Unmanned aerial vehicles (UAVs) are increasingly considered as a key technology for the evolution of 6 G network, addressing limitations related to the fixed deployment of conventional base stations (BSs). In this paper, we investigate the dynamic optimization of an Area Spectral Efficiency (ASE) for UAV-enabled BSs over a realistic 3D terrain model. The goal is to maximize ASE by Line-of-Sight (LoS) queries approach to estimate the coverage area between UAV-enabled BS with ambient active user equipment (UEs), which are distributed within the 3D terrain model. Then proposed route selection strategy is able to successfully seek a minimum path and balance path efficiency. The results reveal that the proposed algorithm is able to successfully seek a minimum path and optimize the LoS coverage simultaneously, achieving similar performance in terms of ASE compared to the PSO method, highlighting its potential for enhancing future wireless communication networks in dynamic and challenging scenarios.

Migrating services in an edge-cloud environment poses unique challenges, including heterogeneous environments, potential failures, and uneven resource distribution. This paper studies and evaluates reactive and predictive migration approaches to support live migration in case of edge cloud computing failures. Telemetry information related to edge cloud computing has been considered to trigger migration, whereas deadlock prevention algorithm has been used to determine and select the target device to migrate services. The paper evaluates these strategies by comparing resource utilization, assessing differences between predictive and reactive migration and handling multiple migrations for tenants hosting several applications. Experimental results have shown that predictive migration can reduce the downtime of the hosted services. Additionally, the total migration cost can be increased for both scenarios where the containers can be migrated to different edge devices due to lack of available resources.

This paper investigates the physical layer security (PLS) issue in reconfigurable intelligent surface (RIS) aided millimeter-wave rotary-wing unmanned aerial vehicle (UAV) communications under the presence of multiple eavesdroppers and imperfect channel state information (CSI). The goal is to maximize the worst-case secrecy energy efficiency (SEE) of UAV via a joint optimization of flight trajectory, UAV active beamforming and RIS passive beamforming. By interacting with the dynamically changing UAV environment, real-time decision making per time slot is possible via deep reinforcement learning (DRL). To decouple the continuous optimization variables, we introduce a twin- twin-delayed deep deterministic policy gradient (TTD3) to maximize the expected cumulative reward, which is linked to SEE enhancement. Simulation results confirm that the proposed method achieves greater secrecy energy savings than the traditional twin-deep deterministic policy gradient DRL (TDDRL)-based method.

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains ($VFC$model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the$VFC$model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the$VFC$model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that$VFC$can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated.

6G is the future of intelligent connectivity artefacts with Artificial Intelligence (AI) at its backbone. The Multi-Access Edge Computing (MEC) based 6G enabled infrastructure helps in achieving the required zero latency for autonomous Intelligent Transport Systems (ITS) with features of low power consumption, lower end-to-end latency, minimal processing/transmission overheads, higher throughput and reliability. MEC are prone to a deadlock due to the limited amount of available computational resources, resulting in fatal delays in Vehicle-to-Vehicle (V2V), Vehicles to Road Side Unit (RSU) and RSU to ITS communication. The unresolved deadlock may entail higher energy consumption that can adversely affect the Quality of Service (QoS) in terms of safety and reliability with potential threats of causing fatal accidents. Therefore, it is almost imperative to resolve the deadlocks from MEC to comply with the QoS parameters of MEC based autonomous vehicles. The asserted goals can be achieved by employing an intelligent and adaptive deadlock resolution strategy. In this paper, a deadlock-aware, and collaborative edge decision algorithm has been proposed for facilitating the seamless communication of autonomous vehicles over MEC. Additionally, deadlock avoidance and prevention schemes have been evaluated using Bankers resource request avoidance algorithm, wound wait algorithm and wait-die algorithms for resource provisioning in collaborative MEC. Furthermore, the effectiveness of deadlock avoidance and prevention algorithms in real-time scenarios has been analyzed in MEC systems. The metrics used for a comparative analysis in this research include Round-trip time, Queue wait-time and CPU utilization. The proposed algorithm shows promising results when compared with prevalent techniques.

