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The manufacturing industry is regarded as one of the most demanding verticals with respect to URLLC requirements. A Device-to-Device (D2D) communication system is a key enabler that has been introduced to support URLLC. To enhance spectrum utilisation, D2D links can share access of radio spectrum resources occupied by cellular users. Spectrum reuse may result in performance degradation due to mutual co-channel interference of co-existing cellular and D2D users. Resource sharing in a D2D-based cellular network for wireless industrial applications is investigated in this article. The presented schemes aim to maximise the overall system throughput while maintaining the Quality of Service/Experience (QoS/QoE) requirements. Interference is managed by jointly considering admission and power control based on the relative distance between devices. A priced-Deferred Acceptance (p-DA) scheme with an incentive-based stability is developed to match D2D users to cellular resources. Numerical simulations show that the p-DA scheme is able to achieve better performance than the conventional DA algorithm and is close in performance to the presented centralised optimisation approach.

In this study, we demonstrate how we can quantify environmental implications of large-scale events and traffic (e.g., human movement) in public spaces, and identify specific regions of a city that are impacted. We develop an innovative data fusion framework that synthesises the state-of-the-art techniques in extracting pollution episodes and detecting events from citizen-contributed, city-specific messages on social media platforms (Twitter). We further design a fusion pipeline for this cross-domain, multimodal data, which assesses the spatio-temporal impact of the extracted events on pollution levels within a city. Results of the analytics have great potential to benefit citizens and in particular, city authorities, who strive to optimise resources for better urban planning and traffic management.

The emerging paradigm of urban computing aims to infer latent patterns from various aspects of a city's environment and possibly identify their hidden correlations by analyzing urban big data. This article provides a fine-grained analysis of air quality from diverse sensor data streams retrieved from regions in the city of London. The analysis derives spatio-temporal patterns, that is, across different location categories and time spans, and also reveals the interplay between urban phenomena such as human commuting behavior and the built environment, with the observed air quality patterns. The findings have important implications for the health of ordinary citizens and for city authorities who may formulate policies for a better environment.

The ability to manage the distributed functionality of large multi-vendor networks will be an important step towards ultra-dense 5G networks. Managing distributed scheduling functionality is particularly important, due to its influence over inter-cell interference and the lack of standardization for schedulers. In this paper, we formulate a method of managing distributed scheduling methods across a small cluster of cells by dynamically selecting schedulers to be implemented at each cell. We use deep reinforcement learning methods to identify suitable joint scheduling policies, based on the current state of the network observed from data already available in the RAN. Additionally, we also explore three methods of training the deep reinforcement learning based dynamic scheduler selection system. We compare the performance of these training methods in a simulated environment against each other, as well as homogeneous scheduler deployment scenarios, where each cell in the network uses the same type of scheduler. We show that, by using deep reinforcement learning, the dynamic scheduler selection system is able to identify scheduler distributions that increase the number of users that achieve their quality of service requirements in up to 77% of the simulated scenarios when compared to homogeneous scheduler deployment scenarios.

International audience; The 2018 2nd International Workshop on Context-aware, Autonomous and Smart Architectures (CASA 2018) was held in conjunction with the 12th European Conference on Software Architecture (ECSA 2018), on September, 24th, 2018 in Madrid, Spain. The main topic of the workshop concerned the design of software architectures for dynamic, autonomous, and adaptive context-aware smart systems. Such systems address changes and cope with uncertainties at runtime. Therefore, the software architecture of such system should ensure the basis for their dynamicity, adaptivity, and autonomy. These basis should be solid because they represent the backbone of a system, and in the same time flexible because they address dynamicity during execution. The one-day workshop aimed to bring together researchers and practitioners from the academic and the industrial world to share and discuss their approaches, solutions, methodologies, ideas, and open questions about the software architecture design for context-aware, autonomous, and smart systems. The workshop enabled discussions and put the basis for future collaborations among the participants.

