Socially-Aware Interference Mitigation Game in Body-to-Body Networks

International audience—Wireless wearable devices have recently gained increasing attention from industry in fields such as health, fitness, and entertainment. In this paper, we consider a dynamic system composed of several Body-to-Body Networks (BBNs) based on wearable technology, and we analyze the joint mutual and cross-technology interference problem due to the utilization of a limited number of channels by different transmission technologies (i.e., ZigBee and WiFi) sharing the same radio spectrum. To this end, we propose a game theoretical approach to address the problem of Socially-aware Interference Mitigation (SIM) in BBNs. Our approach considers a two-stage channel allocation scheme: a BBN-stage for inter-WBANs' communications and a WBAN-stage for intra-WBAN communications, involving mutual and cross-technology considerations at each stage. We develop best response algorithms that converge fast to Nash equilibrium points. Simulation results show the efficiency of SIM game in optimizing the channel allocation in BBNs. Index Terms—Body-to-Body Networks, 2.4 GHz ISM band, Cross-Technology Interference, Channel Allocation, Game Theory, Nash Equilibrium

A two-stage game theoretical approach for interference mitigation in Body-to-Body Networks

International audienceIn this paper, we identify and exploit opportunities for cooperation between a group of mobile Wireless Body Area Networks (WBANs), forming a Body-to-Body Network (BBN), through inter-body interference detection and subsequent mitigation. Thus, we consider a dynamic system composed of several BBNs and we analyze the joint mutual and cross-technology interference problem due to the utilization of a limited number of channels by different transmission technologies (i.e., ZigBee and WiFi) sharing the same radio spectrum. To this end, we propose a game theoretical approach to address the problem of Socially-aware Interference Mitigation (SIM) in BBNs, where WBANs are " social " and interact with each other. Our approach considers a two-stage channel allocation scheme: a BBN-stage for inter-WBANs' communications and a WBAN-stage for intra-WBAN communications. We demonstrate that the proposed BBN-stage and WBAN-stage games admit exact potential functions, and we develop a Best-Response (BR-SIM) algorithm that converges to Nash equilibrium points. A second algorithm, named Sub-Optimal Randomized Trials (SORT-SIM), is then proposed and compared to BR-SIM in terms of efficiency and computation time. We further compare the BR-SIM and SORT-SIM algorithms to two power control algorithms in terms of signal-to-interference ratio and aggregate interference, and show that they outperform the power control schemes in several cases. Numerical results, obtained in several realistic mobile scenarios, show that the proposed schemes are indeed efficient in optimizing the channel allocation in medium-to-large-scale BBNs

A Comprehensive Review of D2D Communication in 5G and B5G Networks

The evolution of Device-to-device (D2D) communication represents a significant breakthrough within the realm of mobile technology, particularly in the context of 5G and beyond 5G (B5G) networks. This innovation streamlines the process of data transfer between devices that are in close physical proximity to each other. D2D communication capitalizes on the capabilities of nearby devices to communicate directly with one another, thereby optimizing the efficient utilization of available network resources, reducing latency, enhancing data transmission speed, and increasing the overall network capacity. In essence, it empowers more effective and rapid data sharing among neighboring devices, which is especially advantageous within the advanced landscape of mobile networks such as 5G and B5G. The development of D2D communication is largely driven by mobile operators who gather and leverage short-range communications data to propel this technology forward. This data is vital for maintaining proximity-based services and enhancing network performance. The primary objective of this research is to provide a comprehensive overview of recent progress in different aspects of D2D communication, including the discovery process, mode selection methods, interference management, power allocation, and how D2D is employed in 5G technologies. Furthermore, the study also underscores the unresolved issues and identifies the challenges associated with D2D communication, shedding light on areas that need further exploration and developmen

Submission to the Attorney-General’s Department on the Exposure Draft Telecommunications and Other Legislation Amendment Bill 2015

