wireless and computing resources exhibit opposite trends; the wireless resources favor from smaller cells, where high attenuation losses are avoided, and the computing resources favor from larger cells, where statistical multiplexing allows for computing more tasks. We also show that small cells with low loads have high per-request costs, even when the wireless resources are increased to compensate for the low multiplexing gain at the servers.

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Over-the-air computation (AirComp) has recently emerged as an efficient analog method for data acquisition from wireless sensor devices. In essence, AirComp exploits the signal superposition property of a multiple access channel to estimate functions of the transmitted data points. Unless devices are excluded from participation, state-of-the-art AirComp methods do not achieve unbiased function computation, thereby introducing systematic errors in the acquired function. In this paper, we propose a new AirComp scheme that employs retransmissions to achieve probabilistically unbiased function computation. We solve a power control problem that minimizes the bias subject to a peak transmission power constraint. We show that the optimal power control follows a greedy structure that maximizes the devices' contribution to the received function at every retransmission. Numerical results show that the proposed scheme can achieve unbiased function computation with a few retransmissions and drastically reduce the mean squared error in the function estimation compared to the current state-of-the-art.

Edge intelligence is a scalable solution for analyzing distributed data, but it cannot provide reliable services in large-scale cellular networks unless the inherent aspects of fading and interference are also taken into consideration. In this paper, we present the first mathematical framework for modelling edge video analytics in multi-cell cellular systems. We derive the expressions for the coverage probability, the ergodic capacity, the probability of successfully completing the video analytics within a target delay requirement, and the effective frame rate. We also analyze the effect of the system parameters on the accuracy of the detection algorithm, the supported frame rate at the edge server, and the system fairness.

This paper addresses the problem of load-balancing in a typical mobile edge computing scenario, where mobile users need to select one of the edge servers in their vicinity to offload their tasks. To achieve load balancing, while also limiting the control overhead, we propose δ-DJSQ, a fully-distributed joint-the-shortest-queue policy, where servers provide sporadic status updates over unreliable channels, and users select the least loaded server according to their local belief. We formally prove that the necessary conditions of system stability are also sufficient, namely, the system is stable when the task generation rate is lower than the task service rate, the status update rate is positive, and the probability that a user receives a status update is positive as well. We show that δ-DJSQ is robust to update delays and to message losses, and achieves competitive performance results compared to static load-balancing solutions, both for homogeneous and for heterogeneous servers.

In this paper, we consider a mobile edge computing (MEC)network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA)is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA)as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.

Age of information has been recently proposed to quantify the freshness of information, e.g., in cyber-physical systems, where it is of critical importance. Motivated by wireless camera networks where multi-view image processing is required, in this paper we propose to extend the concept of age of information to capture packets carrying correlated data. We consider a system consisting of wireless camera nodes with overlapping fields of view and a set of processing nodes, and address the problem of the joint optimization of processing node assignment and camera transmission scheduling, so as to minimize the maximum peak age of information from all sources. We formulate the multi-view age minimization (MVAM) problem, and prove its NP-hardness under the two widely used interference models as well as with given candidate transmitting groups. We provide fundamental results including tractable cases and optimality conditions of the MVAM problem for two baseline scenarios. To solve MVAM efficiently, we develop an optimization algorithm based on a decomposition approach. Numerical results show that by employing our approach the maximum peak age is significantly reduced in comparison to a traditional centralized solution with minimum-time scheduling.

Accurate prediction of user mobility allows the efficient use of resources in our ubiquitously connected environment. In this work we study the predictability of the users' next location, considering a campus scenario with highly mobile users. We utilize Markov predictors, and estimate the theoretical predictability limits. Based on the mobility traces of nearly 7400 wireless network users, we estimate that the maximum predictability of the users is on average 82%, and we find that the best Markov predictor is accurate 67% of the time. In addition, we show that moderate performance gains can be achieved by leveraging multi-location prediction.

In this paper we evaluate the effect of increased wireless spectral efficiency on the performance of mobile edge computing. Specifically, we study the energy minimization of computation offloading for a multicarrier non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. A joint radio-and-computational resource allocation problem is formulated, in which three different resources should be appropriately allocated, including subcarriers, transmission power and computational resources. The formulated resource allocation problem belongs to mixed integer nonlinear programming (MILNP) and is NP-hard. We propose therefore a heuristic solution consisting of two steps, NOMA clustering and subcarrier allocation, and joint computational resource and power allocation. Our numerical results show that NOMA based MEC significantly outperforms its OMA counterpart, especially in scenarios with strict delay limits, where both the transmission and the computational resources become scarce.

