In this paper, we develop a machine learning model to detect active eavesdroppers in a Massive Multiple Input Multiple Output (MIMO) system. Massive MIMO systems are naturally immune to passive eavesdroppers, but this is dramatically degraded by active eavesdroppers. We propose two machine learning-based schemes, i.e. a Support Vector Machine (SVM) based scheme and a Naive-Bayes (NB) based scheme, to classify and detect the presence of an active eavesdropper. Then, we apply a Deep Neural Network (DNN) for detecting the presence of an active eavesdropper. We first build structured datasets based on the Received Signal Strength (RSS) and then apply SVM classifiers, NB classifiers, and DNN to those structured datasets. We built a machine learning model based on a realistic scenario where the Channel State Information (CSI) of the channels (legitimate users and eavesdroppers) is unknown. We exploit the massive MIMO technique features to improve the performance of the detection models. The work presented here provides insights into the design of DNN and new machine learning-based secure transmission schemes in Massive MIMO.

The deployment of Internet of Things (IoT) systems spans a large variety of applications, each with unique requirements. Many of these applications relate to the management of various cyber–physical systems, including road and rail traffic, electricity, water, food/goods transportation and storage, smart building management, crime/safety management, underwater systems, etc. Within this context, a growing concern is the deployment of IoT systems in extreme environments which may occur either because of the nature of the application or due to external factors. Some prominent examples of the former are 1) the IoT deployments in hazardous environments such as management of chemical or nuclear plants, management of underwater oil/gas infrastructure, mining operations, etc.; 2) IoT systems deployed specifically to manage accidents and disasters; and 3) IoT systems deployed in arctic/antarctic regions where they routinely experience extreme levels of changes in terms of temperature, wind conditions, sunlight availability, compression, etc. Such IoT systems generally are designed to specifically operate in the challenging environment they must operate in, and thus may be expected to be rather robust. However, the IoT systems designed to manage the physical infrastructures such as those in urban settings may also need to handle unprecedented and unexpected stresses due to the worldwide phenomena of aging physical infrastructure, demand that far exceeds the designed capacity, and increasingly extreme operating conditions due to climate change. This special issue covers all such scenarios and thus represents a vast and rich area for innovations.

In this paper we present and evaluate the performance of a routing and link scheduling algorithm for millimeter wave backhaul networks. The proposed algorithm models the end user behavior as being selfish, i.e., it considers users always aiming to maximize their individual utility, rather than the global optimization objective. In order to forward packets through the backhaul network, the Shapley value method is applied, which is shown to induce solutions with reduced latency. The performance of the proposed algorithm is evaluated in terms of the total delay as well as the price of anarchy, which represents the inefficiency of a scheduling policy when users are allowed to adapt their rates in a selfish manner and reach an equilibrium. A relaxed version of the problem is also presented, which provides a lower bound on the value of the optimal solution. According to simulation results, the system employing the proposed algorithm outperforms in terms of delay and price of anarchy a system considering a First-In-First-Out packet forwarding policy, as well as a system employing local search global optimization, under which users aim at optimizing the overall delay in the network.

Due to ever-increasing data and resource-hungry applications, the need for new spectrum by mobile networks keeps increasing. Unlicensed spectrum is still expected to play a crucial part in meeting the capacity demand for future mobile networks. But if this will be a reality, fair coexistence attained via practical and efficient channel access procedures would be necessary. In designing such channel access schemes, awareness of the number of nodes contending for the channel resource will be required. This paper investigates a node number estimation approach using channel idle time and analysed via machine learning (ML) techniques. When multiple nodes access the same unlicensed channel, varying idle times can be associated with a statistical distribution. In this paper, a statistical distribution of the idle-time slots over the channel is used to characterise and analyse the channel contention based on the number of nodes. Three ML model-based approaches are evaluated and the results confirm the proposed solution’s viability but also reveal the best-performing ML technique of the three, for the task of node number estimations.

Safety-critical vehicular applications require significant computation and communication resources and have strict performance requirements. Therefore, individual bandwidth reservation schemes are used to support such processes. Such schemes allow vehicles to place a cost-efficient smart reservation request alongside providing guaranteed bandwidth resources. However, efficient reservation is difficult to achieve due to uncertainty in both; future reservation requirement (i.e., demand) and network operator (NO) bandwidth cost. In order to solve this problem, a Heuristic Greedy Update Smart Reservation algorithm (HG-USR) is proposed by formulating bandwidth reservation cost problem. The primary objective of this formulation is to minimize the total reservation cost in certain problem scenarios, such as exact-booking, under-booking, and over-booking over time as the vehicle moves through the driving path. Extensive numerical studies have been carried out with the help of our proposed model. The experimental outcomes show that vehicle can successfully minimize the total cost of bandwidth resources as compared to prediction-based bandwidth reservation cost and immediate reservation request based approaches.

The long term evolution (LTE) has already been commercially implemented for nearly a decade. Despite the fact that it is constantly being updated throughout new releases, the establishment of the fifth generation (5G) mobile networks has been started. In this research, we study the performance of our LTE/5G testbed using software defined radio (SDR) at the Technical University of Chemnitz (TUC) in Germany. The goal is to discover certain critical performance metrics and provide thoughtful considerations in the context of remote/autonomous driving and vehicle-to-everything (V2X) communication. Investigating the channel conditions of the testbed in terms of the link-budget and delay-spread, we evaluate and compare the latency and data-rate performance of the system. We present a review with a discussion of the limitations that must be carefully considered to meet the requirements in beyond-5G and future mobile networks technology.

Bandwidth is a valuable and scarce resource in mobile networks. Therefore, bandwidth reservation may become necessary to support time-sensitive and safety-critical networked vehicular applications such as autonomous driving. Such applications require individual and deterministic approaches for reservations. This is challenging as vehicles usually have insufficient information to reason about future driving paths as well as future network resources availability and costs. In particular, the optimal time for a vehicle to place a cost-efficient reservation request is crucial. If a reservation is conducted too early, the uncertainty in path prediction may become high resulting in frequent cancellations with high costs. If a reservation is requested too late, resources may no longer be available. In this paper, we study the optimal timing for a given vehicle to place a bandwidth reservation request for an upcoming trip. Our proposal is based on predicting bandwidth costs using well-selected temporal machine learning techniques while achieving high accuracy levels. The proposed reservation scheme relies on a corpus of real-world traffic data. The experimental results prove that the model can effectively learn to find an optimized timing for bandwidth reservation. In addition, our model may allow vehicles to save considerably costs compared to the baseline of an immediate reservation scheme.

In recent years, there has been a notable surge in the Internet of Things (IoT) applications. Increasingly, IoT devices are being attacked. Network intrusion detection is a tool to detect any presence of malicious activities in a network. Machine learning (ML) techniques are increasingly used for classifying network traffic. However, results from state-of-the-art studies have shown that training ML classifiers with imbalanced datasets affect their classification performance, resulting in net-work categories with fewer training instances getting classified wrongly. This study presents a Stack ensemble ML classifier for network intrusion detection in an IoT network using the Bot-IoT dataset for the classifier evaluation. According to preliminary results, the classifier showed lower metric scores for minority network categories. We applied Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance. Follow-up experiment results for the SMOTE-Stack outclassed Stack and other state-of-the-art classifiers.

Intrusion detection systems (IDS) protect networks by continuously monitoring data flow and taking immediate action when anomalies are detected. However, due to redundancy and significant network data correlation, classical IDS have shortcomings such as poor detection rates and high computational complexity. This paper proposes a novel feature selection and extraction technique (FI-PCA). Feature Importance (FI) and Principal Component Analysis (PCA) are used to preprocess the network dataset (PCA). FI identifies the most important features in the data, while PCA is used to reduce dimensionality and denoise the data. In order to detect anomalies, we employ three single classifiers: Decision Tree (DT), Naive Bayes and Logistic Regression. Preliminary results, however, show that these classifiers have achieved average classification metric scores. On this basis, we use the Stack Ensemble Learning Classifier (ELC) method of combining single classifiers to improve the classifier's performance further. Experimental results on varied feature dimensions of an IoT (Bot-IoT) dataset indicate that our proposed technique combined with the Stack ELC can maintain the same level of classification performance for reduced dataset features. A comparison of our result with state-of-the-art classifiers’ classification performance shows that our classifier is superior in terms of accuracy and detection rate. At the same time, a remarkable decrease is recorded for both training and test time.

In this paper, we evaluate and make a comparison of the channel estimation performance for three different frame structures of IEEE 802.11p, IEEE 802.11bd-draft and a unique-word (UW)-based physical layer (PHY). As in vehicle-to-everything communication the wireless channel conditions may vary significantly depending on the environment and vehicle velocity, severe fading in both time and frequency domains may occur. Through simulation results, we show that the UW-based PHY achieves an interference-free performance of channel estimation via a low complexity technique, whereas the 802.11bd would need to employ a high complexity approach in order to achieve a comparable estimation performance.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

The Fifth Generation (5G) mobile communications together with software defined networking (SDN) and network function virtualization (NFV) are expected to enable a wide range of vertical use-cases. Different vertical industries with diverse service streams and sets of requirements should leverage the advanced capabilities of 5G networks through a single infrastructure to support the desired Quality of Service/Experience (QoS/QoE). In this paper, we focus on the Transport vertical and we study four novel service categories, each one consisting of one or more related scenarios, within the framework of the 5G Health, Aquaculture and Transport (5G-HEART) 5G PPP Phase 3 project. The first pass analysis of the envisioned vehicular services and their underlying operation, combined with the mapping of the mostly high-level functional user requirements to quantitative network Key Performance Indicators (KPIs) via a thorough and concise methodology, is essential for future testing with real pilots. Furthermore, our work paves the way towards efficient network slicing by exploring the interrelations between the identified KPIs and the respective target values that must be simultaneously satisfied over the same physical network infrastructure, in the context of the three 5G generic services.

Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the airinterface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.

There has been increasing interest in deploying Internet of Things (IoT) devices to study human behavior in locations such as homes and offices. Such devices can be deployed in a laboratory or “in the wild” in natural environments. The latter allows one to collect behavioral data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants’ daily routines, which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this article, we present MakeSense, an IoT testbed that enables real-world experimentation for large-scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed: one in homes and one in offices.

In this paper we present a co-primary spectrum sharing algorithm for the Quality of Service (QoS) enhancement of uplink Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems. We consider the limitations that are resulting from the fact that each user can only be provided with only contiguous sets of resource blocks (following the constraints of the localized SC-FDMA physical layer), and the effect of the limited, or even lack of, knowledge of each user's buffer status and packet delays in the uplink. The sharing of available resources is based on the operator spectrum access priority, an estimation of the packet delays in the uplink direction, the average delay and data rate of earlier allocations, and the power per resource block. Simulation results show that the proposed algorithm considerably improves the performance in terms of packet loss rate, goodput, and fairness.

This paper proposes a novel spectrum utility (SU) metric that assesses the efficiency of spectrum usage by a set of heterogeneous applications. Unlike the traditional spectrum efficiency (SE), the proposed metric does not blindly consider the achievable bit- rate, but captures the most relevant performance metrics for each of the considered applications. Specifically, it is formulated as an aggregated utility that combines the satisfaction level with respect to the various requirements with an innovative pricing model based on it to derive the total revenue generated for the spectrum owner. To get insight into the usefulness of the proposed metric, the proposed methodology is instantiated for an illustrative use case, where a mixture of delay- sensitive (i.e., interactive video) and -tolerant (i.e., file transfer) applications are established in dense indoor deployments. The obtained results reveal that the proposed SU significantly outperforms the legacy SE in assessing how efficiently a limited frequency spectrum is utilised from the perspective of the total revenue, particularly when the quality- of- experience (QoE) perceived during video sessions is degraded. This calls for a novel SU-aware ecosystem, where the spectrum sharing models, billing policies and resource allocation mechanisms (e.g., medium access control (MAC) and radio resource management (RRM)) are jointly revisited to maximise the overall SU.

As sensors are adopted in almost all fields of life, the Internet of Things (IoT) is triggering a massive influx of data. We need efficient and scalable methods to process this data to gain valuable insight and take timely action. Existing approaches which support both batch processing (suitable for analysis of large historical data sets) and event processing (suitable for realtime analysis) are complex. We propose the hut architecture, a simple but scalable architecture for ingesting and analyzing IoT data, which uses historical data analysis to provide context for real-time analysis. We implement our architecture using open source components optimized for Big Data applications and extend them, where needed. We demonstrate our solution on two real-world smart city use cases in transportation and energy management.

This paper proposes a novel context-aware user- driven strategy to efficiently exploit all available bands and licensing regimes in ultra-dense deployments without prior knowledge about each combination. It relies first on fuzzy logic to estimate the suitability of each radio access technology (RAT) to support the requirements of various applications. Then, a fuzzy multiple attribute decision making (MADM) approach is developed to combine these estimates with the heterogeneous context components to assess the in-context suitability. Based on this metric, a spectrum management strategy is proposed to support interactive video sessions for a set of Bronze and Gold subscriptions. The results reveal that the proposed approach always assigns Gold users to the well-regulated licensed band, while switches Bronze users between licensed and unlicensed bands depending on the operating conditions. This results in a significant improvement of the quality-of-experience (QoE) compared to a baseline that exploits only licensed bands. Then, a comparative study is conducted between the available options to exploit unlicensed bands, namely Offloading and Sharing. The results show that the best option strongly depends on the existing load on WLAN. Therefore, a combined approach is proposed to efficiently switch between both options, which achieves the best QoE for all considered loads.

User fairness and spectrum efficiency are conflicting objectives in cellular system optimization given that users share limited spectrum resources. Users at the cell edge are more likely to be unfairly treated due to their disadvantageous locations, where they experience high path losses and strong interferences if co-channel transmission exists. In this paper, a cell edge boundary is obtained and dynamically updated for each scheduling period to divide the users into cell centre and cell edge users, a tailored scheduling scheme is then performed for each group of users accordingly. Simulation results show that the boundary helps the schedulers achieve better balance between spectrum efficiency and fairness while providing the best energy efficiency, especially for smaller cell sizes (e.g. urban macro cells).

Standardization activities have already begun on 5G to support very high system requirements. A common consensus toward achieving these targets is to permit transmission over a wide spectrum range. The departure from a dedicated licensed-only spectrum regime to a shared spectrum model poses new challenges for next generation mobile communication systems. Cognitive Radio Technologies is an enabler for such opportunistic spectrum utilization. Coexistence will be inevitable in licensed shared and unlicensed bands and thus one key problem requiring solutions is efficient resource allocation in a dynamic spectrum access regime. Fairness is a performance metric in sharing of resources among wireless systems and research gaps in fair spectrum allocation problems need to be addressed. In this paper a time-based fairness analysis is conducted to study under a random user distribution scenario, the performance level with respect to throughput and channel occupancy time across all coexisting networks. Time-based fairness is achieved by selection of an optimal contention window for all contenders on the unlicensed channel. Early numerical results show that a time- based fairness approach to coexistence significantly improves overall system throughput and channel utilization.

With technologies developed in the Internet of Things, embedded devices can be built into every fabric of urban environments and connected to each other; and data continuously produced by these devices can be processed, integrated at different levels, and made available in standard formats through open services. The data, obviously f a form of `big data', is now seen as the most valuable asset in developing intelligent applications. As the sizes of the IoT data continue to grow, it becomes inefficient to transfer all the raw data to a centralised, cloud-based data centre and to perform efficient analytics even with the state-of-the-art big data processing technologies. To address the problem, this article demonstrates the idea of "distributed intelligence" for sensor data computing, which disperses intelligent computation to the much smaller while autonomous units, e.g., sensor network gateways, smart phones or edge clouds in order to reduce data sizes and to provide high quality data for data centres. As these autonomous units are usually in close proximity to data consumers, they also provide potential for reduced latency and improved quality of services. We present our research on designing methods and apparatus for distributed computing on sensor data, e.g., acquisition, discovery, and estimation, and provide a case study on urban air pollution monitoring and visualisation.

The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of complex event processing (CEP) for many Internet of Things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called adaptive moving window regression for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from intelligent transportation systems. Our proposed architecture is reliable and can be used across different fields in order to predict complex events.

Time series analysis aims to extract meaningful information from data that has been generated in sequence by a dynamic process. The modelling of the non-linear dynamics of a signal is often performed using a linear space with a similarity metric which is either linear or attempts to model the non-linearity of the data in the linear space. In this research, a different approach is taken where the non-linear dynamics of the time series are represented using a phase space. Training data is used to construct the phase space in which the data lies on or close to a lower-dimensional manifold. The basis of the non-linear manifold is derived using the kernel principal components derived using kernel principal component analysis where fewer components are retained in order to identify the lower-dimensional manifold. Data instances are projected onto the manifold, and those with a large distance between the original point and the projection are considered to be derived from a different underlying process. The proposed algorithm is able to perform time series classification on univariate and multivariate data . Evaluations on a large number of real-world data sets demonstrate the accuracy of the new algorithm and how it exceeds state-of-the-art performance.

Deriving trust relationships from real-world social interactions may contribute significant information towards social behaviour understanding. The level of trust among people constitutes an insightful parameter for describing the social context but also an important measure for security and privacy in pervasive systems. Current works for deriving trust relationships either consider only on-line social networks to create trust networks or focus on users' on-line social interactions. This article presents MobTrust, an opportunistic sensing system that derives and quantifies trust relationships among people through smartphones based on the detected real-world social interactions. A real-world social graph is derived from users' daily social interactions by also considering snapshots of their social relation. A hybrid model was developed to quantify users' trust relationships based on the extracted real-world social graph, the estimated social relations and the contextual information provided by the detected social interactions. As a proof of concept, a real-world evaluation of the system is performed. The evaluation shows that MobTrust can reliably derive and quantify trust relationships, having as such the potential of empowering a variety of real-world scenarios that can leverage such knowledge.