5G New Radio (NR) is touted as a pivotal enabling technology for the genuine realization of connected and cooperative autonomous driving. Despite numerous research efforts in recent years, a systematic overview on the role of 5G NR in future connected autonomous communication networks is missing. To fill this gap and to spark more future research, this article introduces the technology components of 5G NR and discusses the evolution from existing cellular vehicle-to-everything (V2X) technology toward NR-V2X. We primarily focus on the key features and functionalities of the physical layer, sidelink communication and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques. Moreover, we envisage and highlight the potential of machine learning for further performance enhancement in NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases.

With development of 5G and Beyond communication technologies and the recent achievements in autonomous driving, technical solutions to improve road safety have attracted great attention. In this paper, we present a collision avoidance system implemented using a 1/10 scale vehicle, as a research platform for autonomous driving connected a vehicular network. While the collision avoidance system exploits data fusion to make decisions relevant to predicting potential collision events, the effectiveness of the fusion of data obtained from in-vehicle sensors and vehicular communication is evaluated within a testbed environment.

We investigate and compare two radio resource management (RRM) approaches for a reliable Device to Device (D2D) enabled cellular network in an industrial environment. The D2D links have target Quality of Service (QoS) requirements in terms of minimum reliability and data rate while cellular users have minimum throughput requirements. The aim is to maximise the overall system throughput while ensuring the minimum (QoS) requirements are met. Interference is managed by jointly considering admission and power control based on interdistances between devices. The deferred acceptance (DA) game theoretic algorithm is used to perform matching and compared to a centralised optimisation approach. Numerical simulations are used to verify the performances of the two algorithms.

The 2017 1st International Workshop on Context-aware, Autonomous and Smart Architectures (CASA 2017) was held in conjunction with the 11th European Conference on Software Architecture (ECSA 2017), on September, 12th, 2017 in Canterbury, United Kingdom. The goal of this one -day workshop was to bring together researchers and practitioners from academic environment and from the industry to share their solutions, ideas, visions, and doubts in the design of software architectures for context-aware, autonomous, and smart solutions. The workshop focused on architectural aspects offering a complementary vision of such solutions with respect to the available application- and user-oriented perspectives on context-awareness, autonomy, and smartness. Furthermore, the workshop aimed to enable discussions, partnerships, and collaborations among the software engineers interested in these solutions.

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 (ML) for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) 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 (ITS). Our proposed architecture is reliable and can be used across different fields in order to predict complex events.

In the fifth and beyond (5G/B5G) communication, wireless networks are evolved towards offering various services of different use cases and, therefore, need to span a wide range of requirements. While different services will be supported at the same time, radio resource management needs to consider the different requirements. In addition, as wireless systems are capable to support multi-connectivity, radio resource allocation becomes more challenging. In this context, we introduce a many-to-many matching game, and develop a distributed radio resource allocation algorithm supporting multi-connectivity. Simulation results demonstrate that the proposed approach improves the QoS levels of UEs by up to 14.9% considering their service requirements.

This paper formulates an optimization problem that maximizes an aggregate utility that captures the “in-context” suitability of available radio access technologies (RATs) to support adaptive video streaming subject to a single-homing constraint. To efficiently solve the considered problem, a novel network-assisted quality-of-experience (QoE)-driven methodology is devised, and its impact on the end-user devices is evaluated. The proposed approach is evaluated and benchmarked against its distributed and centralized counterparts from a cost-benefit perspective. The results reveal that the proposed strategy significantly outperforms its distributed counterpart, and performs differently with respect to its centralized counterpart depending on the number of video clients. At low loads, it performs similarly with much less control overhead. At high loads, the proposed strategy scales up well, while the centralized approach gets overwhelmed by an increasing uplink signaling. A practicality analysis of the proposed strategy for battery-powered devices reveals that its gain in terms of uplink signaling outweighs its cost in terms of processing load, which results in a drastic reduction of the consumed energy. Therefore, the proposed solution provides a win-win situation, where the video clients can sustain good QoE levels at reduced energy consumption, while the network can accommodate more users with existing capacity.