This paper presents an analysis of a large trace of user associations in a university wireless network, which includes around one thousand access points over five campuses. The trace is obtained from RADIUS authentication logs and its merit is in its recency, scale and duration. We propose a methodology for extracting association statistics from these logs, and look at visiting time distributions and processes of user arrivals to access points. We find that a large fraction of the network - around half of all access points - experiences time-varying Poisson arrival process, and association distributions can be modeled by two-stage hyper-exponential distributions at most of the access point. While network associations in campus wireless networks have been extensively studied in the literature, our study reveals changing patterns in user arrival processes and association durations, which seem to be characteristic for networks of predominantly mobile users, and allows the use of tractable network occupancy models.

This paper addresses the power allocation challenge for the downlink transmission in non-orthogonal multiple access (NOMA) systems applying multiple input multiple output transceivers. We consider the case when users are paired to form NOMA clusters, and share a common power budget. We provide low complexity power allocation methods within the clusters and across the clusters, that, together, maximize the sum-rate of the network, while guaranteeing a minimum quality of service for the users with weak channel condition. We show that compared to equal power allocation for the clusters, the proposed power allocation scheme improves the system fairness significantly, without decreasing the aggregate performance.

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms' performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.

Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than 10% quality degradation compared to the best achievable performance.

We consider the problem of performing distributed visual analysis for a video sequence in a visual sensor network that contains sensor nodes dedicated to processing. Visual analysis requires the detection and extraction of visual features from the images, and thus the time to complete the analysis depends on the number and on the spatial distribution of the features, both of which are unknown before performing the detection. In this paper, we formulate the minimization of the time needed to complete the distributed visual analysis for a video sequence subject to a mean average precision requirement as a stochastic optimization problem. We propose a solution based on two composite predictors that reconstruct randomly missing data, on quantile-based linear approximation of feature distribution and on time series analysis methods. The composite predictors allow us to compute an approximate optimal solution through linear programming. We use two surveillance video traces to evaluate the proposed algorithms, and show that prediction is essential for minimizing the completion time, even if the wireless channel conditions vary and introduce significant randomness. The results show that the last value predictor together with regular quantile-based distribution approximation provide a low complexity solution with very good performance.

In hierarchical cognitive radio networks, unlicensed secondary users can increase their achievable rates by assisting licensed primary user transmissions via cooperation. In this paper, a novel approach to maximizing the transmission rates in the secondary network by optimizing the relay selection, the secondary transmit powers, and the cooperative relaying power splitting parameters is proposed. The resulting optimization problem is mixed integer and nonconvex, which makes it NP hard to find the optimal solutions. Therefore, centralized and distributed solution methods to find near-to-optimal solutions of this challenging problem are proposed. The methods are based on iteratively solving the secondary relay selection by a greedy approach, and the optimal power allocation problem by a fixed-point approach together with alternating direction method of multipliers. It is established that both centralized and distributed solution methods always converge. The numerical results illustrate how the performance of the proposed solution methods depend on the primary performance margins, and show that they give a near-to-optimal solution in few iterations.

Nodes in a sensor network are traditionally used for sensing and data forwarding. However, with the increase of their computational capability, they can be used for in-network data processing, leading to a potential increase of the quality of the networked applications as well as the network lifetime. Visual analysis in sensor networks is a prominent example where the processing power of the network nodes needs to be leveraged to meet the frame rate and the processing delay requirements of common visual analysis applications. The modeling of the end-to-end performance for such networks is, however, challenging, because in-network processing violates the flow conservation law, which is the basis for most queuing analysis. In this work we propose to solve this methodological challenge through appropriately scaling the arrival and the service processes, and we develop probabilistic performance bounds using stochastic network calculus. We use the developed model to determine the main performance bottlenecks of networked visual processing. Our numerical results show that an end-to-end delay of 2-3 frame length is obtained with violation probability in the order of 10-6. Simulation shows that the obtained bounds overestimates the end-to-end delay by no more than 10%.

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Enabling visual sensor networks to perform such tasks can be achieved by augmenting the sensor network with processing nodes and distributing the computational burden among several nodes, in a way that the cameras contend for the processing nodes while trying to minimize their completion times. In this paper, we formulate the problem of minimizing the completion time of all camera sensors as an optimization problem. We propose algorithms for fully distributed optimization, analyze the existence of equilibrium allocations, and evaluate their performance. Simulation results show that distributed optimization can provide good performance despite limited information availability at low computational complexity, but the predictable and stable performance is often not provided by the algorithm that provides lowest average completion time.

