The reconfigurable intelligent surface (RIS) technology allows one to engineer spatial diversity in complex cellular networks. This paper provides a stochastic geometry framework for the system-level performance assessment of RIS-assisted networks. To account for the inherent randomness in the spatial deployments of base stations (BSs) and RISs, we model the RIS placements as point processes (PPs) conditioned on the associated BSs, which are modeled by a Poisson point process (PPP). We assume that the system uses the orthogonal frequency division multiplexing (OFDM) technique to exploit the multipath diversity provided by RISs. The downlink coverage probability and ergodic rate can be evaluated when RISs operate as batched powerless beamformers. The resulting analytical expressions provide a general methodology for assessing the impact of a parameterized RIS model on system performance. These RIS PPs can be adapted based on the deployment strategy. We focus on modeling the RISs as a Matérn cluster process (MCP), where each RIS cluster is a finite PPP within a ring centered on its associated BS. This model connects link-level knowledge to system-level impacts, such as overall interference and the effects of imperfect channel state information (CSI). It also evaluates key RIS deployment parameters, including batch size and RIS density. Furthermore, we analyze a variant of RIS placement in which RISs are deployed around coverage holes to demonstrate the framework’s flexibility and applicability. Numerical evaluations of the analytical expressions and Monte-Carlo simulations jointly validate the proposed analytical approach and provide valuable insights into the design of future RIS-assisted cellular networks.

Conventional hybrid automatic repeat request (HARQ) uses single-bit feedback to boost retransmission reliability; however, its limited granularity restricts advanced link adaptation (LA) for optimizing performance beyond reliability. This paper presents a novel LA approach for retransmissions in 6G networks, targeting spectral efficiency (SE) optimization through effective modulation and coding scheme selection within the HARQ framework. In a dense urban setting with urban macro cell deployment and file transfer protocol model 3 (FTP3) traffic, our optimal LA solution improves average downlink (DL) physical resource block utilization by 31.37 % over Chase combining (CC), a gain inherently linked to SE optimization. Following 3GPP release 18 study item technical report's recommendation to include LA information in HARQ feedback, we address feedback design limitations of the optimal solution with a quantized LA metric. This metric is incorporated into our novel enriched HARQ feedback (EHF) at the user equipment, enabling EHF to surpass single-bit conventional feedback that only conveys transport block decoding status. Evaluated under varying feedback bit counts and LA quantization levels, our design achieves significant gains-e.g., a 20.26 % increase over CC in average DL throughput per FTP3 packet with 4-bit feedback-offering flexibility in application-specific bit sizing.

Reconfigurable intelligent surfaces (RISs) are a promising technology for enhancing cellular network performance and yielding additional value to network operators. This paper proposes a techno-economic analysis of RIS-assisted cellular networks to guide operators in deciding between deploying additional RISs or base stations (BS). We assume a relative cost model that considers the total cost of ownership (TCO) of deploying additional nodes, either BSs or RISs. We assume a return on investment (RoI) that is proportional to the system's spectral efficiency. The latter is evaluated based on a stochastic geometry model that gives an integral formula for the ergodic rate in cellular networks equipped with RISs. The marginal RoI for any investment strategy is determined by the partial derivative of this integral expression with respect to node densities. We investigate two case studies: throughput enhancement and coverage hole mitigation. These examples demonstrate how operators could determine the optimal investment strategy in scenarios defined by the current densities of BSs and RISs, and their relative costs. Numerical results illustrate the evolution of ergodic rates based on the proposed investment strategy, demonstrating the investment decision-making process while considering technological and economic factors. This work quantitatively demonstrates that strategically investing in RISs can offer better system-level benefits than solely investing in BS densification.

This paper investigates a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, users, and services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performance of the proposed solution, advanced dynamic system-level simulations are conducted. The findings reveal that the proposed scheduler offers a notable improvement in increasing XR capacity up to 45%, while keeping the same enhanced mobile broadband (eMBB) cell throughput as compared to the well-known baseline schedulers.