Fifth generation (5G) envisages a “hyperconnected society” with an enormous number of interconnected devices, anywhere and at any time. Edge computing plays a pivotal role in this vision, enabling low latency, large traffic volumes, and improved quality of experience. The advent of 5G and edge computing encourages vertical industries to develop innovative services, which can meet the challenging demands coming from consumers. However, economic feasibility is the ultimate factor that determines the viability of a new service. Hence, effective techniques for the economic assessment of such services are needed. This paper analyzes the provision of immersive media services in crowded events, through a cloud‐enabled small cell network owned by a neutral host, and offered in multitenancy to different mobile network operators. We initially develop a planning model to predict the required compute, storage, and radio resources. Taking into account dynamic factors such as service penetration and price evolution, we then provide a number of economic indices, such as net present value, internal rate of return, and expected payback period to assess the viability of a potential investment in a 5G infrastructure for immersive media services. The presented analysis will guide small cell network operators in the provision of 5G innovative media services.

Part 1: 4th Workshop on “5G – Putting Intelligence to the Network Edge” (5G-PINE 2019); International audience; In the new fascinating era of 5G, new communication requirements set diverse challenges upon existing networks, both in terms of technologies and business models. One among the essential categories of the innovative 5G mobile network services is the enhanced Mobile Broadband (eMBB), mainly aiming to fulfill users’ demand for an increasingly digital lifestyle and focusing upon facilities that implicate high requirements for bandwidth. In this paper we have discussed eMBB as the first commercial use of the 5G technology. Then, we have focused upon the original context of the 5G-DRIVE research project between the EU and China, and we have identified essential features of the respective eMBB trials, constituting one of the corresponding core activities. In addition, we have discussed proposed scenarios and KPIs for assessing the scheduled experimental work, based on similar findings from other research and/or standardization activities.

In this paper, a novel priority assignment scheme is proposed for priority service networks, in which each link sets its own priority threshold, namely, the lowest priority the link is willing to support for the incoming packets without causing any congestion. Aiming at a reliable transmission, the source then assigns each originated packet the maximum priority value required along its path, because links may otherwise discard the incoming packets which do not meet the corresponding priority requirements. It is shown that if each source sends the traffic at a rate that is reciprocal to the specified highest priority, a bandwidth max---min fairness is achieved in the network. Furthermore, if each source possesses a utility function of the available bandwidth and sends the traffic at a rate so that the associated utility is reciprocal to the highest link priority, a utility max---min fairness is achieved. For general networks without priority services, the resulting flow control strategy can be treated as a unified framework to achieve either bandwidth max---min fairness or utility max---min fairness through link pricing policy. More importantly, the utility function herein is only assumed to be strictly increasing and does not need to satisfy the strictly concave condition, the new algorithms are thus not only suitable for the traditional data applications with elastic traffic, but are also capable of handling real-time applications in the Future Internet.

There has been increasing interest in deploying IoT devices to study human behaviour 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 behavioural 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 paper, 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., Comment: 20 pages, 11 figures

In this paper, we present a spectrum sharing scheme for Device to Device (D2D) deployments in a wireless Internet of Things (IoT) industrial environment. Interference management for this scheme relies on the relative distance between participating users or devices. The D2D links can access spectrum by sharing resources with cellular users or by using dedicated channels. The presented scheme maximises the throughput of admitted D2D links without compromising the Quality of Service (QoS) requirements of users in the network. An optimisation approach is adopted to solve the spectrum sharing problem resulting in an efficient allocation and throughput maximisation for all participating users and devices.

In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods&mdash, FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products&rsquo, lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region., IEEE ACCESS 2018

Mobile sensing techniques have been increasingly deployed in many Internet of Things-based applications because of their cost efficiency, wide coverage, and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high-missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.

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.

