Time Sensitive Networking (TSN) is fundamental for the reliable, low-latency networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome by using Target Wake Time (TWT), which enables the access point to instruct Wi-Fi stations to wake up and transmit in non-overlapping TWT Service Periods (SPs), and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), with the objective to schedule TWT SPs that maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, due to TASP being NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Using a TWT simulator based on ns-3, we compare TASPER to several baselines, including HSA, a state-of-the-art solution originally designed for WirelessHART networks. We demonstrate that TASPER obtains up to 24.97% lower mean transmission rejection cost and saves up to 14.86% more energy compared to the leading baseline, ShortestFirst, in a challenging, large-scale scenario. Additionally, when compared to HSA, TASPER also reduces the energy consumption by 34% and reduces the mean rejection cost by 26%. Furthermore, we validate TASPER on our IIoT testbed, which comprises 10 commercial TWT-compatible stations, observing that our solution admits more transmissions than the best baseline strategy, without violating any AoI deadline.

Acoustic communications are the most exploited technology in the so-called Internet of Underwater Things (IoUT). UnderWater (UW) environments are often characterized by harsh propagation features, limited bandwidth, fast-varying channel conditions, and long propagation delay. On the other hand, IoUT nodes are usually battery-powered devices with limited processing capabilities. Accordingly, it is necessary to design optimization algorithms to address the challenging propagation features while balancing them with the limited device capabilities. To address the constraints of the nodes in energy and processing resources, it is crucial to adjust the transmission parameters based on the channel conditions while also developing communication procedures that are both lightweight and energy-efficient. In this work, we introduce a novel Multi-Player Multi-Armed Bandit (MP-MAB) framework for modulation adaptation in Multi-Hop IoUT Acoustic Networks. As opposed to widely used, computation-demanding Deep Reinforcement Learning (DRL) techniques, MP-MAB algorithms are simple and lightweight and allow to iteratively make decisions by selecting one among multiple choices, or arms. The framework is fully-distributed and is able to dynamically select the best modulation technique at each IoUT node by leveraging on high-level statistics (e.g., network throughput), without the need to exploit hard-to-extract channel features (e.g., channel state). We evaluate the performance of the proposed framework using the DESERT UW simulator and compare it with state-of-the-art centralized solutions based on Deep Reinforcement Learning (DRL) for cognitive and heterogeneous networks, namely DRL-MCS, DRL-AM, PPO, SAC, as well as with a multiple-agent, distributed version of the PPO. The results highlight that, despite its simplicity and fully-distributed nature, the proposed framework achieves superior performance in UW networks in terms of throughput, convergence speed, and energy efficiency. Compared to DRL-MCS and DRL-AM, our approach improves network throughput by up to 33% and 20%, respectively, and reduces energy consumption by up to 18% and 16%. When compared to PPO, SAC, and Multi-PPO, the proposed solution achieves up to 11%, 34%, and 38% higher throughput, and up to 7%, 17%, and 33% lower energy consumption, respectively.

Reliable communication in UnderWater (UW) environments is challenged by limited bandwidth, high temporal variability, and significant propagation delays. Orthogonal Frequency Division Multiplexing (OFDM) has emerged as a promising solution for UW communication; however, its performance heavily depends on dynamic subcarrier selection to adapt to fluctuating channel conditions. This paper presents MERMAID, a novel framework based on a Combinatorial Multi-Armed Bandit (CMAB) approach for adaptive subcarrier selection in Underwater Acoustic Networks (UANs). MERMAID dynamically activates subcarriers based on real-time Packet Delivery Ratio (PDR) feedback to maximize reliability while minimizing energy consumption. Unlike Deep Reinforcement Learning (DRL) techniques, which require extensive training and high computational overhead, MERMAID is specifically designed for resource-constrained UW nodes, enabling fast convergence and lightweight operation. We implement MERMAID in the DESERT simulator and benchmark its performance against state-of-the-art DRL baselines. Simulation results show that MERMAID improves PDR by up to 21.15% and reduces energy consumption by up to 45.40%. These findings highlight MERMAID as a scalable, efficient, and high-performance solution for real-world Internet of Underwater Things (IoUT) applications. The framework aligns with the 6G vision by advancing intelligent and energy-efficient connectivity in harsh environments, highlighting its potential to extend communication paradigms to UW networks.

UnderWater (UW) communication channels pose unique challenges due to their limited bandwidth, significant temporal variability, and long transmission delays. Addressing these challenges calls for the implementation of robust protocols capable of dynamically adjusting transmission parameters in response to channel conditions. In such a perspective, the development of intelligent algorithms capable of quickly adapting to current channel conditions based on measurements is of crucial importance. Indeed, this allows to adapt the signal transmission characteristics to the channel conditions, and to accordingly ensure optimal performance. In this perspective, this paper introduces a Multi-Player Multi-Armed Bandit (MP-MAB) framework for smart modulation adaptation in UnderWater Acoustic (UWA) Networks. Our solution is specifically tailored to run on resource-constrained UW nodes, thanks to the simplicity and low-complexity of MAB. Our framework can leverage real-time throughput statistics to dynamically select the optimal modulation technique for multi-hop signal transmission in UW scenarios. Notably, this happens in a fully-distributed way on a per-node basis, as each UW node runs a local MAB agent to autonomously select the best modulation to use according to its own channel conditions. Using the DESERT UW simulator, we evaluate the performance of our proposed framework and compare it with alternative state-of-the-art learning approaches. Results demonstrate the higher efficiency and responsiveness of our algorithm compared to the alternatives, despite its simplicity and fully-decentralized nature.

Job offloading represents one powerful key-enabler for delay-sensitive applications in Intelligent Transport Systems (ITS). Vehicles can rely on job offloading to move their jobs to the Road Side Units (RSUs) devices, which are typically equipped with better computing power. Ultimately, this procedure can help relieve the computational burden on the vehicles, to the benefit of the overall processing latency. However, fixed vehicular infrastructure cannot guarantee ubiquitous, ever-present support for job offloading, especially in the case of hard-to-reach, remote areas where the power grid and/or connectivity are not present. One way to solve this issue is to resort to portable, batterypowered RSUs, which can be installed potentially everywhere and allow for greater flexibility. However, this calls for a management framework that strikes the trade-off between RSU battery-life on one hand, and processing latency on the other. Moreover, this framework should be able to work in a fully-distributed way, without the need of an existing infrastructure. With all of this in mind, our contribution is two-fold. First, we introduce the idea of MEC-in-a-box (M-Box) stations, batterypowered RSUs that can operate even in the absence of a fixed infrastructure or connectivity. Second, in order to support the operation of M-Box stations, we design MANTRA, an offloading framework for vehicular networks that balances energy consumption and processing latency in the M-Box stations. MANTRA allows the M-Box stations to autonomously i) turn on and off their processing units to alternatively increase their processing power or save energy, and ii) manage the amount of jobs offloaded towards the other M-Box stations to balance the network load. We evaluated the performance of MANTRA vs other approaches, including a centralized solution with a full knowledge of the system. First, the results highlight how MANTRA is able to match the performance of the centralized approach, and is also capable of surpassing all the other baselines in various experimental scenarios. Index Terms

Underwater (UW) communication channels experience limited bandwidth, high time variability and much longer delays as compared to traditional terrestrial channels. This makes communication in UW scenarios particularly challenging. One way to cope with this issue is to employ reliable smart protocols that can dynamically adjust transmission parameters based on channel conditions. The effectiveness of these solutions also relies on the design of intelligent algorithms that can forecast channel conditions based on measurements and adapt the transmission characteristics of signals according to the current state of the UW channel, in order to always guarantee the best performance. In this work we propose AMUSE, the first Multi-Armed Bandit-based algorithm for smart modulation adaptation in Underwater Acoustic Networks. Note that AMUSE is specifically designed to suit resource-constrained UW nodes thanks to its simplicity and low-complexity. In particular, AMUSE relies on the current Packet Delivery Ratio (PDR) statistics to select in real time the best modulation technique to use for multihop signal transmission in the aforementioned UW scenarios. By employing the DESERT simulator we compare the performance achieved using AMUSE to those obtained using alternative state-of-the-art learning approaches. The results show that, in spite of its simplicity, our algorithm is more efficient and responsive than the other considered approaches.