EXECUTIVE SUMMARY As has been stated in previous submissions to Government, the Associations acknowledge Government’s desire to protect telecommunications infrastructure and the information transmitted across it from unauthorised access and interference. Indeed, Australian Carriers, Carriage Service Providers and Carriage Service Intermediaries (C/CSPs) and other industry participants have an active and vested interest in ensuring that the nation’s networks and communications infrastructure are robust and resistant to external attack. Industry is, however, unable to support the proposed Telecommunications Sector Security Reform (TSSR), as described in the exposure draft legislation, for reasons including that it constitutes regulatory ‘over-reach’ in the form of a framework that: will face challenges protecting communications networks, i.e. it will not deliver the increased protection the proposed reforms are aiming to achieve; is out of step with regulatory approaches to protecting networks adopted in other countries, including the UK, USA and Canada, thereby putting Australia at a disadvantage in fighting cyber threats and undermine Industry’s ability to support these important peers; hands unjustifiably significant additional and intrusive powers to Government and places regulatory burdens on Industry that will undermine its ability to protect against and respond to cyber attacks; risks being highly disruptive to the deployment of new network technologies that are more robust in preventing cyber attacks; will be a significant deterrent to technological investment in Australia; imposes additional costs on Industry and (ultimately) consumers undermining Australia’s competitiveness at a time when digital innovation is an important area for growth for Australia; fails to offer protection/indemnity to C/CSPs against the risk of civil litigation through ‘safe harbours’, thereby limiting information sharing and the ability to quickly respond to threats and to jointly engage in preventative action; carries the risk that competition in infrastructure supply will be reduced, to the detriment of all Australians; lacks transparency; and fails to provide adequate consultative mechanisms and avenues of appeal

The Internet of Humans: Optimal Resource Allocation and Wireless Channel Prediction

Recent advances in information and communications technologies (ICT) have accelerated the realization of the Internet of Humans (IoH). Among the many IoH applications, Wireless Body Area Networks (BANs) are a remarkable solution that are revolutionising the health care industry. However, many challenges must be addressed, including: a) unavoidable inter-BAN interference severely degrading system performance. b) The non-stationarity and atypical dynamics of BAN channels make it extremely challenging to apply predictive transmit power control that improves the energy efficiency of the network. In this context, this thesis investigates the use of intelligent and adaptive resource allocation algorithms and effective channel prediction to achieve reliable, energy-efficient communications in BAN-enabled IoH. Firstly, we investigate the problem of co-channel interference amongst coexisting BANs by proposing a socially optimal finite repeated non-cooperative transmit power control game. The proposed method improves throughput, reduces overall power consumption and suppress interference. The game is shown to have a unique Nash equilibrium. We also prove that the aggregate outcome of the game is socially efficient across all players at the unique Nash equilibrium, given reasonable constraints for both static and slowly time-varying channels. Secondly, we address the problem of overlapping transmissions among non-coordinated BANs with multiple access schemes through intelligent link resource allocation methods. We present two non-cooperative games, employed with a time-division multiple access (TDMA) based MAC layer scheme that has a novel back-off mechanism. The Link Adaptation game jointly adjusts the sensor node's transmit power and data rate, which provides robust transmission under strong inter-BAN interference. Moreover, by adaptively tuning contention windows size an alternative game, namely a Contention Window game is developed, which significantly reduces latency. The uniqueness and existence of the games' Nash Equilibrium (NE) over the action space are proved using discrete concavity. The NE solution is further analysed and shown to be socially efficient. Motivated by the emergence of deep learning technology, we address the challenge of long-term channel predictions in BANs by using neural networks. Specifically, we propose Long Short-term Memory (LSTM)-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2s over non-stationary BAN on-body channels. An incremental learning scheme, which provides continuous and robust predictions, is also developed. We also propose a lightweight NN predictor, namely 'LiteLSTM', that has a compact structure and higher computational efficiency. When implemented on hand-held devices, 'LiteLSTM' remains functional with comparable performance. Finally, we explore the theoretical connections between BAN on-body channels' characteristics and the performance of NN-based power control. To analyse wide-sense stationarity (WSS) characteristics, different stationarity tests are performed for a range of window lengths for on-body channels. Following from this, we develop test benches for NN-based methods at corresponding window lengths using empirical channel measurements. It is observed that WSS characteristics of the BAN on-body channels have a significant impact on the performance of NN-based methods

Operator-based approaches to harm minimisation in gambling: summary, review and future directions

In this report we give critical consideration to the nature and effectiveness of harm minimisation in gambling. We identify gambling-related harm as both personal (e.g., health, wellbeing, relationships) and economic (e.g., financial) harm that occurs from exceeding one’s disposable income or disposable leisure time. We have elected to use the term ‘harm minimisation’ as the most appropriate term for reducing the impact of problem gambling, given its breadth in regard to the range of goals it seeks to achieve, and the range of means by which they may be achieved. The extent to which an employee can proactively identify a problem gambler in a gambling venue is uncertain. Research suggests that indicators do exist, such as sessional information (e.g., duration or frequency of play) and negative emotional responses to gambling losses. However, the practical implications of requiring employees to identify and interact with customers suspected of experiencing harm are questionable, particularly as the employees may not possess the clinical intervention skills which may be necessary. Based on emerging evidence, behavioural indicators identifiable in industryheld data, could be used to identify customers experiencing harm. A programme of research is underway in Great Britain and in other jurisdiction

Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond