This paper investigates equilibrium strategies in networked control systems subject to false data injection (FDI) attacks, employing a game-theoretic approach. Our framework characterizes the dynamics that revolves around the freshness of the data from multiple sensors using the age of incorrect information (AoII) metric. The interaction between legitimate transmitters, aiming to minimize the system AoII and their transmission costs, and a malicious adversary, aiming to maximize AoII while managing FDI costs, is modeled as a non-cooperative game. We analytically demonstrate the existence and uniqueness of a Nash equilibrium (NE) and derive explicit conditions characterizing equilibrium resource allocation strategies. We also present examples of applications related to secure healthcare and automotive control. The numerical results validate our theoretical findings, highlighting the strategic impact of the system parameters, including drift rates, FDI and transmission costs, and resource constraints. Our analysis yields actionable guidelines for enhancing sensor security through parameter tuning and resource allocation.

We investigate a scenario where multiple sources independently and voluntarily contribute status reports, which are then aggregated through a federated process. To address the challenge of partial participation in distributed systems, we introduce the age of federated information (AoFI), a novel metric that quantifies data freshness. This metric is specifically designed to bridge the gap between classical age of information, which is unsuitable for collaborative tasks, and the often impractical age of correlated information, which requires full participation. To model distributed optimization across multiple independent sources, we adopt a game-theoretic framework. In this framework, users strategically minimize their individual penalty, computed as a global-local combination of the overall AoFI on the common receiver’s side and their individual energy expenditure. We derive the worst-case Nash equilibrium of this game and compare its efficiency with the centralized optimization optimum. Our efficiency analysis reveals a critical design tradeoff for practical industrial internet of things (IIoT) deployments: while decentralized coordination is highly efficient in high-participation regimes, performance in low-participation regimes is paradoxically optimized by actively restricting the number of sources to prevent strategic inefficiencies.

Goal-oriented communication entails the timely transmission of updates related to a specific goal defined by the application. In a distributed setup with multiple sensors, each individual sensor knows its own observation and can determine its freshness, as measured by Age of Incorrect Information (AoII). This local knowledge is suited for distributed medium access, where the transmission strategies have to deal with collisions. We present Dynamic Epistemic Logic for Tracking Anomalies (DELTA), a medium access protocol that limits collisions and minimizes AoII in anomaly reporting over dense networks. Each sensor knows its own AoII, while it can compute the belief about the AoII for all other sensors,based on their Age of Information (AoI), which is inferred from the acknowledgments. This results in a goal-oriented approach based on dynamic epistemic logic emerging from public information. We analyze the resulting DELTA protocol both from a theoretical standpoint and with Monte Carlo simulations, showing that it is significantly more efficient and robust than classical random access, while outperforming state-of-the-art scheduled schemes by at least 30%, even with imperfect feedback.

We study a control system where sensor measurements are transmitted to a remote station. Information may become outdated due to system drift or compromised by malicious false data injection. To quantify the impact of staleness and inaccuracy in the information at the receiver’s side, we use Age of Incorrect Information (AoII). In particular, we consider the Excess AoII above a certain threshold as our key objective to minimize, which we argue to be a sensible goal for many real-time control systems. We adopt a game-theoretic framework to model the strategic interaction between a transmitter, which aims to minimize both Excess AoII and transmission costs, and a malicious agent, which seeks to maximize the same Excess AoII metric while minimizing its own costs. Our analysis reveals the existence of a Nash equilibrium for this game, and we investigate how the system parameters influence the adversary’s decision to attack, identifying the conditions under which an attack becomes advantageous or not.

Age of Incorrect Information (AoII) is particularly relevant in systems where real time responses to anomalies are required, such as natural disaster alerts, cybersecurity warnings, or medical emergency notifications. Keeping system control with wrong information for too long can lead to inappropriate responses. In this paper, we study the Peak AoII (PAoII) for multisource status reporting by independent devices over a collision channel, following a zero-threshold ALOHA access where nodes observing an anomaly immediately start transmitting about it. If a collision occurs, nodes reduce the transmission probability to allow for a resolution. Finally, wrong or lost feedback messages may lead a node that successfully updated the destination to believe a collision happened. The PAoII for this scenario is computed in closed-form. We are eventually able to derive interesting results concerning the minimization of PAoII, which can be traded against the overall goodput and energy efficiency, but may push the system to the edge of congestion collapse.

Internet of things (IoT) applications require up-todate information about the system conditions. This can often be provided from multiple alternative sources that sense the environment, but act without centralized coordination. In this paper, we consider a scenario where multiple sources can provide information for a number of tasks of the IoT application, assuming that the information content of multiple sources is generally redundant, yet one single source is generally insufficient for all tasks. In so doing, we seek for the minimization of the age of federated information (AoFI), a metric describing the age of information from multiple sources, considering that the epochs of successful updates are only those where all tasks are covered. At the same time, we would like to contain the number of active sources for cost reasons. To this end, we tackle the problem through a game-theoretic approach, where individual sources act as players minimizing a linear combination of AoFI and activation cost. We prove that this framework identifies efficient Nash equilibria very close to the optimum performance. However, the latter can only be achieved through centralized control, whereas the former allows for distributed implementation, which is key in IoT scenarios.

This study investigates the integration of multiple biological signals to assess the impact of sleep deprivation on attention levels. Electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG) data from sleep-deprived patients were analyzed with performance outcomes from the Psychomotor Vigilance Test (PVT), which measures response times. The primary objective was to develop a robust predictive model for the level of drowsiness based on these signals. By leveraging machine learning models, the study demonstrated the feasibility of signal-based assessments for predicting drowsiness levels. Random Forest achieved the highest accuracy when using reaction times as the true labels. It also showed promising agreement with the subjective evaluation of the alertness levels, highlighting conditions where the individuals may risk to underestimate their drowsiness. The results underscore the potential of biological signals to improve understanding of sleep deprivation’s impact on cognitive performance and potentially contribute to develop robust drowsiness detection systems for practical contexts.

In-ear EEG has recently emerged as a promising avenue to assess cognitive workload using minimally obtrusive sensors, thus promoting continuous and ubiquitous health monitoring. However, concerns around the quality and representativeness of data collected with this new technology need further investigations. In this work, we utilize a dataset related to a participant engaged in various mathematical tasks while wearing an in-ear EEG device. We apply signal processing techniques and feature extraction methodologies to analyze the EEG data. Feature vectors were constructed from each data segment, and subsequently used to train various machine learning classifiers to discriminate between different levels of cognitive workload. Moreover, we investigate the effectiveness of feature selection methods, to reduce the dimensionality of the feature space and potentially improve classifier performance. The results indicate that in-ear EEG, together with proper processing in terms of feature selection and machine learning, can adequately differentiate cognitive workload levels. Our findings proved the convenience of carrying on the investigation of this new kind of technology to promote a healthcare service closer to patients.

In the era of smart cities and Industry 4.0, Digital Twin (DT) technologies have emerged as transformative tools for optimizing urban and industrial systems. We explore the application of Green DTs (GDTs) in the context of ThirdParty Logistics (3PL) to enhance sustainability and operational efficiency. By integrating real-time data with predictive analytics, GDTs enable the optimization of delivery networks, minimizing resource consumption and carbon emissions, while addressing challenges such as traffic congestion and reverse logistics. We investigate a 3PL scenario, involving a largescale delivery network, focusing on the misalignment between environmental goals of the central operator and the profitdriven strategies of third party providers. We employ a gametheoretic approach to evaluate inefficiencies through the Price of Anarchy (PoA) and the Price of Stability (PoS). The results demonstrate the potential of GDTs to dynamically model agent behavior, optimize route planning, and enhance collaboration in decentralized supply chain networks to reduce emissions.

We use Bayesian game theory to investigate the interaction between a system controller and an additional unknown agent in a cyber-physical system. The system controller performs some monitoring for real-time operation management, with the aim of minimizing the age of incorrect information (AoII). The additional agent reports some extra information, which ideally can serve to aid the controller and meet the same objective of decreasing AoII, but it is uncertain whether these actions are useful or correspond to (possibly international) false data injection in the system. The controller only has information in terms of probability of the legitimacy of this extra agent through a common prior, and also knows that, in case it is malicious, it will try to increase AoII instead. Our analysis reveals that, under rational behavior, an adversary can effectively masquerading as a sensor injecting legitimate data, as the controller can hardly distinguish the behavior of a true helper from that of an attacker. However, under variable data drift, the strategic behavior of the external agent can give away their type.

We investigate the minimization of the age of information (AoI) of an AI-powered application that requires timely processing of data generated by a multitude of users. We consider that sequences of inference tasks generated at individual terminals can either be processed locally with a tiny machine learning (ML) model or be offloaded to a more powerful ML model residing on an edge computing facility shared by all users. Since the local ML model is less powerful, its inferences may have low confidence. When this happens, the user is forced to repeat the inference with the more powerful edge ML model. The choice between local processing or offloading follows a randomized-alpha policy, where the local ML model, while less powerful, offers the advantage to alleviate congestion of the edge server. The AoI model follows the frameworks presented in the literature for multiple sources sharing the same queue. Local processing instead works as a single-server dedicated queue, but we account for the imperfections of the tiny ML model by including a failure probability in the local server. Tasks that are processed locally but eventually fail to achieve a minimum confidence level are offloaded to the edge server, resulting in a longer overall processing time. We derive a queueing model of the entire system based on some bounds from the literature. Our results show the trade-offs between processing latency, inference accuracy, and system congestion, highlighting the importance of optimizing task allocation strategies.