In this paper we propose MARBLE, a distributed framework based on multi-player multi-armed bandit algorithms that aims to support job offloading procedures in FANETs to comply with strict job latency requirements while also minimizing the energy consumption of the system, thus also increasing the Unmanned Aerial Vehicles (UAVs) flight duration. To demonstrate the effectiveness of the framework, we conduct an extensive evaluation campaign and compare MARBLE with several baselines, including a centralized, oracle-based approach. Our results show that MARBLE outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed manner, in compliance with the latency and energy efficiency requirements. Most notably, MARBLE also improves the FANETs flight duration of up to 69% as compared to the baselines.

Time Sensitive Networking (TSN) is fundamental for the low-latency, reliable, and energy-efficient networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome using Target Wake Time (TWT) to instruct Wi-Fi stations to wake up and transmit in non-overlapped TWT Service Periods (SPs) and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), whose objective is to schedule TWT SPs as to maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, since the TASP is NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Finally, we compare TASPER with several baselines through numerical analysis and simulations, which we performed using a TWT-compatible simulator based on ns-3. We demonstrate that TASPER schedules traffic with up to 21.23% higher priority-weighted admission ratio and saves up to 7.42% energy compared to the ShortestFirst strategy, all while satisfying AoI constraints for 99.5% of transmissions.

Unlike traditional terrestrial scenarios, communication channels in underwater environments face severe limitations in bandwidth and experience long propagation delay. In order to address these issues, reliable techniques capable to dynamically adapt transmission parameters to time-varying channel conditions are necessary. Actually, their effectiveness primarily relies on an accurate characterization of the underwater communication channels, which is often obtained through predictive models. In this paper, we compare the performance of different types of SNR-based predictive models (i.e., Markov models, Hidden Markov models) in terms of balance between accuracy and complexity. We also provide a Kalman filter-based prediction of SNR values and compare this prediction with the performance achieved with the Markov models above mentioned. The models we have considered to carry out the comparison analysis have been developed based on real shallow water traces taken over the Tyrrhenian Sea, Italy.

Underwater communications suffer from numerous challenges typically associated with relevant signal attenuation, long propagation delay, limited available bandwidth, and high error rates that severely affect underwater transmission performance. Therefore, it is crucial to apply adaptive or predictive techniques to ensure the best possible performance and guarantee reliability in underwater communication, especially in rapidly changing environments. Using adaptive (i.e., reactive) or predictive (i.e., proactive) methods, it is possible to avoid data retransmission, improve the lifetime of underwater nodes, reduce maintenance frequency and the necessary equipment replacement and recharge, and consequently optimize performance in general. In this regard, many works in the literature propose various adaptive or predictive techniques for UnderWater Acoustic (UWA) networks, which we critically classify and discuss in this qualitative survey.

In this paper, we present SDR-LoRa, an open-source, full-fledged Software Defined Radio (SDR) implementation of a LoRa transceiver. First, we conduct a thorough analysis of the LoRa physical layer (PHY) functionalities, encompassing processes such as packet modulation, demodulation, and preamble detection. Then, we leverage on this analysis to create a pioneering SDR-based LoRa PHY implementation. Accordingly, we thoroughly describe all the implementation details. Moreover, we illustrate how SDR-LoRa can help boost research on the LoRa protocol by presenting three exemplary key applications that can be built on top of our implementation, namely fine-grained localization, interference cancellation, and enhanced link reliability. To validate SDR-LoRa and its applications, we test it on two different platforms: (i) a physical setup involving USRP radios and off-the-shelf commercial devices, and (ii) the Colosseum wireless channel emulator. Our experimental findings reveal that (i) SDR-LoRa performs comparably to conventional commercial LoRa systems, and (ii) all the aforementioned applications can be successfully implemented on top of SDR-LoRa with remarkable results. The complete details of the SDR-LoRa implementation code have been publicly shared online, together with a plug-and-play Colosseum container.

The optimization of job offloading procedures in modern vehicular networks is a problem of utmost importance. In this regard, this paper proposes MANTRA, a distributed framework based on multi-player multi-armed bandit (MP-MAB) algorithms for latency- and energy-aware job offloading in vehicular networks. The main goal of MANTRA is to support procedures of job offloading in green vehicular networks to achieve a target tradeoff between energy consumption and job processing latency. In particular, MANTRA is intended to run on so-called MEC-in-a-box (M-Box) devices, portable battery-powered Road Side Units (RSUs) specifically designed to work without mobile connectivity and of a fixed power grid.To demonstrate MANTRA effectiveness, we model the vehicular network using the queueing theory for M/M/m/K systems. We run an extensive evaluation campaign and compare MANTRA with several baselines, including a centralized, oracle-based approach. In such a way, we demonstrate how MANTRA outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed way in terms of job processing latency and network outage probability.

This work develops a learning-based framework for energy-efficient power control in multi-carrier wireless networks. The problem is formulated as the maximization of the network global energy efficiency, defined as the ratio between the network sum-rate and the total consumed power, and is tackled by a novel approach which merges tools from learning, non-cooperative game theory, and fractional programming theory. The proposed algorithm is provably convergent, enjoys near-optimal performance, while requiring a much lower complexity than previous alternatives.

Wireless sensor networks (WSNs) are an integral part of an Internet of Things (IoT) ecosystem. In fact, a typical IoT service deployment considers an integrated network of switches and sensors (as well as actuators). In order to manage this heterogeneous network infrastructure, the use of software defined networking (SDN) technologies is anticipated. However, existing SDN-enabling technologies consider only isolated networks and there has been no concrete solution that treats the whole network in a unified way. In this paper, a network operating system is proposed as a way to address the above issue. Starting from the open network operating system, which currently supports only OpenFlow networks, the proposed solution leverages SDN-WISE to integrate WSNs and achieves unified system representation and treatment. The proposed system has been prototyped and is publicly available in open source. Experiments have been executed demonstrating that, by exploiting the proposed solution, it is also possible to improve efficiency of the overall network.

Flow-induced molecular communications are a promising communication paradigm based on the exchange of molecule concentrations in a continuous fluid, guided and directed through microfluidic pipes. An emerging approach to implement this type of communications is based on discrete (or droplet-based) microfluidics. It exploits the idea to represent binary information by encapsulating the signaling molecules inside droplets, which are dispersed in an immiscible fluid and delivered, through micro pipes to the destination. The objective of this paper is to study a binary droplet-based microfluidic system for flow-induced molecular communications from an information theoretical perspective. To this end, we first show that the binary discrete microfluidic channel is characterized by memory and anticipation. Accordingly, we provide a mathematical model based on a finite-state Markov chain. Finally, we evaluate the capacity of the binary droplet-based microfluidic channel.

