The growth of massive IoT (m-IoT) has introduced new challenges in ensuring scalable and secure communication. MQTTv5 addresses scalability through features like shared subscriptions, which distribute messages among multiple subscribers based on load-balancing policies. Furthermore, existing Quality of Service (QoS) mechanisms in MQTT can be leveraged to improve the reliability of data exchange within this large and heterogeneous ecosystem. However, when combined with higher Quality of Service (QoS) levels, these features can be exploited by malicious subscribers to trigger excessive and redundant retransmissions and degrade system performance. In light of these considerations, this paper analyzes the associated threats and proposes a mitigation strategy that profiles the number of retransmissions generated by each subscriber to identify anomalous behavior. Subscriber activity is evaluated using a trust-based approach, which isolates potentially malicious subscribers into a SUSPICIOUS group. A token bucket-based message delivery scheduler is then applied to this group to regulate and limit the rate of message delivery accordingly. The approach enhances security and efficiency in MQTT-based m-IoT deployments by mitigating protocol-level abuse. Simulation results confirm the effectiveness of the proposed solution – in terms of data extraction rate, queuing time, and retransmission – in comparison with the traditional strategies available in the MQTTv5 standard, such as random and round-robin message scheduling strategies.

5G has initiated a strong trend toward network softwarization, which is expected to intensify with 6G. At the core of this shift, Software-Defined Networking (SDN) enables a programmable control plane but also broadens the attack surface. This work examines volumetric random-source Distributed Denial of Service (DDoS) attacks in SDN, where the exploitation of table misses in OpenFlow devices generates massive PACKET-IN floods toward the controller, potentially disrupting flow rule management or even compromising network stability. To address this security issue, an entropy-based detection mechanism is proposed, also introducing a dynamic and network-aware computation of the proposal’s hyper-parameters, such as the entropy threshold and the PACKET-IN message window used to analyse the network behavior. Additionally, a mitigation technique is applied by exploiting the meter table provided by the OpenFlow specification, limiting the impact of malicious entities. Experiments show that the proposed solution adapts to network conditions and attack rates, outperforming static entropy-based approaches that ignore traffic dynamics.

In the new Software-Defined Networking (SDN) paradigm some challenges such as suitable traffic load distribution strategies, which can negatively affect the network performance, are faced. In this paper, a Multi-Resource Aware Load-Balancing strategy exploiting Software Defined Networking (SDN) programmability is presented. Our approach, leveraging the SDN controller, distributes the web requests to a web server cluster according to four metrics: CPU, Memory, Network Traffic, and Response Time of each web server. Through a comprehensive experimental campaign, we evaluated and assessed the advantages of the proposed load-balancing approach against basic methods like random selection and round-robin and also against recent advancements in load-balancing strategies that are considered more sophisticated and intelligent.

The LoRa (Long Range) protocol is designed for long-range communication with minimal energy consumption, thus making it ideal for Internet of Things (IoT) deployments where long distances between devices should be covered. In LoRa networks, communications happen over free unlicensed bands, where jamming attacks represents a huge threat. Thus, providing the correct level of security is crucial, especially when considering the huge number of devices that characterize the Massive IoT (M-IoT) scenario. This work presents a selective jamming attack that aims to reduce the quality of the communications between legacy devices and network server, in a LoRaWAN (LoRa for Wide Area Network) IoT deployment. This paper showed that the LoRaWan standard does not provide itself any solution to this problem. For this reason, a potential countermeasure that implements both an attack mitigation and an intrusion detection mechanism is proposed in this work.

In the rapidly evolving landscape of the IoT, the deployment of Wireless Sensor Networks (WSNs) plays a crucial role in enabling smart environments. Future WSN IoT deploy-ments are characterized by a massive number of interconnected devices, necessitating robust communication protocols to handle the vast amount of data generated and transmitted in the so-called Massive IoT (M-IoT) context. Focusing on the con-strained nature of sensors, the MQTT protocol introduces, with the MQTTv5 specification, a pivotal technology for enhancing resource utilization and connectivity efficiency in WSN IoT scenarios: the shared subscription mechanism. In the context of M-IoT WSNs, security vulnerabilities are further exacerbated, potentially leading to high-scale damage. Based on these consider-ations, the objective of this work is to provide a security-oriented examination of the shared subscription feature proving how malicious users can exploit it to induce an indefinite starvation status among the legacy subscribers of the IoT deployment. We highlighted the extent of the damage of the shared subscription attack proposing a countermeasure that takes into account both the light nature of MQTT and the impact that it could have in future M-IoT deployment testing it under real IoT traffic patterns.

Intrusion Detection Systems (IDS) are widely used to secure networks and detect malicious traffic. The proliferation of IoT devices has heightened the focus on protecting these vulnerable devices. Modern IDSs leverage AI to enhance attack detection, but the complexity of network traffic poses challenges in identifying attack traits, which are crucial for reliable and confident classification. In light of these considerations, this work introduces a new comprehensive framework for analyzing datasets, first reducing features using statistical methods like the Pearson Correlation Coefficient, and then applying the Shapley Value from game theory to better understand attacks. This approach standardizes dataset analysis and enhances the reproducibility of machine learning experiments, addressing a key challenge in AI-based IDS research.

Flying Ad-hoc Networks (FANETs) are a key technology for 6G networks due to their flexibility and rapid deployment, but ensuring security remains a challenge. The integration of programmable 6G networks with FANETs offers a promising solution to enhance network security. This paper proposes a resource-aware load-balancing strategy using Software-Defined Networking (SDN) to distribute network flows across drones running a Virtualized Intrusion Detection Function (VIDF). Additionally, a dynamic activation approach is introduced, enabling the SDN controller to quickly activate VIDF functions on drones in a 6G-enabled FANET. Performance evaluations demonstrate the benefits of the proposed strategy.

The integration of mobile sensing into public transportation vehicles offers an interesting opportunity for scalable, real-time environmental data acquisition. This study explores a novel approach in which public transportation vehicles serve as dynamic platforms for atmospheric monitoring, equipped with embedded sensor units capable of measuring key environmental parameters. Data collected on the move is transmitted to cloud infrastructure using cellular communication technologies. The research introduces and evaluates three distinct data acquisition and transmission operational modes, aiming to balance measurement accuracy with energy efficiency. Both GPRS and LTE are employed to assess the impact of network choice on system performance. The main focus of the paper is on quantifying the energy requirements of each configuration, providing critical insights into the trade-offs between communication frequency, data volume, and power consumption. The findings contribute to the development of sustainable, vehicle-based monitoring systems suitable for smart cities and intelligent transport applications.

Future 6G networks will be called upon to support increasingly sophisticated applications based on the massive use of Artificial Intelligence (AI). This poses serious challenges to mobile devices that do not have adequate computational and energy capacity to support the effective execution of AI tasks. According to the emerging Split-AI paradigm, 6G networks can help user devices to perform complex AI tasks by distributing and executing a Neural Network (NN) collaboratively among different processing nodes, including network nodes in the 6G User Plane (UP). However, the extent to which these offloading mechanisms can improve the end-to-end latency of AI tasks is limited by the computational resources available in an operator’s network. The presence of Graphical Processing Units (GPUs) in some elements of the UP can certainly help, but it is necessary to quantify the cost of the task acceleration in terms of increased energy consumption entailed by GPU usage. In this work, we study on the example of Split-AI, the impact of GPU acceleration on inference time and energy consumption. Thereby, we demonstrate that GPU inclusion can reduce the latency induced by the DNN computation by up to 90%, with moderate energy consumption increase of 22% at most.

Vehicular Ad Hoc Networks (VANETs) are increasingly evolving beyond traditional traffic safety applications to support intelligent environmental monitoring across urban and highway settings. As concerns over air pollution, noise pollution, and greenhouse gas emissions rise, utilizing vehicles as mobile sensor platforms offers a promising solution for real-time, largescale environmental data collection. This paper investigates a system in which a vehicle, equipped with an environmental monitoring unit, gathers atmospheric data and transmits it to a cloud server. Three distinct data management strategies are proposed and evaluated, using two communication technolo-gies-GPRS and LTE-to compare their effectiveness. The study focuses particularly on assessing the energy consumption of the overall monitoring system. Experimental results highlight the key differences among the three strategies and the impact of the communication technologies on data transmission efficiency and energy usage.

The software-driven and virtualized architecture of modern networks-pioneered by 5G and evolving toward 6G-enables novel applications such as Massive IoT (mIoT), characterized by numerous resource-constrained and heterogeneous devices. This surge in connected devices demands new network designs and communication protocols. MQTT version 5 (MQTTv5) addresses flexibility and scalability but lacks built-in security measures. Features like Quality of Service (QoS) can be exploited by malicious devices to induce retransmissions and disrupt operations. While MQTTv5 introduces the shared subscription mechanism with load-balancing to mitigate such risks, standard strategies (e.g., Random, Round Robin) are inadequate. Hence, a security-and network-aware load-balancing policy is needed. To this end, this paper proposes QTM-MQTT (QoS-aware Trust-based Mitigation for MQTT Channel Saturation): an intelligent, informed load-balancing mechanism designed for shared subscription scenarios. The proposed strategy prioritizes message delivery to high-trust subscriber nodes while isolating low-trust, potentially malicious ones, thereby reducing unnecessary retransmissions and mitigating channel saturation. The mechanism is evaluated through simulations conducted in a realistic Urban Macro Cell (UMa) 5G channel scenario, as specified by the 5G NR TR 38.901 3GPP standard. Simulation results confirm the effectiveness of the proposed solution in terms of improvements in Signal-to-Interference-plus-Noise Ratio (SINR), Data Extraction Rate (DER), and channel overhead reduction in comparison with the strategies available in the MQTTv5 standard, namely Round Robin and Random.

The convergence of blockchain technology and software-defined networking (SDN) presents a promising solution for enhancing network security. Leveraging the decentralized architecture of blockchain and its inherent security properties, this work proposes a blockchain-based framework to strengthen trust and secure communication among SDN controllers in a multi-domain environment. The framework employs a decentral-ized reputation mechanism based on an adaptive approach that dynamically adjusts reputation parameters based on network status and the average reputation of participating nodes. This system uses the Practical Byzantine Fault Tolerance (PBFT) consensus protocol to implement a reliable process for rewarding and penalizing controllers based on their behavior, thus identifying and excluding potentially malicious nodes. Simulation results confirm that the adaptive reputation metric effectively captures node behavior by incorporating network conditions and node-specific data, leading to a more responsive and precise detection process. This dynamic approach improves detection precision, minimizes false positives, and reduces response delays, making it well-suited for diverse and evolving network environments.

This study explores the integration of Machine Learning (ML) techniques for the classification of traffic signs within simulated vehicular environments. Specifically, the research investigates and compares two training paradigms: centralized learning, enhanced through Transfer Learning (TL) to leverage pre-trained knowledge and reduce training time, and Federated Learning (FL), a decentralized method that allows for collaborative training across distributed nodes. The work involves the implementation and evaluation of two deep learning (DL) models: a classical CNN architecture from existing literature (LeNet-5) and a custom-designed CNN model tailored for vehicular contexts. This study contributes to developing cooperative learning frameworks between vehicles and infrastructure, aiming to enhance traffic safety and efficiency.

As artificial intelligence gains popularity, concerns over regulation — particularly data privacy — are also growing. Federated Learning (FL) has emerged as a solution to address data privacy concerns through its distributed learning approach, enabling collaboration across diverse data sources without centralized data pooling. FL is increasingly valued for its adaptability to complex, large-scale scenarios but also faces significant challenges. In particular, this paradigm can be vulnerable to poisoning attacks, where adversaries may compromise data or local updates to disrupt the learning process and can suffer from resource limitations, as many clients have constrained energy and communication capacities, further complicating robust implementation. This paper presents FedAECS, an Adversary-aware and Efficient Client Selection algorithm designed to address these challenges. Experimental results on the MNIST dataset demonstrate FedAECS’s superior performance over state-of-the-art aggregation methods in accuracy, robustness against Byzantine and backdoor attacks, and energy efficiency.

The standard protocol design of LoRaWAN limits the gateway to a passive forwarding role, preventing it from mitigating packet collisions or identifying the involved nodes. To address this limitation, this work proposes a protocol extension that enables the gateway to function as an active monitoring entity, capable of early packet classification and selective filtering with minimal protocol overhead. Building upon this capability, an intrusion detection mechanism is proposed. The proposal is based on the analysis of the frequency and the distribution of collisions involving specific nodes. Using the statistic related to the collisions, the gateway can detect anomalous behaviors indicative of malicious interference, such as in the case of Denial-of-Service (DoS) and Distributed-Denial-of-Service (DDoS) attacks. Through these enhancements, the proposed method strengthens network resilience by transforming the LoRaWAN gateway into an intelligent intrusion detection entity that can enhance the security of LoRaWAN deployments.