Sensor networks, especially when deployed in a field with little supervision, are vulnerable to a broad range of attacks. In this paper, we study a scenario where multiple competitive adversaries inject false content in the sensed data with the intent of impairing network control. We use game theory to analyze the different behavior of adversaries acting independently or in a coordinated fashion. This analysis ultimately results in the evaluation of efficiency metrics for the utility of uncoordinated attackers, based on the Age of Incorrect Information (AoII), which is compared to the coordinated case. Our numerical results show that generally the lack of coordination is detrimental for the two attackers. With the exception of few edge cases, competition leads the attackers to be more concerned with prevailing over each other than actually compromising the system.

We analyze a sensing system where multiple sources transmit status updates to a common receiver. We assume that the correlation of transmitted information allows updates from one source to enhance the information freshness of others. We study the objective of minimizing individual information staleness, quantified by the Age of Information (AoI), at the receiver's end. We evaluate both centralized and distributed optimization strategies. In the former case, we select the globally optimal transmission rates for each source to minimize the total average AoI of the system. For distributed optimization, sources are seen as players in a non-cooperative game of complete information, for which we compute the Nash equilibria. As an example, we consider a fixed correlation budget shared among two sources and evaluate the transmission rates depending on the specific level of correlation. Our results show that, under a centralized approach, it is convenient that only the source with more influential content transmits, while the other source reduces its data injection rate. In contrast, independent transmission in a distributed setup leads to greater congestion and higher average AoI. However, as correlation increases, the performance of the distributed system approaches that of the centralized model, indicating that decentralized management becomes effective in highly correlated scenarios.

Measurements of one-way delay in 5G networks are essential for evaluating system performance in various practical scenarios. This paper presents a comprehensive study comparing three different packet generators on real-world deployments and their associated challenges. We explore different topolo-gies, types of terminals, and network congestion conditions, discussing the suitability and limitations of different packet generators. Moreover, we propose practical considerations to overcome limitations in capturing dynamic and heterogeneous 5G network environments and provide insights for system designers in precisely measuring and characterizing one-way delay in 5G networks for real-world scenarios.

Mobile computing faces challenges due to the resource constraints of mobile devices, such as limited computing power, energy, and connectivity. These limitations hinder the use of high-complexity classifiers and wireless transmissions. To address this issue, we propose a novel collaboration paradigm between mobile devices and edge servers, where the edge server assists the mobile devices by dynamically retraining a low-complexity classifier to adapt to temporal changes in data distribution. We propose a novel approach called drift control protocol (DCP) which is inspired by TCP congestion control mechanism. DCP aims to strike a balance between low-complexity classifier retraining frequency and communication costs with the edge server. It adjusts the update rate of the classifier on the mobile device based on distribution drift characteristics and controls the number of input samples sent to the edge server to improve accuracy. We evaluate and study different versions of DCP using synthetic and real datasets We demonstrate that DCP keeps the error bound, while reducing the burden of the communication cost by 90% for the mobile nodes, which makes our proposal suitable for online domain adaptation.

Real-time applications in the Internet of things (IoT) commonly require to schedule status updates from remote sensors to minimize age of information (AoI), a metric that captures the freshness of received data. Oftentimes, this problem is tackled assuming that sensors operate over an indefinite time horizon and can decide when to transmit data leveraging the knowledge of the current AoI level at the receiver, even when the communication channel is unreliable. Such a modeling approach, however, neglects some key aspects of most practical IoT systems, where the frequency of status reporting is limited due to resource constraints, such as energy limitations, and tracking the outcome of the updates would require additional consumption of resources to acquire a feedback. In this paper, instead, we investigate the optimal schedule of updates over a finite time horizon for a resource-constrained sensor that is allowed to perform a limited number of updates. We discuss the role of the feedback from the receiver, and whether it is convenient to ask for it whenever this causes additional energy consumption and consequently allows the transmission of a lower number of updates. We analytically identify regions for the feedback cost and the reliability of the channel where making use of feedback may or may not be beneficial. Our study covers both thegenerate-at-willcase, in which a sensor can produce a fresh reading whenever it wants to communicate with the receiver, and anexogenoussetting, where the transmitter cannot decide when new status updates are produced. The results highlight some interesting trade-offs, providing useful design hints for the protocol operation of IoT remote sensing systems.

Data freshness is extremely important for real-time applications and generally measured with age of information (AoI). Related studies typically assume that fresh data can be generated at any time. However, in industrial Internet of Things (IIoT) applications, such as alerting, monitoring, or task-oriented operations, data generation is often exogenous and occurs within a finite window. This motivates our analysis, where we investigate AoI-minimizing scheduling for status updates from an IIoT source that generates fresh data only at random intervals. We differentiate between infinite and finite horizons, with the latter being more aligned with IIoT tasks. For each scenario, we examine both agnostic (predefined and unchangeable) and source-aware scheduling, based on the probability of fresh data generation and the duty cycle. We provide a tight bound for source-aware scheduling in the infinite-horizon case and exact expressions for the other scenarios. We assess the increase in AoI from sporadic data generation, finding worst-case factors of 3 for agnostic scheduling and 2 for source-aware scheduling. However, these estimates are pessimistic when the data generation probability is at least an order of magnitude higher than the duty cycle. In such cases, the AoI increase is less than 20% for agnostic scheduling and almost negligible for source-aware scheduling.

We evaluate a modification of irregular repetition slotted ALOHA (IRSA) involving intermediate decoding and early transmission termination by some nodes, upon their decoding success. This is meant to avoid unnecessary transmissions, thereby reducing energy consumption. We expect this to be particularly useful at low loads, where most transmissions can be avoided as they do not often result in a collision and are therefore redundant. To validate this proposal, we observe that most of the literature related to IRSA considers an asymptotic heavily loaded regime; thus, we also present a model of energy consumption and success probability for frames of limited length and low offered loads. Thanks to our analysis, also confirmed by simulation, we are able to show that the proposed technique is able to reduce IRSA energy consumption by minimizing transmissions, while preserving performance gains over standard ALOHA. For example, we are able to get a 33% energy saving at offered loads around 10% without affecting throughput.

The Metaverse is an immersive online world, accessed through headsets, seamlessly integrating virtual and augmented reality. Users navigate this digital realm through avatars, participating in real-time activities such as work, meetings, concerts. The real-time nature of the Metaverse prompts an analysis using age of information, a metric that tracks information freshness. In this environment, where users actively seek continuous stimuli, sustaining high attention is vital. We propose a game-theoretic analysis of user-server interactions for enduring cooperation, where we incorporate a discount factor to quantitatively compare present and future actions. We derive closed-form solutions for the infinite horizon game and obtain lower bounds for the discount factor chosen by the entities and upper bounds for the communication cost sustainable in order to achieve long-lasting cooperation. This enriches our understanding of temporal dynamics in ensuring information freshness, providing insight into the dynamic interplay between users and the Metaverse environment.

We consider an Internet of thing scenario, where a set of sensors collect data and exchange them with a common receiver. We analyze their interaction, considering a shared goal to minimize Age of information at the receiver's side. We argue that a fully collaborative setup, albeit generally succeeding in this task at first, often leads to resource wastage in the long run. We try to achieve a similar level of cooperation through a purely opportunistic mechanism, in which nodes are driven by selfish objectives, but still aware of the ultimate goal of maximizing information freshness. We show how our proposed approach, allowing fewer nodes to participate in the task (up to one order of magnitude), results in a better resource management, still improving the long-term average age of information. At the same time, a target number of participating nodes can be set, e.g., to a given fraction of the network, by properly tuning the individual objectives and the communication costs.

Age of Information (AoI) is a metric often used to represent the freshness of the information exchanged between a sensing source and a receiver. We consider a system where these two nodes are connected through an error-prone time-slotted channel, and a relay node is also present to assist the transmission. We consider both the sensor and the relay as intermittently and independently active nodes, whose activity rate may be adjusted, resulting in different levels of freshness and corresponding energy costs. To this end, the activity pattern can either follow a Bernoulli random process or a periodic duty cycle with adjustable duration. After computing the expected AoI and the complete Peak Age of Information (PAoI) distribution for both cases, we consider a fully distributed game theoretic duty cycle optimization, in which the two nodes independently tune their own activity rate, finding a balance between freshness and cost. The equilibrium of the resulting game is found to be both efficient from the perspective of the resulting performance and computationally lightweight for a distributed robust control implementation.

Within a recent line of research, age of information is supported as an alternate network performance metric with respect to throughput or delay, to evaluate the performance of medium access techniques, especially for remote sensing applications. Analytical investigations based on game theory have shown how selfish players can behave efficiently in random access systems if they are driven by AoI-based objectives. We extend this kind of reasoning to the case of a slotted ALOHA system with capture. We present a fully analytical derivation of the general framework and its main results. We provide a quantitative characterization for the strength of capture in relation to the efficiency of the resulting Nash equilibrium, which provides extremely useful insights for a distributed system management. We apply our analysis to some scenarios of interest, in particular the case of exponentially distributed powers, for which we obtain a closed-form relationship. We highlight the impact of the system parameters, specifically the cost coefficient and the capture threshold, towards achieving an efficient allocation that represents an equilibrium for the network management. It is ultimately shown that, when capture is strong, as quantified through precise conditions (the system is driven towards a Nash equilibrium achieving near-optimal performance).