Mobile radio networks have been evolving towards the integration of services and devices with a diverse set of throughput, latency, and reliability requirements. To support these requirements, 3GPP has introduced Multi Connectivity (MC) as a more flexible architecture for 5G New Radio (NR), where multiple radio links can be simultaneously activated to split or duplicate data traffic. Multi connectivity improves single user performance at the cost of higher interference due to the increase of radio transmissions, which negatively affects system throughput. This paper analyzes the problem of admission control and resource allocation in multi connectivity scenarios, considering different requirements and 5G NR features. Specifically, we formulate two optimization problems that leverage the features of the Packet Data Convergence Protocol (PDCP) layer, which controls the flow of data packets of the data radio bearer: the PDCP Split-Bearer Decision (PSD) and the PDCP Duplication Decision (PDD) problems, which are tailored for the enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (uRLLC) services, respectively. We further provide heuristic approaches, specifically designed for the PSD and PDD problems, to effectively solve both these problems. Numerical results in realistic network deployments confirm that our solutions can effectively allocate radio resources increasing admission rate and system throughput, while guaranteeing the required reliability level.

Extended Reality (XR) applications introduce challenging requirements for radio mobile systems in terms of capacity and latency. At the same time, XR devices needs to minimize power consumption to extend battery lifetime and limit dissipated heat. Addressing capacity and latency requirements without excessively increasing power consumption of user devices requires the evolution of 5G power saving schemes. In this work, we propose an Adaptive DRX (ADRX) scheme and design a control policy to optimally adjust active duration of the DRX cycle in order to satisfy XR requirements and minimize energy consumption. To this end, we formulate the problem of selecting the user active time as a stochastic online optimization problem and present a control policy to optimally solve it. Numerical results confirm that the proposed solution increases the number of satisfied users and reduce power consumption compared to standard DRX mechanism. In particular, in a Dense Urban scenario where standard DRX cannot fulfill the requirements of any XR user, ADRX can serve up to 4-5 users per cell with 10% power saving gain.

Industrial applications introduce new and complex requirements in terms of reliability and latency for wireless communication systems. In particular, 3GPP has recently identified the need for communications being ultra reliable as well as robust against consecutive packet errors. These requirements call for new approaches that span multiple layers to encompass the latency-reliability trade-offs compared to classical error correction schemes like (Hybrid) ARQ. For this purpose, techniques like puncturing, power boosting, and data duplication have been introduced in 5G NR to enable transmission preemption and overriding, and data redundancy. To alleviate their radio inefficiency cost, this paper presents network coding schemes that proactively correct packet errors caused by simultaneous or consecutive leg transmission failures. In particular, we demonstrate that the proposed schemes are able to increase the reliability of single and consecutive packet transmissions while reducing the associated traffic increase as compared to data duplication.

Classic network control techniques have as sole objective the fulfillment of Quality-of-Service (QoS) metrics, being quantitative and network- centric. Nowadays, the research community envisions a paradigm shift that will put the emphasis on Quality of Experience (QoE) metrics, which relate directly to the user satisfaction. Yet, assessing QoE from QoS measurements is a challenging task that powerful Software Defined Network controllers are now able to tackle via machine learning techniques. In this paper we focus on a few crucial QoE factors and we first propose a Bayesian Network model to predict re- buffering ratio. Then, we derive our own novel Neural Network search method to prove that the BN correctly captures the discovered stalling data patterns. Finally, we show that hidden variable models based and context information boost performance for all QoE related measures.

The surge of video traffic is a challenge for service providers that need to maximize Quality of Experience (QoE) while optimizing the cost of their infrastructure. In this paper, we address the problem of routing multiple HTTP-based Adaptive Streaming (HAS) sessions to maximize QoE. We first design a QoS-QoE model incorporating different QoE metrics which is able to learn online network variations and predict their impact on representative classes of adaptation logic, video motion and client resolution. Different QoE metrics are then combined into a QoE score based on ITU-T Rec. P.1202.2. This rich score is used to formulate the routing problem. We show that, even with a piece-wise linear QoE function in the objective, the routing problem without controlled rate allocation is non-linear. We therefore express a routing-plus-rate allocation problem and make it scalable with a dual subgradient approach based on Lagrangian relaxation where subproblems select a single path for each request with a trivial search, thereby connecting explicitly QoE, QoE and HAS bitrate. We show with ns-3 simulations that our algorithm provides values for HAS QoE metrics (quality, rebufferings, variation) equivalent to MILP and better than QoS-based approaches.

The centralized control in Software Defined Networks paves the way for new services like Bandwidth Calendaring (BWC), where the possibility to shift temporally future bandwidth requests allows to efficiently use network resources. Assuming perfect knowledge of the calendar for all future bandwidth reservations is unrealistic. In this paper, we study the online version of the BWC problem presented in [1], where for unpredictable incoming demands an admission decision, scheduling and path allocation must be taken instantaneously. We design an algorithm for solving the online version of the BWC problem and proposes two heuristic approaches to exploit the scheduling flexibility of demands. Our numerical results reveal that the proposed solution approach outperforms state-of-the art methods by up to 70% in terms of accepted traffic.

Content Delivery Networks (CDN) are witnessing the outburst of video streaming (e.g., personal live streaming or Video-on-Demand) where the video content, produced or accessed by mobile phones, must be quickly transferred from a point to another of the network. Whenever a user requests a video not directly available at the edge server, the CDN network must (1) identify the best location in the network where the content is stored, (2) set up a connection and (3) deliver the video as quickly as possible. For this reason, existing CDNs are adopting an overlay structure to reduce latency, leveraging the flexibility introduced by the Software Defined Networking (SDN) paradigm. In order to guarantee a satisfactory Quality of Experience (QoE) to users, the connection must respect several Quality of Service (QoS) constraints. In this paper, we focus on the sub-problem (2), by presenting an approach to efficiently compute and maintain paths in the overlay network. Our approach allows to speed up the transfer of video segments by finding minimum delay overlay paths under constraints on hop count, jitter, packet loss and relay node capacity. The proposed algorithm provides a near-optimal solution, while drastically reducing the execution time. We show on traces collected in a real CDN that our solution allows to maximize the number of fast video transfers.

Network Functions Virtualization (NFV) has recently gained momentum among network operators as a means to share their physical infrastructure among virtual operators, which can independently compose and configure their communication services. However, the spatio-temporal correlation of traffic demands and computational loads can result in high congestion and low network performance for virtual operators, thus leading to service level agreement breaches. In this paper, we analyze the congestion resulting from the sharing of the physical infrastructure and propose innovative orchestration mechanisms based on both centralized and distributed approaches, aimed at unleashing the potential of the NFV technology. In particular, we first formulate the network functions composition problem as a non-linear optimization model to accurately capture the congestion of physical resources. To further simplify the network management, we also propose a dynamic pricing strategy of network resources, proving that the resulting system achieves a stable equilibrium in a completely distributed fashion, even when all virtual operators independently select their best network configuration. Numerical results show that the proposed approaches consistently reduce resource congestion. Furthermore, the distributed solution well approaches the performance that can be achieved using a centralized network orchestration system.

Load-balancing and network optimization in SDN networks require efficient flow splitting during the path computation phase. The way flow splitting is typically implemented in switches is to map the output of an hash function computed on the headers of incoming flows to the content stored in a Ternary Content Addressable Memory (TCAM), a very efficient but scarce resource. Although a large TCAM budget means that the flow distribution can more accurately model a fractional ideal, the distribution of flow volume amongst the paths is constrained in reality to use only a limited number of TCAM rows. In this paper, we present a flow splitting algorithm that maximizes the total number of demands allocated in the network according to the TCAM size constraints and, at the same time, minimize the total routing cost. Although the problem is NP-hard, we show through simulations that we can achieve good approximations of the optimal solution in a reasonable amount of time.

Software Defined Networking enables centralized network control and hence paves the way for new services that use network resources more efficiently. Bandwidth Calendaring (BWC) is a typical such example that exploits the knowledge of future to optimally pack the arising demands over the network. In this paper, we consider a generic BWC instance, where a carrier network operator has to accommodate at minimum cost demands of predetermined, but time-varying, bandwidth requirements. Some of the demands may be flexible, i.e., can be scheduled within a specific time window. We demonstrate that the resulting problem is NP-hard and we propose a scalable problem decomposition based on column generation. Our numerical results reveal that the proposed solution approach is near-optimal and outperforms state-of-the art methods based on relaxation and randomized rounding by more than 20% in terms of network cost.