We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource -constrained visual sensor network (VSN). Our results show high variability in the density, the spatial distribution , and the octave layer distribution of the interest points. The high variability implies that balancing the processing load among the sensor nodes is a very challenging task, and obtaining a priori information is essential, e.g., through prediction . Our results show that if a priori information is available about the images, then Top- M interest point selection, limited , octave-based processing at the camera node, together with area-based interest point detection and extraction at the processing nodes, can balance the processing load and limit the transmission cost in the network . Complete interest point detection at the camera node with optimized descriptor extraction delegation to the processing nodes in turn can further decrease the transmission load and allow a better balance of the processing load among the network nodes.

Energy efficiency has been the driving force behind the design of communication protocols for battery-constrained wireless sensor networks (WSNs). The energy efficiency and the performance of the proposed protocol stacks, however, degrade dramatically in case the low-powered WSNS are subject to interference from high-power wireless systems such as WLANs. In this paper we propose COG-MAC, a novel cognitive medium access control scheme (MAC) for IEEE 802.15.4-compliant WSNS that minimizes the energy cost for multihop communications, by deriving energy-optimal packet lengths and single-hop transmission distances based on the experienced interference from IEEE 802.11 WLANs. We evaluate COG-MAC by deriving a detailed analytic model for its performance and by comparing it with previous access control schemes. Numerical and simulation results show that a significant decrease in packet transmission energy cost, up to 66%, can be achieved in a wide range of scenarios, particularly under severe WLAN interference. COG-MAC is, also, lightweight and shows high robustness against WLAN model estimation errors and is, therefore, an effective, implementable solution to reduce the WSN performance impairment when coexisting with WLANs.

Enabling visual sensor networks to perform visual analysis tasks in real-time is challenging due to the computational complexity of detecting and extracting visual features. A promising approach to address this challenge is to distribute the detection and the extraction of local features among the sensor nodes, in which case the time to complete the visual analysis of an image is a function of the number of features found and of the distribution of the features in the image. In this paper we formulate the minimization of the time needed to complete the distributed visual analysis for a video sequence subject to a mean average precision requirement as a stochastic optimization problem. We propose a solution based on two composite predictors that reconstruct randomly missing data, and use a quantile-based linear approximation of the feature distribution and time series analysis methods. The composite predictors allow us to compute an approximate optimal solution through linear programming. We use two surveillance videos to evaluate the proposed algorithms, and show that prediction is essential for controlling the completion time. The results show that the last value predictor together with regular quantile-based distribution approximation provide a low complexity solution with very good performance.

We consider controlling and balancing the processing load in a visual sensor network (VSN) used for detecting local features, such as BRISK. We formulate a prediction problem with random missing data, and propose two regression-based algorithms for data reconstruction. Numerical results illustrate the performance of the proposed algorithms, and show that backward regression combined with the last value predictor can be used for controlling and balancing the processing load in VSNs with good performance.

This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.

We address the challenge of energy efficiency in hierarchical spectrum sharing networks with dynamic traffic. We consider a primary and a cognitive secondary transmitter-receiver pair, where the secondary transmitter can utilize cooperative transmission to relay primary traffic while superimposing its own information. The secondary user meets a dilemma in this scenario. By choosing cooperation, it can transmit a packet immediately, but it has to bear the additional cost of relaying. Otherwise, it can wait for the primary user to become idle, which increases the queuing delay that secondary packets experience. To solve this dilemma and trade off delay and energy consumption, we propose dynamic cooperative secondary access control that takes the state of the spectrum sharing network into account. We formulate the problem as a Markov decision process and prove the existence of a stationary policy that is average cost optimal. We evaluate reinforcement learning to find optimal transmission strategy when the traffic and link statistics are not known. We demonstrate that dynamic cooperation is necessary for the secondary system to be able to adapt to changing network conditions and show that optimal sequential decision can significantly improve the tradeoff of the energy consumption and the delay.

We consider a spectrum sharing network consisting of a primary and a cognitive secondary transmitter-receiver pair, where the secondary transmitter can cooperatively relay primary traffic. If the secondary user chooses not to cooperate, it can transmit only when the channel is sensed idle. Otherwise, it relays the primary packet and transmits its own packet in the same time slot while guaranteeing the performance of the primary transmission. Choosing cooperation, the secondary user can transmit a packet immediately even if the primary queue is not empty, but it has to bear the additional cost of relaying. We consider a cognitive system, where, to solve this dilemma, the secondary user decides dynamically on when to cooperate. We derive the bounds of the stable-throughput region of the system, and formulate the problem as a Markov decision process (MDP). We prove the existence of a stationary policy that is average cost optimal. Numerical results show that the optimal dynamic secondary access can trade off between the gain and the cost of cooperation, and the average cost can be decreased significantly.