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a uniqueNash equilibrium(NE). Then, we evaluate thePrice of Anarchy(PoA) of the NE, i.e., the efficiency of the NE as compared with the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner’s profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

This paper proposes computationally efficient algorithms to maximize the energy efficiency in multicarrier wireless interference networks, by a suitable allocation of the system radio resources, namely, the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system global energy efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging nonconvex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms.

Computer based support for clinical and health-related procedures is growing in the last decades. However, the vast majority of information systems adopted in health structures are legacy systems, which do not often allow to export data easily and also are usually desktop oriented. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents eIMES 3D Mobile (standing for Evolution Imaging System 3D for Mobile), a system which is based on the eIMES 3D system and supports clinicians for images studies with dedicated functions for the mobile environment. The tool has been developed within a project called ReCaTuR for RAre Cancer Network (i.e., Network of Rare Cancer), aiming to define a network for the management, organization and distribution of medical information. Moreover, it has been implemented following the specifications by the oncology department of an Italian Hospital. eIMES 3D allows to start a medical interdisciplinary collaboration among different teams, geographically distributed in the network, so that obtaining the integration of skills, expertize, knowledge and experiences with the final aim of clinical case resolution. eIMES 3D provides an hardware infrastructure that allows to connect multiple devices, as well as to create workstations (WorkSpaces) that independently and asynchronously can request information to the central database containing the 3D imaging data. Il also allows to share information among a network of mobile devices. The ability to build plug-in modules enables to easily implement new features in eIMES 3D Mobile, thus ensuring its further development and its sustainability.

In the SDN/NFV ecosystem, network services are provided as single Virtual Network Functions (VNFs) or chains of them, each instantiated and executed on dedicated servers. So far, the chaining of those virtual functions, which is also known as the service chain composition problem, has been mostly performed by Telco Operators (TOs) for the great advantages they receive in terms of Capex and OpEX. However, such a fully centralized approach generally results in solutions which do not scale well with the number of customers. The aim of this paper is to provide a distributed, scalable and efficient solution to the service chaining problem. Specifically, we develop an VNF marketplace system where third-party VNF providers sell VNF as a service (VNFaaS) and adapt their pricing policies according to network dynamics. Also, we leverage on game theory to provide a (theoretically proven) efficient distributed solution which accounts for monetary costs, communication latencies and congestion of computational resources.

The goal of this paper is two-fold. First, to expand an opportunistic network coding protocol for wireless networks, called PlayNCool, in order to incorporate new mechanisms to improve performance in the presence of packet losses. In particular, exploiting additional helper nodes to improve the quality of each link and even across neighbouring links and using simulations to show that an additional reduction of packet transmission in the order of 40% is possible. Second, to advocate for the use of network coding (NC) jointly with software defined networking (SDN) providing an implementation of the expanded PlayNCool protocol using SDN. This implementation uses an architecture developed with OpenFlow switches and a Pox controller to periodically gather statistics related to packet losses and the number of packets coded, recoded and decoded on a per link basis in order to adapt the configuration for the PlayNCool protocol optimally. The measurements carried out were validated with theoretical and simulated results.

The network function virtualization (NFV) paradigm has gained increasing interest in both academia and industry as it promises scalable and flexible network management and orchestration. In NFV networks, network services are provided as chains of different virtual network functions (VNFs), which are instantiated and executed on dedicated VNF-compliant servers. The problem of composing those chains is referred to as the service chain composition problem. In contrast to centralized solutions that suffer from scalability and privacy issues, in this paper, we leveragenon-cooperativegame theory to achieve a low-complexity distributed solution to the above-mentioned problem. Specifically, to account for selfish and competitive behavior of users, we formulate the service chain composition problem as an atomic weightedcongestion gamewith unsplittable flows and player-specific cost functions. We show that the game possesses a weighted potential function and admits a Nash equilibrium (NE). We prove that the price of anarchy is upper-bounded, and also propose a distributed and privacy-preserving algorithm which provably converges toward an NE of the game in polynomial time. Finally, through extensive numerical results, we assess the performance of the proposed distributed solution to the service chain composition problem.

Softwarization of networks allows simplifying deployment, configuration, and management of network functions. The driving force toward this evolution is represented by software defined networking that allows more flexible and dynamic network resource allocation and management. The efficient allocation and orchestration of network resources is of extreme importance for this softwarization process, and many centralized solutions have been proposed. However, they are complex and exhibit scalability issues. So, distributed solutions are to be preferred but, in order to be effective, should quickly converge towards equilibrium solutions. In this paper, we focus on making distributed resource allocation and orchestration a viable approach, and prove convergence of the relevant mechanisms. Specifically, we exploit game theory to model interactions between users requesting network functions and servers providing these functions. Accordingly, a two-stage Stackelberg game is presented, where servers act as leaders of the game and users as followers. Servers have conflicting interests and try to maximize their utility; users, on the other hand, use a replicator behavior and try to imitate other user’s decisions to improve their benefit. The framework proves the existence and uniqueness of an equilibrium, and a learning mechanism to converge to such equilibrium is proposed. Numerical results show the effectiveness of the approach.

In this paper, we consider a jammed wireless scenario where a network operator aims to schedule users to maximize network performance while guaranteeing a minimum performance level to each user. We consider the case where no information about the position and the triggering threshold of the jammer is available. We show that the network performance maximization problem can be modeled as a finite-horizon joint power control and user scheduling problem, which is NP-hard. To find the optimal solution of the problem, we exploit dynamic programming techniques. We show that the obtained problem can be decomposed, i.e., the power control problem and the user scheduling problem can be sequentially solved at each slot. We investigate the impact of uncertainty on the achievable performance of the system and we show that such uncertainty leads to the well-known exploration-exploitation tradeoff. Due to the high complexity of the optimal solution, we introduce an approximation algorithm by exploiting state aggregation techniques. We also propose a performance-aware online greedy algorithm to provide a low-complexity sub-optimal solution to the joint power control and user scheduling problem under minimum quality-of-service requirements. The efficiency of both solutions is evaluated through extensive simulations, and our results show that the proposed solutions outperform other traditional scheduling policies.

Jamming attacks are able to partially or completely disrupt wireless communications. To overcome such a harmful attack, optimal scheduling of user transmissions should be achieved. Providing effective scheduling policies is a hard task which is made more complicated when reactive jamming attacks triggered by user transmissions are considered and no information about the jammer is available, e.g., the triggering threshold is not known. In this paper, we address the problem of maximizing network performance and guaranteeing minimum QoS requirements when reactive jamming attacks are ongoing. Specifically, to maximize network performance and avoid the triggering of the jammer, we formulate and solve a joint user scheduling and power control problem. The proposed solution is then assessed through numerical simulations.

In this paper, we propose a solution to support big data processing with MapReduce inside a wireless sensor network (WSN). The proposed framework provides a simple MapReduce interface, whose implementation can be dynamically loaded into the appropriate nodes. The execution of map and reduce functions inside the network is achieved by leveraging software defined networking principles and in particular by enhancing the SDN-WISE protocol on both the Controller and the sensor node sides. On the Controller side, the proposed framework introduces a functionality, which evaluates the optimal deployment of the processing functions in the network taking the typical WSN requirements into account and enforces it by instructing the flow tables to forward traffic from the mappers to the appropriate reducers. Then, on the node side, SDN-WISE is integrated with Contiki, a widely popular operating system for sensor nodes, which has the capability to load and execute new functions without service disruption, i.e., on the fly. The proposed framework is evaluated considering different network topologies and approaches about the data processing location.

Abstract Support of Voice over Internet Protocol (VoIP) services in wireless mesh networks requires implementation of efficient policies to support low‐delay data delivery. Multipath routing is typically supported in wireless mesh networks at the network level to provide high fault tolerance and load balancing because links in the proximity of the wireless mesh gateways can be very stressed and overloaded, thus causing scarce performance. As a consequence of using multipath solutions, lower delay and higher throughput can be supported also when a given path is broken because of mobility or bad channel conditions, and alternative routes are available. This can be a relevant improvement especially when assuming that real‐time traffic, such as VoIP, travels into the network. In this paper, we address the problem of Quality of Service (QoS) support in wireless mesh networks and propose a multipath routing strategy that exploits the Mean Opinion Score (MOS) metric to select the most suitable paths for supporting VoIP applications and performing adaptive load balancing among the available paths to equalize network traffic. Performance results assess the effectiveness of the proposed approach when compared with other existing methodologies. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper SDN-WISE, a software defined networking (SDN) solution for wireless sensor networks, is introduced. Differently from the existing SDN solutions for wireless sensor networks, SDN-WISE is stateful and pursues two objectives: (i) to reduce the amount of information exchanged between sensor nodes and the SDN network controller, and (ii) to make sensor nodes programmable as finite state machines so enabling them to run operations that cannot be supported by stateless solutions. A detailed description of SDN-WISE is provided in this paper. SDN-WISE offers APIs that allow software developers to implement the SDN Controller using the programming language they prefer. This represents a major advantage of SDN-WISE as compared to existing solutions because it increases flexibility and simplicity in network programming. A prototype of SDN-WISE has been implemented and is described in this paper. Such implementation contains the modules that allow a real SDN Controller to manage an OMNeT++ simulated network. Finally, the paper illustrates the results obtained through an experimental testbed which has been developed to evaluate the performance of SDN-WISE in several operating conditions.

The use of wirelessly internetworked miniaturized biomedical devices is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper, we propose for the first time to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing the theoretical feasibility of using ultrasonic waves in human tissues and by deriving an accurate channel model for ultrasonic intrabody communications. Then, we propose a new ultrasonic transmission and multiple access technique, which we refer to as Ultrasonic WideBand (UsWB). UsWB is based on the idea of transmitting information bits spread over very short pulses following a time-hopping pattern. The short impulse duration results in limited reflection and scattering effects, and the low duty cycle reduces the impact of thermal and mechanical effects, which may be detrimental for human health. We then develop a multiple access technique with distributed control to enable efficient simultaneous access by mutually interfering devices based on minimal and localized information exchange and on measurements at the receiver only. Finally, we demonstrate the performance of UsWB through a multiscale simulator that models the proposed communication system at the acoustic wave level, at the physical (bit) level, and at the network (packet) level. We also validate the simulation results by comparing them to experimental results obtained with a software-defined testbed.

The timing channel is a logical communication channel in which information is encoded in the timing between events. Recently, the use of the timing channel has been proposed as a countermeasure to reactive jamming attacks performed by an energy-constrained malicious node. In fact, while a jammer is able to disrupt the information contained in the attacked packets, timing information cannot be jammed, and therefore, timing channels can be exploited to deliver information to the receiver even on a jammed channel. Since the nodes under attack and the jammer have conflicting interests, their interactions can be modeled by means of game theory. Accordingly, in this paper, a game-theoretic model of the interactions between nodes exploiting the timing channel to achieve resilience to jamming attacks and a jammer is derived and analyzed. More specifically, the Nash equilibrium is studied in terms of existence, uniqueness, and convergence under best response dynamics. Furthermore, the case in which the communication nodes set their strategy and the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by considering both perfect and imperfect knowledge of the jammer's utility function. Extensive numerical results are presented, showing the impact of network parameters on the system performance.

Cognitive radio systems allow opportunistic secondary users (SUs) to access portions of the spectrum that are unused by the network's licensed primary users (PUs), provided that the induced interference does not compromise the PUs' performance guarantees. To account for interference constraints of this type, we consider flexible spectrum access pricing schemes that charge SUs based on the interference that they cause to the system's PUs, and we examine how SUs can react to maximize their achievable transmission rate in this setting. We show that the resulting noncooperative game admits a unique Nash equilibrium under very mild assumptions on the pricing mechanism employed by the network operator and under both static and ergodic (fast-fading) channel conditions. In addition, we derive a dynamic power allocation policy that converges to equilibrium within a few iterations (even for large numbers of users) and that relies only on local-and possibly imperfect-signal-to-interference-and-noise ratio measurements; importantly, the proposed algorithm retains its convergence properties even in the ergodic channel regime, despite its inherent stochasticity. Our theoretical analysis is complemented by extensive numerical simulations that illustrate the performance, robustness, and scalability properties of the proposed pricing scheme under realistic network conditions.

In this paper, we examine the problem of cost/energy-efficient power allocation in uplink multi-carrier orthogonal frequency-division multiple access (OFDMA) wireless networks. In particular, we consider a set of wireless users who seek to maximize their transmission rate subject to pricing limitations and we show that the resulting non-cooperative game admits a unique equilibrium for almost every realization of the system's channels. We also propose a distributed exponential learning scheme which allows users to converge to the game's equilibrium exponentially fast by using only local channel state information (CSI) and signal to interference-plus-noise ratio (SINR) measurements. Given that such measurements are often imperfect in practical scenarios, a major challenge occurs when the users' information is subject to random perturbations. In this case, by using tools and ideas from stochastic convex programming, we show that the proposed learning scheme retains its convergence properties irrespective of the magnitude of the observational errors.

A covert channel is a communication channel that creates a capability to transfer information between entities that are not supposed to communicate. A relevant instance of covert channels is represented by timing channels, where information is encoded in timing between events. Timing channels may result very critical in tactical scenarios where even malicious nodes can communicate in an undisclosed way. Jamming is commonly used to disrupt this kind of threatening wireless covert communications. However jamming, to be effective, should guarantee limited energy consumption. In this paper, an analysis of energy-constrained jamming systems used to attack malicious timing channels is presented. Continuous and reactive jamming systems are discussed in terms of their effect on the achievable covert channel capacity and jammer energy consumption. Also, a simple experimental set up is illustrated and used to identify proper operating points where jamming against malicious timing channels is effective while achieving limited energy consumption.

The use of miniaturized biomedical devices implanted in the human body and wirelessly internetworked is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the well-understood limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper we propose to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing the feasibility of using ultrasonic propagation in human body tissues and by deriving an accurate channel model for ultrasonic intra-body communications. Then, we propose a new ultrasonic transmission and multiple access technique, which we refer to as Ultrasonic WideBand (UsWB). UsWB is based on the idea of transmitting information bits spread over very short pulses following a time-hopping pattern. The short impulse duration results in limited reflection and scattering effects, and its low duty cycle reduces the thermal and mechanical effects, which are detrimental for human health. We then develop a multiple access technique with distributed control to enable efficient simultaneous access by interfering devices based on minimal and localized information exchange and on measurements at the receiver only. Finally, we demonstrate the performance of UsWB through a multi-scale simulator that models the proposed communication system at the acoustic wave level, at the physical (bit) level, and at the network (packet) level.