Modelling service deployment on distributed, heterogenous and dynamic environments usually involves the integration of multiple software components under a common deployment. Such deployments have gained focus lately, not only because of the rise of the Edge Computing paradigm but also due to Clouds and micro-Clouds Computing infrastructures. A novel approach for this deployment is Virtual Function Chains, according to which a service is decomposed to a set of Virtual Functions and each Virtual Function is undertaken by a different device. We propose a Virtual Function Chain Simulation (VFCSIM) Framework, an opensource library for building simulation models of both edge and cloud networked systems, based on Virtual Function Chains. VFCSIM's central structure is a heterogeneous network, which can describe, via scripting, a wide set of devices, network links, cloud resources, cost models and services. A case study is presented, according to which final users request on demand surveillance services from an edge network. After describing the simulation setup steps, both the summarization results and the real-time probing metrics are presented. VFCSIM is expected to enhance and facilitate the deployment of the Virtual Function Chaining paradigm both to cloud and edge infrastructures.

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

This paper introduces a novel distributed AI model for managing in real-time, edge based intelligent analytics, such as the ones required for smart video surveillance. The novelty relies on distributing the applications in several decomposed functions which are linked together, creating virtual chain functions, where both computational and communication limitations are considered. Both theoretical analysis and simulation analysis in a real-case scenario have shown that the proposed model can enable real-time surveillance analytics on a low-cost edge network. Finally, a caching mechanism is proposed and evaluated, reducing further the operational costs of the edge network.

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource-constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit-based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluating the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting reliable data analysis through the blockchain based computational crowdsourcing in MAC.

Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spend millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.

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The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics.

The routing protocols play a vital role in saving energy, especially by minimizing the time a packet takes to travel from source to destination. The aim of energy-aware routing protocols is to select a route that engages routers in such a way that the overall energy consumption is minimized. In this paper, a relationship between resource utilization and energy consumption is stated, further, a resource-aware dynamic routing algorithm for SDN is proposed. The contribution of this paper is a queuing theory-based approach that measures the average waiting time of nodes and links based on their utilization and finds a path that costs the least time. The paper also proposes a framework for implementing routing algorithm over an SDN. Performance of the algorithm is verified using a GNS3 based implementation with an Opendaylight controller.

The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives.

This work proposes a detection scheme that identifies non-conforming behavior in a VoIP network, based on statistical analysis and hypothesis testing. VoIP networks are a popular, low-cost alternative for telephony that offer lower rates especially for long-distance calls. Other services such as FollowMe, enhance the traditional voice-oriented nature of these networks. Consequently several security concerns such as fraud calls, are related to the high availability required by a VoIP system. Fraud calls account for an average loss of 3% to 5% of the operators' revenue. Thus the detection and prevention of the users from behaving in a non-conforming way, becomes crucial. A trustworthy and secure management and billing scheme is necessary, to guarantee the proper operation. This work proposes a behavioral control scheme for the VoIP clients. An initial training period defines the normal behavior. Then statistical analysis and t-testing is employed to extract results regarding the users' profiles, with pre-defined confidence levels. A Buffer zone creates a more flexible decision-making process. The scheme also offers the ability to configure its parameters, in order to react appropriately under different network conditions and detect possible misuses. It is implemented and its rational operation is verified via several simulation scenarios.

Peer-to-peer streaming has emerged as prominent networking approach for the delivery of real-time video content. The dynamic nature of the peers which is characterized by unpredictable arrivals or departures affects connectivity, stability and consequently the perceived video quality. This phenomenon is more evident in tree-based streaming. This work proposes enhanced failover mechanisms for peer-to-peer streaming over multiple multicast trees. Contrary to existing approaches, the proposed mechanisms take into account the order of arrival of peers and the diversity among different trees in order to provide backup links for the retrieval of missing packets during temporary disconnections. The proposed approaches have been evaluated through simulations both in terms of connectivity and video quality.

