Real-time Internet-of-Things (IoT) analytics in smart cities demand millisecond-scale responsiveness and energy efficiency requirements that challenge cloud-only deployments, which struggle with latency and bandwidth constraints. In brief, this paper presents a unified three-layer edge-fog decision stack comprising (i) queueing-theoretic performance modeling, (ii) NSGA-II Pareto-based offline trade-off mapping, (iii) a training-free Genetic-Algorithm Offloader for online adaptation, (iv) formal tail-latency guarantees, and (v) comprehensive validation through both simulation and a Raspberry Pi/Jetson prototype. We introduce a novel three-layer decision framework that couples a queueing-theoretic edge-fog pipeline with offline Pareto optimization and online adaptation. Our key contributions are: (1) an analytical model, validated through simulation, demonstrating edge latency of 10 ± 2 ms and fog latency of 40 ± 10 ms (processing time only, excluding network RTT); (2) NSGA-II-based offline optimization yielding Pareto-optimal configurations that reduce energy and cost by 32.6% while adding only 0.7 ms of latency; (3) A novel training-free Genetic-Algorithm Offloader (GAO) for real-time adaptation, eliminating the costly training phase required by DRL; (4) Formal tail-latency guarantees via Kingman’s bounds that ensure provable SLA compliance; and (5) a comprehensive evaluation against 15 baselines, including state-of-the-art DRL and hybrid approaches. In simulation, GAO achieves 2.03 ms 99th-percentile latency (4.7× better than the strongest DRL baseline, GNN-SAC, and 8× better than vanilla DQN), 100% SLA compliance, and 25% energy reduction. On a Raspberry Pi 5 and NVIDIA Jetson Orin Nano hardware prototype, GAO achieves 18.2 ms p99 latency and outperforms Azure IoT Edge, KubeEdge, and Eclipse ioFog by over 30% on latency and 22% on energy.

Efficient Medium Access Control (MAC) protocols are crucial for time-sensitive transmissions of safety and non-safety messages. The IEEE 802.11p standard requires enhancements for varying channel conditions, including error-prone environments. This paper proposes HVMAC, a hybrid VANET MAC protocol that enhances Quality of Service (QoS) for time-sensitive data traffic applications. It categorizes service channels into contention and scheduled channels. The HVMAC protocol is modeled with Markov chains and evaluated against IEEE 802.11p on offered load, average delay, throughput, reliability, and energy consumption (upto 90%). To improve HVMAC security during safety messages (SM) and service advertisement messages (SAM) transmission, a Post-Quantum Weighted Anonymous Authentication (PQWAA) is proposed. The weighted certificate authority (WCA) assigns priority weights to vehicles, for effective traffic management and resource distribution. PQWAA ensures secure authentication and message integrity using quantum-resistant cryptography and pseudonym-based key derivation. Integrating PQWAA with HVMAC ensures energy efficiency and secure communication for both safety and non-safety applications, offering a comprehensive solution for modern VANET environments. The proposed protocol is analyzed with existing techniques based on the packet delivery ratio, throughput, and average packet delay, values of 99%, (45-50)Mbps, and 50 ms. Also, the HVMAC protocol shows 81.23% lower channel collision than TDMA-MAC.

ABSTRACT The increasing demand for ultra‐reliable, low‐latency, and high‐throughput connectivity in dense urban environments presents significant challenges for next‐generation 6G networks. Traditional cellular networks, with their fixed cell boundaries and centralized base station control, are inadequate to meet the dynamic needs of such environments. A promising solution is the cell‐free network architecture, where a distributed set of access points (APs) jointly serve users without fixed cell boundaries. However, efficient access point selection and accurate user localization are crucial to achieving high performance in such networks. This paper presents a decentralized approach using Belief‐Desire‐Intention eXtended (BDIx) agents for dynamic AP selection and localization within a cluster‐based cell‐free 6G communications network. Various clustering algorithms (K‐means, DBSCAN, self‐organizing maps, MeanShift, ClusterGAN, and Autoencoders) are evaluated for their ability to optimize network throughput, energy efficiency, and spectral utilization. A hybrid localization framework, such as centroid‐based, differential circles, and multilateration methods, is employed to achieve accurate user positioning. The results demonstrate that machine learning‐based clustering methods, notably Gaussian mixture model (GMM), self‐organizing map (SOM), and ClusterGAN, offer significant improvements in throughput (up to 46.3%) and power reduction (up to 32.8%) over traditional methods. Regarding localization, deep learning models such as MLP, CNN, and TCN outperform deterministic methods, achieving sub‐meter accuracy with minimal errors (MeanDist < 1 m, > 0.999). Overall, the proposed solution enhances system scalability, energy efficiency, and positioning accuracy, establishing a promising foundation for future 6G networks. In our reference implementation, we instantiate the pipeline with a GMM for AP/UE clustering and a multilayer perceptron (MLP) regressor for localization.