The forthcoming sixth generation (6G) of networks is expected to enable disruptive Internet of Things (IoT) applications by fostering a seamless integration between networking and Artificial Intelligence (AI). In addition, the exponential increase in interconnected devices is challenging the current 5G networks, urgently requiring a redesign of current networking architectures and communication protocols. To address these challenges, IoT application-layer protocols are undergoing significant redesigns to enhance scalability, efficiency, and adaptability. A prominent example is MQTTv5, which introduces several advanced features, including shared subscriptions. While these advancements contribute to more efficient resource utilization and network scalability, they also introduce new security and reliability concerns that must be carefully addressed. Based on a previous work of the same authors that found a vulnerability in the shared subscription mechanism that makes it prone to a Distributed Denial of Service (DDoS) Starvation attack, this paper proposes QLB-MSS: Queuing-Based Load Balancing for MQTTv5 Shared Subscriptions, a load balancing mechanism to Prevent DDoS Starvation. This approach allows the MQTT broker to intelligently allocate messages within a shared subscription group, ensuring balanced load distribution and preventing device overload while effectively mitigating the considered attack.

The emergence of the SDN paradigm represents a pivotal milestone in the development and realization of programmable networks. This paper explores the severity of the malicious flow rules injection attack over SDN controllers. More specifically, the paper focuses on injecting malicious flow rules with the objective of causing flow rule conflicts as a mean to induce ambiguity in the SDN controller decision-making process and, therefore, diverting the expected behavior of the whole network. Building on these considerations and leveraging a rigorous existing definition of various types of flow rule conflicts, this work proposes CRAD-TPS: Conflicting Rule Attacks Detection through Trust-based Penalty System, a trust-based detection mechanism to identify sources of conflicting flow rules. The obtained results shows the benefits of the proposed detection, which is crucial to allow for a prompt action to restore the expected behavior of the network.

In Internet of Things (IoT) networks, where the demand for efficient and low-power communication is crucial, long-range (LoRa) technology offers significant advantages due to its energy efficiency and extended-range capabilities. However, LoRa networks, operating on unlicensed frequency bands, are particularly vulnerable to jamming attacks. In the context of massive IoT (M-IoT) deployments, that are characterized by a huge number of interconnected devices, ensuring robust security is therefore essential to maintain reliable and uninterrupted communication. This study examines selective jamming attacks targeting LoRaWAN (LoRa for Wide Area Networks), demonstrating how these attacks can degrade communication quality between legacy devices and the network server. It highlights the lack of inherent countermeasures within the current LoRaWAN standard to address such threats. To fill this critical security gap, we propose and validate a new countermeasure, demonstrating its effectiveness in mitigating the impact of jamming attacks. By strengthening the security of LoRa within M-IoT networks, we ensure the reliability and resilience required to support the vast number of devices in future IoT deployments.

In the Internet of Things (IoT) domain, resource-constrained devices are highly susceptible to malicious interference due to limited duty cycles, finite energy reserves, and an inability to perform clear channel assessment. These vulnerabilities are further exacerbated in low-power wide-area networks (LPWANs), where the absence of centralized coordination renders the network an attractive target for jamming attacks. In this work, we introduce a novel split and classification mechanism designed to enhance LoRa packet delivery under both constant and reactive jamming conditions. Our approach partitions each LoRa frame into sub-packets and applies a realtime classification algorithm to identify and mitigate corrupted segments, thereby improving the effective data extraction ratio (DER). We validate the mechanism through extensive simulated scenarios across diverse network densities. Comparative analysis against the standard LoRa protocol demonstrates a significant increase in packet success rate and commensurate gain in DER, without incurring additional energy overhead.

The proliferation of IoT devices, amplified by 6G, has heightened security risks. To counter these, experts are improving Intrusion Detection Systems (IDSs) using Machine Learning (ML) and Deep Learning (DL) algorithms, which rely on Feature Selection (FS) to identify key features and optimize performance in detecting attacks. In this context, this paper proposes a novel informed FS technique that exploits XAI (eXplainable Artificial Intelligence). The proposed algorithm extends the use of Shapley Value in XAI by enhancing it with an accuracy-driven binary search with the aim of finding the most representative feature and therefore reducing the dimensionality of a Network Intrusion Dataset, improving its detection ability. Through a comprehensive experimental campaign, the proposal has been validated and its benefits, which are not only limited to reduced training and testing time, but also drastically streamlining the AI model complexity. Finally, a comparison with other standard FS techniques is also provided to further highlight the advantages of the proposed algorithm. The proposal has been validated through a comprehensive experimental campaign in which results show the benefits. The experimental section shows the performance of the model when trained on the set of features returned by DeepSHAP and on the set returned by our framework, showing a drastic reduction in training and testing time.

The advent of fifth-generation (5G) networks has enabled the rise of the massive IoT (mIoT) paradigm, characterized by large-scale, heterogeneous, and interconnected devices leading to breakthrough applications like telemedicine, AR/VR, and autonomous systems. In this context, the security of communication protocols becomes critical. Among these, MQTT is a widely adopted protocol for IoT communications. Its latest specification, MQTTv5, introduces the shared subscription feature to improve scalability and reduce message overhead on constrained devices. However, prior work by the same authors revealed a critical vulnerability in this mechanism, allowing attackers to induce starvation of legitimate subscribers. An initial mitigation was proposed, based on limiting the number of subscribers per group using fixed statistical thresholds. While effective, this approach compromises scalability by potentially excluding legitimate nodes. To address this, we propose a novel adaptive, trustbased token bucket mechanism that regulates message delivery without restricting group size. By dynamically adjusting delivery rates based on subscriber trust, the system mitigates attacks while preserving scalability. Experimental results demonstrate the superior effectiveness and efficiency of the proposed solution compared to the previous approach.

The integration of Software-Defined Networking (SDN) in Flying Ad-Hoc Networks (FANETs) introduces centralized control and programmability, enhancing network flexibility and UAV resource optimization. This study evaluates and compares the performance of SDN-based FANETs using two platforms: ns-3 and Mininet-WiFi. A multi-layer architecture was implemented to analyze network behavior under different scenarios, including single-hop, multihop, multipath, and mesh topologies. Performance metrics such as bandwidth, jitter, round-trip time (RTT), and packet loss were assessed. The results reveal that Mininet-WiFi consistently outperforms ns-3, achieving up to 87% higher bandwidth, 26 times lower packet loss, and significantly reduced jitter and latency. The study highlights key limitations in ns-3, such as excessive packet fragmentation, retransmissions, and high processing overhead due to VXLAN tunneling. Recommendations for improving FANET-SDN emulation, including MTU optimizations, alternative tunneling methods, and enhanced controller configurations, are provided. These findings serve as a foundation for future research on optimizing SDN-based UAV networks for real-world deployments.

Recent advances in hardware and software technologies have led to the design of many pervasively distributed IoT devices that can generate/consume data and manage multiple sensors and actuators, paving the way for new applications and services. However, these new features, at the same time, can easily become an enticing “grab point” for attackers, unlocking a newer and larger attack space and exposing things to greater vulnerability. In this perspective, the objective of this document is the improvement of the IoT publish/subscribe architecture and the MQTT protocol with more scalable and dynamic additional security mechanisms, which can provide end-to-end security while reducing overhead and traffic load on the broker. Building upon our prior published research, the proposal further extends and advances the concept of “security layers” between which devices with priority-aware topics can easily switch to reduce protocol overhead and increase flexibility. Each topic has associated security characteristics that clients negotiate with each other, thus saving the broker from managing any security primitives. The proposed security mechanism called Dynamic and Lightweight Security over Topics MQTT (DLST-MQTT), is compared with the standard MQTT and TLS-MQTT in terms of bandwidth consumed, CPU, and RAM usage. Additionally, security levels with relevant scores are defined, and two security update procedures taking advantage of topic priorities are designed and evaluated. • A lightweight ECC-based security reduces the overhead for constrained IoT devices. • End-to-end security management in MQTT can improve system scalability. • Security levels linked to topics allow a compromise between security and resources. • Dynamic security allows adapting to changing risks, preserving device resources. • Security hierarchy linked to topic/sub-topic allows different connections/clients establishment.

Vehicular Edge Computing (VEC) is a key enabler of real-time intelligence in next-generation transportation systems. However, conventional Federated Learning (FL) in VEC typically depends on static edge-server aggregation, resulting in high communication overhead, increased latency, and poor responsiveness under dynamic mobility. To overcome these challenges, we propose Proximity-Aware Federated Learning (PA-FL), a decentralized framework that integrates vehicle-to-vehicle (V2V) collaboration and edge-assisted synchronization to enhance learning efficiency, scalability, and robustness. PA-FL introduces three core innovations: (i) Collaborative Local Aggregation, where vehicles perform proximity-based model fusion before forwarding updates to the edge, reducing uplink traffic and accelerating convergence; (ii) Adaptive Neighbor Selection, which dynamically filters peers based on spatiotemporal proximity and link stability to ensure context-relevant learning; and (iii) Context-Aware Synchronization, which adjusts aggregation frequency based on vehicular density and mobility to improve energy efficiency and learning consistency. Extensive experiments demonstrate that PA-FL achieves an average accuracy of 87.08% ± 0.49, surpassing state-of-the-art FL baselines by over 13% in accuracy and 11% in F1 score. It reduces task failure rates across all proximity ranges and lowers per-round energy consumption to 0.038 J, achieving a 6× improvement in communication efficiency. Delay per communication round is also reduced to 0.85 seconds, supporting real-time responsiveness. These results validate PA-FL as a resilient and scalable framework for symbiotic FL where vehicles collaboratively learn from local context while contributing to global intelligence in AI-integrated, 6G-enabled vehicular edge environments.

In emergency scenarios, the network infrastructure must remain reliable and continuously available to ensure connectivity to people and optimal performance in supporting different types of applications, including real-time services. When terrestrial infrastructure is compromised during emergencies, Flying Ad Hoc Networks (FANETs) can offer a quick and effective solution for re-establishing connectivity in affected areas. The dynamic coverage provided by a swarm of UAVs (Unmanned Aerial Vehicles) during a disaster could be crucial for people inside the affected areas. In high-demand and critical situations, the performance of FANETs may deteriorate due to several factors, including simultaneous user connections, high traffic volumes, limited energy resources of network devices, and interference arising within the same geographic region. To address these challenges, this paper proposes a novel, bio-inspired recruitment algorithm that aims to guarantee good performance of FANETs in energy constrained scenarios by efficiently recruiting UAVs to cover the demand of end users connected to the network. In such a scenario, when additional UAVs can not be reachable using the on-earth network infrastructure and multi-hop routing, the recruiting can be supported through a multi-layer hybrid architecture that integrates CubeSats to forward recruiting requests to potential UAVs located far from the network. This approach not only enhances the connectivity of end users but also ensures that the network can efficiently be adapted to the demands of users in emergency situations. • Design of a multi-layer hybrid CubeSat-UAV network for extending coverage. • A novel bio-inspired recruiting algorithm with a joint metric. • Joint metric: hop number, energy level, recruiting time, bandwidth of FANET nodes.

New machine learning (ML) paradigms are being researched thanks to the current widespread adoption of AI-based services. Since it enables several users to cooperatively train a global model without disclosing their local training data, Federated Learning (FL) represents a new distributed methodology capable of attaining stronger privacy and security guarantees than current methodologies. In this paper, a study of the properties of FL is conducted, with an emphasis on security issues. In detail, a thorough investigation of currently known vulnerabilities and their corresponding countermeasures is conducted, focusing on aggregation algorithms that provide robustness against Byzantine failures. Following this direction, new aggregation algorithms are observed on a set of simulations that recreate realistic scenarios, in the absence and presence of Byzantine adversaries. These combine the Distance-based Krum approach with the Statistical based aggregation algorithm. Achieved results demonstrate the functionality of the proposed solutions in terms of accuracy and convergence rounds in comparison with well-known federated algorithms under a correct and incorrect estimation of the attackers number.

In this paper, an AI-driven Intrusion Detection System (IDS) in an SDN environment is proposed exploiting the holist view of the SDN controller of the network. In order to detect malicious traffic, the proposed system makes use of Machine and Deep Learning (ML/DL) techniques and, based on a well-known network dataset called CSE-CIC-IDS2018, it trains Decision Tree (DT), Random Forest (RF), and Deep Neural Network (DNN) models. The main task of the proposal is a deep analysis of the network traffic features using two techniques, Pearson's correlation coefficient and the Mean Decrease Impurity (MDI), in order to find the most important features and reduce the dataset size and model complexity guaranteeing optimal system performance in terms of server's response time.