The explosive growth of new multimedia applications over mobile networks has attracted intensive research over the years. The main problem in this area is posed by the immense data rate required for multimedia applications in conjunction with the scarcity of wireless resources. To alleviate this shortcoming, this paper proposes combining mobility management and application layer filtering in order to optimize and maintain video quality. The design of the proposed solution is described in depth, as well as the integration of this approach within the LTE Evolved Packet Core architecture. Finally, experimental results are presented to demonstrate the performance of the proposed mechanism.

This paper studies the evaluation of the video streaming over Mobile Peer-to-Peer (MP2P) networks using Scalable Video Coding. The proposed research framework exploits the MP2P diversity characteristics where each node acts as a Mobile Peer for every neighboring node in a relay-based communicating path. Mobile Peers use a common look-up table to request video streaming resources in order to estimate the video packets transfer duration in the end-to-end path. The collaborative streaming is achieved through the data packets replication policy, which uses a bounded upper time limitation for the video packets of each layer (Use of Scalable Video). Each node/device utilizes a specified amount of capacity for both channel and storage purposes. Different scenarios have been introduced in this paper towards evaluating the video streaming policy in the MP2P system, using a real-time probabilistic Fractional Brownian Motion (FBM) and a Random Waypoint (RWP) Mobility model. In both models, nodes move according to certain probabilities, location and time. Intermittent connectivity occurs while nodes claim video streams, whereas promiscuous caching policy enables -where possible- recoverability of lost packets offering priority to packets from the base layer. The simulation results show that FBM provides better objective and subjective quality results for two video sequences resolution, in the MP2P network configuration.

Coordinated Multipoint systems (CoMP) have spurred considerable research activity in recent years as they enable both capacity and coverage improvement over conventional cellular systems with open issues in the design of radio resource management algorithms for this type of systems with the challenge of multiple sub-carriers resources available in the OFDMA (orthogonal frequency division multiple access) based systems. To partially solve these issues this paper proposes a user scheduling and resource allocation algorithm for distributed broadband wireless systems based on OFDMA (orthogonal frequency division multiple access) for the 3D video streaming service. The study considers a Manhattan network which is one of the most promising scenarios for CoMP. The algorithm combines the concepts of maximum carrier-to-interference scheduling and antenna selection to increase throughput and ensure zero intra-cell interference. The results show that the use of CoMP can provide significant advantages over conventional deployments in the 3D video streaming services.

Multiple description coding and path diversity is known to improve the perceptual quality of the received video. This paper considers a machine learning based technique to predict the perceived video quality, in terms of Mean Opinion Score for MDC Stereoscopic video. The perceived experience is expressed as a function of the three-dimensional video representation (color-plus-depth and left-right), the path diversity characterized by asymmetric packet losses between two available paths and the interpolation using a single description delivered over a reliable link as opposed to using multiple unreliable delivered descriptions.

This paper presents a stereo client within an end-to-end video communication ecosystem. The key characteristics of the stereo 3D Client are the following: “Open SVC Decoder” extensions to handle and parse on-the-fly RTP flows for both streams (Left-Right View), dynamic jitter buffer management, 3D Synchronization between the two views and Error concealment. QoE measurements have been carried out to evaluate the robustness of the Client within an end-to-end video communication platform. It is shown that stereo video quality is substantially higher when using the error concealment techniques.

Scalable video enables flexible video delivery over IP networks. The adaptation of the video content to the network configuration and the networking conditions is the great advantage of scalable video. In this research work, on-demand H.264 SVC video delivery over wireless multi-hop single-frequency networks is studied. The main objective is to achieve the highest levels of video quality possible while ensuring fairness among competing video flows over a wireless network. With the main barrier being interference, each video receiver has to decide from which peer it will receive a specific layer. The problem of peer selection and layer allocation is formed as a Mixed Integer Linear Program (MILP). A relaxation as a Non-Linear Program (NLP) is also provided. Simulation results based on real input video sequences verify the validity of the theoretical approach.

VoIP traffic over Mobile Ad-Hoc Networks (MANETs) faces many interacting challenges such as the mobility of the nodes that leads to continuous link breakages, the restricted bandwidth availability, the different characteristics of the existing routing protocols, the used voice codecs, the number of the mobile nodes and the background traffic. These factors introduce variability in the latency and the packet loss rate, which in turn severely affects the voice quality that is closely related to the voice codecs used by the VoIP network. This paper examines the effects of these factors to the quality of the VoIP traffic over a MANET via extensive simulations. It also presents ways to improve the performance, based either on the routing layer or on the application layer with the aggregation of multiple voice payloads to a single packet. This aggregation significantly improves the performance of the G.711 codec, while the impact to the other codecs is negligible.

This paper presents a novel cross-layer scheme for video transmission of streams with heterogeneous characteristics over LTE-based wireless systems. The proposed cross-layer scheme is organized by taking into account parameters from the Application layer (I-frames and P-frames), MAC Layer (scheduling packets according to their importance) and Physical Layer (linear precoding). All these parameters are considered within a novel resource allocation algorithm, which guarantees a weighted rate balancing across users, which is proportional to the bit rate of each video stream. Simulation results show that the proposed cross layer scheme can efficiently support video delivery both in terms of individual video quality and system overall performance.

The 11 papers in this special issue cover a range of topics and can be logically organized in three groups, focusing on QoE-aware media protection, QoE assessment and modelling, and multi-user-QoE management.

Real-time rate control techniques are necessary to applications such as video streaming in order to adapt video sending rate to network conditions so that the perceived video quality is optimised. In this paper two realtime rate control schemes, are presented. The first one uses the currently available bandwidth in order to perform rate control by selecting the optimum QP (Quantization Parameter). The second rate control scheme is based on measurements of the current packet loss in order to select the optimum QP parameter. Through extensive simulations on a test-bed environment both schemes are studied under diverse network conditions and are compared against the commonly used Rate-Distortion optimization scheme, which is embedded on a video encoder.

In this paper, a novel cross-layer scheme is presented for video transmission over LTE-based wireless systems. The proposed cross-layer scheme takes into account parameters from the application layer (I-based versus P-based packets), MAC Layer (Scheduling packets according to their importance) and Physical Layer (Linear Precoding). All these parameters are considered within a novel resource allocation algorithm with transmission rate constraints suitable for video applications. Simulations results have shown that the proposed cross-layer scheme performs better in terms of system throughput and perceived video quality against similar cross-layer schemes.

This paper presents a novel chunk-based resource allocation scheme for MIMO-OFDMA multiuser downlink channel. In chunk-based resource allocation, a number of contiguous subcarriers of each OFDM symbol are considered as a chunk and resource allocation is performed on chunk-by-chunk basis. Herein, an optimization problem is formulated that aims to maximize system sum rate under an average power constraint per chunk when Zero Forcing Beamforming (ZFB) is used for inter-user interference elimination within chunks. Under this framework, a low-complexity resource allocation algorithm is presented that exploits frequency and space correlation of wireless channels and jointly solves the problems of chunk allocation and power allocation. Simulation results show that the proposed algorithm performs closely to the optimal solution of the examined problem.

This paper proposes a dynamic trust establishment protocol that enables the nodes of an ad hoc network to establish security associations among each other in a distributed and peer-to-peer manner. The basis of the protocol is a node-to-node security handshake using a network-wide key that every node is preconfigured with. This way a security association is established between the involved nodes. The information regarding such an association is propagated to the rest of the trusted nodes, resulting in the formation of a secure network overlay. The protocol is dynamic in the sense that the nodes keying material is periodically renewed by a set of leader nodes in order to enhance the system security. Although generic, our protocol is best suited to emergency ad hoc networks, where the aforementioned assumptions about the node preconfiguration and the reliability of the leader nodes are applicable. The proposed protocol extends previous work on authority-based trust establishment schemes by using a renewal process of the nodes' keying material and by being independent of the underlying routing protocol and the nodes' communication capabilities. Simulation results show that the performance of the protocol depends directly on the network connectivity, the number of leader nodes and the node mobility level.