Data Processing Units (DPUs) are becoming increasingly popular, especially for use in conjunction with Warehouse-Scale Computers (WSCs) due to their ability to handle networking functions and data-centric workloads. Cost-performance, energy efficiency, network 1/0, and batch processing workloads are important design factors for WSCs. Recent developments in AI and the never-ending increase in demand for data processing, cloud computing, and HPC set the optimisation of all those design factors as a high priority. DPUs can be utilised to achieve significant improvements in all those areas. This includes in-line network processing and upcoming enhanced security paradigms such as post-quantum cryptography (PQC) for quantum re-silient communications or software-defined perimeters (SDP) for confidential computing implementations. Being P4-enabled and dRMT-based, DPUs allow for the reconfigurability of the network traffic without the need to change the hardware. However, the network performance on such devices is only sometimes deter-ministic since the actual traffic and the rules, both of which have to do with packet processing, are not known during compile time. In this paper, we envision how the network performance on DPUs can be accelerated. We describe the challenges that negatively impact the bandwidth and latency: the complex steering pipeline and the massive runtime needed to optimise. These challenges arise from the lack of information during compile time that is only known during runtime. Thus, we envision optimising during runtime by leveraging DPUs' reconfigurability on the network 110. For this, we discuss the significant factors that must be considered to accelerate the network performance on such devices and we propose a solution for them.

Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized.

The limitations of the networks of Internet of Things (IoTs) and Wireless Sensor Networks (WSNs) in terms of computational power, memory and connectivity give rise to several issues that need to be tackled, mostly dynamically, to achieve their tasks. Definitively, a critical factor for the proper operation of these networks is to maintain the connectivity between the nodes, especially in a wireless mesh setting, where communication is performed in hop-by-hop fashion. A method that gains significant research interest for tackling the aforementioned issues is the employment of mobile nodes or as they are frequently called, mobile elements. In this work, we propose a scheme that utilizes carriers to transport mobile nodes to the required points in the network. We provide both a high level description of the concept and also a detailed algorithmic solution. The proposed solution is evaluated through a case study, where hot-spots are created due to congestion in the network and mobile elements are being used to resolve the problem. The experimental results demonstrate that the proposed algorithm can effectively restore the network operation. We believe that our proposed approach can be used to solve similar types of problems.

As Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) applications evolve, the need for robust protocols, capable to guarantee extended lifetime and high throughput, increases. Mobility of devices either in terms of mobile nodes or mobile sinks is a promising solution that can assist, for example, topology control and congestion mitigation. Such factors significantly contribute to the extension of the lifetime and the throughput of wireless ad-hoc networks. In this work we review and classify algorithms that introduce the characteristic of mobility in WSNs. We consider WSNs to be both a subset and a predecessor to IoT, and thus, consider that existing mobility solutions can be adapted for use in IoT. Finally, open problems and future directions are discussed that include wireless power transfer, network fault detection, and real-world/testbed evaluation of algorithms.

Fog computing-based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial Internet of Things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this article, a novel architectural design of an SDN-based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem to optimize the physical resource block allocation, the assignment of user equipments, and radio unit, and the downlink transmit beamforming, by considering imperfect channel state information. To solve the ntractable MINLP, we exploit the successive convex approximation approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units, by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

Security breaches are an imminent threat in the Internet of Things (IoT) as smart diversified devices are now interconnected to serve a specific application. General security guidelines may fail to prevent attacks from penetrating the network and as a result an attack may immerse in the network causing irreversible damage. Detecting the attack at an early stage can minimize the effects of the attack. Using the Support Vector Machine (SVM) supervised machine learning technique in Intrusion Detection Systems (IDS) has shown that routing layer attacks can be detected by monitoring node and network activity. The current work extends on the topic of SVM detection models, by introducing Decentralized Dedicated IDS agents placed at key positions within the network to monitor it and raise an alarm when a malicious node is within its vicinity. The detectors were trained and evaluated with three main attacks and variations of them and achieve high classification and accuracy rates.