Localization is crucial for various applications, this includes resource coordination in small and ultra-small cells, as well as the whole range of Location Based Service (LBS). Multilateration is a localization technique that is based on distance measurements between multiple reference nodes and a target node. This paper introduces a multilateration localization approach that uses Singular Value Decomposition (SVD) for 3D indoor positioning. It also provides a mathematical multilateration formulation which considers the coordinates of the reference nodes and the relative distance between transmitting nodes. In practical deployments, the relative distance can be estimated using RSSI; we apply Kalman filtering to the RSSI measurements aiming to get a more accurate RSSI value. The approach is complemented by using two selection methods which help chosing the best nodes for multilateration computation. The paper concludes with a discussion of the experimental evaluation results obtained.

To improve the inter-working of future 5G systems with existing technologies, this paper proposes a novel context-aware user-driven framework for network selection in multi-RAT environments. It relies on fuzzy logic to cope with the lack of information usually associated with the terminal side and the intrinsic randomness of the radio environment. In particular, a fuzzy logic controller first estimates the out-of-context suitability of each RAT to support the QoS requirements of a set of heterogeneous applications. Then, a fuzzy multiple attribute decision making (MADM) methodology is developed to combine these estimates with the various components of the context (e.g., terminal capabilities, user preferences and operator policies) to derive the in-context suitability level of each RAT. Based on this novel metric, two spectrum selection (SS) and spectrum mobility (SM) functionalities are developed to select the best RAT in a given context. The proposed fuzzy MADM approach is validated in a dense small-cell environment to perform a context-aware offloading for a mixture of delay-sensitive and best-effort applications. The results reveal that the fuzzy logic component is able to efficiently track changes in the operating conditions of the different RATs, while the MADM component enables to implement an adjustable context-aware strategy. The proposed fuzzy MADM approach results in a significant improvement in achieving the target strategy, while maintaining an acceptable QoS level compared to a traditional offloading based on signal strength.

The advent of IoT has resulted in a trend towards more innovative and automated applications. In this regard, occupancy detection plays an important role in many smart building applications such as controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems. Most of the current techniques for detecting occupancy require multiple sensors fusion for attaining acceptable performance. These techniques come with an increased cost and incur extra expenses of installation and maintenance as well. All of these methods are intended to deal with only two states; when a user is present or absent and control the system accordingly. In this paper, we have proposed a non-intrusive approach to detect an occupancy state in a smart office using electricity consumption data and introduced a novel concept of third state as standby for dealing with situations when the user lefts his seat for small breaks. We demonstrated our approach using electricity data collected within our research centre and detected occupancy state with efficiency up to 94%. Furthermore, our solution does not require extra equipment or sensors to deploy for occupancy detection as smart energy meters are already being deployed in most of the smart buildings.

Quantifying social interactions requires accurate, reliable and real-time recognition, of both users' interpersonal distance and relative orientation. DARSIS is the outcome of our research towards fulfilling these requirements based upon a non-intrusive opportunistic mechanism that solely relies on sensors and communication capabilities of off-the-shelf smartphones. We developed a novel hierarchical classifier for interpersonal distance estimation, produced by a substantive training set of Bluetooth Received Signal Strength Indicator (RSSI) and a concrete feature selection process. The presented relative orientation estimation mechanism addresses problems associated with lack of facing direction information in prior works, independently of the wearing position. In addition, DARSIS introduces a collaborative sensing scheme which allows on-the-fly exchange of facing direction information between users and facilitates the interpersonal distance recognition process by sharing RSSI values among devices. We show that the proposed interpersonal distance estimation models outperform state-of-the-art solutions and achieve up to 93.52% accuracy while DARSIS as a coherent system detects accurately 81.40% of the interactions in a real-world environment.

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed algorithm performs resource allocation using information on the downlink packet delay, the average delay and data rate of past allocations, as well as the downlink users' buffer status in order to minimize packet segmentation. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, fairness, and average delay. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

In this paper, an optimal centralized approach to topology control (TC) is adopted where the network topology is established considering interference, k-connectivity and routing constraints. This optimization problem however involves link scheduling and power assignment under SINR constraint, which is an NP hard problem even for a small number of nodes. An exact solution beyond six nodes has not been found so far. Opting for heuristics rather than exact approach, the proposed algorithms in the literature, either cannot guarantee the quality of the solution, or approximate the interference (protocol interference model) rather than using realistic SINR models. Here, at first we present a novel formulation for the optimal solution and analyse its limits. We then propose a novel approximation algorithm using column generation (CG) together with knapsack transformation on the SINR constraint. Particle Swarm Optimization (PSO) is integrated into the CG, to provide robust initial feasible patterns. The results show that, CG-PSO with knapsack transformation increase the solvable instances three fold in terms of number of nodes, in comparison to the state-of-art approaches. The links are scheduled with less power and shorter scheduling lengths,while the proposed algorithm also reduces the computation time at lower penalty cost.

To guarantee the quality of service for different delay-sensitive sessions while enhancing resource utilization in home machine-to-machine network is a challenging issue. To solve this problem, we propose a Distributed Admission Control Algorithm with Soft Resource Allocation (DACA-SRA) for hybrid medium access control in home machine-to- machine networks. The proposed DACA-SRA can enable resource (i.e. transmission opportunities) while achieving guaranteed quality of service for real- time sessions in the contention-free period. This can let contention-free period to serve additional real-time sessions having stringent delay but loose throughput demands. Moreover, it can let the real-time sessions which fail in obtaining resources in the contention-free period to temporarily utilize resources in contention-access period through contention. These real-time sessions can be admitted later when resources in the contentionfree period become sufficient. Simulation results demonstrate that the DACA-SRA can improve network utilization (i.e. accommodating additional real-time sessions to reserve resources in the contention-free period) in practical cases where real-time sessions with diverse quality of service requirements co- exist.

Interference is one of the most limiting factors when trying to achieve high spectral efficiency in the deployment of heterogeneous networks (HNs). In this paper, the HN is modeled as a layer of closed-access LTE femtocells (FCs) overlaid upon an LTE radio access network. Within the context of dynamic learning games, this work proposes a novel heterogeneous multiobjective fully distributed strategy based on a reinforcement learning (RL) model (CODIPAS-HRL) for FC self-configuration/optimization. The self-organization capability enables the FCs to autonomously and opportunistically sense the radio environment using different learning strategies and tune their parameters accordingly, in order to operate under restrictions of avoiding interference to both network tiers and satisfy certain quality-of-service requirements. The proposed model reduces the learning cost associated with each learning strategy. We also study the convergence behavior under different learning rates and derive a new accuracy metric in order to provide comparisons between the different learning strategies. The simulation results show the convergence of the learning model to a solution concept based on satisfaction equilibrium, under the uncertainty of the HN environment. We show that intra/inter-tier interference can be significantly reduced, thus resulting in higher cell throughputs.

In this paper, we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the localized Single Carrier-Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. The optimal resource allocation is formulated as a discrete connected cake-cutting problem, where different agents are allocated consecutive subsequences of a sequence of indivisible items. This problem is NP-hard, therefore a suboptimal algorithm is introduced, which performs resource allocation using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, and fairness. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

The occurrence of short but recurrent opportunistic contacts between static infrastructure and mobile devices largely characterizes recent Internet of Things (IoT) applications for Smart Cities and Smart Buildings scenarios. In order to efficiently exploit such existing communication opportunities to access services, share and collect data, IoT applications cannot rely on standard discovery mechanisms that periodically probe the environment to discover resources. Discovery protocols resilient to different contact opportunities and able to optimize energy consumption when device contacts are not present are therefore required in order to avoid waste of energy especially in battery operated user devices. Additionally, such new discovery protocols need to optimize the time available for communication operations, while being able to adjust to application requirements, and balance energy consumption with respect to latency for contacts discovery. To this aim, we introduce CARD, a Context Aware Resource Discovery framework that leveraging Q-Learning techniques extends the functionalities of asynchronous neighbor discovery protocols, while being capable to reduce energy wastage and discovery latency. Simulation results show that CARD performs better than existing approaches and is resilient to a variety of real scenarios characterizing Smart Cities and Smart Building deployments.

Backup paths are usually pre-installed by network operators to protect against single link failures in backbone networks that use multi-protocol label switching. This paper introduces a new scheme called Green Backup Paths (GBP) that intelligently exploits these existing backup paths to perform energy-aware traffic engineering without adversely impacting the primary role of these backup paths of preventing traffic loss upon single link failures. This is in sharp contrast to most existing schemes that tackle energy efficiency and link failure protection separately, resulting in substantially high operational costs. GBP works in an online and distributed fashion, where each router periodically monitors its local traffic conditions and cooperatively determines how to reroute traffic so that the highest number of physical links can go to sleep for energy saving. Furthermore, our approach maintains quality-of-service by restricting the use of long backup paths for failure protection only, and therefore, GBP avoids substantially increased packet delays. GBP was evaluated on the point-of-presence representation of two publicly available network topologies, namely, GÉANT and Abilene, and their real traffic matrices. GBP was able to achieve significant energy saving gains, which are always within 15% of the theoretical upper bound.

Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword-based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalized discounted cumulative gain values.

In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.

As the number of Linked Data sets increases with more and more interconnections defined between them, querying a single data set is no longer enough for users who need data from mixed domains. The requirement to query data from different data sets motivates the research into federated queries. Network latency is one of the key factors which affect the performance of a federated query. The influence of network latency can be minimised by decreasing the number of remote requests, which is related to the number of joins. In this paper, we provide a mechanism for federated querying based on subject and sameAs grouping techniques. Exploiting the benefits of proposed grouping methods, the number of joins during a federated query has been reduced, thus improving the performance of the entire query. We have evaluated our approach against other existing approaches, using an existing benchmark suite and found that our approach performs better than comparable approaches for queries that are not highly selective.

The decentralised and ad hoc nature of femtocell deployments calls for distributed learning strategies to mitigate interference. We propose a distributed spectrum awareness scheme for femtocell networks, based on combined payoff and strategy reinforcement learning (RL) models. We present two different learning strategies, based on modifications to the Bush Mosteller (BM) RL and the Roth-Erev RL algorithms. The simulation results show the convergence behaviour of the learning strategies under a dynamic robust game. As compared to the Bush Mosteller (BM) RL, our modified BM (MBM) converges smoothly to a stable satisfactory solution. Moreover, the MBM significantly reduces the interference collision cost during the learning process. Both the MBM and the modified Roth-Erev (MRE) algorithms are stochastic-based learning strategies which require less computation than the gradient follower (GF) learning strategy and have the capability to escape from suboptimal solution.

The interference problem to Incumbent Users (IUs) of spectrum is herein addressed so as to prevent obtrusive cognitive transmissions at IU contours. In this paper, we present autonomous distributed power control algorithms for cognitive radios (CRs) in a Rayleigh fading environment with guarantees on IU protection whilst concurrently supporting an increased number of cognitive users. We further consider the user selection problem to maximise system capacity. Based on analysis and simulations we propose an efficient outage based distributed user removal algorithm that alleviates the interference environment for IUs and CRs alike with a substantial increase in number of supported CRs.

In this paper we present an analytical framework that aims to improve the energy efficiency of traffic offloading via Wireless Local Area Networks, taking into account the energy consumption for both data transmission and network discovery operations. More specifically, the network scanning period is optimized in order to minimize the energy consumption in a vehicular scenario where a user moves along a road covered by a long range cellular network and a number of randomly deployed Wireless Local Area Networks. The performance of the system that performs periodic network scanning with the optimal period is compared against a sub-optimal system that does not take into consideration the user and network context information when determining the network scanning period. According to performance evaluation results, the use of the optimal network scanning period achieves significant improvement in terms of energy consumption and network detection delay.

Collaborative spectrum sensing has attracted significant research attention in the last few years and is widely accepted as a viable approach to improve spectrum sensing reliability. Fusing data from multiple opportunistic users (OUs) in order to produce reliable sensing results implies a reliance on the OU to provide correct information. In the presence of malfunctioning or selfish users, performance of collaborative spectrum sensing deteriorates significantly. In this study, the authors propose mechanisms for the detection and suppression of such deleterious OUs (DOUs) for hard and soft decision fusion. More specifically, a credibility‐based mechanism for hard decision fusion using a hard decision combining beta reputation (HDC‐BR) system is introduced. The authors proposed method does not require knowledge of the total number of deleterious users in advance. In HDC‐BR, the fusion centre assigns and updates weights to each user's decisions based on an individual user credibility score, which is calculated using the BR system. The presence of DOUs in soft decision‐based collaborative spectrum sensing has even more adverse effects on system performance. The authors also propose a scheme for the case of soft decision fusion to detect and eliminate falsified user observations at the fusion centre using a modified Grubbs test; they refer to it as soft‐decision combining‐modified Grubbs (SDC‐MG). They compare the performance of the proposed methods with malicious user detection schemes proposed in the literature as well as with the case where no DOU suppression scheme is implemented, and conclude that SDC‐MG performs much better than HDC‐BR in a low signal‐to‐noise ratio regime.

Spectrum sensing, in particular, detecting the presence of licensed or incumbent users in licensed spectrum, is one of the pivotal tasks in cognitive radio network. In this study, the authors tackle the spectrum sensing problem by using statistical test theory and derive novel spectrum sensing approaches. The authors apply the classical Kolmogorov–Smirnov (KS) test under the assumption that the noise probability distribution is known. However, as in practice, the exact noise distribution is unknown, a sensing method for Gaussian noise with unknown noise power is proposed in this article and referred to t ‐sensing. The proposed sensing scheme is asymptotically robust and can be applied to non‐Gaussian noise distributions. A closed form equation determining the miss‐detection probability for the t ‐sensing is derived. The authors compare with the performance of our sensing algorithms with the energy detector and Anderson–Darling (AD) sensing methods proposed in literature. Simulation results show that the proposed sensing methods outperform both ED‐ and AD‐based sensing, especially for the case when the received signal‐to‐noise ratio is low.

This study investigates collaboration among neighbouring base stations for the downlink of multi‐carrier cellular networks, in the absence of a centralised control unit, which is a defining characteristic of future wireless networks. The authors propose a novel scheme for collaboration in resource allocation among a cluster of three neighbouring base stations. In this scheme, the results of an initial calculation are shared among neighbouring cells, then the scheduling decision is made locally and independently by each cell. This scheme does not require complex and iterative calculation. The information is exchanged only once during each scheduling epoch, which results in reduced overhead on the backhaul links. The scheme is implemented in a distributed manner. Simulation‐based performance analysis demonstrates effectiveness of the proposed collaborative resource allocation scheme among the neighbouring base stations for multi‐carrier systems, particularly for the users located near the cell edges.

The ability to reliably and autonomously identify unused frequency bands plays an extremely important role in cognitive radio networks. Relying on the spectrum sensing, ongoing licensed operation must not be compromised and the secondary spectrum usage efficiency should be maintained. Thus, it is critical to ensure that the confidence level of the estimated signal status satisfies the primary user's requirement, while keeping the delay and computational complexity to a minimum. This study provides a comprehensive comparison in terms of performance, reliability and complexity of standalone sensing schemes for various cognitive radio application areas. The authors first give some new results on reliability performance, and then evaluate the sensing time required to achieve the target performance. Finally, the authors compare the computational complexity of various sensing approaches by calculating the number of arithmetic operations required by each approach.

Energy consumption in ISP backbone networks has been rapidly increasing with the advent of increasingly bandwidth-hungry applications. Network resource optimization through sleeping reconfiguration and rate adaptation has been proposed for reducing energy consumption when the traffic demands are at their low levels. It has been observed that many operational backbone networks exhibit regular diurnal traffic patterns, which offers the opportunity to apply simple time-driven link sleeping reconfigurations for energy-saving purposes. In this work, an efficient optimization scheme called Time-driven Link Sleeping (TLS) is proposed for practical energy management which produces an optimized combination of the reduced network topology and its unified off-peak configuration duration in daily operations. Such a scheme significantly eases the operational complexity at the ISP side for energy saving, but without resorting to complicated online network adaptations. The GÉANT network and its real traffic matrices were used to evaluate the proposed TLS scheme. Simulation results show that up to 28.3% energy savings can be achieved during off-peak operation without network performance deterioration. In addition, considering the potential risk of traffic congestion caused by unexpected network failures based on the reduced topology during off-peak time, we further propose a robust TLS scheme with Single Link Failure Protection (TLS-SLFP) which aims to achieve an optimized trade-off between network robustness and energy efficiency performance.

Supporting quality of service (QoS) while fulfilling high efficiency of bandwidth utilization is challenging in wireless mesh networks. To deal with this issue, hybrid medium access control (MAC) protocols are effective candidates because they can achieve QoS support and better resource sharing at the same time. However, in multi-hop communication environments, hybrid MAC protocols suffer from interference and low bandwidth efficiency. To solve these problems, in this paper, we propose a distributed interference-aware admission control algorithm (DIACA) with soft resource allocation for hybrid MAC protocols suitable for IEEE 802.11 wireless mesh networks; a scheme for providing QoS improvement for real-time sessions (RTSNs) while enhancing the efficiency of bandwidth utilization. The proposed DIACA possesses a function for interference probing, making each node recognize their interfering counterparts. Further, through support of interference detection, concurrent transmissions can be achieved by letting non-interfering nodes transmit data simultaneously along a route which improves the efficiency of spatial reuse of bandwidth. In addition, the DIACA can implement soft resource allocation for RTSNs having delay requirements but loose (or low) throughput demands. By using soft resource allocation, a transmission opportunity can be shared by different RTSNs with low data rates and each of the RTSNs can obtain satisfactory QoS. Simulation results indicate that the proposed admission control algorithm can significantly enhance the bandwidth utilization of wireless channel and can improve QoS for RTSNs.

Backbone network energy efficiency has recently become a primary concern for Internet Service Providers and regulators. The common solutions for energy conservation in such an environment include sleep mode reconfigurations and rate adaptation at network devices when the traffic volume is low. It has been observed that many ISP networks exhibit regular traffic dynamicity patterns which can be exploited for practical time-driven link sleeping configurations. In this work, we propose a joint optimization algorithm to compute the reduced network topology and its actual configuration duration during daily operations. The main idea is first to intelligently remove network links using a greedy heuristic, without causing network congestion during off-peak time. Following that, a robust algorithm is applied to determine the window size of the configuration duration of the reduced topology, making sure that a unified configuration with optimized energy efficiency performance can be enforced exactly at the same time period on a daily basis. Our algorithm was evaluated using on a Point-of-Presence representation of the GÉANT network and its real traffic matrices. According to our simulation results, the reduced network topology obtained is able to achieve 18.6% energy reduction during that period without causing significant network performance deterioration. The contribution from this work is a practical but efficient approach for energy savings in ISP networks, which can be directly deployed on legacy routing platforms without requiring any protocol extension.

