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.

Oceans and seas are today mostly unexplored. In the aim of supporting their exploration, the vision of an Internet of Underwater Things (IoUT) emerged recently, indeed targeted at providing a degree of pervasiveness and connectivity in our seas, similarly to what happens in traditional terrestrial networks. This foreseen pervasiveness should however not be detrimental, from a sustainability point of view, to the environment. Accordingly, in this paper we present a semantic-based integrated sensing and communication solution specifically designed for the IoUT. We consider an acoustic underwater network where smart sensor nodes perform image collection and analysis to detect rare marine species. Based on the analysis of the collected data, the smart devices, equipped with a software radio, set physical layer parameters such as transmission gain, payload length, and modulation scheme, so that they communicate relevant data but minimize their impact on rare marine creatures identified in the area. This implies reduction in the sea noise level which could be dangerous for mammals, such as whales or dolphins. AI techniques are employed to classify images collected through sensors and accordingly tune transmission parameters. Performance analysis assess the effectiveness of the approach.

Next-generation networks aim to extend the Internet of Everything (IoE) vision to terrestrial, aerial, and underwater domains. However, when considering acoustic underwater sensing and communications, as traditionally done in the marine scenarios, challenges such as limited bandwidth, high latency, and energy constraints need to be faced. To address these limitations, this paper proposes a distributed AI-based framework for the realization of the Internet of Underwater Things (IoUT), enabling smart sensor nodes to perform localized data processing and adaptive communication. Our system integrates intelligent computing devices with underwater sensing capabilities, allowing cameraequipped nodes to classify marine species using lightweight AI models deployed directly on the nodes. Instead of transmitting raw data, nodes share only classification results, drastically reducing data volume. Software-defined radio (SDR) systems dynamically adapt transmission parameters (e.g., transmission power, modulation, etc.) to minimize energy consumption and environmental impact on marine ecosystems. This decentralized methodology enhances scalability, reduces latency, and saves resources while enabling real-time decision-making in dynamic environments. Performance evaluations using GNU Radio simulations prove reliable data delivery, measured by bit error rate (BER), throughput, and latency, while prioritizing marine ecosystem preservation. Results highlight the system's ability to balance efficiency, sustainability, and ecological conservation in next-generation underwater networks in view of realizing the vision of an Internet of Underwater Things.

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.

In vehicular networks, decentralized cooperative learning strategies have gained significant attention due to the lower communication overhead they involve when compared to centralized cooperative learning approaches like Federated Learning. Decentralized solutions enable vehicles to collaboratively train Machine Learning (ML) models by exchanging parameters without relying on a central server. However, conventional model-sharing methods still suffer from high communication overhead and increased vulnerability to poisoning attacks. This paper presents SPARE , a gossip-based cooperative learning protocol that leverages Vehicle-to-Vehicle (V2V) communication to enhance communication efficiency by exchanging selected model parameters. SPARE selects vehicle nodes for model updates and transmits only the most significantly updated layers, reducing redundancy and improving efficiency. This selective exchange minimizes communication resource consumption and enhances privacy, as the complete model is never shared across the network. We assess the proposed approach using a real-world driving dataset, featuring data from multiple drivers along the same route. Experimental results prove that our method achieves efficient learning with significantly lower communication overhead, demonstrating its suitability for deployment in resource-constrained vehicular networks.

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.

According to the Ethicnet paradigm, which has been recently proposed, communication networks are called to consider Key Value Indicators (KVI)s besides the traditional Key Performance Indicators (KPI)s. The actual realization of the Ethicnet, however, requires several issues to be addressed. One of the most important is the identification of the KVIs to be considered and the way in which they can be measured, controlled, and monitored. This paper contributes to the discussion regarding such topic by answering fundamental questions regarding the identification of the KVIs, the measurement of the extent to which a given network solution contributes to the KVIs, and the definition of requirements in terms KVIs.

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.