We consider resource allocation with aggregation for different types of traffic in heterogeneous networks, including WLANs. While mobile data traffic is expected to increase, efficient management of multiple bands including unlicensed band becomes increasingly important. In this context, we formulate a resource allocation problem using utility functions for heterogeneous traffic and propose a novel algorithm that considers the estimated UE speed, traffic types and channel quality. Simulation results illustrate performance of the proposed algorithm in terms of higher utility value and fairness, even at high traffic loads. Additional improvements in resource utilization through estimating UE speed and allocating low-mobility UEs to Wi-Fi are shown.

We present a new approach for mobility load balancing (MLB) and user association in dense small cell scenarios. This Self Organizing Network (SON) approach relies on Knapsack Optimisation (KO) to evenly distribute users across a cluster of small cells subject to constraints. It is shown that the new technique referred to as (MLB-KO) achieves substantial improvements (better than four times reduction) in blocking ratios compared to the case when no MLB strategy is deployed. Comparisons with other approaches relying on Cell Range Expansion (CRE) and Almost Blank Subframes (ABS) are also presented highlighting the effectiveness of the new approach as a centralised self optimisation technique for future dense small cell network deployments.

The immediacy of social media messages means that it can act as a rich and timely source of real world event information. The detected events can provide a context to observations made by other city information sources such as fixed sensor installations and contribute to building ‘city intelligence’. In this work, we propose a novel unsupervised method to extract real world events that may impact city services such as traffic, public transport, public safety etc., from Twitter streams. We also develop a named entity recognition model to obtain the precise location of the related events and provide a qualitative estimation of the impact of the detected events. We apply our developed approach to a real world dataset of tweets collected from the city of London.

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.

Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.

Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence.

What activities take place at home? When do they occur, for how long do they last and who is involved? Asking such questions is important in social research on households, e.g., to study energy-related practices, assisted living arrangements and various aspects of family and home life. Common ways of seeking the answers rest on self-reporting which is provoked by researchers (interviews, questionnaires, surveys) or non-provoked (time use diaries). Longitudinal observations are also common, but all of these methods are expensive and time-consuming for both the participants and the researchers. The advances of digital sensors may provide an alternative. For example, temperature, humidity and light sensors report on the physical environment where activities occur, while energy monitors report information on the electrical devices that are used to assist the activities. Using sensor-generated data for the purposes of activity recognition is potentially a very powerful means to study activities at home. However, how can we quantify the agreement between what we detect in sensor-generated data and what we know from self-reported data, especially non-provoked data? To give a partial answer, we conduct a trial in a household in which we collect data from a suite of sensors, as well as from a time use diary completed by one of the two occupants. For activity recognition using sensor-generated data, we investigate the application of mean shift clustering and change points detection for constructing features that are used to train a Hidden Markov Model. Furthermore, we propose a method for agreement evaluation between the activities detected in the sensor data and that reported by the participants based on the Levenshtein distance. Finally, we analyse the use of different features for recognising different types of activities.

In currently deployed wireless networks, the lack of dynamic control across wireless resources leads to suboptimized and unbalanced spectrum loads, especially in below 6GHz bands, which in turn creates a (perceived) capacity bottleneck in end users. This issue can be addressed through the adoption of the eDSA (extended Dynamic Spectrum Access) paradigm, which consists of enhanced resource management techniques with three degrees of freedom: densification, rationalized traffic allocation over heterogeneous wireless technologies, and better load balancing across the available spectrum. The objective of this paper, triggered by the ongoing work in the SPEED-5G research project, is twofold: i) present a survey of the most promising enhanced techniques for 5G networks at MAC and RRM level, and ii) propose inter-RAT mechanisms to cope with the above mentioned issues, mainly focusing on access aspects of the next-generation networks in below and above 6GHz bands.

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.

Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

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.