Semantic modeling for the Internet of Things has become fundamental to resolve the problem of interoperability given the distributed and heterogeneous nature of the "Things". Most of the current research has primarily focused on devices and resources modeling while paid less attention on access and utilisation of the information generated by the things. The idea that things are able to expose standard service interfaces coincides with the service oriented computing and more importantly, represents a scalable means for business services and applications that need context awareness and intelligence to access and consume the physical world information. We present the design of a comprehensive description ontology for knowledge representation in the domain of Internet of Things and discuss how it can be used to support tasks such as service discovery, testing and dynamic composition.

This paper proposes an extension to Session Initiation Protocol (SIP) for contextualized service delivery in a service delivery platform (SDP) that enables device specific multimedia delivery. SIP separates between session establishment and description and is thus, amenable to be extended for advanced implementations which make it an ideal platform for service creation. Device specific multimedia delivery needs rich and flexible device descriptions, and our approach proposes advanced device descriptions through semantic technologies. The proposed SIP extensions have been implemented on a SIP Application Server which functions as SDP in IP Multimedia Subsystem (IMS). The validation of the proposed extensions is shown through an Android SIP client application that acts as a device browser and recommender for different multimedia services to users. An example device user agent (UA) application has also been implemented on a laptop.

In cognitive radio networks, the arrival of Primary Users (PUs) may force Secondary Users (SUs) to terminate their ongoing sessions or degrade their Quality of Service (QoS) level. Given the time-varying spectrum availability, an immediate challenge arising is to support the QoS of SUs under spectrum mobility. In this paper, we propose an optimal decision-making framework for joint admission control, eviction control and bandwidth adaptation in cognitive radio networks. The problem is formulated as a Semi-Markov Decision Process (SMDP) and the optimal decision for each system state is derived to maximize the long-term network revenue as a function of the spectrum utilization, the SU blocking probability and the bandwidth adaptation cost under the SU dropping probability constraint. It is shown that the derived optimal strategy outperforms the threshold-based channel reservation schemes with/without bandwidth adaptation. And among the schemes with bandwidth adaptation, more performance improvement can be achieved by the proposed one when the bandwidth adaptation cost is taken into account.

In this paper, we propose a utility-based spectrum aggregation algorithm to enhance the performance of a cognitive radio network considering multiple objectives: (i) maximization of overall throughput, (ii) reduction of channel switching, (iii) reducing the number of sub-channels comprising the aggregate channel, aimed at opportunistic spectrum use by secondary users (SUs). These three objectives are integrated into a weighted sum utility function. The weight associated with each objective can be set differently (typically done manually) depending on the metric to be optimized. In this article however, we propose and evaluate an automatic mechanism for setting weights. The proposed algorithm including the learning module allows for automated (no manual intervention) adaptable setting of objective- function weights depending on the environment changes and its performance is also shown via the simulation results.

Cognitive radio network is defined as an intelligent wireless communication network that should be able to adaptively reconfigure its communication parameters to meet the demands of the transmission network or the user. In this context one possible way to utilize unused licensed spectrum without interfering with incumbent users is through spectrum sensing. Due to channel uncertainties, single cognitive (opportunistic) user cannot make a decision reliably and hence collaboration among multiple users is often required. Here collaboration among large number of users tends to increase power consumption and introduces large communication overheads. In this paper, the number of collaborating users is optimized in order to maximize the probability of detection for any given power budget in a cognitive radio network, while satisfying constraints on the false alarm probability. We show that for the maximum probability of detection, collaboration of only a subset of available opportunistic users is required. The robustness of our proposed spectrum sensing algorithm is also examined under flat Rayleigh fading and AWGN channel conditions.

Multi-hop wireless communication is playing a key role in today's communication networks. Despite several advancements in communication protocols and technologies, QoS provisioning is still a challenging issue in multi-hop wireless communications. In this paper, we propose a novel scheduling mechanism based on resource reservation for guaranteeing the QoS performance of real-time sessions (RTSNs) in IEEE 802.11e-based multi-hop wireless networks (MHWNs). Since RTSNs are sensitive to end-to-end delay in addition to their throughput requirements, our scheme defines a novel bandwidth reservation mechanism which guarantees resources to high priority services (i.e. RTSNs) for meeting both the delay and the throughput requirements. Moreover, the concurrent transmission (CT) mechanism implemented in the pre-configured contention-free period (CFP) further enhances the bandwidth utilization by means of letting chosen CT partners to transmit data simultaneously without collision. Simulation results show that the proposed scheduling mechanism significantly increases the efficiency of the bandwidth usage and successfully optimizes the QoS performance of RTSNs while providing fairness for other traffic sessions.

Guaranteeing quality of service (QoS) is one of the most critical challenges in IEEE 802.11-based wireless networks. This study proposes an analytical framework to evaluate hybrid medium access control (MAC) scheduling mechanisms with distributed resource reservation (RR), that was proposed for the IEEE 802.11e-enhanced distributed channel access protocol for guaranteeing QoS. The hybrid MAC scheduling mechanisms split the airtime into service intervals with contention-free period for QoS guaranteed real-time sessions (RTSNs), and contention access period for other traffic sessions. The distributed RR ensures that the resources are allocated to RTSNs without the support of a centralised controller–this makes it suitable for ad hoc networking applications. The proposed analytical framework models the QoS (i.e. delay and throughput) performance of RTSNs with dedicated resources in a distributed environment, and also estimates the overall capacity of the network. Moreover, the derived models can be used to investigate the impact of changes to individual system parameters, such as service interval or size of transmission opportunity. The simulation results show that the proposed analytical framework precisely models the QoS performance of RTSNs and predicts the optimum resource allocation for improved network capacity.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of pre-defined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. We show that, when the symbol constellation is improper, the joint codebook based precoding and equalization can be designed accordingly to achieve improved performance compared to the conventional system.

Emerging applications in Multihop Wireless Networks (MHWNs) require considerable processing power which often may be beyond the capability of individual nodes. Parallel processing provides a promising solution, which partitions a program into multiple small tasks and executes each task concurrently on independent nodes. However, multihop wireless communication is inevitable in such networks and it could have an adverse effect on distributed processing. In this paper, an adaptive intelligent task mapping together with a scheduling scheme based on a genetic algorithm is proposed to provide real-time guarantees. This solution enables efficient parallel processing in a way that only possible node collaborations with cost-effective communications are considered. Furthermore, in order to alleviate the power scarcity of MHWN, a hybrid fitness function is derived and embedded in the algorithm to extend the overall network lifetime via workload balancing among the collaborative nodes, while still ensuring the arbitrary application deadlines. Simulation results show significant performance improvement in various testing environments over existing mechanisms.

We consider a multiple femtocell deployment in a small area which shares spectrum with the underlaid macrocell. We design a joint energy and radio spectrum scheme which aims not only for co-existence with the macrocell, but also for an energy-efficient implementation of the multi-femtocells. Particularly, aggregate energy usage on dense femtocell channels is formulated taking into account the cost of both the spectrum and energy usage. We investigate an energy-and-spectral efficient approach to balance between the two costs by varying the number of active sub-channels and their energy. The proposed scheme is addressed by deriving closed-form expressions for the interference towards the macrocell and the outage capacity. Analytically, discrete regions under which the most promising outage capacity is achieved by the same size of active sub-channels are introduced. Through a joint optimization of the sub-channels and their energy, properties can be found for the maximum outage capacity under realistic constraints. Using asymptotic and numerical analysis, it can be noticed that in a dense femtocell deployment, the optimum utilization of the energy and the spectrum to maximize the outage capacity converges towards a round-robin scheduling approach for a very small outage threshold. This is the inverse of the traditional greedy approach.

Providing adaptive web services from mobile hosts is a new approach in mobile web services to cope resource scarcity of mobile network environment. This approach is explored through investigating some mechanisms to allow continuous and reliable service provisioning. However, there is a clear limitation in terms complexity and size of the services that may be executed on mobile hosts. In this paper, Simple Partial Offloading mechanism is studied to facilitate mobile web service adaptation through distributing the execution of mobile web services and modeling the transfer of required location-based information. The distribution can be classified into Forward or Bounce offloading while the transfer modeling is based on either Frontend or Backend scheme. Hence, four distinct types of mobile web service frameworks have been implemented; each of these architectures represents a different strategy for achieving adaptive and distributed web services. The paper describes the four prototypes that allow performance evaluation using resource intensive applications. The results presented show that basing distributed mobile hosted services on Backend Bounce Offload strategy is more suitable for mobile network environment.

Clustering algorithms are widely used in Wireless Sensor Networks (WSNs), which however incurs significant energy consumption at Cluster Headers(CHs). Therefore, a re-clustering operation is typically used to balance the workload, where different CHs are selected and clusters are reorganized. However, a considerable number of control messages is initiated during this process which inevitably consumes on-board node energy. Hence, the question of how often the network should perform the re-clustering operation needs to be addressed. In this paper, a distributed re-clustering solution is proposed, which provides an energy-efficient re-clustering rate to conserve node energy while also equalizing the node energy consumption across the network. The proposed algorithm calculates the approximate amount of energy required to reorganize the clusters and to deliver the sensory data. By properly predicting the levels of the energy consumptions values, the appropriate frequency of performing the re-clustering operation can be determined, which reduces control message overhead. To the best of our knowledge, this is the first work that analytically analyzes the overhead in re-clustering a WSN, groups re-clustering rounds to reduce this overhead, and simultaneously equalizes node lifetimes. Performance results show that the proposed algorithm outperforms two other popular clustering algorithms in node energy conservation and node lifetime equalization.

The interference management problem in cognitive radio networks is in this paper, tackled from the transmitter power control perspective so that transmissions by cognitive radios does not violate the interference level thresholds at incumbent users. We modify cellular distributed power control algorithms to suit the cognitive radio framework by exploiting spectrum use and radio environment knowledge for incumbent user location estimation in worst-case scenario. Most literature employs worst-case analysis to guarantee robustness thereby trading off optimality. We therefore, propose a stochastic approach which allows the cognitive radio network, access the extra capacity based on incumbent user outage information with guarantees on interference protection to the incumbent user at all times. This paper therefore shows that the proposed distributed power control strategy is robust with the benefit of increased spectral efficiency compared to its worst case counterpart.

Network virtualization has been recognized as a promising solution to enable the rapid deployment of customized services by building multiple Virtual Networks (VNs) on a shared substrate network. Whereas various VN embedding schemes have been proposed to allocate the substrate resources to each VN requests, little work has been done to provide backup mechanisms in case of substrate network failures. In a virtualized infrastructure, a single substrate failure will affect all the VNs sharing that resource. Provisioning a dedicated backup network for each VN is not efficient in terms of substrate resource utilization. In this paper, we investigate the problem of shared backup network provision for VN embedding and propose two schemes: shared on-demand and shared pre-allocation backup schemes. Simulation experiments show that both proposed schemes make better utilization of substrate resources than the dedicated backup scheme without sharing, while each of them has its own advantages.

Providing non interrupted Web Services from resource limited mobile devices needs to be done in a rather light-weight manner. Processing and communication will drain the battery rapidly, hence, both should be kept at a minimum. This paper describes the outcomes of an investigation into simple offloading mechanisms that facilitate provision of adaptive and distributed Restful mobile web services from resource constrained mobile devices. Offloading considers the distributed hosts processing as well as communication capabilities. Using queuing theory, the performance gained from distributing mobile web service tasks is explored. In addition, the theoretical boundaries of different flavours of offloading mechanisms are presented. The analytical, as well as the experimental results show the differences in performance between these mechanisms.

Collaboration towards a goal involves groups of entities collectively possessing characteristics required to accomplish the goal. Facilitating collaborations in pervasive environments requires the automated formation of such groups. The group formation process is especially challenging in decentralised environments where there is no single central entity that can coordinate the formation process. It is also important that the group formation mechanisms are generic in nature so that they can be utilised in heterogeneous target environments regardless of their domain and requirements. This paper proposes a generic approach for automating group formation in decentralised environments.

Dynamic spectrum allocation (DSA) has been cited as a promising mechanism for managing the radio spectrum for coexisting systems. The goal of the DSA scheme is to increase the performance of networks in the shared spectrum, by providing a more efficient way of utilisation. This work addresses analytically the impact of multi-cell, multi-operator interference on the overall spectrum when multiple operators co-exist and share a common pool of radio resources. We propose a centralised DSA scheme that is able to capture the interference level and interact dynamically to minimise interference and enhance spectrum utilisation while maintaining a satisfactory level of QoS. Furthermore, a concise system model and framework able to describe the interaction among different operators is presented. The DSA algorithm has been investigated for co-located and displaced cellular networks. The simulation results indicate that the proposed DSA algorithm significantly outperformed the fixed spectrum allocation (FSA) ensuring minimum level of interference in the system. The QoS of the overall system has been improved in the DSA compared to traditional FSA. Moreover, the proposed algorithm enhanced the spectrum utilization by 26% guaranteeing that all operators are given fair access to the shared spectrum.

With the exponential growth of internet communications and the rise to prominence of environmental concerns, the energy consumption of the Information Communications Technology industry has become an issue of major importance. One area that has not been yet fully explored is the evaluation of the computational loads required in the processing and forwarding of traffic. While significant work has been performed towards optimizations especially of wireless protocols for energy efficiency “outside the devices” through energy-optimizing mechanisms that deal with the characteristics of the transmission medium, the computational loads required by the protocols' operations and mechanisms themselves “within the devices” have not been yet investigated adequately. Towards this end, in this paper we put forward a method suitable for the measurement and profiling of the processing requirements of the operations, as well as for the traffic loads, generated by both wired and wireless protocols. As we show, this method can be used to determine specific operations, functionality sets and configurations that increase the protocols' and the resulting overall network energy use. It can be used, therefore, to derive recommendations for further protocol optimizations towards energy efficiency as well as practical rules for the selection of specific protocols depending on the higher level applications and the specific deployment environment under which they operate.

Quality of Service (QoS) provisioning in multi-hop wireless networks (MHWNs) has become a promising and challenging topic in the wireless communication research community. In this paper, a novel distributed MAC scheduling protocol with reservation mechanism is proposed for enhancing the QoS performance of IEEE 802.11e-based MHWNs. The proposed mechanism guarantees resources in pre-configured contention-free period (CFP) for real-time sessions (RTSNs) in the network in a distributed manner. Here, resource reservation (RR) for multiple sessions can be made without any collision through an effective signalling process which coordinates the RR for RTSNs. Distributed admission control (AC) ensures that the existing RTSNs are not violated from QoS guarantees. In addition, a concurrent transmission (CT) mechanism is implemented within the pre-configured CFP. This is to further improve the bandwidth utilization by synchronizing the reserved transmissions among different nodes which do not interfere with each other. Simulation results indicate that the proposed scheduling mechanism can address the problems posed by inter- and intra-flow interference and can achieve guaranteed QoS for admitted RTSNs while providing fairness for the other sessions in the network.

Femtocells being small low powered base stations provide sufficient increase in system capacity along with better indoor coverage. However, the dense deployment of femtocells face the main challenge of co channel interference with macrocell users. In this paper, this interference problem is addressed by proposing a novel downlink power control algorithm for femtocells. The proposed algorithm gradually reduces the downlink transmit power of femtocells when they are informed about a nearby macrocell user under interference. This information is given to the femtocells by the macrocell base station through a unidirectional downlink broadcast channel. Simulation results show that the algorithm causes the macrocell to accommodate large number of femtocells within its area, whereas at the same time protecting the macrocell users from any harmful interference.

Dynamic Spectrum Access (DSA) is widely seen as a feasible solution to the problem of illusive radio spectrum clogging. The fundamental concept of DSA is to opportunistically access unutilised spectrum bands while respecting the rights of privileged incumbent users. DSA and spectrum policies depend on meticulous statistics of spectrum opportunities as well as spectrum occupancy models. In this paper, we develop a model to characterise the number of spectrum opportunities available using the probability and approximation theory. In particular, we derive the probability mass function (PMF) of the total number of spectrum opportunities available depending on the probability of each channel being free. We further analyse the complexity involved in calculating the PMF of the total number of available channels at any time and location and develop approximate models. Numerical results are provided showing that the proposed approximation models of spectrum opportunities achieve good accuracy at significantly lower computational cost.

Dynamic spectrum management is a promising solution for network operators to efficiently utilise the limited radio spectrum and guarantee operator's profit by increasing capacity as well as generating more spectrum opportunities for opportunistic use. This paper presents a novel algorithm for efficient spectrum management to optimise spectrum utilisation between two sharing UMTS cellular operators. It is shown that proposed solution approach increases revenue of sharing operators without sacrificing the quality of service on either network. A multioperator UMTS simulation tool is also developed to evaluate the performance of proposed algorithm. The simulation results show that the proposed algorithm achieves high efficiency of spectrum utilisation and gains up to 33% can be achieved for both uniform and non-uniform distribution of traffic.

Increased spectrum efficiency has been demonstrated with the use of cognitive radios, however with increased likelihood of interference to the incumbents of spectrum. Several studies solved the interference problem from the transmitter power control perspective, so as to curtail excessive cognitive interference powers; however, neglecting the effect of secondary terminal mobility. We show by simulation that such assumption of terminal immobility in the power control algorithm would fail in time variant cases resulting in increased levels of interference to the Incumbents as well as serious degradation in QoS within the cognitive radio network. We model the link gain evolution process as a distance dependent shadow fading process and scale up the target signal to interference ratio to cope with time variability. This paper therefore, proposes a mobility driven power control algorithm for cognitive radios based on sensing information, which ensures that the interference limit at the Incumbents is unperturbed at all times while concurrently maintaining the QoS within the cognitive radio network.

Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.