Emerging from the symbiotic combination of nanotechnology and communications, the field of nanonetworking has come a long way since its inception more than fifteen years ago. Significant progress has been achieved in several key communication technologies as enablers of the paradigm, as well as in the multiple application areas that it opens. In this paper, the focus is placed on the electromagnetic nanonetworking paradigm, providing an overview of the advances made in wireless nanocommunication technology from microwave through terahertz to optical bands. The characteristics and potential of the compared technologies are then confronted with the requirements and challenges of the broad set of nanonetworking applications in the Internet of NanoThings (IoNT) and on-chip networks paradigms, including quantum computing applications for the first time. Finally, a selection of cross-cutting issues and possible directions for future work are given, aiming to guide researchers and practitioners towards the next generation of electromagnetic nanonetworks.

Nanotechnology is enabling the development of devices on a scale ranging from one to a few hundred nanometers. At this scale, a nanomachine is defined as the most basic functional unit, integrated by nano-components which can carry out sensing and actuation. Coordination and information communication among several nanomachines expand the potential applications of individual devices both in terms of complexity and range of operation. The resulting nanonetworks can cover wide areas, to reach unprecedented locations in a non-invasive way. Moreover, the integration of nanonetworks with classical networks and ultimately with the Internet results in a new networking paradigm, which is referred to as the Internet of Nano-Things (IoNT) by Akyildiz and Jornet one and a half decades ago.

1 The evolution of smart objects towards the Internet of Softwarized Things (IoST) in 6G networks will provide the society with dynamic and programmable systems of interconnected smart devices interacting with little to no human intervention. One pivotal aspect of this evolution is represented by edge learning, which brings machine-learning algorithms at the network edge to achieve massive connectivity, ultra-low latency, energy efficiency, security and privacy. Unfortunately, in many application scenarios commonly envisioned for 6G, edge learning is not feasible neither locally in the smart objects, due to their computation and energy limitations, nor by servers at the edge of the cabled network, because not connected with adequate powerful links. To this purpose, this paper proposes ODEL, an On-Demand Edge-Learning framework that uses a Flying Ad-hoc NETwork (FANET) to bring computing and networking facilities on-site for edge learning. ODEL is based on a marketplace employing a non-cooperative game theoretic approach: UAVs are provided by different third-party providers in exchange of some economic gain. A non-linear optimization problem is formulated in order to determine the optimal distribution of flows that maximizes revenue for each UAV provider, and is solved by means of the Variational Inequality (VI) theory.

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.

In this work i-WSN League, a comprehensive hardware/software framework for the support of distributed training and inference is introduced. For what concerns the hardware, in i-WSN League two types of nodes are considered, namely, head nodes and common nodes. Head nodes are resource rich nodes that have the capabilities for training artificial neural network. Common nodes collect data and can execute inference only. In i-WSN League, all nodes are grouped in Clusters, each with a Cluster Head (selected among the head nodes), which is the only node responsible for training. To this end, data coming from all nodes in the Cluster can be utilized. This, however, involves large exchange of data which might be unsustainable by common nodes. Thus, only part of the data collected by common nodes is sent to the Cluster Heads and a network of Cluster Heads will implement distributed learning in a peer-to-peer fashion. As compared to state of the art literature, the key contributions of our work are related to the combination of gossiping and clustering to adapt the operations executed by each node to its capabilities, with the aim of minimizing the energy consumption in resource limited nodes, while preserving accuracy. In this paper i-WSN League is assessed in a simple scenario in which a wireless sensor network monitors the air pollution in a large city. Performance results obtained by considering auto-encoders prove the effectiveness of the proposed scheme as well as its balanced energy consumption and fairness in resource consumption distribution.