Multicast is necessary in several wireless multihop communication scenarios. Accordingly, it has received a lot of attention in the past, and several multicast protocols have been proposed. Nevertheless, traditional solutions typically incur poor efficiency when there is a large number of nodes, topology changes occur frequently, and/or the traffic load is low. Geographic multicast has been recently exploited to solve the problems mentioned above. However, these solutions require exchange of topology information that, again, can lead to excessive overhead. In this paper, we propose a new geographic multicast protocol denoted as GEM, which is inspired by the Euclidean Steiner Tree (EST) theory and does not require any information exchange for routing purposes. Therefore, it is very efficient and scalable in wireless networking scenarios where other schemes achieve low performance, especially in terms of energy consumption. In this paper, we also derive some key properties of GEM that allow us to characterize the protocol performance. As a major contribution, we show that these properties are quite general and apply to a wide range of algorithms inspired by the EST. Simulation results assess the derived properties and confirm the effectiveness of the proposed GEM scheme.

In the recent past several network scenarios have emerged where transmit-only nodes - i.e., nodes without receiving capabilities - are deployed. Such nodes cannot perform carrier sensing and cannot be synchronized. Therefore, they have to apply an Aloha-like medium access control. However, it is well known that Aloha achieves low goodput due to the possibility to incur in collisions, and this results in poor energy efficiency too. In order to achieve better performance, in this paper a scheme called Timing-Channel Aloha (TC-Aloha) is introduced which exploits the timing channel. The timing channel is the logical communication channel established between a transmitter and a receiver in which the information is transferred by means of the timing of events. Another feature of TC-Aloha is that it enables multiple transmissions of the same information to improve the communication reliability. In this paper the TC-Aloha scheme is described in detail and an analytical framework is derived for the evaluation of its performance. The numerical results assess the advantages of TC-Aloha over traditional solutions.

Intra-body sensor networks can radically change the approach to personal health control and care. However, they still need several research issues to be solved. One major issue is related to the need for minimizing the transmitted energy in order to both increase battery duration and reduce heating of biological tissues which may raise health concerns. In this paper a scheme named Intra-Body Transmission Protocol (IB-TxP) is introduced which achieves reduction in the transmitted energy by exploiting the so-called timing channel approach. This is based on establishing a logical channel between a sender and a receiver in which information is transferred in the timing between transmissions. Accordingly, part of the information is encoded in the timing between events, so lowering energy consumption and tissues heating associated to the real bits transmission. An analytical framework is derived for the assessment of IB-TxP and its comparison to traditional transmission schemes. Numerical results show the effectiveness of IB-TxP.

Information-centric networking (ICN) is a new networking paradigm which is attracting increasing attention from the scientific community. ICN focuses on content handling and distribution rather than host-to-host communications. Satellite systems have always played a key role in content distribution and therefore, they are expected to be fundamental components of the information-centric networking. However, it is not obvious whether solutions that are being developed for terrestrial ICNs are effective in satellite scenarios as well. More specifically, focus of this paper is on in-network content caching, which is an important feature characterizing all ICN proposals but, as we will show, should be re-designed to address the specific characteristics of satellite networks. Indeed, in this paper a novel caching scheme named SatCache is proposed which exploits the broadcast nature of satellite communication media and creates a profile of the preferences of network users in order to estimate their potential interest in a given content. Simulation results given in the paper show the effectiveness of the proposed scheme.

Nanomachine-to-neuron communications are envisaged as a futuristic communication scenario with enormous impact especially on medical applications. One of the crucial aspects to be considered in the study of any communication environment is represented by the amount of information that can be reliably transmitted over the channel, that is, its capacity. In this paper we evaluate such capacity. To this purpose we consider that the communication channel between a nanomachine and a neuron has been proved to introduce a noise. This however cannot be modeled by means of a traditional additive white Gaussian noise as assumed in the traditional Shannon communication theory. Capacity and bit error rate are useful metrics to characterize the theoretical bounds for communications and the maximum achievable throughput.

This paper deals with a novel forwarding scheme for wireless sensor networks aimed at combining low computational complexity and high performance in terms of energy efficiency and reliability. The proposed approach relies on a packet-splitting algorithm based on the Chinese Remainder Theorem (CRT) and is characterized by a simple modular division between integers. An analytical model for estimating the energy efficiency of the scheme is presented, and several practical issues such as the effect of unreliable channels, topology changes, and MAC overhead are discussed. The results obtained show that the proposed algorithm outperforms traditional approaches in terms of power saving, simplicity, and fair distribution of energy consumption among all nodes in the network.

The network-centric warfare concept pursues information superiority as the key strategic advantage. However, information can have a different strategic value depending on its criticality and, thus, communication systems should provide higher priority to critical data. In this paper, performance of different approaches to achieve such differentiation in TDMA satellite-based tactical communication systems are provided. More specifically, we will evaluate the impact of the correlation between the criticality level of information sources that are in proximity to each other. To this purpose, a Markovian model of the process modeling the criticality level dynamics of information sources belonging to the same group is defined and implemented in the ns-2 simulation environment. Simulation results demonstrate that the correlation between the criticality level of information sources in proximity to each other significantly impact the performance of the communication system.

In this paper we propose a routing approach aimed at enhancing the quality of Voice-over-IP (VoIP) transmissions in multipath Wireless Mesh Networks. The proposed approach executes a path switching based on the estimation of the Mean Opinion Score (MOS) for each available path of the mesh network. Performance results conducted through simulations show that the proposed approach, when compared to a random choice of the available paths, is highly effective in improving the performance of the VoIP calls in both static and dynamic scenarios.

One of the most important applications of wireless sensor networks (WSNs) undoubtedly is the emergency management. In particular, sensor nodes can be helpful for both preventing and recovering emergency situations. During critical emergency situations, having a WSN easy to access and manage is a mandatory requirement. So far, sensor networks have been based on custom protocols that make difficult to access and configure sensors by users that are different from the original designers. Today, with the advent of IPv6, integration between a sensor network and Internet becomes feasible. In this paper, we discuss the IPv6 capabilities that can be effectively exploited jointly with 6LoWPAN, and we illustrate a testbed based on an implementation of IPv6 over IEEE 802.15.4. We demonstrate that, by using an open source module we have purposely developed to run over 6LoWPAN, a user equipped with some few common network utilities can remotely access the WSN and change run-time the state and configuration of every single sensor node.

The incessant decrease in size, cost, and energy consumption of wireless devices is likely to boost the number of wireless devices dramatically. These will be deployed in several real world objects that we use in our daily life as envisioned by the Internet of Things (IoT) concept. Designing effective and efficient objects' location update and search mechanisms, is very important but is extremely difficult in the IoT scenarios. In fact, the number of mobile objects composing the IoT will be huge; this implies that signaling traffic generated for location update and discovery purposes can explode. Also, objects might currently be located where positioning solutions are not available or are not accurate enough to support most of the envisioned added value IoT applications. In this paper it is studied how object group mobility can be exploited to achieve accurate location update and search in the IoT, while reducing the signaling overhead. Object group mobility is a phenomenon that naturally emerges as usually moving objects are carried by a human or a vehicle together with several other objects. In this paper i) the concept of object group mobility is introduced and it is explained how and to which extent this can be used to reduce signaling overhead and improve accuracy in object location information; ii) the concept of collective-agent is introduced to further decrease signaling overhead in the IoT scenarios where object group mobility emerges, iii) an analytical framework is derived to assess the advantages of exploiting object group mobility in IoT scenarios.