Creating Internet-of-Things (IoT) solutions that can be deployed at scale requires adequate experimentation environments. In the area of experimentation, two trends can be observed. First, there is a shift from lab-based, controlled experiments to experimenting "in the wild": researchers tend to augment the users' natural environments and observe how people integrate a new solution into their everyday lives. Second, when a substantial investment in setting up an experimentation infrastructure has been made, it makes sense to open it to a wide community of researchers; the concept of Experimentation-as-a-Service (EaaS) is emerging along these lines. SmartCampus, an IoT testbed developed at the University of Surrey, fits the both trends very well. It involves real users in a natural setting, as IoT devices are deployed in the users' offices. Further, several user-centric experiments conducted in the SmartCampus were driven by external researchers, i.e., people who do not belong to the team that developed the testbed. In this paper we report on lessons learned from such IoT experiments. After a brief overview of SmartCampus and the experiments themselves, we offer a simple experiment stakeholder model, which identifies key actors and interfaces between them. We then focus on issues related to the external experimenters who take advantage of the experimentation "service." That focus is motivated by our realization that EaaS, while attractive in principle, gives rise to a number of non-trivial challenges.

The Internet of Things (IoT) paradigm aims to realize heterogeneous physical world objects interacting with each other and with the surrounding environment. In this prospect, the automatic provisioning of the varied possible interactions and bridging them with the digital world is a key pertinent issue for enabling novel IoT applications. The introduction of description logic-based semantics to provide homogeneous descriptions of object capabilities enables lowering the heterogeneity and a limited set of interactions (such as those with stationary objects with fixed availability) to be deduced using classical reasoning systems. However, the inability of such semantics to capture the dynamics of an IoT system as well as the scalability issues that reasoning systems encounter if too many descriptions have to be processed, necessitate that such approaches should be used in conjunction with others. Towards this aim, this paper proposes an automated rule-based association mechanism for integrating the digital IoT components with physical entities along temporal-spatial-thematic axes. To address the scalability issue, this mechanism is distributed over a federated network of nodes, each embodying a set of objects located in the same geographical area. Nodes covering nearby geographical areas can share their object descriptions while all nodes are capable of deducing interactions between the descriptions that they are aware of.© 2013 Elsevier B.V.

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.

The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.

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).

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-ofthe- 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.

We consider the resource allocation with aggregation of multiple bands including unlicensed band for heterogeneous traffic. While the mobile data traffic including high volume of video traffic is expected to increase significantly, an efficient management of radio resources from multiple bands is required to guarantee the quality of service (QoS) of different traffic types. In this context, we formulate an optimal resource allocation by using different utility functions for heterogeneous traffic and the two-step resource allocation algorithm including resource grouping has been proposed. Simulation results demonstrate that the proposed algorithm enhances the connection robustness and shows good performance in terms of higher utility value of inelastic traffic even at high traffic loads by steering elastic traffic to unlicensed band.

Although 5G promises advanced features such as low latency, high data rates and reliability as well as high socio-economic value, the business opportunities of the proposed solutions have not yet been examined. In this paper, the SESAME approach along with spectrum sharing options and indicative use cases are initially described. The incentives for SESAME adoption along with the value proposition and creation are analyzed. Finally, a reference model describing the role and interactions of the involved players as well as the revenue streams is provided.

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.

Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the ``Things''. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.

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 article, we propose mechanisms for the detection and suppression of such deleterious opportunistic users (DOUs) for hard and soft decision fusion. More specifically, a credibility based mechanism for hard decision fusion using a beta reputation system (HDC-BR) is introduced. Our proposed method does not require knowledge of the total number of deleterious users in advance. In HDC-BR, the fusion center assigns and updates weights to each user’s decisions based on an individual user credibility score which is calculated using the beta reputation system. The presence of DOUs in soft decision based collaborative spectrum sensing has even more adverse effects on system performance. We 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; we refer to it as SDC-MG. We 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 (SNR) regime.