The operation of Internet of Things (IoT) networks and Wireless Sensor Networks (WSN) is often disrupted by a number of problems, such as path disconnections, network segmentations, node faults, security attacks, etc. A method that gains momentum in resolving some of those issues is the use of mobile nodes or nodes deployed by mobile robots. The use of mobile elements essentially increases the resources and the capacity of the network. In this work, we propose a scheme that utilizes mobile nodes for the creation of alternative paths from source to sink by also accounting the energy levels of the nodes as a contributing factor regarding the creation of alternative paths. We offer both a high level description of the concept and also a detailed algorithmic solution. The evaluation of the solution was performed in a case study of resolving congestion in the network. Results have shown that the proposed algorithm can significantly contribute to the alleviation of the problem of congestion in IoT and WSNs and can easily be used for other types of network problems.

Software-defined networking (SDN)-based virtual Fog computing radio access network (Fog-RAN) architecture is known as a potential solution to cope with massive traffic loads in 5G and beyond networks. Dynamic radio and computational resource allocation is needed to handle the fluctuating traffic loads, aiming to reduce power consumption and enhancing user satisfaction. In this paper, we propose a dynamic resource allocation for a SDN-based virtual Fog-RAN. We formulate a mixed-integer non-linear problem to minimize the network power consumption by optimizing the user equipment (UE) - radio units (RUs) association, the physical resource block (PRB) allocation, and the RU power allocation, according to the real-time traffic loads. Then, exploiting the obtained results in the previous problem, we formulate a multiple knapsack problem to optimize the assignment between the active RUs and the virtual baseband units (vBBUs). Finally, we perform a simulation study to analyse the impact of the proposed resource allocation on network power consumption, vBBUs resources savings, and user satisfaction so to validate the performance gains achieved.

More and more Internet of Things (IoT) devices are deployed around the world, due to the convenience and extra functionality they enable. This growth, while great for the industry as a whole, has come at a price with respect to ensuring and maintaining security and privacy. Having that in mind, one of the most common solutions to the IoT security problem is to update the devices frequently. Recently, LoRa Alliance has released a new specification (FUOTA) on how to perform firmware updates using LoRa technology. In this paper, we propose a blockchain-based framework to securely update the firmware of the IoT devices using the LoRa communication protocol. As a first step, we perform an evaluation of the firmware update procedure using different network sizes and different firmware sizes. The evaluation shows that there is a need to use more gateways that will collaborate to increase the reliability and the performance of the firmware update process.

In this paper we are experimenting with an intrusion detection system (IDS) for IoT. The IDS under consideration is employing a machine learning techniques for detecting novel at-tacks in the IoT network. We examine detection based on Support Vector Machines (SVM). The detection models were trained and evaluated for Selective Forward and Blackhole network routing layer attacks using IoT-testbed data and achieved up to 99.8% Accuracy rates and 100% Recall values.

In this work, we present an authentication mechanism based on Blockchain to provide a secure communication for wireless communications assisted UAV sensing system for maritime IoT critical applications, by deploying a private Blockchain network that is connected to a fusion center (FC) in the terrestrial area. The received packets would be validated by the FC, based on the stored IDs on the Blockchain, to avoid intrusion into the network. We further analyze the effect of Blockchain on the network performance in terms of delay and throughput to demonstrate the suitability and effectiveness of the proposed authentication mechanism.

Machine learning models have long be proposed to detect the presence of unauthorized activity within computer networks. They are used as anomaly detection techniques to detect abnormal behaviors within the network. We propose to use Support Vector Machine (SVM) learning anomaly detection model to detect abnormalities within the Internet of Things. SVM creates its normal profile hyperplane based on both benign and malicious local sensor activity. An important aspect of our work is the use of actual IoT network traffic with specific network layer attacks implemented by us. This is in contrast to other works creating supervised learning models, with generic datasets. The proposed detection model achieves up to 100% accuracy when evaluated with unknown data taken from the same network topology as it was trained and 81% accuracy when operating in an unknown topology.

Congestion control and avoidance in Wireless Sensor Networks (WSNs) is a subject that has attracted a lot of research attention in the last decade. Besides rate and resource control, the utilization of mobile nodes has also been suggested as a way to control congestion. In this work, we present a Mobile Congestion Control (MobileCC) algorithm with two variations, to assist existing congestion control algorithms in facing congestion in WSNs. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. Simulation results show that both variations can significantly contribute to the alleviation of congestion in WSNs.

A number of sensor network applications are envisioned to be applied to industry settings where the existence of mobile nodes (MN) is required. In critical applications, the realtime monitoring of a MN must always be available, something that requires the existence of a suitable mobility protocol to control the handoff procedure. In this paper, we use an industrial WSN setting to perform a comprehensive performance evaluation of different mobility handling solutions based on single-and multiple-metric options. The results show that Fuzzy Logic-based Mobility Controller (FLMC), the multiple-metric approach we used (based on Fuzzy Logic), performs better compared to any single metric-based approach under a varying set of conditions. More specifically, we demonstrate that the Fuzzy Logic -based approach can efficiently control the handoff triggering procedure and provide high reliability (low packet loss) under different mobility models, different radio propagation models, and different topologies.

Applying Wireless Sensor Networks to industry settings requests the dynamic reaction of mobile nodes. In critical applications, such as, e-health monitoring of workers, the existence of a suitable protocol to efficiently handle the hand-off procedure is essential for the real-time monitoring of mobile nodes. The main contribution of this paper is a comprehensive performance evaluation considering different single and multiple metric options. The results confirm that the implementation of a fuzzy method to control the triggering procedure outperforms solutions that are based only on single metric approaches.

In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node.We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96% - 100%.

Estimating the rate, with which the buffers of the nodes in a Wireless Sensor Network (WSN) fill-up, can contribute to the efficient operation of congestion control algorithms. In this paper we attempt, using a macroscopic fluid dynamic model, to estimate the rate with which the buffers of nodes fill-up, when congestion occurs in the network. This estimation can become a useful tool for the development of particular mechanisms, for congestion or overload control algorithms, that can improve their performance.

Mobility management is a crucial problem for wireless mobile communication, especially in industrial wireless sensor networks. Our mobility approach resides on the fact we need to adaptively combine globally available information in order to produce a successful handoff. In this paper, we propose an adaptive fuzzy logic mobility system to support the handoff procedure in industrial wireless sensors. The results clearly show that the proposed adaptive fuzzy logic mobility controller performs better compared to the non-adaptive fuzzy logic controller, in terms of packet loss, power consumption, and on-time triggering.

In this paper, we deal with mobility management for wireless sensor networks and we focus on the first phase of a handoff procedure which is concerned with the mechanics of measuring important parameters and initiating (triggering) the handoff decision process. We introduce a range of metrics that could potentially be used in triggering a handoff and we focus on two easy-to-find local values, namely the Received Signal Strength Indicator (RSSI) and the Local Link Loss. We investigate these metrics on their ability to provide correct triggers for the decision process, taking into account different threshold values, averaging methods, averaging windows, and hysteresis margins. We evaluate the performance of the different metrics and methods using not only the resulting end-to-end packet loss, the handoff triggers, the eventual handoffs performed, and the handoff success rate, but also using the new concept of on-time triggering. Our evaluation provides a unique insight into the mechanics of the handoff trigger and decision phases.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Placement of nodes in Wireless Sensor Network (WSNs) is often massive and random. Topology control algorithms focus in lowering the initial network topology, by reducing active nodes and links, thus saving resources and increasing network lifetime. Currently, most algorithms and schemes in WSNs, construct shared, core-based trees, with the sink as a root for this purpose. In this paper, we study whether trees that initiate from each source, called source-based trees, can assist in this purpose and provide, under specific circumstances, an efficient topology control solution. Simulation results show that source-based trees can provide proper topology and routing control solution in WSNs, when are employed along with "resource", congestion control algorithms in WSNs.

Mobility management in Wireless Sensor Networks is considered to be of an up most importance for today's critical applications. In this paper, we present a soft mobility management solution where the mobility procedures are supported by fuzzy logic techniques. Our solution was designed and implemented to support the movement of a mobile worker inside an oil refinery area. The results show that the proposed system provides high reliability and control over the handover actions.