Recently, Flying Ad Hoc Networks (FANET) have been proposed to empower 5G networks to support complex missions and provide ubiquitous connectivity to heterogeneous devices. However, it is needed to cope with the limited UAV capabilities (e.g., limited available energy to supply engines and computing elements, limited computing capabilities), as well as with the need to provide network and application services as foreseen in highly dynamic and time varying 5G ecosystems. This paper presents for the first time a comprehensive framework that integrates a FANET with a 5G network, with the aim of providing services that can be even chained with each other. This model is comprehensive in the sense that it takes into account physical constraints of the devices, as well as features and requirements of traffic flows. For this framework, the paper proposes a mathematical optimization model, allowing Virtual Function (VF) placement and chaining, aimed at minimizing energy consumption and service unsatisfaction probabilities of the FANET as a whole without employing heuristics for the solution of the problem. Two placement strategies named MLP and WMP are introduced and compared with the standard placement strategy named NoShP. An extensive numerical analysis shows that MLP and WMP allow us to well catch network dynamics and to reduce the number of virtual functions needed while decreasing the power consumption, so increasing UAV flight time and network lifetime.

Underwater networks are characterized by undesirable time variability of the channel conditions due to Doppler effect, serious multipath and variable impulse response. Energy efficiency is a primary concern in these scenarios, because nodes batteries cannot be recharged or replaced. Accordingly, in order to increase network lifetime and effectiveness of underwater channel transmissions, in this paper, we present a reinforcement learning approach for communication in multihop underwater networks. The proposed methodology employs a Markov underwater channel model to characterize the link status that allows the relay device to choose the most efficient next hop node to forward data towards a remote gateway device for connection to the terrestrial internet. Simulation results are presented to show the effectiveness of the proposed methodology in terms of energy consumption and latency performance so allowing to increase network lifetime.

Body Area Networks (BANs) have attracted a lot of research interest in the last decades as also witnessed by standardization activities and European Commission fundings. Today, commercial devices, which implement simplified BAN monitoring have appeared, although they usually imply expensive subscription costs or need for a supporting device carried by the user (e.g. a smart phone) which, in case of elderly people, cannot be easily held. However, the integration between commercial devices and external sensors located on/in the body is still an open issue due to the limited processing capabilities of low cost commercial devices. Also, no tools for anomaly detection aimed at reliable healthcare monitoring are currently commercially available. In this paper, we focus on Cloud-Assisted BANs and evolve this vision according to the emerging paradigm of edge computing. We present design, implementation and experimentation of a wireless BAN system which performs data transmission using a commercial, cheap, off-the-shelf gateway smart watch. A mechanism for prompt anomaly detection at the edge node is also supported for the purpose of reliable healthcare monitoring as well as pre-filtering of the data at the smart device itself. Also, in order to reduce the overhead caused by propagation of useless and time-correlated data, and to guarantee a prompt action in case of emergency, edge network nodes located closer to the patient BAN are exploited since they can execute machine learning algorithms to process large amounts of data and activate potential alerts in a shorter time and without overloading the cloud. In this work we describe a real system and evaluate the effectiveness of the approach in terms of false alarm probability.

In this paper an analytical framework based on ordinary differential equations (ODE) is derived which models the propagation of worms injecting false traffic information in a Vehicular Sensor Networks (VSN) by exploiting V2V communications. This is a relevant problem because faulty traffic information can deviate traffic flows causing a decrease in the vehicle density in certain areas which results in a reduction of the sensing power of the VSN. Therefore, in this work the interactions between worm infection propagation and the mobility of infected vehicles are analyzed. The proposed framework is applied to an urban scenario in which mobility traces are generated by exploiting the urban mobility simulator SUMO.

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.

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design.