Software-Defined Network (SDN) controllers include mechanisms to globally reconfigure the network in order to respond to a changing environment. While iterative methods are employed to solve flow optimization problems, demands arrive or leave the system changing the optimization instance and requiring further iterations. In this paper, we focus on the general class of iterative solvers considering an exponential decrease over time in the optimality gap. Assuming dynamic arrivals and departures of demands, the computed optimality gap at each iteration Q(t) is described by an auto-regressive stochastic process. At each time slot the controller may choose to apply the current iteration to the network or not. Applying the current iteration improves the optimality gap but requires flow reconfiguration which hurts QoS and system stability. To limit the reconfigurations, we propose two control policies that minimize the flow allocation cost while respecting a network reconfiguration budget. We validate our model by experimenting with a realistic network setting and using standard Linear Programming tools used in the SDN industry. We show that our policies provide a practical means of keeping the optimally gap small within a given reconfiguration constraint.

By offloading the control plane to powerful computing platforms running on commodity hardware, Software Defined Networking (SDN) unleashes the potential to operate computation intensive machine learning tools and solve complex optimization problems in a centralized fashion. This paper studies such an opportunity under the framework of the centralized SDN Admission Control (AC) problem. We first review and adapt some of the key AC algorithms from the literature, and evaluate their performance under realistic settings. We then propose to take a step further and build an AC meta-algorithm that is able to track the best AC algorithm under unknown traffic conditions. To this aim, we exploit a machine learning technique called Strategic Expert meta-Algorithm (SEA).

SDN unleashes the potential to perform computational intensive machine learning algorithms to solve complex routing problems. This demo presents an architecture for the SDN controller that integrates several online routing algorithms for the real-time admission control of new connection requests. The demonstrator permits to compare the evolution of the network according to the admission decisions taken by different online algorithms and to simulate future scenarios to support strategic decisions aimed at improving the infrastructure.

Demand-Side Management (DSM) systems represent an efficient method to improve the performance of Smart Grid infrastructures by controlling users' power loads. In this paper, we focus our analysis on fully distributed DSM systems especially designed to reduce the peak demand of groups of residential users. In our proposed scheme, each appliance decides autonomously its scheduling using only limited information on the energy price fixed by the retailer, thus greatly reducing the system complexity as well as the need of information exchanges. We develop two schedule-selection policies based on the Proportional Imitation Rule, where at each iteration all appliances switch to a new schedule with a probability proportional to the cost difference between the actual and cheapest schedules of the previous iteration. We analyze the proposed learning methods based on realistic instances in several use-case scenarios, and show their effectiveness in terms of cost reductions (both local and system-wide) as well as convergence speed to stable and efficient system equilibria.

This work focuses on Visual Sensor Networks (VSNs) which perform visual analysis tasks such as object recognition. There, the goal is to find the image in a reference database which is the closest match to the image captured by camera sensor nodes. Recognition is performed by relying on visual features extracted from the acquired image, which are matched against a database of labeled features in order to find the closest image match. The matching functionalities are often implemented at a central controller outside the VSN. In contrast, we study the performance trade-offs involved in distributing the matching functionalities inside the VSN by letting sensor nodes performing parts of the matching process. We propose an optimization framework to optimally distribute the matching task to in-network sensor nodes with the goal of minimizing the overall completion time of the recognition task. The proposed optimization framework is then used to assess the performance of distributed matching, comparing it to a traditional, centralized approach in realistic VSN scenarios.

In the past years, researchers have been advocating for flexible 802.11 devices that dynamically adapt to the varying network conditions, looking for efficient alternatives to the 802.11 standard MAC. In this work we demonstrate that this flexibility is readily available at the MAC level, and its operation can be tuned by re-programming the firmware inside the wireless chipsets that are built on relatively generic hardware modules. We show this by implementing the new amendment IEEE 802.11aa in legacy equipments by simply coding the frame exchange schemes at the firmware level. Nevertheless, we claim that the lack of flexibility in the way these modules interact results in a bottleneck that severely degrades performance. In our work, we prove this inefficiency of the 802.11 hardware architecture that hinders high throughput features, as in our case study of 802.11aa reliable multicast. To solve this problem, we provide new directions for the revision of the current hardware architecture and propose a new vision for the future design of wireless chipsets.