Exploiting path diversity to enhance communication reliability is a key desired property in Internet. While the existing routing architecture is reluctant to adopt changes, overlay routing has been proposed to circumvent the constraints of native routing by employing intermediary relays. However, the selfish inter-domain relay placement may violate local routing policies at intermediary relays and thus affect their economic costs and performances. With the recent advance of the concept of network virtualization, it is envisioned that virtual networks should be provisioned in cooperation with infrastructure providers in a holistic view without compromising their profits. In this paper, the problem of policy-aware virtual relay placement is first studied to investigate the feasibility of provisioning policy-compliant multipath routing via virtual relays for inter-domain communication reliability. By evaluation on a real domain-level Internet topology, it is demonstrated that policy-compliant virtual relaying can achieve a similar protection gain against single link failures compared to its selfish counterpart. It is also shown that the presented heuristic placement strategies perform well to approach the optimal solution.

Formal verification tools have been extensively used in the past to assess the correctness of protocols, processes, and systems in general. Their most common use so far has been in identifying whether livelock or deadlock situations can occur during protocol execution, process, or system operation. In this paper we aim to showcase that an additional equally important and useful application of formal verification tools can be in protocol design and optimization itself. This can be achieved by using the tools in a rather different context compared to their traditional use. That is not only as means to assess the correctness of a protocol in terms of lack of livelock and deadlock situations but rather as tools capable of building profiles of protocols, associating performance related metrics, and identifying operational patterns and possible bottleneck operations in terms of metrics of interest. This process can provide protocol designers with an insight about the protocols' behavior and guide them towards further protocol design optimizations. We illustrate these principles using some existing protocol implementations as case studies.

The most common use of formal verification methods and tools so far has been in identifying whether livelock and/or deadlock situations can occur during protocol execution, process, or system operation. In this work we aim to show that an additional equally important and useful application of formal verification tools can be in protocol design and protocol selection in terms of performance related metrics. This can be achieved by using the tools in a rather different context compared to their traditional use. That is not only as model checking tools to assess the correctness of a protocol in terms of lack of livelock and deadlock situations but rather as tools capable of building profiles of protocol operations, assessing their performance, and identifying operational patterns and possible bottleneck operations. This process can provide protocol designers with an insight about the protocols' behavior and guide them towards further protocol design optimizations. It can also assist network operators and service providers in selecting the most suitable protocol for specific network and service configurations. We illustrate these principles by showing how formal verification tools can be applied in this protocol profiling and performance assessment context using some existing protocols as case studies.

Cognitive radios have been proposed as a solution to the spectrum underutilisation problem and have been proven to increase spectrum efficiency whilst providing opportunities for futuristic technologies. However, increased levels of interference are expected with the introduction of secondary spectrum access, therefore, proper interference management within the cognitive framework is imperative. The interference management problem is herein tackled from the transmitter power control perspective so that transmission by cognitive radio network does not violate the interference level thresholds at the primary receiver. We propose a fully distributed power control framework for cognitive radio network exploiting spectrum use and radio environment knowledge. The proposed algorithm called Distributed Power Control with Primary Protection via Spectrum Sensing has the ability to satisfy tight QoS constraints for cognitive radios as well as to meet interference constraints for primary users.

Resource reservation is one of the effective solutions in guaranteeing the quality-of-service (QoS) in communication networks. In this paper, we propose a novel resource reservation scheme for dynamically managing the bandwidth resources in IEEE 802.11e-based wireless local area networks (WLANs). The proposed solution consists of two components namely (i) dynamic medium access control (MAC) scheduler and (ii) adaptive admission control algorithm. Once resources occupied by certain stations become idle due to the end of QoS data session, the proposed MAC scheduler will either release the resources to the non-real-time traffic flows (NRTTFs) or re-allocate them to other real-time traffic flows (RTTFs) which require dedicated resources. The re-allocating process is implemented with the support of the novel adaptive admission control algorithm (AACA) that decides which RTTF can gain access to the available resources in a distributed manner. Evaluation of our proposed scheme is conducted in several scenarios through extensive ns2 simulation. The simulation results indicate that the proposed distributed resource reservation scheme significantly improves the efficiency of resource distribution with real-time and non-real time traffic flows in WLANs, while guaranteeing the QoS for the admitted RTTFs.

Emerging applications in Wireless Sensor Networks (WSNs) demand notable in-network processing capacities rather than simple data gathering and dissemination. Therefore, the performances of the network like latency and energy consumption are greatly affected by how the various application requirements are mapped to the processing nodes in the network. This paper investigates intelligent task mapping and scheduling techniques based on Genetic Algorithm (GA), and proposes a novel task allocation model and a multi-hop communication model to schedule both computation and communication activities in the WSN environment. A hybrid fitness function which balances the energy consumption among collaborative sensor nodes with application tolerable delays is presented to extend the network lifetime. Simulation results show that the proposed algorithm has a better capability of balancing the network lifetime with latency constraints in both homogeneous and heterogeneous networks.

The cognitive radio (CR) is known as a radio that can reconfigure its transceiver parameters based on the environmental awareness. The opportunistic radio (OR) is considered in this work, with a narrower definition where the awareness is limited to the spectrum knowledge. The decision making framework is employed as a crucial entity to control the behaviour of the OR. The main purpose is to enable an efficient spectrum usage while avoiding the interference to other users. This study describes the proposed OR decision making framework including the flow of context information as an input process to the decision making engine, the context filtering and the reasoning mechanisms in which the decision optimisation is achieved using a genetic algorithm (GA)-based approach. The system stability of the GA-based reasoning engine is tested through simulations. Then, the experimental study is performed on a test platform for a practical proof of the concept. The test platform is based on the Ettus USRP (Universal Software Radio Peripheral) hardware and the GNU Radio open source software. Several tests were carried out to observe the OR capabilities of the proposed decision making framework. Test environment settings together with the observation results are provided in this study, covering the spectrum sensing and opportunistic channel allocation in the industrial, scientific and medical (ISM) band of 2.4 GHz.

Cognitive radio has been identified as a potential candidate to increase spectrum utilisation by exploiting spectrum holes on a non-interfering basis. Spectrum sensing is the key functionality to make sure that the cognitive radio is aware and will not disturb the operation of the licensed users. Collaborative spectrum sensing is needed to overcome deleterious channel effects such as fading or the hidden node problem and to increase sensing reliability and accuracy. This paper presents a novel optimisation framework for collaborative spectrum sensing in the presence of imperfect reporting channels. It is also shown in this paper that in fading channels spectrum sensing performance can be significantly improved by using multiple antennas at the cognitive radio terminal. A weighted optimised collaborative spectrum sensing scheme with multiple antenna cognitive terminals, which maximises global probability of detection at the fusion centre using genetic algorithm is presented. Based on an individual user local conditions, the algorithm assigns optimal weight to the user observation at the fusion centre. Simulation results illustrate that significant collaborative and spatial diversity gains can be achieved by the proposed spectrum sensing framework.

Cognitive Radio (CR) introduces the idea of system awareness and intelligent adaptability in order to provide more efficient spectrum utilization especially in spectrum scarcity environment, which is happening in wireless communications. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor radio environment and detect unused spectrum. A fast and accurate detection approach is very vital since it provides interference avoidance to the existing users, which in many cases are licensed users. Much work has been undertaken to propose spectrum sensing techniques; however narrowband spectrum sensing is focused. In the case of high spectrum utilization, which increases spectrum scarcity, wideband spectrum sensing needs to be employed to provide better chance of detecting spectrum opportunity. In this paper, the application of wavelet transform technique for spectrum opportunity detection in cognitive radios is documented. Edge detection using wavelet analysis is employed for the wideband spectrum sensing. The paper presents research approach as well as system testing, which involves the development of the test platform used to obtain live spectrum sensing results. The implementation results prove the practicality of the approach as it provides fast and accurate spectrum opportunity detection.

Applications/services that cater to groups of users (or entities) in ubiquitous computing environments must utilise the concept of context-awareness in order to provide group adapted content or services. Most existing frameworks that support group applications/services focus on lower level issues such as group membership management or group communication, failing to give attention to context-awareness, while those that address context-awareness are mostly application specific. However, the introduction of a generic framework for the above situation can be challenging for several reasons, such as the heterogeneity of the applications/services and the complexity of interpreting group information compared to single user information. This paper proposes a generic framework for group knowledge management that overcomes these challenges. The framework supports the above mentioned applications/services in extracting group knowledge regarding the involved entities. A prototype implementation including functionality to query group knowledge is presented as well.

Multimedia services are becoming increasingly popular among mobile users. Ontology and related technologies have been introduced into the multimedia domain as a means to provide declarative formal representations of the domain knowledge and thus to enable intelligent multimedia processing, such as media format adaptation. The range of devices available to access media content becomes increasingly heterogeneous and at the same time ubiquitous. Users expect to access their services and content without restrictions in time or location. Users have many and different gadgets/devices with network connectivity at their disposal to receive content, ranging from their smart phones, car audio systems to laptops, or office PCs, etc. Hence there is a need to link the discovery and the description of these ambient device with multimedia domain knowledge representations in order to facilitate a ubiquitous multimedia experience. The contribution of this work is an approach for mapping device descriptions, which are leveraged on the resource discovery protocol UPnP to OWL ontology instances. The ontology instances chosen are compliant with the MPEG-21 DIA OWL-formatted ontology. This approach bridges the gap between non-semantic description mechanisms of the legacy device/services discovery protocol with the semantic multimedia domain knowledge representation.