IoT devices are becoming more pervasive and integrated within our everyday lives, and networks of these devices are able to help humans accomplish complex and critical tasks. In such a network, IoT devices cooperate using Machine-to-Machine ($M2M$) communications to carry out such tasks. It becomes vital, therefore, to protect such a type of communication most often carried out by resource-constrained devices that cannot afford the overhead of a standard security protocol such as TLS. In this paper, a security framework for MQTT-M2M communications is proposed, considering a smart food retail shop use case. Specifically, we considered SlowIte, Data Forgery, Man-In-The-Middle, and Dictionary attacks that target the communication among the smart devices of food retail shop, proposing a mitigation method for each of them. To prove the benefits the proposal can provide, in terms of CPU/RAM utilization and network bandwidth consumption, we compared it with the baseline MQTT security standard, i.e. TLS.

An important challenge in edge computing service management is maintaining good quality of service and low latency for end-users. Edge services must be hosted near the end user, necessitating sophisticated management of virtualized resources in the edge infrastructure. In the context of edge computing, users' location and mobility patterns are essential components of resource allocation and service transfer. As a result, it is of the utmost importance to assess the effectiveness of the proposed management solutions in a city environment with realistic mobility. The purpose of this study is to investigate the simulation of edge computing and to present an integrated solution environment that makes use of two validated simulators for the modeling of urban mobility and edge computing services.

Message Queuing Telemetry Transport (MQTT) protocol is a lightweight communication protocol, widely adopted in IoT applications. The last OASIS standard document defines the new MQTTv5 specification, introducing a breakthrough feature named shared subscriptions. It is meant to bolster and make the message distribution among the subscribers of an MQTT publisher-broker-subscriber deployment more efficient. The shared subscription mechanism leverages a load-balancing technique to guide the distribution of the messages leading to an overall saving that is not only limited to network resources but also extends to the workload imposed on the involved clients. Despite its benefits, shared subscriptions introduce new security vulnerabilities that must be carefully studied and analyzed as well as mitigated without burdening the lightweight nature of MQTT. In light of these considerations, this paper aims to provide an initial security-oriented point-of-view analysis of this new feature by means of a Proof-of-Concept experimental setup in which the load-balancing approach exploited by the shared subscription mechanism is maliciously leveraged to induce indefinite starvation among the legacy subscribers of the MQTT-enabled IoT deployment. After proving the shared subscription attack magnitude damage, we present a proposal for a potential countermeasure also designed to not affect the lightness of MQTT.

This paper introduces an error positioning analysis making use of a proposed cooperative multi-technology localization technique that leveraging WiFi, Ultra WideBand (UWB), LIght Detection And Ranging (LIDAR) signals and an Extended Kalman Filter (EKF) is able to guarantee a more precise positioning estimation and a signal map reconstruction. The analysis of the positioning error aims to show how the quality of the used sensors can affect the values estimated by the filter. In particular, we have considered three different classes of WiFi receiver sensitivity for evaluating how the receiver quality can affect the error positioning estimation. Moreover, we have also varied the number of WiFi emitters giving an indication of the goodness of the proposed approach in respect with other techniques. The numerical simulations are carried out considering a system in which sensors with different quality of sensing are taken into account. The results obtained have shown the greater robustness of the proposed strategy compared to others based on multilateration, especially when dealing with low-quality sensors, that is receiver with bad sensitivity, and with different number of WiFi emitters inside the considered area. It emerges that the adopted approach leads to acceptable results even in the presence of unreliable sensor measurements.

Federated Learning (FL) introduces an innovative approach with the potential to enhance privacy and security compared to current methods. This is accomplished by enabling multiple users to collectively train a global model without disclosing their individual training data. Despite its advantages, this paradigm is vulnerable to poisoning attacks, wherein an adversary manipulates local model updates and injects them into the aggregating process. In this paper we propose a novel Robust and Dynamic Gradient Filter (RDGF) aggregation algorithms that provide resilience against model poisoning and backdoor attacks. In detail, the gradients received undergo initial processing to produce distance-based scores for clients, which are then compared to the average value of these scores. This comparison serves to eliminate malicious gradients before the final aggregation. We evaluate RDGF with well-known federated algorithms in a series of simulations that replicate realistic scenarios. These scenarios encompass situations with various percentages of Byzantine adversaries. The attained results showcase the effectiveness of the solutions in terms of both robustness and accuracy.

Federated Learning (FL) presents a novel methodology that has the potential to improve privacy and security when compared to existing methods. This is achieved through allowing numerous users to collaboratively train a machine learning model without sharing the personal data. Notwithstanding its merits, this paradigm could be exposed to poisoning attacks, where an adversary alters local model updates and inserts them into the aggregation process. There are many methods in the literature to ensure robustness against possible attackers, however, the resource-unreliable nature of clients is often not taken into account. In this paper we propose a Robust and Energy Efficient Client Selection (REECS) algorithms that provide an effective resilience against poisoning attacks. In particular, REECS calculates a score for each local model received and associates it with the residual energy of the corresponding client. This process is used to temporarily eliminate from the aggregation the less performing clients with less available energy resources. The attained results showcase the effectiveness of the solutions in terms of both efficiency and resilience.

The 6G wireless communication networks have an important role to play in meeting the demand for massive connections from IoT devices. This poses major challenges in terms of the efficiency and reliability of LPWAN networks, such as LoRa. LoRa technology has emerged as a promising solution for long-range, low-power communications, offering an efficient alternative for resource-constrained IoT devices. However, for its successful integration into the 6G ecosystem, it is crucial to address issues such as channel optimisation and minimising collisions in simultaneous transmissions. LoRa networks face the challenge of handling concurrent transmissions of IoT devices, which can affect network efficiency and packet delivery rate. To solve these problems, this study explores strategies involving carrier frequencies and time shifting, evaluating the performance in different scenarios, with the aim of improving channel capacity and reducing collisions. The results of this research will satisfy the high demand for massive IoT connections using concurrent LoRa transmissions, improving the efficiency of IoT networks by increasing the packet delivery rate in a LoRa IoT gateway.

Due to its powerful learning capabilities and diverse applications, federated learning (FL) is becoming increasingly popular in the fields of wireless communications and machine learning (ML). Furthermore, since it enables several users to cooperatively train a global model without disclosing their local training data, FL represents a new methodology capable of attaining stronger privacy and security guarantees than current approaches. In this paper we propose new distance-statistical aggregation algorithms that provide robustness against Byzantine failures. In detail, a new class of aggregation algorithms is compared with the well-known federated algorithms on a set of simulations that recreate realistic scenarios (e.g. in the absence and presence of Byzantine adversaries). Achieved results demonstrate the functionality of the solutions in terms of accuracy and communication overhead, also under a correct and incorrect estimation of the attackers number.

The Adaptive Data Rate (ADR) method used in LoRa networks to manage data rate, airtime, fairness, and energy consumption has proven to be not so efficient in scenarios with heavy traffic load and high node density. This issue led to intense research efforts, aimed at creating more efficient algorithms, able to manage dynamically contentions and data transmissions. Specifically, the goal of this paper the proposal a strategy to assign communication parameters, such as Spreading Factors (SF) and channel frequencies, to competing nodes and at the same time balance the load on different channels by exploiting the new concept of Interference Region. The performance of the proposed mechanism is compared with three benchmarks, namely the classic ADR, and two previously proposed mechanisms called ALS-LB and EXPLoRa-AT, under homogeneous and heterogeneous traffic conditions.

Massive IoT (M-IoT) is a network paradigm introduced in the fifth-generation (5G) of networks to cope with a huge number of interconnected IoT devices that handle high volumes of data while providing fast and reliable IoT applications. Focusing on the constrained nature of sensors, the Message Queuing Telemetry Transport (MQTT) protocol introduces, with the MQTTv5 specification, a pivotal technology for enhancing resource utilization and connectivity efficiency in M-IoT scenarios: the shared subscription mechanism. However, due to the huge amount of interconnected IoT devices characterizing modern networks, security vulnerabilities are further exacerbated potentially leading to high-scale damage. Based on these considerations, the objective of this work is to provide a security-oriented examination of the shared subscription feature proving how malicious users can exploit it to induce an indefinite starvation status among the legacy subscribers of the IoT Wireless Sensor Network (WSN) deployment. We highlighted the extent of the damage of the shared subscription attack proposing a countermeasure that takes into account both the light nature of MQTT and the impact that it could have in future M-IoT deployment testing it under real IoT traffic patterns.

In critical scenarios like natural disasters, the physical infrastructure of the network may be heavily damaged or completely torn down making difficult communications and rescue activities. In such cases, due to their nature, Unmanned Aerial Vehicles (UAVs) are usually employed to provide a temporary support network by forming a Flying Ad-Hoc Network (FANET). However, this network should be carefully monitored and safeguarded to guarantee its stability by deploying network security appliances, like Intrusion Detection Systems (IDSs). In the wake of these considerations, this paper delves into the utilization of IDS functions deployed on UAVs, named IDS-enabled UAVs, in emergencies leveraging the modern Software-Defined Network (SDN) paradigm. Specifically, the study proposes a dynamic approach to promptly activate IDS-enabled UAVs, in an SDN-FANET, responding to network traffic increasing and/or malicious activities taking place within the network. In order to achieve this objective, a Software-defined IDS with UAV Resource Aware Load Balancing strategy – SURA-LB – is proposed and implemented within the SDN controller, leveraging its programmable nature and its holistic view of the network. The SURA-LB strategy equally distributes the load among the set of IDS-enabled UAVs by considering their resources and energy utilization along with the experienced network traffic conditions. Finally, an extensive experimental campaign shows the benefits of the proposed load-balancing strategy in reducing and fairly distributing the workload among the IDS-enabled UAVs outperforming naive and baseline load-balancing strategies like Random and Round-Robin and, therefore, bolstering the resilience and the reliability of the set of IDS-enabled UAVs while the traffic to be monitored and analyzed increases.

Federated Learning (FL) introduces a novel methodology with the potential to achieve enhanced privacy and security assurances compared to existing methods. This is achieved by allowing multiple users to collaboratively train a global model without revealing their individual training data. Moreover, the popularity of federated learning is on the rise in fields like wireless communications and machine learning (ML), thanks to its robust learning capabilities and versatile applications. In this paper we propose a novel Dynamic Gradient Filter (DGF) aggregation algorithms that provide robustness against Byzantine failures. Specifically, the received updates are first processed to generate clients’ distance based scores, which are compared with the average value of these scores in order to remove malicious updates before final aggregation. DGF undergoes comparison with established federated algorithms through a series of simulations that replicate realistic scenarios, including scenarios both with and without the presence of Byzantine adversaries. We also demonstrate that DGF can be readily tailored to enhance the efficiency of the FL process without compromising its distinctive qualities. The attained results showcase the effectiveness of the solutions in terms of convergence rate, efficiency and resilience.

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.

We consider a generalized version of Carrier-Sense Multiple Access (CSMA), where the contention window size is a constant and the back-off probability distribution can be varied. We address the optimization of a weighted throughput metric, identifying the optimal back-off Probability Density Function (PDF). We give a simple fixed-point algorithm to compute the optimal PDF and prove that the solution is unique. The weighted throughput definition caters for aspects other than the mere channel utilization. It reduces to plain utilization (normalized throughput) when all weights are equal to 1. We also reconnect our result to the classic analysis of saturated non-persistent CSMA, as introduced in the seminal paper by Tobagi and Kleinrock, proving formally that the modeling assumptions of that work, that lead to a Geometric PDF of back-off, actually correspond to the throughput-optimal choice, provided that the ratio of the Geometric PDF is suitably chosen.

SDN (Software Defined Networking) is a widely used networking technology aiming at decoupling control plane and data plane making network management more flexible and dynamic. This is possible thanks to the programmability feature that characterizes the SDN architecture. Despite all the benefits we can gain from the SDN technique, it is affected by some security issues and vulnerabilities. The main attack it suffers the most is DoS (Denial of Service) and its distributed evolution: DDoS (Distributed DDoS). In this work it is presented a DDoS detection mechanism based on a pillar of Information Theory, namely Shannon Entropy but adopted in SDN environment. To make the detection more accurate it is considered also the concept of packet_in window, which indicates the number of packet_in considered in a certain period of time during communications. The experimental results aim to show the best trade-off between the packet_in window size and the value of the measured entropy in each window leading to an as timeliness as possible detection of DDoS demonstrating that a proper tuning phase of the aforementioned parameters is needed to promptly detect a DDoS attack.

Nowadays, Internet of Things (IoT) is a well-known and fast-growing paradigm. It aims to have physical entities that can gather and share information through wireless technologies. For large monitoring environments and large areas it can be suitable to use long-range IoT technologies. The paper focuses on LoRa integrated into Unmanned Aerial Vehicles (UAVs). The purpose of this work is to apply mobile LoRa Gateway (GTW) on a UAV, to cover larger areas where a lot of IoT devices are deployed. In this case, intelligent path and UAVs mobility need to be designed to improve the overall network performance considering heterogeneous traffic conditions triggered by IoT devices. The dynamic UAV coverage must be performed reliably, considering the number of packet losses as low as possible and, at the same time, the power consumption of the drone must be low.

Security and privacy issues are a great challenge in IoT systems. IoT security, of course, depends by the security level offered by communication protocols. In this paper we focus on a secure CoAP (Constrained Application Protocol) proposing a lightweight security mechanism for IoT devices characterized by limited resources. We introduce the feature of dynamic security, through a security levels scheme, to face the problem of devices heterogeneity and to provide more flexibility in choosing the security parameters that best suit the current state of the device, in terms of resources availability. Energy aspects are considered to evaluate the impact of the security on the devices' resources and to estimate the energy saving achieved by the dynamic approach. Performance evaluation has been led out comparing the dynamic Security supported by security levels with the classical secure COAP based on DTLS.

A dynamic deployment of Intrusion Detection Systems (IDS) over drone networks exploiting the new Software-Defined Networking (SDN) paradigm in emergency scenarios is proposed. In such scenarios, maintaining network security can be a key element to consider for efficient and reliable communication. Drones equipped with IDS can play a vital role in monitoring and protecting the communication infrastructure in such scenarios. To this aim, a dynamic and time-sensitive deployment strategy for IDS on a fleet of drones is proposed to provide a high degree of fault tolerance regarding network defense. Moreover, a load-balancing strategy enabled by the SDN controller to better distribute the network traffic load to be analyzed by the IDS is designed.

Free Space Optics (FSO) represent a promising technology for secure communications in several types of architectures: from Quantum Key Distribution Networks (QKDNs) to satellite communications. In this paper, in particular, we take into account terrestrial point-to-point laser communications and evaluate the performance in terms of Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER), taking into account different scenarios, that can reflect real situations in which long distances can be reached in a secure way, guaranteeing an acceptable level of BER. So, after a huge campaign of simulations, we would like to let the scientific community know which are the theoretical limits that such kind of communications can reach. We take into account standard telescopes parameters (available today in the market), while configuring several real situations, in function of, for example, bit-rate, visibility, link distance, etc. A brief survey of the existing works is given, then a clearer performance evaluation of terrestrial FSO links is proposed.

Nowadays, mobile computing has become a key component of telecommunication systems, and the Open Systems Interconnection (OSI) layer operations are affected by the effects of node movements along the roads, from the physical to the routing/transport layers. In particular, routing approaches have been investigated from many years, trying to optimize the performance of the whole considered system, under different points of view. In this paper we are focusing the attention on the analysis of the mobility grade trend for a mobile ad-hoc network environment, as well as on the way it can be a-priori known, in order to have the possibility to study how the dynamics of mobile nodes can be described and in-advance known, with a predicted knowledge of nodes stability (in terms of mobility). Our simulations considered mobility in real geographical maps, and the obtained results confirmed the goodness of our proposed study.

The increasing data produced by IoT devices and the need to harness intelligence in our environments impose the shift of computing and intelligence at the edge, leading to a novel computing paradigm called Edge Intelligence/Edge AI. This paradigm combines Artificial Intelligence and Edge Computing, enables the deployment of machine learning algorithms to the edge, where data is generated, and is able to overcome the drawbacks of a centralized approach based on the cloud (e.g., performance bottleneck, poor scalability, and single point of failure). Edge AI supports the distributed Federated Learning (FL) model that maintains local training data at the end devices and shares only globally learned model parameters in the cloud. This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL, which supports a sustainable learning paradigm using model parameters aggregation at different layers and considering adaptive learning rounds at the edge to save energy but still preserving the learning model’s accuracy. Performance evaluations of the proposed approach have also been led out considering model accuracy, loss, and energy consumption.

Many studies in literature have shown that the bandwidth of an ongoing flow can dynamically change during multimedia sessions and an efficient bandwidth prediction scheme must be employed in order to guarantee the needed Quality of Service. Many contributions in literature focused on some specific reservation schemes for cellular systems, 3G/4G/5G networks, WLAN and so on. However, few of them exploited the trade-off between resource prediction error and bandwidth wastage through a general prediction technique based both on the analysis of Cell Stay Time and on the direction probabilities of hand-in and hand-out events of mobile nodes among wireless cells. Our proposed scheme is based on the stochastic computation and characterization of user movements and it is absolutely general. Two novel metrics such as prediction error e and resource wastage u are defined. Moreover, predictive reservation schemes, admission prediction and resource wastage probabilistically associated to the stochastic hand-in/hand-out movement directions are proposed, with an optimization problem for the optimal pre-reservation of bandwidth in wireless system. A threshold-based reservation scheme has been considered and it has been evaluated in terms of reservation prediction errors and resource wastage, in order to prove the practical effectiveness and consistence of the defined theoretical bounds.

Publish/Subscribe paradigm can support multi-broker management to increase network reliability and support more traffic load. However, in this context, efficient load balancing techniques need to be designed to try benefit by multiple IoT brokers. In this paper a novel optimization problem to minimize the number of topic migration has been formulated and a novel greedy-based load balancing strategy has been proposed to find a solution to the formulated problem. Performance evaluation has been considered in terms of number of topic migrations, traffic load and variance.

The Internet of Things (IoT) growth is exponential and it is providing a massive impact on our lives. It gives the ability to connect any kind of wireless device in a network. The distance between devices can change from a few centimeters to many kilometers. For wide-ranging coverage we need to use Low-Power Wide-Area Networks (LPWANs), and new IoT technologies are emerging such as LoRa. This technology enables energy-efficient wireless communication over exceptionally long distances. In order to be able to evaluate the performance of this type of network, new simulators implementing this technology are increasingly emerging; in fact, several tools and simulators for LoRa technology can already be found online. Of course, there is no simulator that gives the possibility to evaluate the whole protocol stack, but there are different implementations that allow to evaluate different features. This paper has considered the LoRaEnergySim-simulator, written in Python Programming language. It allows the setting up of a simple LoRa network, with a single Gateway (GTW). However, in this paper an extension to the Multi-GTW scenario is presented. This extended-version will support also other features, such as the imperfect SF’s orthogonality and new statistics at device and network levels. Moreover, a performance analysis considering the computational cost when using a Single-GTW or Multi-GTW network will be shown.

Many CSMA based MACs can abruptly degrade their performance when the optimized design parameters do not fit with the considered scenarios. The main issue is the lack of optimal adaptation of the MAC strategies to dynamic network conditions. Novel approaches based on learning, deep-learning, nature-inspired learning are gaining interest for their robustness to dynamic network conditions. In this paper, a simple adaptive MAC strategy based on the Q-learning has been proposed. Our proposal, called Q-CSMA, is able to dynamically tune the contention probability in a slotted CSMA to decrease the number of collisions, also reducing the packet latency. Q-CSMA has been compared with optimal p-persistent CSMA (p-pers-CSMA/ p*) and a sift-based CSMA (sift-CSMA).

In this paper, the proposal of an irrigation system exploiting IoT is provided. The proposed system, based on IoT sensors and smart platforms such as Raspberry PI and Arduino, is able to manage farm operations in term of irrigation. The control of water pumps for irrigation is driven by two important parameters: soil moisture and evapotranspiration. The system uses a Reinforcement Learning approach based on Markov Decision Process in order to learn the right water amount needed by the plants. This approach allows to reduce both water and energy consumption. The proposal has been compared with a conventional irrigation system that normally is based on a soil humidity threshold and takes its decision considering the threshold setting. Conducted experiments show the water and energy saving by the use of the proposed Smart Irrigation system.

This paper proposes a lightweight security mechanism to manage security levels in MQTT protocol reducing the protocol overhead and using a flexible security negotiation in comparison with classical TLS solution applied to application layer protocol in the IoT context. Our proposal considers the security features around the topic and it involves the publishers as the main actors to negotiate the possible security levels on the topics. The proposal supports an end-to-end security features reducing the complexity of the broker that can only forward encrypted packet towards subscribers without performing ciphering or encryption/decryption. The performance of the proposed solutions has been tested considering increasing number of topics and clients and considering some metrics such as processed packets and bytes, processing time and RAM usage. A comparison between the dynamic security approach with MQTT and classical$\text{MQTT}+\text{TLS}$has been also considered.

The classic ADR method used by LoRa for optimizing data rates, airtime and energy consumption in the network has proven to be not very efficient in scenarios characterized by a high number of transmitting nodes. There is therefore a need to implement alternative mechanisms to reduce collisions and packet transmission delay in high dense IoT network scenario with increasing traffic load. The research presented in this paper contributes to satisfy this need by proposing a new mechanism aimed at traffic load balancing that leverages the switching of LoRa devices between Gateways in a multi-Gateway scenario. From the comparison with the classic ADR it clearly emerges how the proposed technique can obtain significantly better performance levels in terms of number of collisions, energy consumption and packet delay.

A novel carrier sense multiple access strategy with collision avoidance (CSMA/CA) balancing contention probability and channel access time is proposed. The approach can be applied to any context where the computational simplicity of the MAC must be preferred to the complexity of the channel access strategy. Our MAC, called Delay-Collision CSMA (DC-CSMA), is a slotted nonpersistent CSMA/CA with nonuniform contention probability distribution, designed to reduce at the same time latency of contenders and preserve a high successful access probability. An utility function aiming at equalizing the effects of these two performance metrics is introduced, and the related theoretical properties and optimal distribution are derived. DC-CSMA is insensitive to the number of contenders and very robust with respect to contention window size, packet length, and impairments such as frame synchronization errors and hidden terminals, and it does not require any adaptive tuning to optimize its performance. Current technologies such as WSN, RFID, IoT devices can benefit from such a simple access technique. The numerical evaluation has been led out considering latency, successful probability and throughput, and DC-CSMA has been compared with other classical strategies such as CSMA with uniformly distributed contention probability, CSMA/$p^{\ast}$and Sift distribution.

In this paper, we focused our attention on the design of an Intelligent Transportation System (ITS) system which offers support to the platooning management to achieve a global optimisation of the groups. In this way, new formations help vehicles to reduce energy consumption exploiting platooning. A novel algorithm based on Grey Wolf Optimization (GWO) to perform the search of the best platoon is considered. We evaluated the system performances of the platoon formation by considering several constraints such as platoon size, platoon speed, average platoon energy consumption. For the analysis of the results, we use the Omnet++ framework for protocol issues and Matlab tools for carrying out algorithm performances.

Mobile Edge Computing (MEC) is a new network paradigm that allows resource management and IT services at the edge of a communication network and, so, closer to the devices guaranteeing low latency and high bandwidth requirements. This characteristic makes MEC paradigm suitable for critical communication services used in collaboration with container-based virtualization and with 5G networks. In this paper, an implementation of a collision avoidance system based on MEC in a VANET environment is proposed. This system makes use of cloud and edge computing and it is able to switch communication from edge to cloud server and vice versa when possible, trying to guarantee the required constraints and balancing the communication among the servers avoiding of overloading edge layer. The simulation results have proved how, in some cases, the MEC-5G combination is the best solution for avoiding collisions in a VANET environment.

This paper presents a novel data dissemination strategy called Geographic Energy-aware Epidemic Routing (GEER) in DTN network. This routing scheme tries to consider the residual node energy and degree centrality to dynamically select the number of nodes where forwarding data and it tries to preserve buffer space reducing the TTL of data sent on nodes with higher degree centrality. A novel estimation of node density differentiated for geographic area is proposed to improve the data forwarding and a buffer data discarding policy has been applied when network becomes congested. GEER has been compared with Energy Aware Epidemic Routing (EAER) and EpSoc routing scheme in terms of Data Packet delivery ratio, overhead and energy consumption.

The advances accomplished in the modern wireless network are leading to the development of efficient antenna devices that are able to achieve high requirements especially as regards the 5G next generation systems. Although a significant number of theoretical recommendations already exist in this context, the most of the used network simulators do not offer the possibility to emulate the next generation systems. The present paper provides an overview about URPA (Uniform Rectangular Planar Array) massive MIMO (Multiple Input Multiple Output) with reference to PER (Packet Error Rate) and channel performance; in addition, a support for emulating such systems, extending Omnet++ network simulator features, is presented.

The paper proposes a comparison between two different coordination protocols for UAVs management in the agriculture domain. In precision agriculture the presence of parasites or the sudden climatic changes represent an important issue for farmers because of the degradation of crops and cultivation quality. Then, the use of the new technologies such as UAVs devices, the new networks such as FANET and the utilization of new sensors and actuators inside UAVs can represent an important aid to the farmers and the agriculture. An important aspect is, surely, the adequate management and coordination of these new devices in order to plan specific strategies able to support adequately the agriculture humans operators.

Platooning in VANET context is gaining a lot of interest in the last years. In order to support properly the car platooning multiple simulation modules need to be integrated to consider a different car mobility model, a different sensor equipment management on board. In this paper, an overview of the main simulator modules to support platooning are presented and a simple platooning management protocol has been proposed in order to improve the performance of electrical vehicles from an energetic point of view.

In this paper, the attention is focused on the design of a hybrid platoon system capable of minimising energy consumption in the vehicular environment. In this paper, we focused our attention on the design of a Intelligent Transportation System (ITS) system which offers support to the platooning management to achieve a global optimisation of the groups. In this way, new formations help vehicles to reduce energy consumption exploiting platooning. In this work, we propose a novel algorithm based on Gray Wolf Optimization (GWO) to perform the search of the best platoon. Moreover, in this work we propose a protocol to accomplish platoon tasks. We evaluated the system performances of the platoon formation by considering several constraints such as platoon size, platoon speed, average platoon energy consumption, and so on. For the analysis of the results, we use the Omnet++ framework for protocol issues and Matlab tools for carrying out algorithm performances.

In this paper a simulator for the management of a team of Unmanned Aerial Vehicles (UAVs) and drones has been proposed. This new network is known as Fly Ad-Hoc Network (FANET), and it is a particular type of Mobile Ad-Hoc Network (MANET) but with some specific aspects that allow to provide new services in future generation networks. One of the possible applications is emergency situations or scenario where drones can provide an additional or complementary access networks supporting web services and multimedia traffic. In this paper a simulator for FANET deploying has been proposed providing the possibility to simulate different scenarios with different coverage areas. New coverage model has been included in the features and also interesting human mobility model to support more realistic users mobility. Moreover, additional modules for traffic pattern generation have been implemented to create scenario where mobile users can activate multimedia calls and traffic on FANET. Some simulations have been led out to show how the simulator works.

Massive MIMO Systems certainly represent one of the most significant technology with regard to 5G systems; they are able to ensure high capacity performance through a better exploitation of the total radiated power with respect, for example, to sectorized and omnidirectional antennas. Unfortunately, because of the important amount of resources involved for achieving high data rates, these types of systems, could be affected from significant waste of energy especially if an appropriate mechanism that handles the energy consumption of nodes is not designed. Therefore, limiting the energy consumption of nodes relating to systems using very high data rates, constitutes a critical task because energy waste occur at the transmitter as well as at the receiver due to different reasons. In view of these considerations, in the present paper, we propose a novel algorithm having variable data rate and a mechanism for optimizing the energy consumption of nodes that addresses the problem both for the transmitter and the receiver.

In recent years, technology use has assumed an important role in the support of human activities. Intellectual work has become the main preferred human activity, while structured activities are going to become ever more automatized for increasing their efficiency. For this reason, we assist to the diffusion of ever more innovative devices able to face new emergent problems. These devices can interact with the environment and each other autonomously, taking decisions even without human control. This is the Internet of Things (IoT) phenomenon, favored by low cost, high mobility, high interaction and low power devices. This spread of devices has become uncontrolled, but security in this context continues to increase slowly. The purpose of this work is to model and evaluate a new IoT security system. The context is based on a generic IoT system in the presence of lightweight actuator and sensor nodes exchanging messages through Message Queue Telemetry Transport (MQTT) protocol. This work aims to increase the security of this protocol at application level, particularly mitigating Denial of Service (DoS) attacks. The system is based on the use of a host Intrusion Detection System (IDS) which applies a threshold based packet discarding policy to the different topics defined through MQTT.

When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account when describing networking activities: in this paper, we first evaluate the effects of node selfishness in opportunistic networks. Then, we propose a routing mechanism for managing node selfishness in opportunistic communications, namely, SORSI (Social-based Opportunistic Routing with Selfishness detection and Incentive mechanisms). SORSI exploits the social-based nature of node mobility and other social features of nodes to optimize message dissemination together with a selfishness detection mechanism, aiming at discouraging selfish behaviors and boosting data forwarding. Simulating several percentages of selfish nodes, our results on real-world mobility traces show that SORSI is able to outperform the social-based schemes Bubble Rap and SPRINT-SELF, employing also selfishness management in terms of message delivery ratio, overhead cost, and end-to-end average latency. Moreover, SORSI achieves delivery ratios and average latencies comparable to Epidemic Routing while having a significant lower overhead cost.

In the design of a Directional Medium Access Control (MAC) protocol for pervasive environments such the Mobile Ad hoc Networks (MANET), one of the most known problems that needs to be properly addressed is the deafness problem. Deafness could produce a large amount of collisions in the network and consequently a not negligible waste of energy. In this paper. we propose to partially mitigate this issue through the implementation of a Round-Robin MAC (RR-MAC) approach that provides for a logical sectorization of the transmission plane, limiting the maximum beamforming time of nodes, in order to reduce the frame collisions in the channel and at the same time to ensure an acceptable overall throughput in the network.

The progress accomplished in the modern wireless network are leading to the development of efficient antenna devices that are able to achieve high level requirements with regard to the 5G next generation systems. Although a significant number of theoretical recommendations can already be found in this context, the most known used network simulators do not provide for a support concerning the most recent wireless network requirements like the IEEE802.11ac and, simultaneously, they do not offer the possibility to use the latest massive MIMO (Multiple Input Multiple Output) antenna systems. In view of this, we propose to extend the default features provided by the Omnet++ network simulator with the intention of actualizing the existing simulation instruments to be suitable also for 5G wireless network communication systems.

In the last years. the rise of new ICT technologies and Artificial Intelligence (AI) methodologies are providing new life to Vehicular Ad Hoc Networks that are becoming always more complex and advanced in providing new services. With the recent advances in wireless communication, and the proliferation of micro-sensor devices, IoT objects, together with recent Networking and Computation paradigm such as Edge and Fog Computing novel frontiers of Autonomous Vehicles can be exploited. Moreover, also the need to reduce CO2 emissions and to provide novel energy harvesting solutions are leading the production of new generation of cars such as Electrical Vehicles (EVs) and Hybrid Vehicles to combine the benefits of the electrical engine and recharging energy stations with renewable energy sources. This talk will cover the broad range of novel EVs, moving towards scalable VANET with on board IoT devices enhanced with new architectures such as Edge layer. Through the introduction of these novel technologies it is possible to think also about Autonomous Vehicles (AVs) with its related research issues to be faced. The keynote will highlight some critical points to be faced in order to deploy AVs in the near future.

The improving of Electrical Mobility (EM) technologies is promoting the birth of novel mobility scenarios where Electrical Vehicles (EVs) are starting to be a remarkable share of the vehicle moving along the streets. New mobility models should take into account several environments parameters to better fit real conditions. In this work, we focus our attention to the slope coefficient of the roads by parsing altitude data from on-line Map services. Achieved results bring up interesting output. They demonstrate the importance of the road slope in case of modeling energy consumption. To achieve more realistic simulations we propose a novel model which takes into account real EV engines behavior. Moreover, communication issues are also been considered in the simulator by building ad-hoc modules for simulating communication standards and protocols. In this work, an all-in-one simulation framework is proposed. This framework can be used out of the box but can be also easily extended thanks to the code organization in modules.

The advances accomplished in the modern wireless network design are requiring increasingly more efficient high-performing antenna devices in order to satisfy the requirements provided for the 5G next generation systems; although a considerable number of theoretical proposals already exist in this field the most common used network simulators do not offer a support for the latest wireless network standards such as the IEEE802.11acand, at the same time, they do not afford the possibility to utilise the latest up-to-date high performance massive MIMO (Multiple Inputs Multiple Output) antenna systems.In view of this, we propose to extend the basic feature offered by the Omnet++ network simulator with the aim of actualizing the current simulation instruments for enabling the emulation of 5G wireless network scenarios.

Nowadays Electrical Mobility (EM) is one of the most interesting topics in vehicular environment.With the increasing adoption of EV an appropriate management of hybrid traffic could be a key factor to drastically reduce environmental pollution and to enhance road safety.EVs were slowly becoming a reality in everyday life.A novel mobility model which takes into account their characteristics become necessary for describing the key factors that influence EM and its performances.Moreover, it is important to build a real world close scenario by using a novel ad-hoc simulator that implements modules which are able to capture EV behaviors.Therefore, remarkable contributions to the mobility model introduced in the simulator are: a slope factor that influence users' speed and behavior as well as vehicle consumption, a Kinetic Energy Recovery System (KERS) emulation for EV to better simulate vehicles' battery life and integrated module for communication issues which will be able to allow Vehicular Ad-Hoc Network (VANET) protocol integration.In this way, a deep analysis on different vehicles distribution ratio has been done as well as their impact on the overall scenario.Results are shown in the dedicated section.

We study a situation where a swarm of robots is deployed to handle multiple different targets in a confined unknown area. The targets are found in real time and each target requires a certain amount of resources. An individual robot may not have sufficient capabilities for its execution, therefore an announcement process can start. We address the issue of how each robot responds itself to one of the discovered targets in an efficient way, considering a dynamic scenario where failure of the robots and unreliable communications unpredictably can occur. We propose a network architecture that incorporates a self-regulating mechanism allowing the distribution among the targets, with the minimal exchange of information. We have conducted experiments for evaluating our proposed approach in a simulated environment, considering different network parameters and studying the scalability and the robustness of the proposed model.

The use of adaptive antenna systems enables the improvement of the overall wireless network capacity, especially in Mobile Ad hoc Networks (MANET) environments in which the quality of the communication is closely related to the Signal-to-Noise-Ratio (SNR) measured in the channel. Unfortunately, most of used adaptive algorithms such as Least Mean Square (LMS) and Constant Modulus Algorithm (CMA), because are strongly related to the step size value and to the error metric estimation, usually present a speed convergence problem. In this paper, a new Variable Error Metric (VMA) adaptive Algorithm is proposed in order to limit the level of dependency between the algorithms and step size also reducing the slowing effect introduced by Mean Square Error (MSE) criterion used for example by LMS algorithm.

Dishonesty detection in mobile and wireless networks is a task that typically relies on watchdog techniques. However, these medium overhearing-based techniques are prone to cause a high energy consumption. In an attempt to address this problem, several proposals adopted trust management as an alternative solution that is able to overcome the shortcomings of cryptography-based solutions when facing inside attacks. Unfortunately, these trust-based solutions remain mostly unable to reduce energy consumption. In this paper we propose a distributed time division-based monitoring strategy to achieve the required security levels while optimizing the energy consumption. Our proposal accounts for both trust and link duration among honest peers to fairly divide the monitoring period, and takes advantage of the periodically exchanged hello messages to make this solution fully distributed. Simulations results evidence the energy efficiency achieved by our proposal, especially for high density scenarios (>120 nodes) where the consumption becomes stable and does not increase with the number of nodes, while ensuring high detection ratios of malicious nodes (>85%).

Smart Antenna Systems (SAS) are providing a strong increasing impact in digital wireless communication systems.Due to their great advantages regarding nodes power saving and coverage enhancing, SAS are largely employed on pervasive network environments such as MANET.Because almost the whole of existing network simulators use omnidirectional antennas on nodes, we propose a new version of Inet framework of Omnet++ that extends its operation also for network scenarios in which nodes are equipped with directional antennas.Furthermore, we created a new directional antenna module that simulates the behaviour of a Phased Array System and a very simple algorithm for power management in channel.The new proposed model presents some modifications to key-modules that are involved in a normal wireless communication scenario, in order to support asymmetrical communications between nodes.

The work proposes a simulation environment for UAVs management in the agriculture domain.In these last years, new technologies can effectively support farmer to face issues and threats such as parasites and sudden climatic changes that can severely degrade the crop or the quality of the cultivated products.However, to properly manage a UAVs team, equipped with multiple sensors and actuators, it is necessary to test these technologies in order to plan specific strategies and coordination techniques able to efficiently support farmers and achieve the targets.At this purpose, this contribution proposes a simulator suitable for the agriculture domain, where it is possible to set many parameters of this domain of interest.

In the last few years the process of monitoring people health status has grown in interest in the researcher community and led to the develop of new devices able to detect and analyse information gathered from many kind of sensors. These devices are commonly designed to monitor or diagnose disease in the medical field. Moreover, a remarkable interest is growing in the field of sports. In amateur activities such as jogging, running, climbing a set of smart devices are used to improve and monitor performances. Another field of interest is represented by those people that wants to monitor their health status by using low cost devices. In this work we propose a two stage fuzzy logic approach in which the device tries to learn and fit customer habits in order to discover outlier warning signals. The two stages approach proposed consists of monitoring the normal activities of the user in order to build a reference of its condition; Then a real-time monitoring and analysis of gathered data from body sensors is accomplished. User status is carried out using a Fuzzy Logic based network. First stage will give us the current activity of the user while second stage will provide information about health status in terms of heart rate.

MANETs are networks composed of mobile nodes connected using the wireless medium to communicate each other. The MANET nature makes it subject to various types of security leaks. External nodes could interfere with the communication, fair nodes could be compromised at any time by malicious agents. Another issue regards the limited amount of energy available to nodes, so their behavior could become selfish under some conditions. Many proposals detect these behaviors using a trust management system, but the activity of packet monitoring is energy-consuming. The main aim of this proposal is the reduction of the energy required by the monitoring system without lowering detection performance. Various simulations show that the achievement of this result is obtained through the application of a probabilistic approach, having also better performance in terms of time needed to detect misbehaviors.

Vehicular ad hoc networks (VANETs) represent a particular mobile technology that permits communication between vehicles, offering security and comfort. Nowadays, distributed mobile wireless computing is becoming a very important communication paradigm, due to its flexibility to adapt to different mobile applications. In this work, an interference aware metric is proposed in order to reduce the level of interference between each pair of nodes at the MAC and routing layer. In particular, this metric with a prediction algorithm is proposed to work in a cross-layered MAC and an on-demand routing scheme in multi-radio vehicular networks, wherein each node is equipped with two multi-channel radio interfaces. The proposed metric is based on the maximization of the average signal-to-interference ratio (SIR) level of the connection between source and destination. In order to relieve the effects of the cochannel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical SIR evaluation. Our solution has been integrated with an on-demand routing scheme but it can be applied to other routing strategies. Three on-demand interference aware routing schemes integrating IEEE 802.11p Multi-channel MAC have been tested to assess the benefits of the novel metric applied to a vehicular context. NS-3 has been used for implementing and testing the proposed idea, and significant performance improvements were obtained: in particular, the proposed policy has resulted in an enhancement of network performance in terms of throughput and packet delivery ratio.

In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.

In this paper, we present a forwarding algorithm for mobile social opportunistic networks which is both centrality-based and energy-aware. In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Centrality-based forwarding schemes use the structurally important nodes (i.e. nodes having many contacts with neighbors) as message relays and have been shown to be good candidates for social forwarding in opportunistic network environments. However, the most central nodes which are structurally more important than other network nodes, have the disadvantage to be used very frequently as message relays, thus consuming their energy very quickly. Here, we exploit the advantage of using a centrality-based scheme which is distributed and needs only the history of contacts as social information. In addition, we modulate the computed node centrality value using the node energy level in order to avoid transmissions in nodes more central but with a limited energy budget. Simulating three real-world mobility traces, we show that our scheme is able to achieve better delivery ratio and overhead ratio while sensibly reducing the energy consumption of nodes.

In this paper we consider Delay Tolerant Network (DTN) as a technology to implement a future network in a People Centric Networking paradigm, using Active RFID carried by people that exchange information with each other. We propose a novel and real Neighbor Discovery (ND) phase on active RFID based DTN using Open Beacon devices. In particular, we propose a solution using the Sift distribution on a probabilistic persistent approach called Sift-Persistent. We simulated P-Persistent, Aloha and our solution using our customized Java simulator. We implemented Sift-Persistent and P-Persistent on Open-Beacon devices, comparing the simulation results and test-beds. Moreover, simulations and real testbed show a coherent behavior validating our proposal in the RFID context. Performance evaluations have been tested in terms of discovered neighbors.

In this work a new protocol for Voice over IP (VoIP) transmissions in wireless ad-hoc networks is proposed. Distributed architecture is necessary when dealing with dynamic environments, such as ports or battlefields, where creating infrastructures becomes expensive or impossible. Mobile ad-hoc networks (MANETs) are based on a peer-to-peer approach and each node participates in the organization of the whole network. VoIP over MANETs is a challenging issue due to the intrinsic distributed nature of the existing peer-to-peer paradigm. This paper proposes a new protocol, capable of ensuring a quality of service (QoS) level for VoIP calls over a MANET and to manage a large number of calls in the system. Novel metric and utility functions are proposed to perform the best path selection from source to destination nodes, respecting the QoS parameters for VoIP quality. In particular, an objective metric such as R-factor is considered, and a flexibility index is defined in order to maximize the number of acceptable VoIP calls. Performance evaluation shows that the proposed approach led to better network management in terms of admitted calls and respected QoS constraints.

In last years, the challenge of providing end-to-end service where end-to-end forwarding paths do not exist is faced by the new emerging approach of Delay Tolerant Networks (DTNs), that support interoperability of “regional networks” by accommodating long delay between and within regional networks, such as space communications, networking in sparsely populated areas, vehicular ad-hoc networks and underwater sensor networking. The common issue of these environments consists in the absence of guarantees about the existence of continuous end-to-end paths between source and destination nodes. In this paper we consider epidemic schemes to solve this problem, proposing an extended approach aimed at the routing optimization in terms of energy consumption and message delivery probability. The performance of our idea have been evaluated through a deep campaign of simulations, verifying that the proposed extended epidemic approach leads the system to an overall enhancement.

Pocket Switched Networks (PSNs) are challenged wireless networks of handheld mobile devices that exploit user mobility to distribute data in an opportunistic way. In PSNs, a fundamental issue is the choice of a suitable relay node for message forwarding. In such environments, disconnections are very frequent and an end-to-end path from source to destination usually does not exist. Consequently, routing protocols are based on a hop-by-hop communication paradigm. Most of these protocols exploit social behaviour characteristics to decide whether an encountered node is a good relay. Social network information is commonly extracted from encounters detected between mobile devices. However, Internet added online social interaction techniques, which are not based on physical meetings and that reflect users' online behaviour. In this paper we present novel routing metrics for social-based routing in PSNs that exploit both offline and online users' social network information. By proposing a model of dynamic online social network that uses information extracted from offline and online user behaviour, we show that routing centrality metrics, combining node centrality extracted from dynamic online social network and centrality extracted from detected social network, improve delivery ratio and in some scenarios even reduce end-to-end delay.

This work considers a cognitive radio (secondary system) that operates under the interference of a WiMAX-like legacy (primary) system. The secondary terminals have knowledge of the codebooks used in the primary system and can apply Opportunistic Interference Cancellation (OIC): if the channel conditions allow, the secondary system can decode and subsequently cancel the interference from the primary system. Contrary to the previous works that utilize the concept of OIC, in this paper we consider practical packet coding, rather than optimal random codebooks in an information-theoretic setting. The key contribution is the mechanism for power control, whose objective is to protect the primary users from a harmful secondary interference. As a dividend, it is seen that in certain regions the proposed power control creates channel conditions that enable the secondary receiver to take advantage of the OIC mechanism. Several power control algorithms have been considered and evaluated in a single and multi-channel scenario. The results clearly indicate the advantage of using power control in conjunction with the OIC concept for achieving spectrally-efficient secondary operation.

Satellite communications represent an important slice of the global communications sector thanks to their broadcast nature. Unfortunately, TCP used on satellite links suffers from a number of well-known performance issues, especially for high altitude satellites with longer delays. In this paper, a new TCP protocol is proposed. This new protocol arises after a careful study of the various protocols present in literature that try to resolve the typical satellite issues. Our TCP protocol is called Available bandwidth adaptable Transport Protocol (ATP) and it takes the advantages of two TPC protocols proposed in literature: Satellite Transport Protocol (STP) and TCP Jersey. In particular, it consists of a reviewed version of STP protocol with the addition of the TCP Jersey Bandwidth Estimation algorithm called Available Bandwidth Estimation (ABE). In order to show the goodness of the proposed protocol we have performed some comparisons between ATP, STP, TCP Jersey and another TCP protocol called Satellite Communication Transmission Protocol (SCPS-TP).

Opportunistic networks are a generalization of Delay Tolerant Networks (DTNs) where communication is challenged by frequent disconnections and encounter patterns between mobile devices are unpredictable. Opportunistic routing protocols attempt to enable the forwarding of messages via encounters between devices, while dealing with the problems of large delays and lack of end-to-end connectivity. To save energy in mobile environments, such routing protocols must minimize unnecessary message replication. This paper presents FSF (Friendlist-based Social Forwarding), an opportunistic routing scheme that exploits both social network information detected through encounters between mobile devices and pre-existing online social network information. To select an effective forwarding node, FSF measures the forwarding capability of a node when compared to an encountered node in terms of node degree cen-trality measured on the temporal encounter network and online tie strength. Simulations with experimental datasets containing encounter records with corresponding Facebook friendlists of the participants show that the proposed routing scheme allows mobile users to achieve good delivery ratio, while forwarding fewer messages than other existing algorithms.

In these last years, many studies have focalized on the design of reliable under water acoustic communication systems. However, the ocean acoustic communication channel exhibits strong amplitude and phase fluctuations and the phenomena of diffraction, refraction and reflection. Due to the complexity of environment, the motions of transducers, sea surface, etc., the underwater acoustic signals exhibit random temporal and spatial frequency fluctuations in both amplitude and phase. These highly space, time and frequency dependent features introduce numerous obstacles for any attempts to establish reliable and long-range underwater acoustic communications. Therefore, it is very important to model a so complex channel. In this paper, we propose a new multipath channel model for shallow underwater acoustic communications. In particular, our model takes into account the effects due to spreading loss, scattering and reflections.

In this paper, we consider the problem of exploring an unknown environment with a team of robots to detect and disarm mines. The goal is to minimize the overall exploration time and to disarm all mines in a landscape. We present two approaches for the coordination of robots. An indirect communication, stigmergy, inspired to biology is used to help robots to cover the overall area in a minimum time. In this way the robot simultaneously explore different regions of their environment. Moreover, a new coordination protocol called Ant-based Task and Robot Coordination (ATRC) is proposed to recruit robots and disarm mines in a minimum amount of time. This approach has been implemented and tested extensively in a simulation environment. The results showed the best convergence time of proposal in term of space discovery in comparison with a well-known algorithm such as the Vertex Ant Walk (VAW). Moreover the effectiveness of proposed protocol is evaluated varying network parameters in the simulation.

Energy awareness for computation and protocol management is becoming a crucial factor in the design of protocols and algorithms. On the other hand, in order to support node mobility, scalable routing strategies have been designed and these protocols try to consider the path duration in order to respect some QoS constraints and to reduce the route discovery procedures. Often energy saving and path duration and stability can be two contrasting efforts and trying to satisfy both of them can be very difficult. In this paper, a novel routing strategy is proposed. This proposed approach tries to account for link stability and for minimum drain rate energy consumption. In order to verify the correctness of the proposed solution a biobjective optimization formulation has been designed and a novel routing protocol called Link-stAbility and Energy aware Routing protocols (LAER) is proposed. This novel routing scheme has been compared with other three protocols: PERRA, GPSR, and E-GPSR. The protocol performance has been evaluated in terms of Data Packet Delivery Ratio, Normalized Control Overhead, Link duration, Nodes lifetime, and Average energy consumption.

The new DVB-S2 standard, introduced by ETSI, is mainly based on three key concepts: best transmission performance, total flexibility and reasonable receiver complexity. The Adaptive Coding and Modulation (ACM) scheme is the tecnique allowing to achieve these main goals. In particular, this tecnique allows to adapt the modulation and code levels, performed at the physical layer, at channel variations in a dynamic way. However, often the others modules, composing a satellite terrestrial terminal, does not take advantage of the ACM potentiality. For this purpose, in this paper we propose an efficient packet scheduling strategy able to operate with the different MODCOD schemes of ACM. In particular, considering ACM policy, Frame efficiency and QoS requirements the scheduling strategy has the main goal of maximizing the overall performances of the system.

Mobile computing and vehicular communications are becoming a very important paradigm for wireless communications, mainly because of their ability to adapt to different mobile applications. In this paper, we propose a new scheme for reducing the interference level during mobile transmissions in the VehiculAr inter-NETworking (VANET) environment, taking the advantage of the multi-channel nature of IEEE802.11p standard. In order to relieve the effects of the co-channel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical Signal-to-Interference Ratio (SIR) evaluation. The attention is focused on the routing level of VANET and we propose an interference aware routing scheme for multi-radio vehicular networks, wherein each node is equipped with a multi-channel radio interface. A new metric is also proposed, based on the maximization of the average SIR level of the connection between source and destination. Our solution has been integrated with the AODV routing protocol to design an enhanced Signal-to-Interference-Ratio-AODV (SIR-AODV). NS-2 has been used for implementing and testing the proposed idea, and significant performance enhancements were obtained, in terms of throughput, packet delivery and, obviously, interference.

In this paper two slot assignment strategies in TDMA MAC protocols for MANET are investigated: continuos (DCSA) and alternate slot (DASA) assignments. After a simple analytic modeling of TDMA systems running DASA or DCSA, simulative results are provided using E-TDMA (Evolutionary TDMA) protocol to validate our analysis. Energy Metrics such as Residual Energy and Energy good put have been evaluated in order to show the benefits and drawback of the investigated strategies.

Vehicular mobile communications are attracting much attention in wireless communications field. In this paper a new routing protocol for the reduction of interference level during mobile transmissions in VANET environment is proposed, considering the availability of different channels in the DSRC spectrum. The attention is mainly focused on the routing level of VANET and we propose an interference aware routing scheme for multi-radio vehicular networks based on a new metric, for the maximization of the average SIR level of the connection between source and destination. The proposed idea has been integrated with the AODV routing protocol to design an enhanced Dynamic-Frequency-Interference-Aware-AODV (DFIA-AODV). The proposed idea has been tested and significant performance enhancements were obtained.