Rise in population aging as well as diffusion of chronic diseases and health consciousness require constant monitoring of health conditions but also lead to increasing costs for the governments. Body Area Networks represent the next frontier in health care and are envisaged as the natural choice to provide detailed and updated information on health status to prevent health risks and diseases. However if, on the one hand, a large research effort has been devoted so far to the investigation of BAN communications on and around the body, on the other hand intra-BAN communications are still a scarcely explored area because of the difficulties and risks in successfully propagating signals inside the body where RF waves cannot be employed. In this paper we propose to use ultrasonic waves for intra-BANs and we investigate on the feasibility of supporting multihopping among different intra-body devices which communicate with a gateway node in charge of transmitting health information to a remote medical center. We discuss system implementation using Universal Software Radio Peripheral (USRP) nodes and test the feasibility of multihop transmissions across a body phantom enhanced with organic tissues. Our analysis shows that multihop transmission is feasible and can be effectively employed to support communications in next-generation E-Health applications.

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 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.

Due to their inherent feature of exhibiting frequent disconnections, propagation of faulty data in Delay Tolerant Networks can be a critical aspect to counteract. Indeed the rare meeting events require that nodes are effective and efficient in propagating the correct information. Accordingly mechanisms to rapidly identify possible faulty or misbehaving nodes should be searched. Distributed fault detection has been addressed in the literature in the context of sensor and vehicular networks, but unfortunately these solutions suffer for long delays in identifying and isolating misbehaving nodes. In this paper instead we propose a fully distributed, easily implementable, and fast convergent approach to allow each DTN node to rapidly identify whether its sensors are producing outliers. The behavior of the proposed algorithm is described by some continuous-time state equation, whose equilibrium is characterized. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can give guidelines in the design of the algorithm.

This paper introduces μ-NET, a microfluidic LAN that supports the exchange of both digital information and biochemical information carried by droplets moving across molecular processors in a microfluidic chip. The μ-NET can be used to support molecular biology applications like DNA, RNA, and protein biosynthesis. The μ-NET is the first realization of a microfluidic networking paradigm that controls movements of droplets in microfluidic chips by exploiting hydrodynamic phenomena only and builds on recent solutions to achieve communications in the microfluidic domain. The μ-NET integrates techniques to represent addressing information, as well as switching and medium access control solutions. In fact, in μ-NET, the address of the molecular processor where a droplet should be sent to is encoded into the distance between droplets; switching is executed to steer the droplets inside the microfluidic device; medium access control is applied to avoid collisions between droplets that may result in their fusion and, thus, loss of the biochemical information. In this paper, the design of μ-NET is presented in detail, and simulation results validating μ-NET operations are shown.

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.

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 feasibility of using microfluidic technologies for communication and networking among Labs-On-a-Chip (LoCs), in order to increase reusability, their effectiveness and flexibility, has been already demonstrated in recent works. LoCs are devices, in the size of few centimeters, characterized by μ-scale channels in which small volumes of fluids, either in the form of a continuous stream or of sequences of discrete droplets, move in order to deliver chemical or biological samples to a given destination entity in the microfluidic circuit. They are usually realized through monolithic devices. Samples are processed by passing them through a preset sequence of fixed microfluidic channels. We propose and assess a pure-hydrodynamic switching element in which the path followed by a sample can be controlled through a series of junctions simply modulating the distance between droplets flowing in the microfluidic channel.

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.

Microfluidics deals with manipulation and control of fluids which flow in constrained sub-millimetric media. In this paper communication concepts and networking approaches, typical of telecommunications, are extended to the microfluidic domain. The work illustrated in this paper investigates on possible approaches to information encoding and evaluation of the corresponding channel capacity as well as design of switching solutions. Based on the results of this study, the Hydrodynamic Controlled Microfluidic Network (HCN) paradigm is proposed, which is based on a pure hydrodynamic microfluidic switching function. The HCN paradigm can be applied to interconnect Labs-on-a-Chip (LoCs) in a microfluidic network in which chemical/biological samples are routed between the LoCs by exploiting hydrodynamic effects only. The resulting LoCs are expected to be highly flexible and inexpensive, and thus to become extremely useful in chemical/biological analysis and synthesis.

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.