Infostation communication is an innovative communication paradigm thought to provide connectivity in isolated areas. In infostation communications, wireless ports are deployed to act as gateways between the remote users and the communication infrastructure. However, users are allowed to exchange data with the infrastructure only when in the coverage area of one of them. In order to provide further connectivity to remote users far away from infostations, mobile nodes are assumed to be used to collect data at remote users not in the range of the infostations and, then, upload this data when they come into proximity of the infostation. In order for this process to be successful, the system stability conditions should be identified. More in depth, we denote collection process the process representing the data packets collected by the mobile node in the time between two consecutive visits of infostations and upload process the process representing the number of data packets delivered by the mobile node to the infostation. In this paper we derive conditions when the average of the upload process is higher than the average of the collection process. To this purpose a diffusion approach inherited from physics is presented and design guidelines for stable system design are identified.

Data aggregation is a promising approach for reducing energy consumption and relaying of redundant data in wireless sensor networks for monitoring applications. However data aggregation implies, on the one hand, an increase in network processing at certain nodes, referred to as aggregators, possibly leading to a higher delivery delay to the sink. On the other hand, the advantages of using aggregation are numerous, especially under the perspective of energy consumption reduction and network lifetime increase. In this paper we provide a closed form expression for the end-to-end delay and network lifetime distributions in a wireless sensor network employing aggregation. These derivations can be used to provide some insights on the expected performance in terms of the metrics above mentioned, when data aggregation is used. For example, if a certain constraint on the maximum tolerable delivery delay is given, ¿*, this can be useful to characterize what is the probability to satisfy this constraint.

The main aim of this paper is to generalize and improve a recently proposed forwarding technique based on the Chinese Remainder Theorem (CRT). The proposed technique outperforms more traditional approaches in terms of both energy efficiency and fair distribution of energy consumption, and requires very few changes to the commonly used forwarding schemes for its implementation. In particular, in this paper it is shown how to apply the CRT-based forwarding method on a realistic wireless network where unreliable erasure channels are considered together with topological changes. Furthermore, an analytical model for the novel forwarding technique has been derived and the trade-off between reliability and energy saving has been investigated.

In this paper an analytical framework is introduced for the optimization of TCP performance through joint channel coding and transmission power management in satellite communications, where both energy and bandwidth are scarce and costly resources. More specifically, the analysis takes into account the use of Low-Density Parity-Check (LDPC) codes as coding scheme for high data rate satellite communications. The analytical framework allows for the evaluation of the transmission power and coding rate that maximize the TCP throughput per unitary cost, which is a metric defined in terms of energy cost and bandwidth cost when related to throughput. Numerical examples demonstrate the usability of the proposed framework.

In this paper we discuss the use of the Chinese Remainder Theorem (CRT) for a novel forwarding scheme in wireless sensor networks. The proposed approach is characterized by a computationally simple packet splitting procedure able to reduce the energy needed for transmission and increase the network lifetime accordingly. Simulation results show that the proposed algorithm outperforms more traditional approaches in terms of both energy efficiency and fair distribution of energy consumption among all nodes in the network.

Sensor networks are characterized by limited energy, processing power, and bandwidth capabilities. These limitations become particularly critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is regarded as an effective technique to reduce energy consumption and prevent congestion. In this paper, we derive a number of significant insights concerning the data aggregation process, which have not been discussed in the literature so far. We first estimate the conditions under which aggregation is a costly process as compared to a no-aggregation approach, by considering a realistic scenario where processing costs related to aggregation of data are not neglected. We also consider that aggregation should preserve the integrity of data, and therefore, the entropy of the correlated data sent by sources can be considered in order to both decrease the amount of redundant data forwarded to the sink and perform an overall lossless process. Our framework can be used to investigate the tradeoff between the increase in data aggregation required to reduce energy consumption, and the need to maximize information integrity.

Delayed real-time (DRT) services are an interesting class of services aimed at supporting real-time transmission through the use of buffering of streams at a receiver or a proxy. This allows to decouple the physical download of realtime streams from the actual playout since streams are delayed before being sent to the playing application. A useful application scenario for these services is represented by employing them in Geo-Satellite networks where different delays and impairments are met downlink when distributing information sent by many content providers to remote users. In this perspective we investigated on the need for optimization of the recovery transport scheme so as to make it responsive to retransmissions priorities reflecting the hurry related to the time available for the recovery of the missing data. The hurry of each retransmission request depends on the length of the different receiver buffers, the amount and the time of lost data and the playout bitrate. Exploiting some previous results, in this paper we propose an Additive Increase Multiplicative Decrease (AIMD) multicast transport protocol aimed at performing bandwidth optimization while avoiding redundant transmissions. To this purpose, we recall the previously introduced DRT Service Architecture and study the multicast transport protocol in the perspective of supporting optimization and fairness in the recovery process.

In a delay-tolerant sensor network where the sink is mobile, a node can conveniently hold an information packet until the sink comes closer. This allows to both decrease the number of wireless hops required to deliver the information to the sink and reduce energy consumption. Thus, mobility of nodes can be exploited to increase energy efficiency in sensor networks. However, a major drawback of the above policy is that it causes delay which may be not acceptable for the application. In this paper a trade-off between energy efficiency and delay is investigated. More specifically, an analytical framework which allows to evaluate the expected delivery cost with respect to the maximum delay acceptable for the application is derived and assessed by comparison with simulation results. Numerical results show that the analytical framework is very accurate and, as expected, accuracy increases as the density of nodes in the network increases.

TCP has serious performance problems when wireless links are involved in the end-to-end connection because of the high bit error rate typical of the wireless medium. Joint power management and error correction can improve the link reliability and increase the throughput performance. In this paper, the tradeoff between power management and error correction is investigated. To this end, an analytical framework aimed at maximizing the throughput per unitary cost, defined as the ratio between the TCP throughput and a cost function, is introduced. The proposed analytical framework is general and some significant results have been derived which do not depend on the specific wireless system and do not rely on any TCP throughput approximation formula. The benefits of joint power management and error control are demonstrated in several relevant case studies. The accuracy of the proposed analytical framework is assessed through ns-2 simulation

Sensor networks are characterized by limited battery supplies. Due to this feature, communication protocols specifically designed for these networks should be aimed at minimizing energy consumption. To this purpose, the sensor's capability of transmitting with different power levels can be exploited. With this in mind, in this paper an integrated MAC/routing protocol, called MACRO, which exploits the capability of sensor devices to tune their transmission power is introduced. The proposed protocol requires that each node only knows its own coordinates and the coordinates of the destination, but does not require any exchange of location information. In order to select the next relay node, a competition is triggered at each hop, so that the most energy efficient relay node is chosen. This is achieved through maximization of a newly introduced parameter, called weighted progress factor, which represents the progress towards the destination per unit of transmitted power. To this aim, an analytical framework which guarantees that MACRO performs the best choice is derived. MACRO performance is evaluated through ns-2 simulation and compared to other relevant routing schemes. Performance results show that the proposed protocol outperforms other solutions in terms of energy efficiency and boosts data aggregation.

No abstract available.

Ad hoc networking enables novel communication paradigms which require the definition of new frameworks and parameters for quality of service (QoS) support. The relevant QoS requirements are frequently antagonist and thus also appropriate tradeoff have to be determined and achieved to fit all of them. As an example, consider the spontaneous networking featured by self-organizing ad hoc networks. Spontaneous networking can happen only if neighboring communication nodes discover each other within a short period of time; however, velocity of discovery is paid in terms of energy consumption. In this paper we consider the tradeoff between energy efficiency and responsiveness in the discovery process. More specifically, our objective is to achieve the highest velocity in the discovery process, given that the energy consumption is lower than a certain threshold, or to minimize the energy consumption given that specific requirements in terms of the discovery velocity are satisfied.