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost; thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.

The rapid integration of digital technologies into physical systems has given rise to cyber-physical systems, where the interaction between the computational and physical components plays a crucial role. This study explores optimal decision-making in event detection and transmission scheduling within cyber-physical systems, emphasizing the crucial aspect of efficient decision-making. We consider the problem of monitoring and reporting about a single event taking place within a finite time window achieving a reward related to the timeliness of the status update. Thus, the objective corresponds to minimizing the age of information between the instant of the event x and the status update time t, with a further penalty for a missed event. The monitoring apparatus decides when to perform the status update without knowing the value of x, but only knowing its statistical distribution. We assume a triangular probability density function for the instant of the event taking place, with a variable average. We provide an analytical derivation of the optimal choice of the status update, highlighting interesting trends, such as the saturation in the value of t as x grows close to the limit of the observation window. This proposed problem and its analytical formalization may serve as a further foundation for the general analysis of optimal monitoring of cyber-physical systems.

Several actuation choices in the Internet of things (IoT) require state information to be up-to-date. In this context, the choice between local processing and offloading to a more powerful remote server can have a significant impact on data freshness. Local processing is indeed typically less reliable due to limited computing power and may require multiple attempts, which can result in data becoming stale. Conversely, remote (i.e., edge/cloud) processing can be more reliable, yet entail longer response times due to data transmission to an external server, possibly performing processor sharing with other tasks. The optimal balance depends on the specific system conditions, especially on the congestion at the remote server, and is in general non-trivial. We explore this tradeoff through a mathematical model, and argue about the derivation of a freshness-efficient local vs. remote split of requests, providing insights on the role played by reliability and latency in selecting the optimal strategy.

We investigate the performance of concurrent remote sensing from independent strategic sources, whose goal is to minimize a linear combination of the freshness of information and the updating cost. In the literature, this is often investigated from a static perspective of setting the update rate of the sources a priori, either in a centralized optimal way or with a distributed game-theoretic approach. However, we argue that truly rational sources would better make such a decision with full awareness of the current age of information, resulting in a more efficient implementation of the updating policies. To this end, we investigate the scenario where sources independently perform a stateful optimization of their objective. Their strategic character leads to the formalization of this problem as a Markov game, for which we find the resulting Nash equilibrium. This can be translated into practical smooth threshold policies for their update. The results are eventually tested in a sample scenario, comparing a centralized optimal approach with two distributed approaches with different objectives for the players.

Intelligent reflecting surfaces (IRSs) are being studied as possible low-cost energy-efficient alternatives to active relays, with the goal of improving coverage in millimeter wave (mmWave) and terahertz (THz) network deployments. In the literature, these surfaces are often studied by idealizing their characteristics: notably, it is often assumed that IRSs can tune with arbitrary frequency the phase-shifts induced by their elements, thanks to a wire-like control channel to the next generation node base (gNB). Instead, in this work we investigate an IRS-aided time division multiple access (TDMA) cellular network, where the reconfiguration of the IRS entails an energy or communication cost, and we aim at limiting the number of reconfigurations over time. We propose a clustering-based heuristic scheduling, which optimizes the cell sum-rate subject to a given number of reconfigurations within the TDMA frame. To this end, we first cluster user equipments (UEs) with a similar optimal IRS configuration, determined through a novel beamforming and IRS iterative optimization algorithm. Then, we obtain a single IRS configuration for each cluster of UEs. Numerical results show that our approach is effective in supporting IRSs-aided systems with practical constraints, achieving up to 85% of the sum-rate obtained by an ideal deployment, while reducing by 50% the number of IRS reconfigurations.

A key challenge of today’s systems is the mismatch between the high computational demands of modern neural network models for data analysis and the severely limited resources of mobile devices. Existing solutions focus on model simplification and task offloading to compute-capable edge servers. The former often leads to performance degradation, whereas the latter requires the transfer of information-rich signals and is subject to the impairments of wireless channels. To address these issues, a framework that establishes a novel form of collaboration between mobile devices and edge servers is proposed herein. The core idea is to deploy lightweight models on mobile devices that are intelligently updated to match the current, and local, distribution of the samples being observed. The framework develops the temporal patterns of the samples to determine the optimal model update policy, as well as channel resources allocated to the mobile users. The performance of the proposed framework is evaluated via extensive experiments with both synthetic and real-world datasets.

This paper investigates data correlation in remote sensing networks and how it can be characterized through diverse models quantifying the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations.

A modern interdisciplinary analysis of social networks implies detecting and investigating relevant socio-psychological linguistic markers that carry insight on the nature and characteristics of the social discourse. Associating markers to specific words is a further important step, allowing for an even richer interpretation. By taking as a working example the social discourse in Twitter, we propose a scalable method called PageRank-like marker projection (PLMP) following a rationale inspired by PageRank to fully exploit the interdependencies in a semantic network, so as to meaningfully project markers from a social discourse level (tweets) to its semantic elements (words). The effectiveness of PLMP is shown with an application example on calls to online collective action.

This paper investigates the decision making process aided by machine learning for biomedical problems and how to improve it through meta assessments of the most relevant features. Classification algorithms are usually trained and exploited with high dimensional datasets (i.e., with an extremely large number of features), which is inefficient and costly. It would be beneficial to identify the most meaningful features that contribute the most to assigning a category to a subject, and in particular, diagnosing a pathological condition. A helpful support can come from cooperative game theory, through the computation of the Shapley value, an indicator of desirable properties according to which the players, in our case the input features, can be ranked. We apply such a framework to a supervised machine learning scenario of a random forest tree classifier applied to heart disease detection. From a publicly available dataset, we identify the most relevant features that can affect the decision, thus obtaining practical guidelines for a compact yet efficient description based on an analytical rationale.

Scheduling updates from remote sensors is a key task for the internet of things (IoT). In this context, the mathematical concept of age of information is often used to capture the freshness of received data. This is, in turn, relevant to optimize the frequency of the exchanges, especially for resource constrained (e.g., energy-limited) sensors. Most investigations on the subject assume that the transmitter can leverage knowledge of the age of information at the receiver side to decide when to send data, even when the communication channel is unreliable. In reality, tracking the outcome of the updates would require additional consumption of resources to acquire a feedback. We investigate the optimal schedule of updates over a finite time horizon for a resource-constrained sensor that is allowed to perform a limited number of updates, as typical of IoT devices. We discuss the role of the feedback from the receiver, and whether it is convenient to ask for it whenever this causes additional energy consumption and consequently allows the transmission of a lower number of updates. We analytically identify regions for the feedback cost and the reliability of the channel where making use of feedback may or may not be beneficial.

In this paper, we consider energy cooperation in a smart grid scenario. We assume that grid nodes act as prosumers, who can generate energy thanks to harvesting procedures, and exploit the presence of a smart grid gateway that enables energy and money transactions between local and external prosumers. We propose to adopt a game theoretic approach, where the prosumers participating in the smart grid can efficiently improve their revenue. We built a simulator, in which we can tune the smart grid price settings, and compared the game theoretic approach with “always sell”, “always buy,” and “random” strategies through smart grid simulations with 500 participants.

The problem of multi-agent robotic survey of an unknown area is approached through a game theoretic frame-work. This is meant to enable cooperation in the group of robotic explorers reflecting their common objectives to minimize the effort in the surveying task, without requiring expensive exchanges of signaling. The game theoretic approach can be applied to avoid any preliminary planning, but just exploiting the ability of the robots to take smart actions based on the state of the environment and the behaviors of other neighboring agents. We discuss how the choice of different utility functions can improve the collaboration among the robots and lead to more efficient results.

We consider a wireless jamming game played by a receiver and a malicious interferer that wants to disrupt communication. The outcome of the game depends on the characteristics of the wireless medium with distance-dependent path loss. The players can leverage this by changing their physical location. We consider both a static scenario where a position is kept forever by the players, and a dynamic one where players can change it over subsequent steps. We also include an optional feature for the receiver to catch the jammer when it is too close, which nullifies its jamming. The conclusion is that, in a mobile scenario, losing players see an improvement of their payoffs (or better, they cut their losses). As a result, we characterize the Price of Mobility, i.e., the benefit obtained thanks to the ability of changing position.

Intelligent autonomous vehicles navigating in a smart city environment need to find a parking spot, and often resort to multi-storey parking facilities. The task of efficiently using parking resources is at odds with the competitive nature of autonomous vehicles operating in a selfish way. In this paper, we model the problem through game theory and we evaluate the efficiency of a distributed decision mechanism. At the same time, we also gain insight on the complexity of identifying efficient solutions and hint that the overall problem is difficult to solve without compromising the inherent selfish objective of each individual vehicle. We also propose some distributed simulation scenarios to capture some aspects of the competition, thereby suggesting possible further analytical studies.

LoRa is a low-power wide-area network solution that is recently gaining popularity in the context of the Internet of Things due to its ability to handle massive number of devices. One of the main challenges faced by LoRa implementations is the allocation of Spreading Factors to the devices. While the assignment of these parameters is virtually simple to execute, scalability and complexity issues hint at its implementation through a game theoretic approach. This would offer the advantage of being readily implementable in vast networks of devices with limited hardware capabilities. Hence, we formulate the SF allocation problem as a Bayesian game, of which we compute the Bayesian Nash equilibria. We also implement the procedure in the ns- 3 network simulator and evaluate the resulting performance, showing that our approach is scalable and robust, and also offers room for improvement with respect to existing approaches.