The opportunistic utilization of third party WiFi access devices to offload customer traffic from the mobile network has recently gained momentum as a promising approach to increase the network capacity and simultaneously reduce the energy consumption of the radio access network (RAN) infrastructure. To foster the opportunistic utilization of unexploited Internet connections, we propose a new and open market where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase dynamically (and adaptively) the network capacity. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity made available by third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose three alternative greedy algorithms that efficiently solve the offloading problem, even for large-size network scenarios.

Nowadays, the maintenance costs of wireless devices represent one of the main limitations to the deployment of wireless mesh networks (WMNs) as a means to provide Internet access in urban and rural areas. A promising solution to this issue is to let the WMN operator lease its available bandwidth to a subset of customers, forming a wireless mesh community network, in order to increase network coverage and the number of residential users it can serve. In this paper, we propose and analyze an innovative marketplace to allocate the available bandwidth of a WMN operator to those customers who are willing to pay the higher price for the requested bandwidth, which in turn can be subleased to other residential users. We formulate the allocation mechanism as a combinatorial truthful auction considering the key features of wireless multihop networks and further present a greedy algorithm that finds efficient and fair allocations even for large-scale, real scenarios while maintaining the truthfulness property. Numerical results show that the greedy algorithm represents an efficient, fair, and practical alternative to the combinatorial auction mechanism.

Virtualization of network functions and services can significantly reduce capital and operational expenditures of telecommunication operators through the sharing of a single network infrastructure. However, the utilization of the same resources can increase their congestion due to the spatio-temporal correlation of traffic demands and computational loads. In this paper, we propose novel orchestration mechanisms to optimally control and reduce the resource congestion of a physical infrastructure based on the NFV paradigm. In particular, we formulate the network functions composition problem as a nonlinear optimization model to accurately capture the congestion of the physical resources. In order to meet both efficiency and load balancing goals of the physical operator, we introduce two variants of such model to minimize the total and the maximum congestion in the network. Our models allow us to efficiently compute the optimal solution in a short computing time. Numerical results, obtained with real ISP topologies and network instances, show that the proposed approach represents an efficient and practical solution to control the congestion in virtual networks. Furthermore, they indicate that a holistic approach that optimizes the virtual system by jointly considering all elements/components would further improve the performance.

Information Centric Networking (ICN) is a novel paradigm that aims at improving the performance of today's Internet by supporting universal caching and multicast content delivery features on every network device.

The Radio Access Network (RAN) infrastructure represents the most critical part for capacity planning, which usually accounts for peak traffic conditions. A promising approach to increase the RAN capacity and simultaneously reduce its energy consumption is represented by the opportunistic utilization of third party Wi-Fi access devices. In order to foster the utilization of unexploited Internet connections, we propose a new and open market, where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase the network capacity and save large amounts of energy. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity of third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose a greedy algorithm that solves the offloading problem in polynomial time, even for large-size network scenarios.

Rate adaptation represents a key functionality of the 802.11 MAC protocol for performance enhancement. Several solutions have been proposed for improving the transmission rate of unicast communications using frame receptions/losses, BER (Bit Error Rate) and SNR (Signal to Noise Ratio) measurements. Nevertheless, rate adaptation for multicast transmissions represents a more challenging tasks due to the complexity of estimating the reception correlation of wireless links. This paper presents a novel scheme for selecting the best transmission rate for multicast communications using the packet reception correlation of the links established among the nodes of the multicast group with the access point. The proposed algorithm has been evaluated on a real-life testbed using commercial wireless cards. The results show that our solution accurately estimates the reception correlation of wireless links, thus considerably increasing the performance of multicast transmissions up to 3x and 5x in terms of throughput and delay, respectively.

