QoS aware radio access technology selection framework in heterogeneous networks using SDN

This paper addresses the problem of radio access technology (RAT) selection in heterogeneous networks (HetNets). Current approaches rely on signal related metrics such as signal to interference plus noise ratio (SINR) for selection of the best network for the wireless user. However, such approaches do not take into account the quality of service (QoS) requirements of wireless users and therefore often do not connect them to the most suitable network. We propose a QoS aware RAT selection framework for HetNets based on software-defined networking (SDN). The proposed framework implements a RAT selection strategy that reflects QoS requirements of downlink flows using a metric called fittingness factor (FF). The framework relies on the flexibility and centralised nature of SDN to implement monitoring and RAT capacity assessment mechanisms that help in the realisation of the selection strategy. The simulation campaign illustrates the important gains achieved by our RAT selection framework in terms of data rates assigned to the wireless users, their satisfaction, and their quality of experience (QoE) compared against other state of the art RAT selection solutions

A dynamic access point allocation algorithm for dense wireless LANs using potential game

This work introduces an innovative Access Point (AP) allocation algorithm for dense Wi-Fi networks, which relies on a centralised potential game developed in a Software-Defined Wireless Networking (SDWN)-based framework. The proposed strategy optimises the allocation of the Wi-Fi stations (STAs) to APs and allows their dynamic reallocation according to possible changes in the capacity of the Wi-Fi network. This paper illustrates the design of the proposed framework based on SDWN and the implementation of the potential game-based algorithm, which includes two possible strategies. The main novel contribution of this work is that the algorithm allows us to efficiently reallocate the STAs by considering external interference, which can negatively affect the capacities of the APs handled by the SDWN controller. Moreover, the paper provides a detailed performance analysis of the algorithm, which describes the significant improvements achieved with respect to the state of the art. Specifically, the results have been compared against the AP selection considered by the IEEE 802.11 standards and another centralised algorithm dealing with the same problem, in terms of the data bit rate provided to the STAs, their dissatisfaction and Quality of Experience (QoE). Finally, the paper analyses the trade-off between efficient performance and the computational complexity achieved by the strategies implemented in the proposed algorithm

Radio Resource Management framework for energy-efficient communications in the Internet-of-Things

The Internet-of-Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature Radio Access Technologies (RATs) such as GSM, LTE,Wi-Fi and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use Low PowerWide Area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a Radio Resource Management (RRM) framework, based on Software-Defined Networking (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information in order to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software-defined radio based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latestIoT technologies, i.e., LoRa and SigFox, operate.We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications

Fine-Grained Radio Resource Management to Control Interference in Dense Wi-Fi Networks

In spite of the enormous popularity of Wi-Fienabled devices, the utilisation of Wi-Fi radio resources (e.g. RF spectrum and transmission power levels) at Access Points (APs) is degraded in current decentralised Radio Resource Management (RRM) schemes. Most state of the art centralised control solutions apply configurations in which the network-wide impacts of the involved parameters and their mutual relationships are ignored. In this paper, we propose an algorithm for jointly adjusting the transmission power levels and optimising the RF channel assignment of APs by taking into account the flows’ required qualities while minimising their interference impact throughout the network. The proposed solution is tailored for an operatoragnostic and Software Defined Wireless Networking (SDWN)- based centralised RRM in dense Wi-Fi networks. Our extensive simulation results validate the performance improvements of the proposed algorithm compared to the main state of the art alternative by showing more than 25% higher spectrum efficiency, satisfying the users’ demands and further mitigating the networkwide interference through flow-based and quality-oriented power level adjustment

SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks

The popularity of Wi-Fi-enabled devices alongside the growing demand for non-licensed spectrum, has made the Wi-Fi networks exceedingly congested. This endangers the efficiency of Wi-Fi and negatively affect the users' experience. The problem is especially pressing in dense areas (e.g. shopping centers) where Wi-Fi channel assignment is more likely to be uncoordinated and the working environment of Wi-Fi Access Points (APs) has become increasingly time-variant. As a result, the availability of Software-Defined Networking (SDN) and network virtualization technologies has motivated the use of centralized resource management as a solution. This paper provides an algorithm for channel assignment functionality in the context of SDN-based centralized resource management, which captures the live status of a Wi-Fi network and is capable of optimising the Radio Frequency (RF) channel assignment process. The APs' network arrangement, the current assignment of the channels and the characteristics of the RF channels in IEEE 802.11 have all been taken into account in the proposed model. The performance of the proposed model in terms of the level of the interference, the spectral efficiency at each AP and the Signal to Interference plus Noise Ratio (SINR) at the user-side is evaluated through simulation and compared against state of the art solutions

A pathway to solving the Wi-Fi tragedy of the commons in apartment blocks

Surprisingly little research has quantified the severity of Wi-Fi congestion in densely populated areas. We performed a high-fidelity 3D simulation of the performance of a realistic Wi-Fi deployment in a typical apartment block. Our results show that congestion leads to significant loss of performance, and that current channel selection procedures have only little effect. Also the strategy that is mostly applied today, i.e. to deploy additional repeaters and access points (APs), fails. As this is a typical example of the “Tragedy of the Commons”, some form of collaboration between AP operators is needed to solve the problem. New channel selection algorithms that optimize Wi-Fi performance on a system level then become possible which, for instance, minimize the mutual interference impact on all APs involved. We validate that such an algorithm indeed leads to an optimized as well as fair assignment, which is a necessary first step towards solving the Tragedy

A centralised Wi-Fi management framework for D2D communications in dense Wi-Fi networks

In Wi-Fi networks, Device-to-Device (D2D) communications aim to improve the efficiency of the network by supporting direct communication between users in close proximity. However, in a congested Wi-Fi network, establishing D2D connections through a locally managed self-organising approach will intensify the congestion and reduce the scalability of the solution. Therefore, a centralised management approach must be involved in orchestrating those actions to guarantee the sufficiency of D2D communications. In this paper, we propose a novel management framework for D2D communications in dense Wi-Fi networks. The proposed framework employs a Software-Defined Networking (SDN) based centralised controller in synergy with a novel Access Point (AP) channel assignment process. This framework is designed to proactively establish and manage D2D connections in Wi-Fi networks considering the available radio resources and the effect of the subsequent interference. Thus, improving the overall performance of the network and providing users with higher data rate. Through simulation, we validate the effectiveness of the proposed framework and demonstrate how D2D deployment considerably improves the Wi-Fi network efficiency especially when the data rate requirements are high. Furthermore, we show that our proposed framework achieves better performance than the widely deployed Least Congested Channel selection strategy (LCC)

Exploiting linked data to create rich human digital memories

Memories are an important aspect of a person's life and experiences. The area of human digital memories focuses on encapsulating this phenomenon, in a digital format, over a lifetime. Through the proliferation of ubiquitous devices, both people and the surrounding environment are generating a phenomenal amount of data. With all of this disjointed information available, successfully searching it and bringing it together, to form a human digital memory, is a challenge. This is especially true when a lifetime of data is being examined. Linked Data provides an ideal, and novel, solution for overcoming this challenge, where a variety of data sources can be drawn upon to capture detailed information surrounding a given event. Memories, created in this way, contain vivid structures and varied data sources, which emerge through the semantic clustering of content and other memories. This paper presents DigMem, a platform for creating human digital memories, based on device-specific services and the user's current environment. In this way, information is semantically structured to create temporal "memory boxes" for human experiences. A working prototype has been successfully developed, which demonstrates the approach. In order to evaluate the applicability of the system a number of experiments have been undertaken. These have been successful in creating human digital memories and illustrating how a user can be monitored in both indoor and outdoor environments. Furthermore, the user's heartbeat information is analysed to determine his or her heart rate. This has been achieved with the development of a QRS Complex detection algorithm and heart rate calculation method. These methods process collected electrocardiography (ECG) information to discern the heart rate of the user

Semantic clustering mechanisms for communication in wireless sensor networks

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