Distributed (cloud, cluster, grid) computing systems are becoming popular due to the huge amount of data available nowadays and the complexity of the computations required to handle them. An efficient allocation of computational resource is key to guarantee service quality in terms of execution time and cost. However, the inherent distributed character of these scenarios prevents them from adopting centralized allocation strategies and suggests that approaches inspired or related to game theory can be used instead. However, most solutions available in the literature propose simple techniques based on static allocation scenarios subsequently finding their outcome as a plain Nash equilibrium, which seems to leave some room for improvement. In this paper, we address this issue by considering instead a Nash bargaining solution obtaining a Pareto optimal solution of the allocation problem. We compare the results of this approach with those of a "myopic" strategy that pursues a Nash equilibrium, and we determine that, while both allocation strategies fully utilize the entire system capacity, a Nash bargaining achieves significantly better performance in terms of time spent by the users in the system. This gives evidence for a high Price of Anarchy of the myopic allocation and points out the need for a better allocation policy that makes a more efficient use of the available resources.

Data collected and transmitted by Internet of things (IoT) devices are typically used for control and monitoring purposes; and hence, their timely delivery is of utmost importance for the underlying applications. However, IoT devices operate with very limited energy sources, severely reducing their ability for timely collection and processing of status updates. IoT systems make up for these limitations by employing multiple low-power low-complexity devices that can monitor the same signal, possibly with different quality observations and different energy costs, to create diversity against the limitations of individual nodes. We investigate policies to minimize the average age of information (AoI) in a monitoring system that collects data from two sources of information denoted as primary and backup sources, respectively. We assume that each source offers a different trade-off between the AoI and the energy cost. The monitoring node is equipped with a finite size battery and harvests ambient energy. For this setup, we formulate the scheduling of status updates from the two sources as a Markov decision process (MDP), and obtain a policy that decides on the optimal action to take (i.e., which source to query or remain idle) depending on the current energy level and AoI. The performance of the obtained policy is compared with an aggressive policy for different system parameters. We identify few types of optimal solution structures and discuss the benefits of having a backup source of information in the system.

In this paper, we propose a new method for jointly compressing EEG and EMG biosignals based on the so-called cortico-muscular coherence, a function that takes into account the simultaneous frequency changes of the brain and the muscles activity, and can be used, e.g., to classify different kinds of movement. It is shown that this method increases the achievable compression rate compared to transmitting EEG and EMG samples separately, while trading-off with the accuracy of the classification. This can be exploited in several kinds of life and health applications e.g., motor rehabilitation and drivers attention monitoring; it could be especially useful for low-power wireless technologies, such as Bluetooth Low Energy or IEEE 802.15.6, whose transmission resources are limited.

This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.

Interactions between pedestrians, cyclists, and human-driven vehicles have become a major concern for traffic safety over the years. The upcoming age of autonomous vehicles will further raise major problems on whether self-driving cars can accurately avoid accidents; on the other hand, usability issues arise on whether human-driven cars and pedestrians can dominate the road at the expense of the autonomous vehicles that will be programmed to avoid accidents. This paper proposes some game theoretical models applied to traffic scenarios, where the strategic interaction between a pedestrian and an autonomous vehicle is analyzed. The games have been simulated to demonstrate the theoretical analysis and the predicted behaviors. These investigations can shed new lights on how urban traffic regulations and inter-vehicle communications could be required to allow for a general improved management of traffic in the presence of autonomous vehicles.

In this paper, we consider energy cooperation in an Internet of Things (IoT) smart city scenario. We assume the presence of interconnecting energy harvesting IoT gateways (GWs), that are endowed with energy harvesting capabilities and whose role is to collect and aggregate data from field sensing devices. Energy cooperation complements and balances the energetic needs to those devices that are neither connected to the power grid, nor satisfactorily served by energy harvesting due to the instability of ambient energy arrivals. The proposed solution entail energy transfers from energy rich gateways to energy scarce ones, i.e., those which are not connected to the power grid. To identify the optimal energy transfer/allocation scheme, we formulate a convex optimization problem that finds the optimal solution for heterogeneous smart systems. With this energy allocation technique, the gateways are unlikely to run out of energy during operation and the gap between energy offer and demand among interconnected gateways is kept to a minimum. We also quantify the performance of the proposed energy transfer policies as a function of network parameters, including: the amount of traffic generated by sensing devices, the number of smart services in the system, and the number of gateways that are connected to the power grid.

The definition of “energy topologies” based on energetic cooperation (exploitation and exchange) between interconnected objects is an important feature that can be implemented in Smart Cities. Based on the presence of energy harvesting devices, it is aimed at providing system-wide sustainability by allowing exchange of stored and supplied energy in a similar fashion to communication of data. In this paper, we investigate the possibility of integrating energy cooperation within the design of the energy topology, or, in other words, by establishing energy links between objects, in particular wireless smart nodes powered by harvesting renewable energy sources. To do so, we construct an optimization model, where it is guaranteed that wireless nodes during operation will not be depleted and the optimal energy transfer does not exceed the energy demands of other communication nodes. We analyze how the system conditions can affect the energy topology, in particular, energy harvesting capabilities, energy levels, and energy thresholds. We also identify some theoretical limits for the system to guarantee complete sustainability, that is, nodes do not go out of charge. Also we demonstrated the effectiveness of the model comparing it with the system operation without applied optimization.

We consider the management of an energy harvesting wireless sensor network, inspired by game theory so as to obtain a distributed multi-agent operation. In particular, we focus on asymmetries in the nodes energetic capabilities, and how do they impact on the resulting performance. We frame the problem as a repeated Bayesian game with asymmetric players and incomplete information, where also the private information available at each node is asymmetric. We find out that instead of a proportionally fair resource utilization, such a situation ends up in an even more unbalanced situation, which leads to an inefficient management where certain nodes are utilized beyond their fair share. Future research directions are identified so as to recover information about asymmetries from the strategic gameplay of the sensors and thus enable a better management.

Due to their distributed management, smart grids can be vulnerable to malicious attacks that undermine their cyber security. An adversary can take control of few nodes in the network and spread digital attacks like an infection, whose diffusion is facilitated by the lack of centralized supervision within the smart grid. In this paper, we propose to investigate these phenomena by means of epidemic models applied to cellular automata. We show that the common key parameters of epidemic models, such as the basic reproductive ratio, are also useful in this context to understand the extent of the grid portion that can be compromised. At the same time, the lack of mobility of individuals limits the spreading of the infection. In particular, we evaluate the role of the grid connectivity degree in both containing the epidemics and avoiding its spreading on the entire network, and also increasing the number of nodes that do not get any contact with the cyber attacks.

In this paper, we analyze hybrid automatic repeat request applied to the transmission of video content over the wireless channel. Retransmission-based techniques are usually applied to queueing systems assuming a homogeneous flow of identical packets, which are all transmitted and possibly retransmitted in the same way. However, multimedia packets are encoded with incremental methods leveraging spatial and temporal redundancy and as such, they have different roles and should be treated differently by the retransmission mechanism. Therefore, our work considers a selective retransmission scheme with unequal error protection applied to a multimedia flow subdivided into distinguishable packets. We assume a binary channel with memory and non-zero round-trip time. We utilize discrete-time Markov chains to model the channel and the transmission/retransmission system. This enables a closed-form derivation of performance metrics via Markov analysis. Numerical results are discussed and possible implications on multimedia communications are evaluated.

Cooperative Spectrum Sharing (CSS) is an appealing approach for primary users (PUs) to share spectrum with secondary users (SUs) because it increases the transmission range or rate of the PUs. Most previous works are focused on developing complex algorithms which may not be fast enough for real-time variations such as channel availability and/or assume perfect information about the network. Instead, we develop a learning mechanism for a PU to enable CSS in a strongly incomplete information scenario with low computational overhead. Our mechanism is based on a Markovian variant of multi-armed bandits (MABs) called superprocess, enhanced with the concept of Upper Confidence Bound (UCB) from stochastic MABs. By means of Monte-Carlo evaluations we show that, despite its low computational overhead, it converges to a low regret solution outperforming baseline approaches such as epsilon-greedy. This algorithm can be extended to include more sophisticated features while maintaining its desirable properties such as low computational overhead and fast speed of convergence.

We consider a multi-stage Bayesian game to model the interaction between an adaptive video streaming client and a congested network adopting a token-based policy for QoS provisioning. The Bayesian type of the network is its level of congestion, which is initially unknown to the client but heavily influences its payoff, so that the client may be interested in estimating it. Thus, we consider the Bayesian Nash equilibrium of the stage game and also we evaluate an iterative estimation process performed by the client throughout the stages, which allow to tune its equilibrium action. We discuss how the initial conditions can gauge the convergence speed of the estimate. We find out that, while the network type may be sometimes hard to evaluate, especially in low congestion scenarios, nevertheless the equilibrium action of the client is still very close to the ideal best response with full knowledge of the network type. We extend this result to the ability of the client to correctly estimate the prior distribution of the network type from multi-stage streaming games.