Ad hoc networking enables novel communication paradigms which require the definition of new quality-of-service (QoS) parameters and frameworks for QoS support. The relevant QoS requirements are frequently antagonist and, consequently, an appropriate tradeoff has to be determined and achieved to fit all of them. As an example, the spontaneous networking featured by self-organizing ad hoc networks is possible only if neighboring communication nodes discover each other within a short period of time; however, velocity of discovery is paid in terms of energy consumption. In this paper, an analytical framework is derived which allows an evaluation of the tradeoff between energy efficiency and responsiveness in the discovery process. More specifically, the analytical framework allows either to achieve the highest velocity in the discovery process, given that the energy consumption is lower than a certain threshold, or to minimize the energy consumption given that specific requirements in terms of the discovery velocity are satisfied. The analytical framework is applied to significant case studies to derive design implications on neighbor discovery algorithms.

Given their broadcast nature, satellite communications are one natural engineering choice for multicast service deployment. In this paper, the throughput performance of transmission control protocol (TCP)-like multicast congestion control is analyzed in hybrid terrestrial/satellite networks. With this objective, an analytical framework based on Markov chains is introduced. The major advantage of the proposed analytical model is its scalability in that the number of states of the Markov chain modeling the system is independent of the number of receivers in the multicast session. This is a very important feature as simulation is unfeasible for large numbers of receivers. The framework is used to evaluate the impact of the long propagation delays, high bit-error rates, and channel asymmetry characterizing hybrid terrestrial/satellite communications. The performance results show that in certain cases, it is more convenient to divide the receivers in an appropriate number of groups and establish a different multicast session toward each of the above groups. Also, the convenience of an acknowledgment (ACK) flow reconstructor is shown.

It is well known that TCP has performance problems when wireless links are involved in the end-to-end connection. This is due to the high bit error rate characterizing wireless links. Appropriate power management and error correction can improve the link reliability observed by TCP and increase the throughput performance accordingly. In the literature, the effects of transmission power and error correction capability on TCP performance have been considered separately, so far. We study the tradeoff between power management and error correction. To this end, an analytical framework to maximize a user satisfaction function, defined as the ratio between the TCP throughput and a cost function, is introduced. The proposed analytical framework does not depend on the specific wireless system and does not rely on any TCP throughput approximation formula. The benefits of joint power management and error control are demonstrated in several relevant case studies.

TCP-Peach has been proposed for IP network scenarios characterized by long round trip times and high bit error rates, such as satellite networks. In the TCP-Peach congestion control scheme, Slow Start and Fast Recovery are replaced with new algorithms called Sudden Start and Rapid Recovery. In this paper protocol refinements for TCP-Peach are proposed to allow it to be utilized in cooperation with FACK/SACK options. The modified protocol has been implemented in the Linux 2.2.17 kernel and its performance has been evaluated in an emulated satellite network environment. Performance results show that the modified TCP-Peach protocol outperforms other TCP implementations.

Current TCP protocols have lower throughput performance in satellite networks mainly due to the effects of long propagation delays and high link error rates. In this paper, a new congestion control scheme called TCP-Peach is introduced for satellite networks. TCP-Peach is composed of two new algorithms, namely Sudden Start and Rapid Recovery, as well as the two traditional TCP algorithms, Congestion Avoidance and Fast Retransmit. The new algorithms are based on the novel concept of using dummy segments to probe the availability of network resources without carrying any new information to the sender. Dummy segments are treated as low-priority segments and accordingly they do not effect the delivery of actual data traffic. Simulation experiments show that TCP-Peach outperforms other TCP schemes for satellite networks in terms of goodput. It also provides a fair share of network resources.

Design of network devices requires accurate models for network traffic. The main difficulty lies in determination of the traffic statistics which effectively influence the network performance, and in construction of a tractable and easy analytical model capturing these statistics. This paper demonstrates that, in order to evaluate performance of a multiplexer in a discrete-time environment, it is necessary to define a model which is able to capture the probability density function, autocovariance function, and the overload probability of the input traffic, simultaneously. The paper also provide an algorithm for the construction of an SBBP model matching these statistics.

The enormous increase in wireless communications has led to the need to configure wireless access networks with cellular structures based on the use of micro- and picocells. A key issue in connection with this is the rerouting of the data flow directed to a user making a handover. The paper considers one of the most widely-used connectionless protocols, the signaling network layer (SNL), which is based on a tree-structured routing architecture. Through an analytical model based on a queueing network, its performance is analyzed in terms of the distribution of the rerouting delay. The proposed model is then applied to a case study to demonstrate its versatility and manageability and to get important insights to the dimensioning of the routing architecture.

Multimode coders are able to exploit the different characteristics of the speech waveform and to take into account the different peculiarities of background noise, thus allowing improvements in both signal reconstruction and network-offered load. In this context the variable rate code excited linear prediction (VR-CELP) coding, that is, a multimode variable bit rate (VBR) coding based on the CELP technique, has been introduced in the literature and is currently being considered for use in various applications, especially in the third-generation UMTS cellular systems. The target of the paper is to introduce an efficient and accurate framework allowing a network designer to analyze the impact of multimode VBR speech coding on the quality of service (QoS) provided by a wireless/wired ATM network. In order to capture the coder output characteristics, we propose to model a VR-CELP voice source by using a switched batch Bernoulli process (SBBP). More specifically, three models are introduced and compared in terms of accuracy and simplicity in determining network performance. As a result of the comparison, a four-state model has been chosen as the best tradeoff. The model is then used to analytically derive the loss probability and the jitter probability density function of an ATM multiplexer loaded by a number of VR-CELP sources. Finally, the proposed paradigm has been assessed in a case study where we demonstrate that, for a given output ATM link capacity and for a number of telecommunication services involving voice transmission, VR-CELP coding performs better than traditional on-off coding.

Thanks to the ever growing diffusion of distributed multimedia applications, VBR compressed video traffic is expected to constitute the majority of the B-ISDN load in the immediate future. At the moment, the MPEG coding scheme is widely used for many types of applications. Therefore, it is crucial to obtain a model for the generation of sequences with the same statistical characteristics of real MPEG traces. In this paper, after demonstrating that intra-GoP (group of pictures) correlation has to be taken into account, an accurate model allowing the generation of an MPEG-like trace is proposed. Moreover, as the MPEG traffic has been demonstrated to be self-similar, the proposed algorithm allows one to generate a trace, fitting the short-range dependence (SRD) and long-range dependence (LRD) behavior and the probability density function of any given MPEG sequence.

Flow theory is a rich, effective theory introduced in order to study real-time network protocols. It is based on discrete mathematics in which a flow of data is represented by an infinite sequence of numbers and is characterized by its upper bounds. Unfortunately up to now flow theory has used a traffic characterization that in some cases can be too loose, thus causing an inefficient network utilization. In this paper we enhance flow theory by providing it with a more accurate traffic characterization and with all the tools necessary to deal with it. As we will see, this enhancement provide a more accurate performance evaluation and thus better network utilization.

Various tracking strategies for the location of a mobile user in a cellular system have been introduced in literature. Up to now they have been evaluated using simple mobility models that do not take into account the various directions in which users often move. We present a comprehensive study of the distance-based, movement-based and multilayer strategies, from which it emerges that the effectiveness of these techniques depends not only on the call-to-mobility rate, a parameter typically referred to in literature, but also on the directional characteristics of the user mobility model.