In the future internet era, the Internet of Things (IoT) has consolidated its presence in the smart cities with a variety of innovative IoT platforms for the provisioning of relevant services. The provisioning of such services requires ubiquity, reliability, high-performance, efficiency, scalability. In order to accomplish this popular trend is to merge IoT and Cloud concepts by combining multi-Cloud IoT architectures. The introduction of cognition was the first step for the IoT success, as it brought essential self-management and awareness capabilities combined with knowledge-generation functionality. The Cloud-IoT architectural vision is paving the way to the next/vital step for the IoT success and for new business value propositions for the IoT world leveraging cloud principles. Towards this direction, in this effort, a set of challenges have been identified, with a set of different research initiatives that aim to address them. A promising architecture for enabling Cloud-IoT services in smart cities is presented together with a case study that reveals the high potential that Cloud-IoT can achieve in the context of smart cities. The work concludes with the lessons learnt through the study and the elaboration of the Cloud-IoT concepts.

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.

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.

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.

Information-centric networking (ICN) is a promising framework providing data through their names. It is expected that ICN will provide many-to-many communications enabling the efficient content sharing. To provide such many-to-many communications, communication tree structures for different groups need to be constructed on the fly. Consequently, we focus on the tree root deployment and selection for the communication structure establishment: namely, what is the impact of the number of root devices and how to select roots for different groups. We propose an optimized root selection algorithm with low computation power consumption. Through simulations, we show that the number of deployed root devices influences the traffic distribution across the network links greatly. Then we use the proposed algorithm for the root selection for different groups. It is observed that the proposed algorithm can reduce the total traffic overhead compared to the random root selection algorithm.

International audience; The 11th EAI International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM 2016) was hosted by CEA-LETI and was held in Grenoble, France, the capital of the Alps. 2016 was the 30th anniversary of wireless activities atCEA-LETI, but Grenoble has a more ancient prestigious scientific history, still very fresh in the signal processing area as Joseph Fourier worked there on his fundamental research, and established the Imperial Faculty of Grenoble in 1810, now the Joseph Fourier University. This year, the main themes of the conference centered on the application of cognitive radio to 5G and to the Internet of Things (IoT). According to the current trend, the requirements of 5G and the IoT will increase demands on the wireless spectrum, accelerating the spectrum scarcity problem further. Both academic and regulatory bodies have focused on dynamic spectrum access and or dynamic spectrum usage to optimize scarce spectrum resources. Cognitive radio, with the capability to flexibly adapt its parameters, has been proposed as the enabling technology for unlicensed secondary users to dynamically access the licensed spectrum owned by legacy primary users on a negotiated or an opportunistic basis. It is now perceived in a much broaderparadigm that will contribute toward solving the resource allocation problem that 5G requirements raise.The program of the conference was structured to address these issues from the perspectives of industry, regulation bodies, and academia. In this transition period, where several visions of 5G and spectrum usage coexist, the CROWNCOM Committee decided to have a very strong presence of keynote speeches from key stakeholders. We had the pleasure of welcoming keynotes from industry 5G leaders such as Huawei and Qualcomm, the IoT network operator Sigfox, and the European Commission with perspectives on policy and regulation. We were also honored to welcome prestigious academic views from the Carnegie Mellon University and Zhejiang University. Along with the keynote speeches, we had the opportunity to debate the impact of massive IoT deployments in future spectrum use with a panel gathering high-profile experts in the field, thanks to the help of the conference panel chair. The committee also wanted to emphasize how cognitive radio techniques could be applied in different areas of wireless communication in a pragmatic way, in a context where cognitive radio is often perceived as a theoretical approach. With this in mind, a significant number of demonstrations and exhibits were presented at CROWNCOM.Seven demonstrations and two exhibition booths were present during the conference,showcasing the maturity level of the technology. In this regard, we would like to express our gratitude to the conference demonstration and exhibit chair for his outstanding role in the organization of the demonstration area in conjunction with the conference local chairs. In addition, we decided to integrate a series of workshops that can be seen as special sessions where specific aspects of cognitive radio regarding applications, regulatory frameworks, and research were discussed. Four such work-shops were organized as part of the conference embracing topics such as modern spectrum management, 5G technology enabler, software-defined networks and virtualization, and cloud technologies. This year, delegates could also attend three tutorials organized as part of the conference. We are thankful to the tutorial chair for organizing these tutorials.