Microfluidics refers to manipulation and control of little amount of fluids flowing into channels of micro-scale size and is the basic technology utilized by Labs-on-a-Chip (LoC) systems. In the recent past researchers have analyzed the possibility to support information exchange between different elements of a LoC. However, support of communication in microfluidic systems relies on the use of appropriate information coding strategies and deep knowledge of channel propagation characteristics. Accordingly, in this paper we compare two different schemes for information coding and evaluate the corresponding capacities depending on the characteristics of the fluids utilized. Furthermore, we discuss how such encoding schemes can be integrated in networking solutions for LoCs.

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.

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.

Support of traffic with QoS requirements in wireless scenarios is a challenging topic and methodologies for supporting different traffic priorities have recently emerged. Unfortunately in wireless scenarios, it is well known that contention problems are met. The latter can be modeled as a game. Accordingly, in this paper we present a game-theoretic model of prioritized data transmission in CSMA/CA networks where nodes are considered as potential cheaters which should be punished through a jamming mechanism when misbehaving and trying to monopolize wireless resources. Then, we also describe the distributed algorithm to be performed by network nodes to implement the model and assess the validity of both the model and the algorithm through simulations. Index Terms: Wireless networks, QoS, game-theory, algorithms, simulation.

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.

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.

Adaptive rate video encoding is required to maximize efficiency when wireless links are involved in the communication. In fact, wireless channels are characterized by high, time-varying bit error rates. To cope efficiently with this problem adaptive forward error correction schemes have been proposed. These schemes introduce an amount of redundancy dependent on the channel conditions. Accordingly, the bandwidth available at the application layer changes: it increases when channel conditions improve, and decreases when channel conditions worsen. Obviously, the encoding parameters must be tuned to adapt the video source transmission rate to the available bandwidth. This adaptation is achieved by means of appropriate feedback laws, which are relationships between the encoding parameters to be used and other variables representing the state of the system. An analytical framework is introduced which can be used for the design of the feedback laws. To this purpose both the channel and the video source are modeled by means of Markov models. The resulting model of the whole system is denoted as SBBP/SBBP/1/K. Analysis is derived which allows to evaluate the most significant performance measures and, therefore, to design optimal feedback laws.

Wireless channels are characterized by high time-varying bit-error rates (BERs). To cope with this problem, several adaptive forward-error-correction (AFEC) schemes have been proposed in the literature. They work locally at the wireless link, adding a variable amount of redundancy to the transmitted data in order to maintain the packet error rate below an acceptable level. However, when such schemes are utilized, the bandwidth offered to the applications changes when channel conditions change. In this paper, the effects of these bandwidth variations are investigated in the case of real-time Motion Picture Experts Group (MPEG) video transmission. The MPEG encoder is controlled in order to adapt its emission rate to the current bandwidth offered by the wireless link. To this end, the encoding quality is diminished by the source rate controller when the transmission rate has to be decreased due to an increase in the channel BER, whereas it is improved when the transmission rate can be increased due to a decrease in the channel BER. A Markov-based model, denoted as SBBP/SBBP/1/K, has been introduced to model the scenario being considered. The analytical framework allows evaluation of the performance of the system and can be used to optimize the design of a video transmission system for wireless channels, providing the instruments to derive the tradeoff between information corruption in the wireless channel and MPEG video encoding quality.

It is well known that TCP is unfair when connections with different round trip times share the same bottleneck. In the literature TCP fairness in wired networks has been widely studied but little effort has been devoted to this issue in wireless networks where new problems arise. Objective of this paper is investigating on the possibility of achieving TCP fairness through power management in wireless CDMA networks. The basic idea is to allow long round trip time connections, which are usually the most disadvantaged, to have higher signal-to-interference ratio levels. An analytical framework is introduced to calculate, for each mobile terminal, the appropriate transmission power giving fairness. The impact of such power management scheme on fairness and overall throughput performance is evaluated by means of both analysis and simulation.

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.

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.