The vision of service personalization for mobile communication environments entails context sensitive service provisioning. The realization of such customizable smart spaces necessitates acquisition and processing of modality context information from a variety of devices in the ambient environment. The heterogeneity of available device capabilities and description formats brings new challenges for a context reasoning engine that formulates content delivery decisions. Specifically, to ensure interoperability with existing application logic, the enabling components should support semantic queries. Secondly, situations where variously formatted context input may not provide enough information to answer queries, should be intelligently handled. Towards this aim, this paper discusses a context reasoning and query interface component as part of a service context manager (SCM) framework that supports semantic querying and handles incomplete context information through a rule-based mechanism. The validation of the approach is provided by showing the mapping of disparate UAProf and UPnP descriptions into the framework and querying of supported modality services.

The authors present the use of a genetic algorithm (GA) model as a solution approach to the dynamic spectrum allocation (DSA) problem considered as a difficult combinatorial optimisation problem. The proposed multi-objective GA model enhances overall spectral efficiency of the network, while optimising its own spectrum utilisation to generate accessible spectrum opportunities for other radio technologies. A novel two-dimensional encoding technique is defined to represent solutions in the problem domain and the technique enables significantly shorter convergence times. A simulation tool has been developed to model the GA-based DSA and to compare the new scheme with the conventional fixed spectrum allocation (FSA) scheme under both uniform and non-uniform traffic distributions. The proposed scheme significantly outperformed the FSA scheme both in terms of spectral efficiency gain and spectral utilisation.

In this paper we investigate the impact that introduction of new ambient networks (AN) functionality will have on usage of system resources and on connection delay. The signalling load for multiple attachment and negotiation procedures is assessed by modelling signalling sequences for a WLAN system enabled with AN technology. The load is computed for varying numbers of users and for users with different levels of "willingness to evaluate and negotiate offers". The results show that the most important parameter is the number of attachment attempts per time unit, which is an indicator of user activity level. In the investigated scenarios, the relative load of signalling is 0.1 - 1.0 % of the transferred user data. The delay depends on the current load situation of the network.

Technology is paving the way for ubiquitous services. However, the delivery environment is characterized by its complexity which emanates from the diversity in end user terminals and networking infrastructures. Hence, adaptation is vital to the portability and personalization of the ever increasing content and services offered to users. This paper proposes a context-aware adaptation manager architecture for adaptation management. The architecture aims to analyse the service delivery context to determine the required adaptations in order to enable efficient ubiquitous accessibility and personalization to the user situation and preferences. This paper presents technologies which have been identified useful for this purpose and how they are applied to deliver the functionality of the Adaptation Manager architecture, including ontology, OWL-DL, SWRL, reasoning and XSTL.

The paper presents an association rule mining based learning approach for multimodal user interface adaptation in mobile environments. High-level knowledge about user preferences in multimodal interaction is inferred, using data mining techniques based on context parameters of the environment. The current approach facilitates automatic selection of multimodality capable interaction devices and their according rendering facilities for media output streams. An overview of the learning subsystem being part of the distributed communication sphere (DCS) management architecture, proposed within the EU IST-027617 project SPICE, will be introduced. Further the design of the learning approach will be discussed, including the definition and adaptation of snapshot data based on environment parameters. Frequent snapshots form the basis for learning, and therefore for the described association rule mining algorithm. Theoretical simulation results are presented and an outlook towards next research steps is given.

Adaptive networks are envisaged to play a significant part in the future, where the time and space variations in the traffic pattern will necessitate the ability to continuously amend the Radio Access Technologies' (RATs') operating parameters. Reconfiguration of communications systems is a facilitator towards this convergence and enables the dynamic adaptation and optimization of the access characteristics. However, such far ranging optimization concept involves many different mechanisms and work areas. Each of these areas provides an answer to a different optimization problem; Dynamic Network Planning and Management (DNPM) provides a load and demand driven optimization of the radio planning of multiple different networks within a given area. Advanced Spectrum Management (ASM) enables short term use of spectrum for services with higher demand. Finally Joint Radio Resource Management (JRRM) coordinates different access schemes and facilitates a more centralized approach to allocation of radio resource. Each of the schemes optimizes spectrum and radio resource usage on a different time scale. ARRM deals with the rather short term allocation, ASM with more medium term spectrum assignments while DNPM assumes time scales up to the range of weeks or months. Consequently, there is need of combining all working areas in the form of a Functional Architecture (FA), where each module represents a concept, aiming at forming part of the global end-to-end reconfigurability architecture. This paper includes a detailed analysis of the Reconfigurability FA, along with a description of the functionality of each of the modules included therein.

Network scenarios beyond 3G assume the cooperation of operators with wireless access networks of different technologies in order to improve scalability and provide enhanced services to their mobile customers. While the selection of an optimised delivery path in such scenarios with multiple access networks is already a challenging task for unicast delivery, the problem becomes more severe for multicast services, where a potentially large group of heterogeneous receivers has to be served simultaneously via shared resources. In this paper we study the problem of selecting the optimal bearer paths for multicast services with groups of heterogeneous receivers in wireless networks with overlapping coverage. We propose an algorithm for bearer selection with different optimisation goals, demonstrating the existing tradeoff between user preference and resource efficiency.

Multimodality is a fact of human communication, increasingly our ways to communicate change and humans do interact with machines (be it a mundane ATM transaction, the calling of an automised call center, or the setting/disarming of a residential alarm system). However, these interactions are mostly limited to single input and output schemes, thus loosing a lot of additional information a human communication partner would sense, Multimodality was perceived to exactly tackle this point. This paper describes a framework and approach to operate multimodal interaction mechanisms in both the fixed as well as the mobile environments. The paper describes a scheme that facilitates the dynamic binding and release of user-interface devices (such as screens, keyboards, etc.) to support multimodal interactions in mobile environments and to enable the user to 'make use' of any possible user interface device available (and allowed), thus supporting the individuals changing communication environment. The principles and basic functionality of an adaptive multimodal human interface-device binding engine are outlined.

The inappropriate or under-use of spectrum offers new perspective for a secondary usage of spectrum between heterogeneous radio access technologies. This spectrum sharing can be achieved at the expense of an appropriate management of the co-channel interference between the different radio access technologies. This paper proposes an extended dynamic channel allocation (eDCA) scheme for providing distributed and real non SIR (signal to interference ratio) based spectrum sharing. The trunking gain performance of eDCA is analyzed and compared to simple fixed channel allocation (FCA), FCA with channel borrowing and DCA for FDMA based systems. Sensitivity analysis of the performance is provided. In particular, results show that careful inter-radio access technologies reuse frequency distance coordination is required between heterogeneous radio systems to ensure that pooling frequencies results in gain to the systems. The appropriate capacity management relies on the activation of the right strategy in the right traffic load condition

This paper introduces a generic architecture that enables the development and execution of mobile multimodal applications proposed within the EU IST-511607 project MobiLife. Mobi Life aims at exploiting the synergetic use of multimodal user interface technology and contextual information processing, with the ultimate goal that the two together can provide a beyond-the-state-of-the-art user experience. And this led to an integrated concept, components of the underlying architecture are described in detail and the interfaces towards the application back-end as well as towards context aware resources are discussed. The paper also positions the current work against existing standardisation efforts and it pinpoints technologies required to support the implementation of a device and modality function within the MobiLife architecture

Dynamic spectrum allocation (DSA) seeks to exploit the variations in the loads of various radio-access networks to allocate the spectrum efficiently. Here, a spectrum manager implements DSA by periodically auctioning short-term spectrum licenses. We solve analytically the problem of the operator of a CDMA cell populated by delay-tolerant terminals operating at various data rates, on the downlink, and representing users with dissimilar "willingness to pay" (WtP). WtP is the most a user would pay for a correctly transferred information bit. The operator finds a revenue-maximising internal pricing and a service priority policy, along with a bid for spectrum. Our clear and specific analytical results apply to a wide variety of physical layer configurations. The optimal operating point can be easily obtained from the frame-success rate function. At the optimum, (with a convenient time scale) a terminal's contribution to revenues is the product of its WtP by its data rate; and the product of its WtP by its channel gain determines its service priority ("revenue per Hertz"). Assuming a second-price auction, the operator's optimal bid for a certain spectrum band equals the sum of the individual revenue contributions of the additional terminals that could be served, if the band is won

Software reconfigurability of air interfaces, the actual reconfiguration processes and the procurement of reconfiguration software are posing substantial threats to the system integrity of wireless communication system. These threats are investigated and reported, and mechanisms to ensure secure reconfiguration procedures are described. The security mechanisms documented form part of the MVCE reconfiguration management architecture (RMA).

Reconfiguration of configurable radio communication platforms contains uncounted difficulties and pitfalls. This paper contributes to the solution of this situation with a proposal of a management architecture capable to prevent mis-configurations and also to ensure reliable reconfiguration of terminals. An introduction to reconfiguration management and its requirements is followed by a description of our reconfiguration management architecture (RMA). Later parts contain an outline of the mechanisms used to implement parts of the RMA.