We investigate a jamming problem in an underwater acoustic sensor network, where nodes try to communicate in spite of an adversary that is attempting to block their communications. We take into account that the attenuation of underwater acoustic channels is strongly dependent on the communication distance and the signal frequency. We frame the problem in a game theoretic setup, as a Bayesian zero-sum game where the sensor network acts as the maximizer of the transmission capacity, while the jammer is the minimizer. In particular, we are interested in evaluating the effect of the nodes' position on the resulting equilibrium. The Bayesian character comes into play to represent the uncertainty on the position information of the nodes. Our evaluations show that for many network configurations, the equilibrium strategy of the jammer is pure. Thus, the transmitters can act as though the jammer only causes a higher level of interference. This allows us to identify positions where the damage caused by a jammer is easier to quantify, but the jammer itself is harder to detect.

The increasing demand for higher data rates has accelerated research on the next generation of mobile cellular networks (5G). One of the key factors of 5G is the use of a larger bandwidth allocated in the millimeter wave (mmWave) frequency spectrum. In particular, one of the candidate bands is the portion of spectrum between 17 and 30 GHz that is currently used by other technologies such as fixed satellite services (FSS) and the cellular network backhaul. In this paper, we analyze the coexistence between mobile services and FSS considering the main characteristics of the mmWave spectrum recently investigated in the literature. Moreover, we present a novel cooperative scheduling algorithm based on a game theoretic framework that exploits the use of analog beamforming at the base stations (BS). Finally, we show that adopting this algorithm ensure that the system meets the regulatory recommendation concerning the interference level at the FSS and at the same time provides a good user spectral efficiency.

The use of a larger bandwith in the millimeter wave (mmWave) spectrum is one of the key components of next generation cellular networks. Currently, part of this band is allocated on a co-primary basis to a number of other applications, such as the fixed satellite services (FSSs). In this paper, we investigate the coexistence between a cellular network and FSSs in a mmWave scenario. In light of the parameters recommended by the standard and the recent results presented in the literature on the mmWave channel model, we analyze different BSs deployments and different antenna configurations at the transmitters. Finally, we show how, exploiting the features of a mmWave scenario, the coexistence between cellular and satellite services is feasible and the interference at the FSS antenna can be kept below recommended levels.

Cooperative Spectrum Sharing (CSS) is an appealing approach for primary users (PUs) to share spectrum with secondary users (SUs) because it increases the transmission range or rate of the PUs. Most previous works are focused on developing complex algorithms which may not be fast enough for real-time variations such as in channel availability. Instead, we develop a learning mechanism for a PU to enable CSS in a strongly incomplete information scenario with low computational overhead. We model the learning mechanism of the PU to discover which SU to interact with and what offer to make to it with a combination of a Multi-Armed Bandit (MAB) and a Markov Decision Process (MDP). By means of Monte-Carlo simulations we show that, despite its low computational overhead, our proposed mechanism converges to the optimal solution and significantly outperforms the ε-greedy heuristic. This algorithm can be extended to include more sophisticated features while maintaining its desirable properties such as the fast speed of convergence.

Data-bundling is a useful technique that decreases the delivery delay of packet streams when they are transmitted over noisy channels and are subject to retransmission-based error control. In this paper, we investigate the packet delay statistics for a fully reliable selective repeat automatic repeat request (SR ARQ) where a data-bundling mechanism is employed. In more detail, we discuss a model for data-bundling to analyze the SR ARQ mechanism over wireless channels based on Markov chains. We evaluate various channel error distributions and analyze the buffer occupancy to check if the data-bundling mechanism provides efficient results. We further analyze the queueing, delivery and overall delay statistics at link layer. We found that using data-bundling can improve the delay performance of the SR ARQ mechanism, especially when bursty channels with heavily correlated errors are considered. Thus, this technique can bring useful improvements for real-time services, multimedia, and other delay-sensitive applications over wireless networks.

In the context of dynamic spectrum access, spectrum sharing among multiple operators has recently emerged as a promising paradigm to improve the efficiency of resource usage. Several theoretical evaluations have proven the benefits offered by pooling the available frequencies so as to tune the capacity offered by the operators according to their different needs, especially the service demands from their users. However, practical aspects concerning the application of sharing techniques are rarely studied, and deserve more detailed investigations. This paper aims at tackling this problem, in particular investigating the impact of asymmetries and dynamics of the user demands on the implementation of spectrum sharing techniques and the resulting performance, especially in terms of fairness among the users, which seems to be often neglected by many studies. We show that in variable traffic conditions, a constantly monitored and updated sharing of frequency bands performs much better than a static allocation simply based on average traffic loads. However, it is possible to choose the update rate of the spectrum allocation so that it does not represent a heavy computational and signaling burden, while retaining most of the improvements brought by the spectrum sharing paradigm.

This paper presents a study of the automatic repeat request (ARQ) technique applied to the transmission of multimedia traffic, e.g., video content. In the literature, retransmission-based techniques are usually investigated by means of queueing theory and assuming a homogeneous flow of identical packets, which are sent and possibly retransmitted all in the same way. However, multimedia packets are the result of an incremental encoding that leverages spatial and temporal redundancy, which is naturally present in the raw data. As a result, the flow is inherently made of packets with different roles, which should also be treated differently by the ARQ mechanism. Thus, we assume that different levels of error protection are applied, and also we model the decoding process at the receiver as accounting for a dependence relationship among the packets. Moreover, since error correlation has a strong impact on the performance, we consider a transmission over a Markov channel where we tune not only the error probability but also the average error burst size. This enables the derivation of several performance metrics in an entirely analytical manner via Markov analysis. Finally, some numerical results are explored and possible applications on the development of guidelines for multimedia transmission are discussed.

Several techniques for wireless networking, such as opportunistic spectrum access, or self-healing networks, may be seen as using a form of cognition, meaning that they mimic reasoning processes of intelligent beings. We propose to expand this cognition-based process by exploiting the parallel processing power of the infrastructure, so as to go beyond cognition as is meant by these approaches. We leverage novel approaches, taken from cognitive science and artificial intelligence, involving not only supervised but also unsupervised learning, and we envision their application to systems for video over wireless. The transmission of multimedia content, and its adaptation to the condition of the communication infrastructure, i.e., the wireless channel or the content delivery network, are envisioned as particularly critical steps for the development of latest generation mobile networks. For this scenario, we propose and evaluate a video classifier based on a Restricted Boltzmann Machine that tries to extract abstract features of videos from the analysis of the sizes of a few coded frames. These features can then be exploited by the communication network itself to optimize video transmission based on its content.

Wireless sensors can be integrated with energy harvesting (EH) devices to enable long-term, autonomous operation, necessitating efficient energy management. Existing research assumes knowledge of the state-of-charge (SOC) of the rechargeable battery; however, accurate SOC estimation in real-world devices is typically costly or impractical. This paper investigates the impact of imperfect SOC knowledge and the design of policies to cope with such uncertainty. The optimization complexity is reduced by decoupling the different time scales of the system: first, the short-term average performance is optimized with respect to fast-varying exogenous state variables, under an average energy consumption constraint, but neglecting battery dynamics; then, the policy dictating the average energy consumption as a function of state variables evolving over longer time scales is optimized, based on the detailed battery dynamics. A local search algorithm is presented to determine a locally optimal policy. The performance degradation compared to the scenario with perfect SOC knowledge is shown to decrease with increasing storage capacity and decreasing uncertainty in the EH source, and is within 5% for most cases of practical interest. Moreover, near-optimal performance is achieved by only a loose SOC knowledge, which distinguishes between high/low SOC levels. Finally, the impact of time correlation in the EH source is investigated. EH state knowledge is shown to be more critical than SOC knowledge, hence precise knowledge of the former can obviate the need for accurate information about the latter.

Multimedia traffic is expected to be widespread in next generation wireless networks, which will be likely based on Orthogonal Frequency Division Multiple Access. While multimedia content is heavily demanding in terms of network resources, it is also inherently adaptable at the application layer, thereby imposing soft QoS constraints, rather than strict requirements on a specific data rate. In this paper, we specifically investigate the suitability of such a medium access control rationale for this kind of traffic. It turns out that, if properly managed, next generation networks can accommodate several multimedia users, thanks to a proper exploitation of user and frequency diversity. However, on the application side a great deal of attention should be paid to take advantage of scalability of the video flows and adaptability of this kind of traffic, to exploit the network capacity at its fullest.

Harvesting-Based Wireless Sensor Devices are increasingly being deployed in today's sensor networks, due to their demonstrated advantages in terms of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of harvesting-based battery-powered sensor devices, focusing on the impact of the battery discharge policy on the irreversible degradation of the storage capacity. A general framework based on Markov chains which captures the battery degradation process is proposed. Based on such model, it is shown that a degradationaware policy significantly improves the lifetime of the sensor compared to "greedy" operation policies, while guaranteeing the minimum required QoS.

Users of video multicast groups are highly heterogeneous in terms of individual channel conditions and requirements for video transmission. Their experienced quality may vary, making it a challenging task for the network to optimally configure the resource management. In this paper, we consider mathematical model to represent layered video content delivery in a multicast group. We compare various network policies to choose the optimum number of transmit opportunities and we investigate the role of feedback, which, if present, dynamically tunes the resource management. We analyze the actual perceived quality of the users as well as how their satisfaction levels vary in the multicast session. Simulation results show that the presence of feedback generally enhances the overall users quality; however, this improvement is heavily related to the resource allocation policy of the operator.