Efficient communication in the crowded ISM band requires the communication networks to be aware of the networking environment and to control their communication protocols accordingly. In this paper we address the issue of efficient WSN communication under WLAN interference. We propose analytic models to describe the WLAN idle time distributions as observed by the WSN nodes, together with efficient methods for parameter estimation. We evaluate how the spectrum sensing capability of the sensors affects the performance of the idle period distribution estimation and conclude that the proposed solutions are accurate enough to support cognitive WSNs.

We consider a cognitive radio network where primary users (PUs) and secondary users (SUs) coexist and share the same spectrum. Secondary users access the spectrum randomly and limit the outage probability experienced by the primaries by controlling their transmission probability and by obeying a primary exclusion region (PER), within which all SUs have to be inactive. We propose a vertical cooperative transmission scheme with three geographic relay selection rules. In our scheme, the primary source-destination pairs select a neighboring idle SU, and use the selected SU as a cooperative relay. We derive analytic models on the primary outage probability considering the effects of the secondary random access, the PER and the different relay selection rules. Our results indicate that the proposed vertical cooperation increases significantly the allowed transmission probability of the SUs both without and with PER.

Cooperative communication has been proposed as a means to increase the capacity of a wireless link by mitigating the path-loss, fading and shadowing effects of radio propagation. In this paper, we evaluate the efficiency of cooperative communication in large scale wireless networks under interference from simultaneous transmissions. Specifically, we consider tunable spatial reuse time division multiplexing and half-duplex decode-and-forward cooperative relaying on a hop-by-hop basis. We show that hop-by-hop cooperation improves the reliability of the transmissions particularly in the low-SINR or in the low-coding rate regimes. Moreover, hop-by-hop cooperative relaying gains 15 - 20% more throughput compared to simple multihopping in the interference-limited regime, if the relay location and the reuse distance are jointly optimized.

We propose cooperative geographic routing (cGeo-routing) for wireless mesh networks by combining cooperative transmission with traditional geographic routing. We model and evaluate two cGeo-routing schemes including Cooperative-Random Progress Forwarding (C-RPF) and Cooperative-Nearest with Forward Progress (C-NFP). We show that cGeo-routing significantly increases the average transport capacity for a single hop in well connected mesh networks, and the gain increases with the transmitted signal-to noise ratio (SNR). Moreover, there exists an optimal topology knowledge range in C-RPF, whereas an optimal node density in C-NFP. Our results also suggest that hop-by-hop cooperation can increase transport capacity in highconnectivity and high-SNR regimes, however, it does not change the transport capacity scaling law of the mesh network.

Cognitive radio operation with opportunistic spectrum access has been proposed to utilize spectrum holes left unused by a primary system owning the spectrum license. The key of cognitive radio operation is the ability to detect weak primary signals and to control the transmission of cognitive users in a way that interference between the two systems is minimized. In this paper we evaluate how a sensor network deployed to provide distributed spectrum sensing can assist cognitive operation. Specifically, we consider sensor networks with regular topology, where a high level of cooperation also means that sensors far from the source of the primary signal are involved in the sensing process. Assuming energy detection and hard-decision combining we derive worst case probabilities of missed detection and false alarm, determine the necessary level of cooperation among the sensors and evaluate how the sensor density and the sensing time affect the performance of distributed sensing.

This paper investigates the efficiency of cooperative transmission when it is applied in wireless multihop networks. We consider regular linear networks and derive the achievable rate-delay tradeoff when selective relaying through a single relay node is used in each hop. We show that relaying achieves significant gain particularly in the high throughput - high delay regime.

A large number of peer-to-peer streaming systems have been proposed and deployed in recent years. Yet, there is no clear understanding of how these systems scale and how multipath and multihop transmission, properties of all recent systems, affect the quality experienced by the peers. In this paper, we present an analytical study that considers the relationship between delay and loss for general overlays: we study the trade-off between the playback delay and the probability of missing a packet and we derive bounds on the scalability of the systems. We present an exact model of push-based overlays and show that the bounds hold under diverse conditions: in the presence of errors, under node churn, and when using forward error correction and various retransmission schemes.