In recent years, Body-to-Body Networks (BBNs) have gained momentum as a means to monitor people behavior and simplify their interaction with the surrounding environment; thus representing a key element of the Internet of Things (IoT) networking paradigm. Within BBNs, several transmission technologies sharing the same unlicensed band (namely the ISM band) coexist, increasing dramatically the level of interference, which in turn negatively affects the network performance. In this paper, we consider an IoT system composed of several BBNs and we analyze the Cross Technology Interference (CTI) problem caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model considering both the Mutual and Cross Technology Interference in order to mitigate the overall level of interference within the IoT system, taking explicitly into account the node mobility. We further develop two heuristic approaches to solve efficiently the interference mitigation problem in large scale network scenarios. Numerical results show that the proposed heuristics represent two efficient and practical alternatives to the optimal solution for solving the CTI mitigation problem in large scale IoT scenarios.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. This paper proposes a novel routing metric, Expected Forwarded Counter (EFW), and two further variants, to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. EFW combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We evaluate the proposed metrics both through simulations and real-life deployments on two different wireless testbeds, performing a comparative analysis with On-Demand Secure Byzantine Resilient Routing (ODSBR) Protocol and Expected Transmission Counter (ETX). The results show that our cross-layer metrics accurately capture the path reliability and considerably increase the WMN performance, even when a high percentage of network nodes misbehave.

In recent years, with the evolution of new and content-rich Internet services, mobile network operators face the challenging task to guarantee ubiquitous access to their customers, while minimizing network deployment costs. In order to foster the opportunistic utilization of unexploited Internet connections of residential users, we propose a new marketplace where mobile network operators can rent the unused capacity of residential users' access devices (e.g., wireless access points or femtocells) when the traffic demand of their mobile customers exceeds the operator's network capacity. We formulate the allocation problem as a combinatorial reverse auction, which prevents market manipulation, and we further propose a greedy algorithm that finds efficient allocations in polynomial time, even for large-size network scenarios. Numerical results demonstrate that our proposed schemes well capture the economical and networking essence of the allocation problem, thus representing a promising approach to enhance the performance of next-generation wireless access networks.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. Several routing protocols have been proposed to improve the packet delivery rate based on enhanced metrics that capture the wireless link quality. However, these metrics do not take into account that some participants can exhibit selfish behavior by selectively dropping packets sent by other mesh routers in order to prioritize their own traffic and increase their network utilization. This paper proposes a novel routing metric to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. Our solution combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We integrated the proposed metric with a well-known routing protocol for wireless mesh networks, OLSR, and evaluated it using the NS2 simulator. The results show that our cross-layer metric accurately captures the path reliability, even when a high percentage of network nodes misbehave, thus considerably increasing the WMN performance.

Abstract Wireless Mesh Networks (WMNs) have emerged recently as a technology for next‐generation wireless networking. They consist of mesh routers and clients, where mesh routers are almost static and form the backbone of WMNs. WMNs provide network access for both mesh and conventional clients. In this paper, we propose DSA‐Mesh, a fully distributed security architecture that provides access control for mesh routers as well as a key distribution scheme that supports layer‐2 encryption to ensure security and data confidentiality of all communications that occur in the backbone of the WMN. DSA‐Mesh exploits the routing capabilities of mesh routers: after connecting to the access network as generic wireless clients, new mesh routers authenticate to a key management service (consisting of several servers) implemented using threshold cryptography, and obtain a temporary key that is used both to prove their credentials to neighbor nodes and to encrypt all the traffic transmitted on wireless backbone links. A key feature in the design of DSA‐Mesh is its independence from the underlying wireless technology used by network nodes to form the backbone. Furthermore, DSA‐Mesh enables seamless mobility of mesh routers. Since it is completely distributed, DSA‐Mesh permits to deploy automatically and incrementally large WMNs, while increasing, at the same time, the robustness of the system by eliminating the single point of failure typical of centralized architectures. DSA‐Mesh has been implemented in Network Simulator, and extensive simulations have been performed in large‐scale network scenarios, comparing it to a static key approach and to a centralized architecture where a single key server is deployed. Numerical results show that our proposed architecture considerably increases the WMN security and reliability, with a negligible impact on the network performance, thus representing an effective solution for wireless mesh networking. Copyright © 2010 John Wiley & Sons, Ltd.