In this work we present an obstacle-aware human mobility model for ad hoc networks. Typical examples where the nodes of mobile ad hoc networks are human-operated are natural or man-made disasters, military activities or healthcare services. In these scenarios, obstacles are an integral part of the areas where such networks are deployed in order to facilitate communication among the firemen, policemen, medics, soldiers, etc. In the proposed mobility model, the nodes of the network move around the obstacles in a natural and realistic way. A recursive procedure is followed by each node according to which every time an obstacle is encountered between the node's current position and the final destination point, the node moves to the obstacle's vertex that is closest to the destination. This process is repeated until the destination is reached. The obstacles are also taken into account in modeling the signal propagation. When a packet is transmitted through an obstacle, the power at which it is received is attenuated by a certain value representing the physical layer phenomena suffered by the signal. The model is implemented as an add-on module in Network Simulator NS-2. A thorough simulation study conducted highlights the differences of the proposed model with other mobility models, by investigating the properties of the resulting network topologies and their impact on network performance.

The paper presents the future trends in network evolution and multimedia services provision and delivery. In the context of Next Generation Networks vision, Mobile and Fixed access networks converge over the IP Multimedia Subsystem (IMS) platform that will control the delivery of user-centric secure broadband quad-play services. The IMS has been well defined for the mobile world; however its integration with fixed line networks requires extended functionalities in the architecture, application and security planes. This study is concerned with the research efforts for extending IMS in order to become the common control architecture that will support the convergence of heterogeneous networks and the integration of quad-play services. Furthermore, the most up-to-date open source code IMS platforms and testing scenarios are presented.

This paper describes a mechanism of forwarding secure state information associated to communication sessions, between middleboxes belonging to different Radio Access Networks (RANs). The transfer of state information among RANs could support service integrity and continuity by maintaining a mobile user's multimedia sessions which may otherwise be dropped and also minimize security vulnerabilities. The paper demonstrates how the context transfer protocol could be employed for this purpose to forward certain security information from the old to the new middlebox to support multimedia session maintenance during mobility and also at the same time notify the previous middlebox to close unnecessary open ports for improved security and resolve vulnerability. A number of test scenarios are used to demonstrate how middleboxes could intervene with multimedia sessions during mobility and show how context transfer can provide a solution for improving the performance in the multimedia session re-establishment as well as enhancing middlebox security.

Internet telephony like any other Internet service suffers from security flaws caused by various implementation errors (e.g. in end-users terminals, protocols, operating systems, hardware, etc). These implementation problems usually lead VoIP subsystems (e.g. SIP servers) to various unstable operations whenever trying to process a message not conforming to the underlying standards. As Internet telephony becomes more and more popular, attackers will attempt to exhaustively "test" implementations' robustness, transmitting various types of malformed messages to them. Since it is almost infeasible to avoid or predict every potential error caused during the developing process of these subsystems, it is necessary to specify an appropriate and robust, from the security point of view, framework that will facilitate the successful detection and handling of any kind of malformed messages aiming to destruct the provided service. In this paper, we adequately present malformed message attacks against SIP network servers and/or SIP end-user terminals and we propose a new detection "framework" of prototyped attacks' signatures that can assist the detection procedure and provide effective defence against this category of attacks

This paper presents the architecture and design of an enhanced VoIP gateway based on the H.323 protocol. The key characteristics of this gateway include: scalability by separating signaling from media transformation and use of media gateway control protocols such as MGCP/MEGACO for communication between the signaling part and the media gateway; lightweight property by using the transport protocol SCTP (stream control transmission protocol) to convey H.323 and MGCP/MEGACO signaling; QoS support through the control of the voice encoding of the VoIP connections. This control is accomplished by estimating the network load and enforces the voice connections to modify the encoding scheme according to the estimated network load.