Energy constraints are undoubtedly a major concern in the creation of algorithms for WSNs. In this paper we examine the issue of extending a wireless sensor network's lifetime, not with the aim of providing a new algorithm, or comparing existing energy-reduction related methods, but with the aim of understanding if this problem can be inherently addressed by other types of algorithms such as those designed for congestion control and avoidance. We claim and prove that Congestion Control Algorithms can also assist in the uniform energy utilization of a Wireless Sensor Network to some extent. A number of WSN congestion control algorithms base their operation on the creation of alternative paths from the source to sink, using the plethora of the network's unused nodes. The creation of alternative paths employs several nodes which are not in the initial shortest path(s) from the source to sink and assist in safely transmitting the observed data. The use of these nodes leads to a balanced energy consumption, avoiding the creation of “holes” in the network.

Recent critical application sectors of sensor networks like military, health care, and industry require the use of mobile sensor nodes, something that poses unique challenges in aspects like handoff delay, packet loss, and reliability. In this paper we propose a novel mobility model that handles those challenges effectively by providing on-time mobility detection and handoff triggering. In that way soft handoffs and controlled disconnections are assured. The proposed solution uses cross-layer information from the MAC and Network layers. Our solution was implemented and evaluated in an experimental testbed, in the context of the European FP7 GINSENG project.

Network Congestion is a highly undesirable situation for every type of network. Especially in low powered Wireless Sensor Networks (WSNs), congestion can be proven critical for the network's proper operation. In case of congestion in a WSN, the network is programmed to react, either by reducing the data rate of the sources or by creating multiple routing paths to the sink, thus avoiding the network's congested point. In this paper we perform a comparison of these two techniques, comparing one algorithm of each category. Results depict the advantages and disadvantages of each category.

In this paper, we present how the HTAP (Hierarchical Tree Alternative Path) algorithm can drastically assist in the uniform energy utilization of a wireless sensor network and the avoidance of energy holes. HTAP is an algorithm designed explicitly for congestion control and avoidance and its operation is based on the creation of alternative paths from the source to sink, using the plethora of network's unused nodes. The creation of alternative paths involves several nodes which are not in the initial shortest path form the source to the sink and assists in safely transmitting the observed data. The use of these nodes leads to a balanced energy consumption, avoiding the creation of holes in the network.

The goal of the GINSENG project is a performance-controlled sensor network suitable for applications with specific network performance requirements, such as plant automation and health monitoring. Any such sensor network must include a deterministic MAC protocol that can meet the application's targets in areas like delivery delays and reliability. We present a description and preliminary evaluation results of GinMAC, designed to be such a MAC protocol. Also included are details of a deployment to the GALP oil refinery in Sines, Portugal.

MBMS bearer services can be provided in each cell by either point-to-point or point-to-multipoint transmission mode but for efficiency a decision has to be made between the two approaches. Initially, the fundamental criterion used to make this decision was the number of MBMS users in a cell (UE counting). In this paper we extend our previous work, which proposed the power transmitted by the base station (BS) as the channel switching criterion (power counting). We evaluate our approach and illustrate its capability to dynamically adapt to continuously varying cell conditions by selecting the transmission mode in a more efficient way, preserving in this way the networkpsilas resources. At the same time we demonstrate the limitations of the conventional approach, in terms of radio resource efficiency. We show that by having the power used by the BS as the limiting factor we achieve optimized capacity during the transmission of MBMS bearer services in UTRAN.

As currently specified by 3GPP, MBMS (multimedia broadcast multicast service) content can be provided in a cell by either point-to-point (P-t-P) or point-to-multipoint (P-t-M) transmission mode, but not both at the same time. In earlier work we highlighted the deficiencies of the current approach and proposed the concept of the ldquodual transmission mode cellrdquo, in which P-t-P (DCH) and P-t-M (FACH) transmissions can coexist and allowed to dynamically adapt through time, to address them. In this paper we further analyze the deficiencies of the current 3GPP approach and enhance our previous work to efficiently address the problem in high mobility scenarios as well. The proposed algorithm takes into consideration both the distribution and the movement of the MBMS userspsila within the cell (using the CPICH Ec/No signal strength as a reference) and dynamically decides which MBMS users should be served using FACH and which should be served using DCH. Simulation results show significant transmission power savings, considerable reduction on the aggregated signaling and processing effort introduced, while QoS requirements are still fulfilled.