In this paper we discuss topology design and dimensioning of sensor networks to achieve full sensing coverage. We consider two ways of deploying the sensors, placing them according to some regular pattern or scattering them randomly, and two ways of activating the sensors, optimally according to some predefined schedule or randomly, when each sensor follows a wake-up schedule independently from the other sensors. We provide analytic expressions for the necessary and sufficient number of sensors that guarantee coverage in these scenarios and determine the cases when deterministic sensor placement or optimal sensor activation can achieve significant gains. We consider sensing with bounded delay and show that the number of sensors to be deployed can be decreased significantly even at low sensing delays.

In this paper we propose and analyze a generalized multiple-tree-based overlay architecture for peer-to-peer live streaming that employs multipath transmission and forward error correction. We give mathematical models to describe the stability properties of the overlay and evaluate the error recovery in the presence of node dynamics and packet losses. We show how the stability of the overlay improves with the proper allocation of the outgoing bandwidths of the peers among the trees without compromising its error correcting capability.

Applications that require low loss probabilities in today's Internet have to employ some end-to-end error-recovery mechanism. For interactive applications with strict delay constraints, the delay introduced by the applied schemes has to be low as well. In this paper we compare two schemes proposed for error recovery for real-time audio applications: media-dependent forward error correction (MD-FEC) and multiple description coding (MDC). We conclude that MDC always performs better than MD-FEC, and that the stationary loss probability plays a key role in the choice of the optimal parameters for these schemes. Combining the analytical results with the loss characteristics of measured traces of VoIP calls we conclude that in the current Internet these schemes give considerable gains for streams with a high code rate only, and for these streams MDC can decrease the average distortion significantly better than MD-FEC.

In this paper we propose an analytical model of a resilient end-node multicast streaming architecture based on multiple minimum-depth-trees that employs path diversity and forward error correction for improved resilience to node churns and packet losses. We study the performance of the architecture in the presence of packet losses and dynamic node behavior. We show that for a given redundancy the probability that an arbitrary node possesses a packet is high as long as the loss probability in the network is below a certain threshold. After reaching the threshold the packet possession probability suddenly drops; the rate decrease gets faster as the number of nodes in the overlay grows. The value of the threshold depends on the ratio of redundancy and on the number of the distribution trees. We study the overlay structure in the presence of node dynamics and conclude that stability can be achieved only if the root node serves a large number of nodes simultaneously

This paper proposes a controlled load service that provides a network state with bounded and well known worst-case behavior. The service is primarily developed for real time applications. The full system for achieving quality of service to the application consists of an admission control combined with forward-error correction. The admission control is used to limit the packet-loss probability to a known value; the error-control coding (i.e., FEC) is then used to raise the quality above the level enforced by the admission control. The basic idea for the admission control is that a host must probe the path to the receiver before sending actual data. It accepts the session if the probe is received with no or at most a moderate amount of loss. The performance evaluation shows clearly that the proposed scheme avoids network congestion and high packet losses even over short time scales.

This paper introduces an approach to solving the fundamental scalability problem of all-optical packet switching wavelength-division multiplexing (WDM) access networks. Current optical networks cannot be scaled by simply adding nodes to existing systems due to the accumulation of insertion losses and/or the limited number of wavelengths. Scalability through bridging requires, on the other hand, the capability to switch packets among adjacent subnetworks on a wavelength basis. Such a solution is, however, not possible due to the unavailability of fast-switching wavelength sensitive devices. In this paper, we propose a scalable WDM access network architecture based on a recently proposed optical switching approach, termed photonic slot routing. According to this approach, entire slots, each carrying multiple packets (one on each wavelength) are "transparently" routed through the network as single units so that wavelength sensitive data flows can be handled using fast-switching wavelength nonsensitive devices based on proven technologies. The paper shows that the photonic slot routing technique can be successfully used to achieve statistical multiplexing of the optical bandwidth in the access network, thus providing a cost-effective solution to today's increasing bandwidth demand for data transmissions.

Photonic slot routing (PSR) is a promising approach to solving the fundamental scalability problem of all-optical packet switched WDM networks. In this approach adjacent subnetworks can exchange wavelength sensitive traffic through bridges using wavelength non-selective devices which can be constructed using proven technologies. With photonic slot routing, packets sharing a common subnetwork destination are aggregated to form a photonic slot which is individually routed in order to reach the destination subnetwork. Photonic slots from different subnetworks can originate contentions at the bridge, leading to potential penalties of slot loss and retransmission. This paper shows how slot contention penalties can be reduced through the use of switched delay lines (SDL) at the bridges. The combination of these two innovative techniques, the photonic slot routing and the switched delay lines, leads to a unique solution that is shown to nearly achieve throughput and delay performance obtainable in absence of slot contentions.