In this work an approach to obtain an accurate high level channel model based on Discrete-Time Markov Chain, useful in some simulation context, is provided. In particular, this model is based on the concept of error trace analysis and on the degradation level of given observation windows: an observation window is fixed and the degradation level of the link, the Packet Error Rate (PER) relative to the specific window, is evaluated. This approach is useful for any wireless transmission scheme, but in our work, we apply it to the Ultra Wideband (UWB) system. Many researchers have already treated UWB channel modeling. However, to the best of our knowledge, all the proposed channel models work at the physical level investigating only some aspects of channel interaction. It has been shown through a comparative analysis, based on the occurrence of correctly and wrongly received packets, that the degradation level approach is more accurate than the classic Gilbert-Elliot model, the 3rd order Markov model and the Markov-based Trace Analysis (MTA) model.

Mobile ad-hoc networks are one of the most active research areas in the wireless communications field. These networks do not need any fixed infrastructure or configuration, easily adapting to difficult scenarios and types of use. Since mobile nodes are often powered by limited batteries, energy is an important issue in MANETs since the lack of a node can lead to the partitioning of the network. Recently, many different strategies have been proposed in order to optimize the energy consumption and prolong the mobile network lifetime, especially at the routing layer. These policies use energy-aware metrics, instead of minimum-hop routing, to achieve this goal. In this paper, the Minimum Drain Rate routing metric is analyzed, which is one of the most promising techniques, with the aim of clarifying the relationship between its parameters and the node energy consumption pattern. Some considerations will be made about this relationship, and they will be demonstrated via simulations. Finally, an adaptive technique will be proposed, in order to improve energy evaluation at the mobile nodes.

Traditional routing protocols deployed to provide communication among mobile nodes in wireless ad hoc networks (MANET) assume a trust and collaborating environment inside the network. However, this assumption is not always true and a hostile or unprotected environment can seriously affect the protocol and network performance. In this work two kinds of approaches are applied to a well-known routing protocol called SAODV in order to improve its performance and to offer more resilience to attack from malicious nodes authenticated by the network. A preventive approach based on a cryptographic mechanism and a reactive approach to detect the anomalous and malicious behavior of nodes are considered. An extension of SAODV to offer Intrusion Detection mechanism (IDM) and trust-based mechanism (TBM) to promote the collaboration of the cooperating node and penalize the selfish nodes are proposed. Simulation results were performed in order to show the effectiveness of our proposal in comparison with AODV and SAODV.

The IEEE 802.16 protocol is one of the most promising technologies in recent years, especially in its recent version that specifies user mobility support. The purpose of this paper is to illustrate a scenario characterized by this protocol and the presence of mobile users. The goal is to create a model that can describe the time variant channel behaviour in terms of probability values, i.e. such as: what is the probability of receiving a wrong packet on the receiver side? In the literature there is a great diffusion of Markov chain based models to describe channel behaviour, but none of these is independent of scenario configurations, and also none has been tested with a realistic dynamic scenario. As a consequence we have attempted to create a new model that can be used independently of the channel configuration.

The mesh distributed mode supported by IEEE 802.16 protocol, with the capability to establish direct links between SSs (Subscriber Station) and with the wide coverage area and the promised bit rate, allows to create interesting scenarios. The protocol defines guidelines to realize request/grant process, but it does not proposes a distributed Call Admission Control (CAC) algorithm. This process is essential to guarantee QoS constraints to admitted connections. In this paper, we present GCAD-CAC (Greedy Choice with bandwidth Availabilities aware Defragmentation): a new call admission control algorithm to guarantee the respect of data flows delay constraints defined by three different traffic classes. In order to reach good results, a Greedy algorithm is used: all the new requests are accepted, but when a higher priority request is received, a lower priority admitted request is preempted. The preemption can leave in data subframe some little gaps not sufficient to new connection admission; these gaps can be collected by GCAD algorithm by activating a bandwidth availabilities defragmentation process. The algorithm quality is proved by a comparison with other two algorithms found in the literature.

Nowadays, in wireless computing, guaranteeing a certain grade of Quality of Service (QoS) during wireless sessions is an important issue, especially when non-tolerant applications are running into the system. Mobility and channel quality often affect system performance, depending on the grade of mobility of hosts. In this paper, providing QoS to mobile hosts belonging to a certain service class (Mobility Independent Predictive - MIP) is the main issue and a statistical multiplexing algorithm is proposed, in order to save an enormous amount of resources; it is based on the Cell Stay Time (CST) distribution analysis for a 2- dimensional (2D) wireless environment under the Smooth Random Mobility Model (SRMM). There are many works in literature about resource pre-reservation: they offer good QoS guarantees, but a very low system utilization, because they do not care about the fact that a passive reservation can remain unused for long time. With the help of the CST statistical multiplexing introduced in this paper, better results are obtained in terms of system utilization, while the Quality of Service and prediction error are maintained at acceptable levels. The proposed idea has been validated through many simulation campaigns, with a good performance analysis in terms of some interesting parameters, like bandwidth system utilization or prediction errors.

Ultra wideband (UWB) is a promising technology thanks to its unique physical characteristics. For this purpose, many works have already treated issues regarding UWB system. Most of them analyzed only first two ISO/OSI levels, that are physical and MAC layers, while too few investigations are yet spent on the routing layer of UWB systems. However, traditional approaches are not valid for UWB systems because they do not take into account of interference between nodes, that is not a trivial problem in these networks. The main goal of our work is to propose a new on demand routing protocol, called interference aware-based ad-hoc on demand distance vector (IA-AODV), based on the concept of interference perceived by the nodes: so, the optimum route is chosen on the basis of the minimum perceived interference, improving in this way the general system performance. To test the proposed protocol a ns-2 based simulator was realized. The protocol was compared with the AODV and IBOR protocols in terms of packet delivery ratio, end-to-end delay and normalized overhead. Simulation results show as our protocol outperforms the others protocols in all considered scenarios: only in few cases IA-AODV presents an overhead slightly higher than AODV.

Ultra wide band (UWB) thanks to its physical characteristics became one of most suitable technology for indoor and outdoor personal communications. Many works have already treated problematic involving UWB transmission channel. However, at the best of our knowledge, all proposed model work at the physical level or they investigate only some aspects of channel interactions. Instead, in our work, we provide an approach to obtain an accurate high level channel model based on Discrete Time Markov Chain (DTMC) modeling useful in every simulative context. In particular, our model is based on the concept of error trace analysis and on degradation level of a given observation window: we do not analyze the single packet, but we fix an observation window and we evaluate the degradation level of the link computing the PER relative to the specific window. This new approach is more accurate than classic Gilbert-Elliot (G-E) model, 3rd order Markov model and Markov based Trace Analysis (MTA) Model as a comparative analysis, based on the occurrence of correctly and wrong received packets, showed.

The IEEE 802.16 protocol is one of the most promising technologies in recent years, especially in its recent version that specifies user mobility support. The purpose of this paper is to illustrate a scenario characterized by this protocol and the presence of mobile users. The first goal is to create a model that can describe the time variant channel behaviour in terms of probability values, i.e. such as: what is the probability of receiving a wrong packet on the receiver side. The second goal is to use the model forecast to guarantee QoS to every type of application. In literature there is a great diffusion of Markov chain based models and of other kind of model, as ARMA or filter model, to describe channel behaviour, but none of these is independent of scenario configurations, and also none has been tested with a realistic dynamic scenario. As a consequence we have attempted to create a new model that can be used independently of the channel configuration to ensure compliance with the QoS bounds.

In this paper we propose a routing algorithm based on Swarm Intelligence studies. In particular, this algorithm is based on AntNet routing with the extensions of novel metrics for the multi-objective optimization, that are minimum hop count and traffic load balancing. In order to build an optimal solution, the proposed algorithm will make use of ANT agents that consist of probe packets sent on the HAPs network. We have chosen as reference network a HAPs mesh in order to get advantages of the dynamic characteristics of these platforms. In this work we perform a comparison of a classical shortest length path and our algorithm that will try to find the minimum hop path respecting a maximum end-to-end delay bound and an equally distribution of the traffic on the HAPs network.

Deep space communications are an important research line in scientific community. The possibility of performing the communication between earth and other planets is an excitant challenge. The main issue of the communication between planet, called interplanetary (IPN) communication, is the intermittent visibility due to planet movement and the long propagation delay. In order to solve these problems a delay tolerant network (DTN) has been deployed. DTN is an architecture particularly useful in scenarios where very long transmission delays or intermittent connectivity are present, such as IPN network. The routers in DTN perform a transmission mechanism similar to the model of mail transfer protocols or the store-and-forward and it permits to determine a hop-by-hop based messages exchange. Our main contribute in this paper is the introduction of an asynchronous congestion control mechanism on the network that it is not present in the traditional DTN protocol. This control tries to resolve buffer overflow, link saturation and useless retransmission problems in order to better utilize the overall system.

Multimedia communications and, in particular, multicast communications are widely used to distribute multimedia contents. When a certain quality of service (QoS) level must be guaranteed in the multicast tree construction then a Steiner tree problem is obtained, therefore a NP-complete problem must be faced. QoS multicast has a great relevance in the research community because it can offer high performances and better resource allocation. This is relevant in the modern networks, which are composed of wireless networks, wired networks, 3G-4G networks, delay tolerant networks and so on. A better resources allocation permits to have a greater number of connected users. In order to obtain these results it is important to have a multicast tree that can satisfy some QoS requirements. In this work two meta-heurist such as genetic algorithms and simulate annealing algorithms have been considered. Moreover, new procedures and new approaches are proposed in order to obtain better algorithms performances.

This paper presents a novel Medium Access Control algorithm for the QoS support over UWB networks. Interference Mitigation techniques are combined with access control to guarantee the Packet Loss and Maximum Packet Delay satisfaction. Two QoS indexes are defined and are considered for the acceptance of a call on the network in order to maintain low the interference level of transmitting stations. Performance evaluations have been considered in terms of data throughput and maximum data packet delay.

QoS guarantee is a very important issue in wireless communications, when mobile nodes move among different coverage areas. This paper presents a novel 2D reservation scheme for WLAN environments. A general prediction technique based both on the analysis of Cell Stay Time (CST) and on the direction probabilities of hand-in and hand-out events of mobile nodes from wireless cells is outlined. In particular, a threshold-based algorithm is presented, trying to take into account the mobility behavior of mobile users, through the analysis of a directional probabilities matrix. An integrated architecture able to make active and passive reservations has been employed as a possible application of the proposed approach. Many simulations have been carried out and a performance analysis has been performed, finding a good tradeoff between bandwidth utilization and prediction error.

Recently there has been a lot of research and development in wireless networking and mobility management, with the main purpose of connection handling between mobile hosts and base stations or access points. QoS guarantee is a very important issue in wireless communications, when mobile nodes move among different coverage areas. This paper presents a general prediction technique based both on the analysis of cell stay time and on the direction probabilities of hand-in and hand-out events of mobile nodes from wireless cells. User mobility has been analyzed, in order to reduce passive resource reservations, with a high gain in system utilization. In particular, a threshold-based algorithm is presented, trying to take into account the mobility behavior of mobile users, through the analysis of a directional probabilities matrix. The performances of the 2D wireless system have been evaluated in terms of prediction error and system utilization; in addition, some comparisons with older prediction schemes have been made.

Multi-mode satellite terminals are actually the most desired devices on the market, thanks to their flexibility and suitability. A multi-mode radio communication terminal is a terminal allowing two or more transmission mode. In the last few years many research resources have been invested in the study of this new type of terminal In this work, we define a multi-mode terminal as a terminal capable of communicating both with a bent-pipe (BP) satellite and with an on-board processor (OBP) satellite and dynamically setting its transmission parameters (e.g. transmission power and code rate). In order to realize this multi- mode functionality, the most suitable technology is the software defined radio (SDR). The SDR is a set of hardware and software technologies that allow one to obtain reconfigurable architectures for network and wireless terminals. The goal of our work is to plan a satellite system based on digital video broadcast with the return channel satellite (DVB-RCS) standard in which the return channel satellite terminals (RCSTs) are able to adapt it to the channel state, configuring the transmission chain via software, respecting a certain quality of service (QoS) constraint on the packet error rate (PER).

Ultra-wideband (UWB) radio is a viable candidate for short-range multiple access communications in dense multipath environments. This paper analyzes the efficiency of direct sequence - UWB physical layer standard proposal in a indoor environment with fixed transmitters and receiver positions. The performance evaluation is carried out using the UWB channel model provided by the IEEE 802.15 channel modelling subcommittee to model the dense multipath indoor environment typical of the UWB system. DS-UWB architecture has been implemented using a Matlab Simulink tool and simulation results are evaluated in terms of bit error rate (BER) vs. transmitter-receiver distance and noise power spectral density. Moreover, a polynomial regression analysis is carried out on simulation results in order to obtain a closed formula to describe, for different scenarios, the BER as a function of data rates, noise power spectral density (PSD) and distance between the transmitter and receiver.