It is beyond any doubt that the unpredictable nature of the today's Internet has tremendous impact on the transmission of video streams. With respect to the realtime nature of video streaming, unpredictable bandwidth, end-to-end delay and packet loss, are all problems that can affect video streaming over the Internet. The problem is worsened when we consider wireless and mobile users. In this paper we adopt Network Adaptation Techniques applied together with Content Adaptation Techniques to achieve graceful performance degradation as network load increases. We present a novel feedback mechanism and a fuzzy oriented decision algorithm which collectively provide video adaptation to network parameters. Our preliminary performance evaluations indicate that the fuzzy-based algorithm can finely sense the available bandwidth of the network path and adapt the video transmission rate accordingly, maintaining acceptable video quality.

The transfer of information over the wireless networks is emerging as a promising business model. In addition, new applications require the transfer of video content in real time. However, the unpredictable nature of wireless and mobile networks in terms of bandwidth, end-to-end delay and packet loss has tremendous impact on the transmission of video streams. In this paper, we adopt Network Adaptation Techniques applied together with Content Adaptation Techniques to achieve graceful performance degradation when network load increases and network conditions deteriorate. We present a new feedback mechanism that provides video adaptation to network parameters, working together with a fuzzy-based decision algorithm. Our preliminary performance evaluations indicate that our algorithm can finely adapt the video stream bit rate to the available bandwidth while providing fairness as well as high and stable objective quality of service.

In this paper we deal with the design issues that need to be considered when creating a mobility protocol for MPLS. We explain what decisions need to made when the connection-oriented static MPLS architecture is to be combined with the connectionless and dynamic mobile IP protocol. In addition to the outlining of the design considerations, we describe a protocol that is based on the interworking of MPLS with Hierarchical Mobile IPv6 (HMIPv6) in an "overlay" fashion. The combined protocol is explained with the use of detailed signaling diagrams.

It is shown that enhanced UMTS is an affordable solution for providing the required network quality and to reduce infrastructure investments in offices scenarios. System capacity results are obtained by using a system level simulator which considers traffic characterisation parameters and services usage in detail, among other. Results for the most profitable cell radius are obtained via an optimisation procedure based in economic aspects. A higher number of pico cells (with a smaller radius, around 30-32 m) can be installed in the future, when costs of deploying and maintaining the network decreases, allowing for supporting higher system capacity, and reducing prices. Our approach is based in a detailed services analysis, which represents a worst case situation relatively to the total services approach, because the later does not discriminate results for the different traffic classes. The impact of call blocking, handover failure, end-to-end delay, and delay variation are taken into account

In this paper we present a soft handover algorithm for wide band CDMA based E-UMTS networks and the associated power control algorithm. The handover and power control algorithms have been developed and implemented in the framework of a system level E-UMTS simulator developed under the 1ST SEACORN project. The paper provides a background on handover design techniques and explains the handover implementation. The soft handover algorithm has been modeled using the guidelines provided in the standards and the literature for W-CDMA intra-frequency handovers. The combination of established handover techniques with enhanced UMTS is a novelty feature of this paper and provides a reference for further performance comparison

This paper presents a new system level simulator that has been developed to evaluate radio resource management (RRM) techniques in UMTS (Universal Mobile Telecommunication Systems) and enhanced-UMTS networks. The simulator includes new implementations for the user equipment, node B, radio network controller nodes and can evaluate parameters associated with UMTS or enhanced-UMTS performance, related to the introduction of RRM functions. The RRM functions implemented deal with admission control, packet scheduling, handover control, load control, and power control. The evaluation of RRM mechanisms is supported by the provision of appropriate radio propagation, traffic generation, and mobility models.

This paper presents the integration of multiprotocol label switching with hierarchical mobile IPv6. The resulting micromobility-based MPLS (M-MPLS) is defined in two modes of operation: overlay and integrated. In an overlay framework MPLS and HMIP operate on their respective layers without having common processes, tables, or signaling. In an integrated framework, related functions are merged. The overall goal of an integrated framework is to facilitate efficient and reliable network operations while simultaneously optimizing network utilization and system performance.

This paper examines the current status of research work on mobile ATM and presents the authors' view on pending technical challenges that require attention and investigation. Mobile ATM is not restricted to the local area and it need not be ground based. This paper examines: (a) location management, (b) connection management, (c) handoff management, (d) routing, and (e) media access for two evolving architectures, namely: (a) wireless ad-hoc ATM networks, and (b) LEO-based satellite ATM networks.