Concerns a fuzzy logic-based system which has been purposely designed to achieve real-time traffic control in high-speed networks using the asynchronous transfer mode (ATM) technique. One of the most critical functions is "policing", which has the task of ensuring that each user source complies with the traffic parameters negotiated in the call setup to avoid network congestion. This function is difficult to implement on account of certain conflicting requirements such as selectivity and responsiveness. This is confirmed by the severe limits affecting the most popular mechanisms proposed so far, based on conventional logic. The capacity to formalize approximate reasoning processes offered by fuzzy logic is exploited to derive rules of behavior for a policer starting from the know-how of an expert. We address two key issues related to the implementation of the fuzzy policer. The first focuses on the possibility of hardware implementation of the mechanism using VLSI technology; we present the design of a VLSI fuzzy processor which exhibits a level of performance of over 3 MFLIPS. The second issue concerns the suitability of applying the fuzzy policer to the policing of several classes of sources to reach high levels of cost effectiveness and scalability.

In ATM networks, usage parameter control is required in order to ensure that each source conforms to its negotiated parameters. To this purpose, several policing methods, such as leaky bucket and window mechanisms, have been introduced in literature. However, traditional methods have proved to be inefficient in coping with the conflicting requirements of ideal policing, that is, a low false alarm probability and high responsiveness. This led us to explore alternative solutions based on artificial intelligence techniques, specifically, in the field of fuzzy systems. We propose a policing mechanism based on fuzzy logic that aims at detecting violations of the parameters negotiated. The main characteristics of the proposed fuzzy policer are simplicity and the capacity to combine a high degree of responsiveness with a selectivity close to that of an ideal policer. Moreover, it can easily be implemented in hardware, thus, enhancing both cost and processing performance. The reported simulation results show that the performance of our fuzzy policer is much better than that of conventional policing mechanisms.

Most of the policing techniques proposed so far in high speed networks using the ATM technique are based on conventional approaches which use a crisp decision making logic. They don't meet the selectivity and responsiveness requirements needed to make a policing efficient. In this paper we propose a policing mechanism based on fuzzy logic. The main characteristics of the proposed fuzzy policer are simplicity and the capacity to combine a high degree of responsiveness with a selectivity close to that of an ideal policer. Moreover, it can easily be implemented in hardware, thus enhancing both cost and processing performance. The simulation results reported show that the performance of our fuzzy policer is much better than that of conventional policing mechanisms.

The anticipated emergence of third-generation mobile systems, referred to as universal mobile telecommunication systems (UMTS), raises the problem of reconsidering the design of the databases destined to contain the user information. In particular, it is expected that the key concepts of the new database architectures will be high distribution and fast updating of information. So far, the problem of determining the most appropriate distributed database (DDB) architectures for third-generation mobile systems has not been widely dealt with in literature. This paper presents a methodology for evaluating hierarchical DDB architectures by means of an analytical model of the data querying operation. This methodology allows for structural alternatives, differing on account of the number of levels and branches in the hierarchy, to be evaluated in terms of query loads and mean response times, according to a given user mobility characterization and a given search protocol operation. By way of illustration, the paper discusses a case study, concerning a query operation arising from a location updating procedure and applied to a hierarchical tree-like DDB in which some structural alternatives are considered.>

Erasure nodes are special devices that have been conceived to improve the system throughput of distributed queue dual bus (DQDB) networks. The authors discuss the overload performance of a network with erasure nodes and the standard bandwidth balancing (BWB) mechanism enabled, when alternative implementation schemes of the erasure node protocol are considered. A numerical method is presented to calculate the maximum throughput in any node of the network, and the conditions for achieving fairness are discussed. The results obtained through the analytical approach are assessed by simulation.

The fair queue dual bus (FQDB) medium-access control (MAC) protocol for dual bus networks, which is intrinsically fair in bandwidth sharing and retains all the positive features of distributed-queue dual-bus (DQDB) systems including simplicity, robustness, flexibility, 100% bandwidth utilization, and zero access delay at very low load, is presented. Features which make this protocol appealing with respect to others DQDB alternatives are represented by the fact that FQDB is compatible with the DQDB standard with respect to the physical arrangement and attachment to the bus, the XOR write tap, the access control field (ACF) length, and the three priority levels. Nevertheless, its properties are attractive also for protocols operating at 1 Gb/s and up. Fairness is formally proved, and it is shown that a bandwidth sharing pattern different from the uniform one can easily be enforced, as may be the case if bridges or other large users are presented. Queueing disciplines, priorities, and implementation issues are discussed, and comparisons obtained by simulation are given.>

The authors deal with a new technique that is fully compatible with the IEEE 802.6 MAN standard and allows for multiple use of a slot for segment transmissions. In particular, this technique increases the efficiency of the distributed-queue dual-bus (DQDB) protocol by allowing pre-use of slots traveling along the bus before they are taken as determined by normal DQDB operation. These slots may be captured by nodes wishing to transmit their queued-arbitrated segments addressed to neighbors. The proposed technique is particularly well suited for the case when information transfer between neighboring nodes is more likely than that between remote ones. Simulation was used to study the throughput, delay, and fairness performance. It is demonstrated that the resulting extension of DQDB, which is called SP-DQDB (slot pre-use DQDB), may be much more efficient than DQDB in several different environments.>

The authors introduce a control strategy for bandwidth management in asynchronous transfer mode (ATM) networks. A two-level hierarchy is defined, where one level performs fixed class-selective call admission control strategies that are periodically dynamically coordinated by a higher-level bandwidth allocation controller. Each admission controller decides to accept or refuse an incoming call on the basis of a class-selective rule designed to maintain a certain quality of service. A decision is taken according to the virtual capacity share that is assigned to the various service classes by the allocation controller. Bandwidth shares are periodically recomputed online by means of the constrained minimization of a cost function that takes cell loss probability and refused traffic into account. The control structure, the strategies, and the optimization algorithm used are described as well as the assumptions underlying the choices made. Initial simulation results are reported.>

The authors present the delay-throughput performance of a new MAC protocol, the fair queue dual bus (FQDB), which relieves dual bus networks from the unfairness problems exhibited by the basic version of the distributed queue dual bus (DQDB) standard. The protocol considered is absolutely fair in bandwidth sharing and does not imply any change in the DQDB transmission system. It retains all the positive features of DQDB including simplicity, robustness, flexibility, 100% bandwidth utilization, and little slot access delay at light load. Comparisons to DQDB with the bandwidth balancing mechanism show that the FQDB delay-throughput performance was always superior while its implementation complexity was comparable.>

An architecture and a media access control (MAC) protocol aimed at improving fairness in dual-bus networks are introduced. The major feature of this proposal is that the distributed operation of updating the queue is made atomic, so that the average bus access delay is equalized over the network. An equalized access gain is provided by mandating that a station waiting for transmission consider itself as positioned in the distributed queue only after a constant delay, which is needed for all the other stations to register the request of the first one. This delay is equal to the propagation time of the request along the entire bus. It is shown that under certain conditions the average access delay results in the one experienced in the distributed queue dual bus (DQDB) by those stations which are placed at the middle of the bus. Consequently, the global end-to-end delay remains much below the limits fixed by the CCITT for delay sensitive services. The actual average delay is even smaller than the one estimated due to the presence in the protocol of the stand-by mechanism which is the same as in the DQDB version. In comparison with the traditional DQDB, the increase in access-delay values is rewarded by the significant improvement in terms of fairness.>