Spectrum sharing has been recently proposed as a promising paradigm to improve the efficiency of resource usage in next generation mobile networks. In particular, non orthogonal spectrum sharing allows the operators to re-use the available frequencies at the cost of higher interference at the receivers. In this paper, we mathematically analyze the performance of this technique and how it is statistically related to the channel coefficients. Moreover, we compare different kinds of schedulers that exploit various aspects of non orthogonal spectrum sharing. Finally, the resulting system performance is assessed, first through an exact statistical framework and then by simulating the schedulers in an LTE scenario with the open-source network simulator ns3.

We investigate the optimization of video transmissions over cellular networks by using the H.264 Scalable Video Coding (SVC) at the application layer and an Adaptive Modulation and Coding (AMC) scheme at the physical layer. We analyze how the cross-layer optimization (XLO) of these two techniques together performs compared to a sequential and independent selection of video packets and Modulation and Coding Schemes (MCS) with no cross-layer optimization (NXLO), in terms of goodput and packet delivery delay. We formulate an analytical model based on a Markov chain representing the wireless channel, where each state is associated to a different channel quality corresponding to a set of possible choices of video layer and MCS. Our numerical results show that XLO significantly outperforms NXLO for video transmissions, thereby pointing out the strong need for cross-layer solutions in video transmission.

Current spectrum allocation policies, imposing exclusive usage of a licensed operator, may lead to inefficient management and waste of resources. Spectrum sharing, i.e., usage by the same frequency band by multiple operators, can improve the efficiency of the allocation. We analyze a scenario where two mobile operators managing neighboring cells also share a fraction of their available spectrum and quantify the performance gain. To this end, we propose a framework based on the definition of the Interference Suppression Ratio, which models effects such as beamforming or directional antennas. Depending on its value, mutual interference among the operators is reduced and sharing gains can be achieved. We implemented this framework in the well known open-source simulator ns-3 and we ran a parametric analysis of the impacting factors, including noise and cell radius. Simulation results confirm that significant gains can be achieved in terms of network capacity and throughput, provided that the Interference Suppression Ratio is above a given value.

This paper discusses a new perspective for the application of game theory to wireless relay networks, namely, how to employ it not only as an analytical evaluation instrument, but also in constructively deriving practical network management policies. We focus on the problem of medium sharing in wireless networks, which is often seen as a case where game theory just proves the inefficiency of distributed access, without proposing any remedy. Instead, we show how, by properly modeling the agents involved in such a scenario, and enabling simple but effective incentives towards cooperation for the users, we obtain a resource allocation scheme which is meaningful from both perspectives of game theory and network engineering. Such a result is achieved by introducing throughput redistribution as a way to transfer utilities, which enables cooperation among the users. Finally, a Stackelberg formulation is proposed, involving the network access point as a further player. Our approach is also able to take into account power consumption of the terminals, still without treating it as an insurmountable hurdle to cooperation, and at the same time to drive the network allocation towards an efficient cooperation level.

Energy Harvesting Wireless Sensor Devices are increasingly being considered for deployment in sensor networks, due to their demonstrated advantages of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of these devices, focusing on the interplay between the battery discharge policy and the irreversible degradation of the storage capacity. We propose a stochastic Markov chain framework, suitable for policy optimization, which captures the degradation status of the battery. We present a general result of Markov chains, which exploits the timescale separation between the communication time-slot of the device and the battery degradation process, and enables an efficient optimization. We show that this model fits well the behavior of real batteries for what concerns their storage capacity degradation over time. We demonstrate that a degradation-aware policy significantly improves the lifetime of the sensor compared to "greedy" policies, while guaranteeing the minimum required QoS. Finally, a simple heuristic policy, which never discharges the battery below a given threshold, is shown to achieve near-optimal performance in terms of battery lifetime.

Relay sharing has been recently investigated to increase the performance of coexisting wireless multi-hop networks. In this paper, we analyze a scenario where two wireless ad hoc networks are willing to share some of their nodes, acting as relays, in order to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian network analysis is exploited to compute the probabilistic relationships between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through the use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

As Energy Harvesting Devices (EHD) become more widely deployed in sensor network platforms, the need arises for "smart" operation policies which can ensure long-term, autonomous and reliable operation. Existing research has relied on the implicit assumption of perfect knowledge of the energy available in the EHD. However, estimating the energy level of the batteries or super-capacitors employed in real-world EHDs, commonly known as State-Of-Charge (SOC), is a non-trivial task. In this paper, we design operation policies that maximize the long-term reward under imperfect knowledge of the SOC. Through an array of simulation results, we quantify the performance degradation due to imperfect SOC knowledge, and show that it increases with decreasing storage capacity and increasing variance in the energy arrival process. In the particular case of a two-state controller, i.e., a controller which knows only if the SOC is HIGH or LOW, we prove that, for a linear reward function, there is no performance loss, while, for a logarithmic reward function, simulations show that the loss is typically less than 5%.

Infrastructure sharing has been recently investigated as a viable solution to increase the performance of coexisting wireless networks. In this paper, we analyze a scenario where two wireless networks are willing to share some of their nodes to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian Network analysis is exploited to compute the correlation between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

Nowadays, many devices in wireless sensor networks are provided with energy harvesting capability to allow for their continuous operation over long periods of time. In principle, the energy level within each sensor should be managed optimally to ensure the best performance. Network engineers, however, often consider optimality under the idealized assumption of perfect knowledge about the State-of-Charge (SOC) of the device. This information is not always realistic or accurate. In our previous work [1], we showed that optimal policies for sensing, transmission, and battery usage should rather consider uncertainty on the SOC of the device. In this paper, we extend that investigation, therein performed in the idealized scenario of i.i.d. energy arrivals, by considering a correlated energy generation process. We show that the knowledge of the SOC and that of the energy generation process are useful in a complementary manner, that is they can be traded for each other. Moreover, the knowledge on the state of the energy generation process can obviate the need for acquiring accurate SOC information. This investigation paves the road for a new line of research in wireless sensor networks, allowing a tighter interaction between the designers of energy harvesting and battery storage mechanisms on the one hand, and the engineers of network operation and control policies on the other.

Wireless network access and the exchange of multimedia flows over the Internet are becoming more and more pervasive in the everyday life. However, simple technological advances in terms of improved network capacity cannot satisfy the increasing demand of such services, since a paradigm shift from the current Internet architecture is required. The EU FP7 MEDIEVAL project tackles this issue by addressing novel architectural frameworks and viable strategies to efficiently deliver video services in a wireless Internet context. This paper reviews the currently ongoing activities of the project for what concerns wireless access, in particular the identification of useful techniques for the considered access technologies (WLAN and LTE-A) and the general definition of architectural schemes to efficiently support video flows.

Cooperation in wireless networks is an important means to improve the resource utilization efficiency. It finds an interesting application in the context of spectrum sharing, where multiple wireless users put their licensed frequency bands in common in order to achieve a better resource usage. Due to the complexity of the problem, mathematical analysis is typically focused on simple scenarios. However, we believe that, in order to obtain a concrete proof of concept of the sharing paradigm, it is mandatory to assess its performance in realistic situations, i.e., with a larger number of nodes and a wider range of applications. Therefore, the support of a proper simulation environment is fundamental for high-quality applied research. In this paper we present and evaluate an original extension of the well known ns-3 network simulator which focuses on multiple operators of the most up-to-date cellular scenarios, i.e., the Long Term Evolution of UMTS employing OFDMA multiplexing. We describe the software architecture that enables the spectrum sharing and, in particular, allows operators to interact in order to agree on a spectrum division. A sample sharing policy is given as well, and a detailed simulation campaign is run to validate the proposed architecture, assess its efficiency, and evaluate the simulation time related to scenarios with an increasing number of nodes.

Video transmission is expected to be the next big thing in personal communication systems. While text messaging applications have already experienced an explosive growth, the exchange of multimedia content is still lacking architectural solutions which enable it to become the next killer application. The EU project MEDIEVAL (MultimEDia transport for mobIlE Video AppLications) aims at filling this gap. In this paper we describe the approaches envisioned by the project for what concerns the data link layer, with special emphasis on wireless access and its related cross-layer issues. After presenting some general project aspects, we will review the state of the art and enumerate several open issues, concerning the identification of the most suitable radio technologies for video support, as well as their integration and required enhancements to enable efficient video transport.

A novel framework for the analysis and optimization of cognitive wireless networks with unslotted time operations and reactive primary users is proposed. In the considered network setting, primary users' channel access is regulated by a carrier sense-based contention mechanism. As the sensing mechanism cannot distinguish between primary and secondary signals, secondary users' activity may interfere with primary users' channel contention, thus biasing the statistics of the stochastic process modeling primary users' transmissions. In fact, a primary user which wakes up during a transmission from a secondary user may sense a busy channel and enter backoff or generate a collision. The proposed framework considers these effects and optimizes the fraction of time a secondary user is allowed to transmit according to a constraint on the minimum throughput achieved by the primary users. Numerical results are presented which illustrate fundamental behaviors and tradeoffs in a network with one primary and one secondary user. Extension to more general scenarios is also discussed.