In last years there has been a lot of research and development in wireless communications and mobility management, with the main purpose of connections handling between mobile hosts and base stations or access points, when different coverage areas are visited. This paper presents a novel 2D reservation scheme for WLANs; a general prediction technique based both on the analysis of cell stay time (CST) and on the direction probabilities of hand-in and hand-out events of mobile nodes from wireless cells is outlined. In particular, a threshold-based algorithm is presented, trying to take into account the mobility behavior of mobile users, through the analysis of a hand-off directional probabilities (HDP) matrix. Users' mobility has been analyzed, in order to reduce passive resource reservations, with a high gain in system utilization. In particular, an integrated architecture able to make active and passive reservations has been employed as a possible application of the proposed approach. The performances of the 2D wireless system have been evaluated in terms of prediction error and system utilization; in addition, some comparisons with older prediction schemes have been made.

A multi-mode radio communication terminal is a tunable terminal for passing and/or receiving bands in at least two radio communication systems. In the last few years many research resources have been invested in the study of this new type of terminal. This study is intended for a multi-mode terminal is a terminal capable of communicating both with a bent-pipe satellite and with an on-board processor satellite. In the literature different technologies are presented and they can be used to realize the multi-mode capability of a terminal, but, the most suitable technology is the software defined radio (SDR). The SDR is a set of hardware and software technologies that allow one to obtain reconfigurable architectures for network and wireless terminals. The goal of this work is to plan a satellite system based on digital video broadcast with the return channel satellite (DVB-RCS) standard in which the return channel satellite terminals (RCSTs) are able to adapt to the channel state, configuring the transmission chain via software, respecting a certain quality of service (QoS) constraint on the packet error rate (PER).

This paper presents a performance evaluation of IEEE 802.16e protocol. The 802.16e PHY/MAC protocol for wireless MAN has been applied to a high altitude platform (HAP) architecture with another protocol family member: 802.16SC (single carrier). In this work an HAP serves a rural scenario with low multipath effects. Two different time-variant wireless channels have been considered: the first one for a HAP- mobile user link that is characterized by path loss and doppler effects and the second one for a mobile user - mobile user link that presents also multipath fading. Many simulation scenarios with different mobility user speeds and different modulations are reported and interesting results concerning the PER of both channel, the first under single carrier (SC), and the second under an OFDM modulation, a specific multicarrier modulation, are presented. Thus PER evaluation under different modulation and for different payload sizes are presented. Simulation results showed good channel performance if the mobility of the user is low or the mobile user is near the vertical axis of the HAP segment, but also if QPSK modulation is used. Finally Matlab elaboration to obtain PER polynomial function are presented.

The low cost of devices and the possibiliy of achieving higher data rates without increasing transmitter power make Ultra-Wideband (UWB) radio a viable candidate for short-range multiple access communications in dense multipath environments. This paper analyses the efficiency of the Direct Sequence - Spread Spectrum (DS-SS) physical layer standard proposal in an indoor environment with fixed transmitters and receiver positions. The modelling and the performance evaluation of a new channel model, where impulse response is also distance-dependent, are outlined. Packet Error Rate (PER) analysis has been carried out by using a polynomial regression technique, through the Matlab tool, on simulation results obtaining an expression for the PER as function of distance and packet length. Simulation results are evaluated in terms of PER vs transmitter-receiver distance.

This papers presents a 2D reservation scheme in WLAN environment. A two-dimensional wireless mobility model called smooth random mobility model (SRMM) has been considered, because it makes the movement of users smoother and more realistic than well-known in literature random mobility models. A general prediction technique based both on the analysis of cell stay time and on the direction probabilities of hand-in and hand-out events of mobile nodes from wireless cells is outlined. Three different reservation policies are considered: the first scheme reserves a fixed number of cells for each hand-off; the second one permits to reserve on a greater number of cells in the first hand-off and a lower number of cells in the next hand-off events; the last policy uses an increasing adaptive reservation scheme, trying to take advantage of the lower prediction error for the first hand-off and the greater prediction error for the next hand-off. Many simulations have been carried out and a comparison between the three reservation schemes has been performed.

The low cost of devices and the possibility of achieving higher data rates without increasing transmitter power make ultra-wideband (UWB) radio a viable candidate for short-range multiple access communications in dense multipath environments. This paper analyses the efficiency of the direct sequence-spread spectrum (DS-SS) physical layer standard proposal in an indoor environment with fixed transmitters and receiver positions. The modelling and the performance evaluation of a new channel model, where impulse response is also distance- dependent, are outlined. Moreover, the performance evaluation of an ideal minimum mean square error (MMSE) receiver is exploited in terms of distance, noise and data rates dependence. packet error rate (PER) analysis has been carried out by using a polynomial regression technique, through the Matlab tool, on simulation results. Simulation results are evaluated in terms of bit error rate (BER) and PER vs. transmitter-receiver distance.

Abstract Network scalability is one of the critical challenges and requirements in routing protocols for ad hoc networks. This paper presents a novel scalable routing protocol called Geo‐LANMAR. The proposed protocol inherits the group motion support of landmark routing (LANMAR) and applies the geo‐routing concept to deliver packets efficiently. In this framework, the integration between geo‐coordinates and table‐driven IP addressing is introduced. There is also an integration of group management with geo‐forwarding and IP group management. Geo‐LANMAR uses link‐state propagation over a virtual topology built on landmarks, and a fisheye like scheme makes this propagation very efficient. The virtual topology helps recover from voids. For extra efficiency, a novel metric called effective traveled distance (ETD) allows us to predict voids or obstacles. With respect to LANMAR, Geo‐LANMAR reduces advertisement update overhead (O/H) and features robust forwarding. Consequently, Geo‐LANMAR is more scalable to large ad hoc networks with group motion. The performance evaluation of Geo‐LANMAR shows that Geo‐LANMAR gives high scalability for large networks in terms of control O/H, end‐to‐end delay, and packet delivery ratio as compared with other routing protocols such as AODV, LANMAR, and GPSR. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents a novel routing protocol called Geo-LANMAR. This routing scheme is able to get full advantages of group motion of mobile nodes to reduce the routing overhead and offer high network scalability. This protocol inherits same advantages of LANMAR protocol regarding the group motion support and its idea is to use the long-distance gee-forwarding for the extra-scope routing such as the terminodes routing and the optimized link-state routing (OLSR) for the intra-scope routing. Together the geo-routing forwarding scheme, a global update propagation scheme based on the hazy sighted link state routing (HSLS) between landmark nodes (cluster heads) is applied. An asymptotic analysis of Geo-LANMAR protocol is proposed and a rule that binds the intra-scope and extra-scope overhead cost is found. The novel routing scheme has been compared with the standard routing protocols such as AODV, GPSR and LANMAR.

A novel routing scheme called GeO-LANMAR routing has been proposed in this paper. This scheme combines the local link-state routing protocol until k-hop and a long-distance greedy forwarding. Geo-LANMAR supports group motion and gives high scalability for large network. It uses the concept of local group area (LGA) to define region where to forward data packets through geo-forwarding scheme. It makes usage of a virtual topology between LGAs and an optimized Hazy sighted link state (HSLS) link-state routing protocols. It combines the benefit of LANMAR protocol in terms of group motion support with the scalability of terminodes routing. A hybrid forwarding scheme (proactive and geo-routing) and a coarse topology knowledge through the HSLS protocol running between LGAs is applied. A performance evaluation of Geo-LANMAR vs. other routing protocols such as AODV, LANMAR and GPSR has been lead out.

This paper presents a novel call admission control (CAC) algorithm based on the statistical multiplexing of VBR traffic. The proposed algorithm is called statistical multiplexing based on discrete bandwidth levels of GOP rate (SMDB) because the solution is based on the discretisation of the GOP rate in a set of bandwidth levels and on the time characteristics of discrete bandwidth levels of MPEG sources. SMDB is compared with another statistical CAC based on the normal/lognormal distribution of the GOP rate (SMND). SMDB overcomes SMND in many situations such as high variance around the average video traffic GOP rate of sources and heavy traffic load. The new CAC has been tested in the new generation satellite platform called digital video broadcasting via return channel satellite (DVB-RCS). A high system utilization and multiplexing gain respecting QoS constraints is obtained

In the past few years, there has been much research in wireless adaptive networking and resource management, in order to examine how to efficiently handle wireless communications between mobile hosts and base stations. In this paper, we focus the attention on the mobile independent predictive (MIP) service class, which provides a passive reservation policy, with the goal of QoS guarantees during hand-off events. We analyzed MIP users' mobility along coverage areas, to obtain a system utilization improvement, by reducing passive resource reservations on cells that users will probably visit. A prediction technique is also proposed and calls are managed in a wireless system by a utility-based algorithm that takes into account the radio-link quality variability by a Markov channel model, based on the IEEE802.11b standard. System performances are evaluated by varying users' speed and system utilization with prediction error percentages are shown. It is observed that the proposed prediction technique causes an increase in system utilization.

This paper focuses its attention on the rate adaptation problem in wireless network with mobile hosts. We define two new quality of service (QoS) indexes. They express the degradation degree of bandwidth and the frequency of bandwidth degradation suffered by data packet flows. These indexes are used to define and propose a new rate adaptation algorithm named degradation degree based fair algorithm (DDFA). The aim of the proposed algorithm is to fairly distribute the effects of QoS degradation among the cell visited by the mobile hosts. The DDFA algorithm has been compared with a fair adaptation algorithm proposed in the literature. The performance evaluation shows that proposed algorithm reduces the QoS degradation of flows without adding control overhead compared to the reference fair algorithm.

This work considers the problem of bandwidth adaptation in wireless mobile environment. In network in which the bandwidth resource changes frequently and an hard quality of service can not be guaranteed, is important to consider an adaptive approach in the bandwidth management. This work proposes new bandwidth adaptation algorithm called average degradation degree and ratio based rate adaptation. This algorithm accounts for the degradation degree and the degradation ratio of the flows as criteria to manage the bandwidth. In the literature many rate adaptation algorithms consider the fairness criteria and the protocol overhead but there is no scheme that accounts for the flow degradation degree maintaining good fairness properties. We compare the proposed algorithm with well known studied schemes: minimum adaptation, fair rate adaptation and average rate adaptation algorithm. The performances of the wireless systems have been evaluated in terms of overhead, fairness, and degradation degree and degradation ratio. Performance measurements show that the proposed algorithm is able to reduce the degradation degree and degradation ratio preserving good fairness properties without adding significative overhead.

The rapid proliferation of the Internet together with the increasing importance of broadband multimedia services has roused the research community's interest, in particular IETF, in QoS traffic. Interesting can be evaluated the QoS architecture and in particular integrated services inside the satellite platform. A multimedia satellite platform called EuroSkyWay has been considered and the IntServ controlled load services (CLS) and the behaviour of the system loaded with controlled load traffic and guaranteed (GS) traffic have been analyzed. The management of CLS is important in order to manage the category the traffic called "better than best-effort" and to guarantee a full interoperation with the terrestrial network. In particular, we have proposed a new way to manage the CLS on satellite systems. The approach is based on reduction of peak bandwidth values of CLS and the increasing of GS request delay tolerance. Different simulations have been outlined and the conformance to QoS parameters for the proposed approach has been verified. Furthermore, the admission control for different values of bandwidth levels declared in satellite admission phase and GS requests timeout have been examined.

This paper focuses on a recently proposed Diff-Serv like architecture called scalable core (SCORE). This one is used in the terrestrial network in order to offer more scalability and deterministic guaranteed services. The integration issues between a scalable terrestrial and a satellite network based on Int-Serv architecture are outlined. The Aggregated RSVP protocol on integrated scalable terrestrial and IntServ satellite network has been introduced in order to guarantee the interoperation between the SCORE architecture and the satellite segment. The system performance in terms of signalling overhead and resource utilization has been set out. The study shows that increasing aggregated bandwidth reservation can improve signalling but can under utilize the terrestrial channel. The satellite network, instead, can improve its static efficiency due to the increased number of calls accepted on the satellite link.

This paper proposes a heuristic for re-computing the forwarding zone of route request packets for the location-aided routing (LAR) protocol in ad hoc wireless networks. We evaluate the influence of a "multi-step" resizing of the request zone on the LAR protocol performance, and make comparisons with other routing protocols (LAR scheme 1, LAR scheme 2, and promiscuous-dynamic source routing). Simulation results show that an accurate choice of the request zone size and of the number of steps for the resizing is successful in reducing the control overhead and increasing the throughput.