We present a framework, based on Markov models, for the analysis of error control techniques in video transmission over wireless channels. We focus on retransmission-based techniques, which require a feedback channel but also enable to perform adaptive error control. Traditional studies of these methodologies usually consider a uniform stream of data packets. Instead, video transmission poses the non-trivial challenge that the packets have different sizes, and, even more importantly, are incrementally encoded; thus, a carefully tailored model is required. We therefore proceed on two different sides. First, we consider a low-level description of the system, where two main inputs are combined, namely, a video packet generation process and a wireless channel model, both described by Markov Chains with a tunable number of states. Secondly, from a highlevel perspective, we represent the whole system evolution with another Markov Chain describing the error control process, which can feed the packet generation process back with retransmissions. The framework is able to evaluate hybrid automatic repeat request with selective retransmission, but can also be adapted to study pure automatic repeat request or forward error correction schemes. In this way, we are able to comparatively evaluate different solutions for video transmission, as well as to quantitatively assess their performance trends in a variety of scenarios. Thus, our framework can be used as an effective tool to understand the behavior of error control techniques applied to video transmission over wireless, and eventually identify design guidelines for such systems.

Online games between remote opponents playing over computer networks are becoming a common activity of everyday life. However, computer interfaces for board games are usually based on the visual channel. For example, they require players to check their moves on a video display and interact by using pointing devices such as a mouse. Hence, they are not suitable for visually impaired people. The present paper discusses a multipurpose system that allows especially blind and deafblind people playing chess or other board games over a network, therefore reducing their disability barrier. We describe and benchmark a prototype of a special interactive haptic device for online gaming providing a dual tactile feedback. The novel interface of this proposed device is able to guarantee not only a better game experience for everyone but also an improved quality of life for sight-impaired people.

Modern communication systems require effective error control techniques for wireless links. This problem is still relevant in many applications involving recently developed IEEE standards such as 802.16, or sensors for environmental monitoring in extreme conditions. A notable example is also underwater communication with acoustic transmission, where long delays may be present. Usually, such studies assume the availability of an error-free feedback channels which is used to perform retransmission requests in an ARQ fashion. This paper discusses the performance of the selective repeat ARQ scheme in terms of packet delay when the feedback that is sent back at the transmitter's side can be erroneous. A non instantaneous noisy feedback and a Bernoulli arrival process, for different traffic intensities, are considered. The system is modeled through discrete time Markov chains, including the channel and the ARQ state. The system equations are derived and solved, and the impact of erroneous feedback is quantified. With respect to other similar contributions, the proposed approach presents the advantage of being directly implementable in any analysis using Markov chains to evaluate the system properties, instead of dedicated models. It can therefore be extended to a plethora of different scenarios, including different channel models and also including FEC features so as to obtain a hybrid ARQ scheme.

This paper presents an analytical framework for the study of hybrid ARQ techniques aimed at the transmission of multimedia content with differential encoding. We propose a Markov model of a selective repeat hybrid ARQ transmission scheme, where we assume that packets have different properties in terms of size, information content, and retransmission limit, so as to capture the differential encoding which characterizes such multimedia data. We consider a non-zero round-trip time channel modeled through a discrete-time Markov chain. We provide an analytical tool for the evaluation of two main performance metrics, namely, throughput and goodput. The model is extremely flexible and allows evaluation of several channel conditions and comparison of different types of ARQ (plain ARQ, Type I and II hybrid ARQ). Our results can be used as an effective tool to define design guidelines of multimedia transmission systems and to understand their performance trends.

In wireless mesh networks, joint optimization of routing and link scheduling within a time-division multiplexing approach is commonly sought to provide end users with high data rates. However, the strategies proposed to this end usually proceed by means of complex optimization models, which also often rely on oversimplified assumptions, especially for what concerns wireless interference. In the present paper, we draw a novel general framework to perform joint routing and scheduling avoiding these limitations. We evaluate sequences of link activation modes, i.e., sets of transmissions which can be performed simultaneously, and we introduce the concept of potential energy of a mesh network, thanks to which we outline efficient selection of link allocation modes in order to jointly solve routing and scheduling. A heuristic strategy derived within this framework is numerically evaluated by means of simulation and is shown to achieve very good performance, obtained with extremely low computational complexity.

We analytically investigate the packet delay statistics of the selective repeat ARQ scheme with non-instantaneous feedback, with correlation both in the channel errors and the packet arrival process. We highlight interesting trends of the delay terms, which can be extremely useful for multimedia real time services over wireless.

In this paper, we present a methodology to obtain a channel description tailored on performance evaluation for incremental redundancy hybrid automatic repeat request schemes. Such techniques counteract channel errors by using data coding and transmitting parts of the codeword over different channel realizations. We focus on coding performance models where the error probability is asymptotically zero if the channel parameters of these realizations fall within a given region. To map this region in a compact but still precise manner, we adopt a finite-state channel model. This approach is quite common in the literature; however, differently from existing work, we propose a novel method to derive efficient channel partitioning rules, i.e., a code-matched quantization of the channel state. Such a representation enables the use of accurate Markov models to study the system performance. Compared to existing channel representation methods, our proposed technique leads to a more accurate evaluation of higher layer statistics while at the same time keeping the computational complexity low.

There is wide consensus that properly taking into account wireless interference is necessary to design high performance link scheduling algorithms for multi-hop networks. However, most approaches in the literature use simplified models, which significantly abstract from the physical behavior of wireless links. Indeed, the main problem in representing the wireless propagation conditions with a proper level of detail is the very large amount of information needed, that includes the wireless link gains between all node pairs. In this paper, we propose a greedy, centralized scheduler which is based on the physical interference model but aims at exploiting local information available at each node, in order to reduce global information exchange and therefore the overhead as well as the computational complexity of the algorithm. We prove the effectiveness of our approach by extensive simulation results. We also show that our system outperforms the most up-to-date benchmark in realistic interference aware schedulers for wireless multi-hop networks.

This paper presents an analytical model for the study of hybrid ARQ techniques on discrete time Markov channels by means of an appropriate Markov chain, which tracks the transmission outcome and can be used to evaluate several performance metrics, including throughput, loss probability, number of retransmissions, and delay. The analysis is carried out with the assumptions that the information frame is encoded by the source with a linear block code and hard decoding is used at the receiver side. We finally present numerical evaluations for the performance of a truncated type II hybrid ARQ technique based on Reed Solomon erasure codes.

In this letter we discuss the exploitation of aggregated mobility patterns in mobile networks including heterogeneous multiple access techniques. We advocate the use of knowledge about neighboring devices to create routing groups (RGs) of adjacent nodes in order to optimize radio resource management. Basically, RGs consist of aggregated logical structures which are built and maintained at the application layer. Their use allows decreased signaling overhead between groups of nodes and access points (AP) and, at the same time, improved connectivity, which is achieved through the exploitation of technology diversity and relaying schemes. We illustrate a simple yet effective analytical model, and validate it through accurate simulation results. Finally, we show the effectiveness of the RG approach in terms of resource efficiency, throughput and multiple access performance.

In this paper we analyze the provisioning of multimedia services over a wireless LAN hot-spot, based on the IEEE 802.11 protocol. We address the issue of defining proper pricing strategies, from the perspective of both evaluating the technical performance and quantifying the economic revenues. We take into account a model for users' behavior that describes all users' choices in a decentralized manner, so that the transmission rate of each node is driven both by multimedia service requirements and by the customer's willingness to pay. The multiple users' medium access mechanism is studied through a simulation analysis based on ns-2. Within this model, the network performance is evaluated and discussed, presenting numerical results which can provide practical insight for pricing setup in a wireless LAN hot-spot. We observe that the impact of the pricing policy on the provider's income and on the satisfaction of the users is critical and especially depends on the shape of the pricing function (flat, linear or hybrid). Additionally, we investigate the provider's task of having a suitable price policy which properly tunes the tradeoff between the two contrasting factors of achieving high revenue and obtaining high satisfaction of the users.

Abstract This paper explores the logical device aggregation of terminals in future generation networks, where the availability of several different radio access techniques is integrated by means of common radio resource management algorithms. In particular, we investigate the creation of routing groups (RGs) among adjacent nodes, which might be beneficial in order to improve connectivity, decrease signaling overhead and increase transmission efficiency. A simple analytical approach is proposed, which allows the performance evaluation of device aggregation algorithms. We measure the performance of establishing RGs with special focus on two metrics of interest: the connectivity of the nodes and the energy consumption. Within this framework, many detailed insights are obtained and presented throughout the paper. In particular, we focus on the effectiveness of these aggregation techniques in improving network connectivity and on the cost incurred in getting the extra information needed to build and maintain group structures. In the final part of the paper, we provide simulation results which further validate our discussion and highlight additional aspects that are to be considered in real scenarios. Our work is a first step in the investigation of the effectiveness of in‐network aggregation of terminals equipped with multiple radio technologies. The results derived in the paper are encouraging and motivate further research on the topic. Copyright © 2007 John Wiley & Sons, Ltd.

This paper investigates routing strategies for mobile and heterogeneous multi-hop wireless networks. We leverage the knowledge about users mobility to improve the efficiency of route discovery and of the following data forwarding phase. In particular, we exploit group mobility behaviors, which allow us to apply a distributed on-line algorithm for the recognition of aggregated mobility patterns. Hence, we adopt a novel routing strategy that uses the aggregate structure formed within this algorithm to simplify the exchange of signaling and data messages. Finally, we demonstrate and quantify the benefits obtained with the proposed technique by means of a simulator for heterogeneous wireless networks.

This paper presents an analytical model for the analysis of hybrid ARQ techniques on discrete time Markov channels by means of Markov chains. The first contribution is an original proposal to track the outcome of the packet transmissions in a generalized hybrid ARQ transmission system, deriving an appropriate Markov chain. Also, an example is given of how to put this Markov chain in relationship with a discrete-time Markov channel description. This framework can be used to evaluate from the theoretical point of view the performance of truncated type II hybrid ARQ techniques, deriving in general some useful insight on the behavior of such systems.

In this paper, we analyze a wireless LAN hot-spot, based on the IEEE 802.11b protocol, and more specifically we address the issue of defining proper pricing strategies, from both perspectives of evaluating technical performance and quantifying the economic revenues. We take into account a model for users' behavior that considers the trade-off between perceived QoS and paid price. This allows us to describe all users' choices in a decentralized manner, so that the transmission rate of each node is driven both by service requirements and by the customer's willingness to pay. After this setup, the multiple users' medium access mechanism is considered through simulation based on ns-2. Within this model, the network performance is evaluated and discussed. First, we investigate the provider's task of having a suitable price policy that gives a satisfactory income. This is connected with the goal of achieving high throughput, but is also dependent on a price setting that is accepted by the users and optimizes resource usage. Finally, we present numerical results which can provide practical insight for pricing setup in a wireless LAN hot-spot.

In this letter we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the m round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular the packet arrival rate, on the delay statistics

In this paper the packet delay statistics of a fully reliable selective repeat ARQ (SR ARQ) scheme is investigated. An N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be non zero, i.e., ACK/NACK messages are received at the transmitter m channel slots after the packet transmission started. The ARQ packet delay statistics is evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, procedures to derive a Markov channel description of a Rayleigh fading process are discussed and the delay statistics obtained from the Markov analysis is compared with that estimated by simulation of the SR ARQ protocol over the actual fading process. The accuracy of the delay statistics obtained from the Markov Channel representation of the actual fading process is investigated by explicitly addressing the effect of the number of states considered in the Markov channel model and the impact of the Doppler frequency. Finally, besides giving a new analysis to obtain link layer statistics over N-state Markov channels, the paper provides important considerations on the adequacy of the widely used Markov modeling approach for the characterization of higher layer performance

In this paper we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular of the packet arrival rate, on the delay statistics

The ambient networks concept targets forthcoming dynamic communication environments, characterized by the presence of a multitude of different wireless devices, radio access technologies, network operators and business actors, which can form instant inter-network agreements with each other. Multi-radio resource management (MRRM) mechanisms, coordinating several radio accesses, fulfill a key role for providing wireless services with improved resource efficiency, coverage and service quality. This paper presents an MRRM concept for Ambient Networks, describes the principal MRRM functions and discusses design criteria.

In this paper, we present a joint economic and technical analysis of a wireless LAN, where we model the user behavior as aimed at the choice of the transmission rate. User rate allocation requests are determined in a decentralized manner by considering the tradeoff between service requirements and willingness to pay the service price. Then, the multiple users' medium access is considered and the resulting allocation is evaluated and discussed. We provide results for what concerns the allocated resource, the number of satisfied users and the revenue coming from the assignment. All these metrics are shown to be connected, since they are strongly influenced by the service appreciation rate; however, they are also dependent on the price setting, which has to be acceptable for the users and allow efficient resource usage. Moreover, we investigate in particular the sensitivity to the number of possible system customers. These considerations are finally extended to gain general insights on the performance of the distributed coordination function of IEEE 802.11b.

In this paper we investigate radio resource management strategies for multimedia networks driven by economic aspects such as users' utility and service pricing. To this end, we discuss a framework in which the impact of both QoS and pricing is accounted for in the users' acceptance rate of the service. The model is general enough to be adapted to different situations and optimization goals. Thus, we discuss possible objectives for the network management under the constraints of meeting quality of service requirements, by satisfying at the same time technical conditions of feasibility. We employ utility functions, in order to account for the additional characteristic represented by the traffic elasticity, and consider the effect of price to have a realistic characterization of economic quantities. The resulting optimization problem is then discussed and analyzed, to derive general insight and identify possibilities for enhancement.

The packet delay statistics of a fully reliable selective repeat (SR) ARQ scheme are investigated. A N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be finite, i.e., ACK/NACK messages are received at the transmitted m channel slots after the packet transmission is started. The ARQ packet delay statistics are evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, a procedure to derive a Markov channel description of a Rayleigh fading process is presented and the delay statistics obtained from the Markovian analysis is compared with those estimated by simulation of the ARQ protocol over the actual fading process. Finally, some discussion on the accuracy of the delay statistics obtained from the Markov channel representation of the actual fading process is reported.

In this paper we discuss utility functions models to study Radio Resource Management. Our goal is to identify the characteristics of the wireless systems which make such theoretical models, though challenging, very useful, as they allow to quantify the Quality of Service and to analytically investigate the users ’ satisfaction. Moreover, we show how, within a utility-based framework, it is possible to also study economic issues, besides more conventional technical aspects such as throughput or system capacity. Thus, when economics are taken into account by considering the financial needs of the provider and the users ’ reaction to prices, we are able to study wireless systems in a more realistic and appropriate way. Another key contribution of this paper is a discussion on how utility functions should be applied to the particular case of the radio resource. To this end, we extend classic economic concepts with an original proposal, better able to model the nature of the wireless services. Finally, by giving both analytical insight and numerical results, we compare different classes of RRM strategies and explore the relationships between Radio Resource Allocation, pricing, provider’s revenue, network capacity and users ’ satisfaction. Categories and Subject Descriptors C.2.1 [Network Architecture and Design]: network communications, wireless communication; I.6.5 [Model Development]: modeling

This paper aims at introducing possibilities of self-organization for packet-switched networks at the scheduling management level. By discussing already existing scheduling strategies, we identify several contrasting needs that should be jointly addressed. The employment of the same algorithm for the whole network leads to low performance. On the other hand, adjusting the management cell-by-cell is not feasible and requires coordination. Hence, we propose a general framework for the scheduler, which can be easily tuned by means of a neural network. In this way, cells are grouped and self-similarities are identified, so that the differentiation in the management is lighter and a significant performance improvement can be achieved. Moreover, a general tunability of the system is introduced, allowing to cut the right trade-off between system complexity and QoS in a simple way.

Over the years, radio resource management has been benchmarked mostly by its technical merits. A service provider, however, must also reckon with economics. When the financial needs of the provider and the satisfaction of the users are considered, common objectives in radio resource management, like maximising throughput or meeting various quality constraints, may no longer be sufficient. We analyse next generation communication systems by including models of economics, which have been presented in the literature, and reasonable considerations to depict the users/provider relationship in a generalised multimedia environment. In particular, we develop a model of users' satisfaction, in which both requested quality of service and price paid are taken into account. The model enables us to investigate how resource allocation dynamics affect operator revenues and to derive some useful insights. Radio resource management can be shown to be highly dependent on economic considerations. The provider's task to determine the best usage of the network capacity is heavily affected by the users' service demand and their reactions to the pricing policy. Thus, the economic scenario needs to be taken into account to exploit the constrained radio resource efficiently. The model is applied to a CDMA cellular system.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. It is assumed that the sender continuously transmits packets whose error process is characterized by means of a two-state discrete time Markov channel. At the receiver these packets are checked for errors and ACK/NACK messages (assumed error-free) are sent back to the sender accordingly. The feedback message is known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been developed in order to find the exact statistics of the delays experienced by ARQ packets after their first transmission.

In this paper, we present a study of admission control in 3G systems. In particular, the behavior of algorithms already presented in the literature is analyzed, with respect to their implementation in UMTS-like systems, and a model of trade-off between the QoS metrics, blocking and dropping probability, is presented. The obtained performance is discussed and analyzed under different points of view. Finally, possibilities to improve fairness and generality of these results open up when a more detailed model for mobility, data rate and discontinuous transmission (DTX) is considered.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. It is assumed that the feedback message is known with no errors at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find exact statistics of the delays experienced by ARQ packets. In this work, in order to reduce the computational complexity of such analysis, an appropriate model is presented. The results obtained from the approximate approach are shown to be in excellent agreement with the ones derived from the exact analysis.

In this paper, we investigate the delay statistics of an aggregate of fully reliable selective-repeat ARQ packets. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. No errors are accounted for in the reverse channel. The feedback message is assumed to be known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find the exact statistics of the delays experienced by ARQ packets. This work presents an extension of the analysis that computes the delay statistics of an aggregate of ARQ packets. This is achieved without increasing the model complexity and allows useful considerations from the point-of-view of higher level protocols.

Energy efficient communications in ad hoc and sensor wireless networks is a very important topic. We study the problem of creating spanning trees of low cost, where low cost can be viewed in terms of global or local energy efficiency. We refer to the algorithms and techniques presented in the literature, and we show an extension to them, called TDPC (time division path changing), which takes into account both global and local efficiency. We show that these techniques can be applied to existing algorithms, improving the performance, and addressing both needs. We show how it is possible, in general, to cut a trade-off between the two contrasting requests of global and local energy efficiency, by using TDPC with a particular class of algorithms.