Multi-radio network optimisation using Bayesian belief propagation

In this paper we show how 5 GHz and “TV White Space” wireless networks can be combined to provide fixed access for a rural community. Using multiple technologies allows the advantages of each to be combined to overcome individual limitations when assigning stations between networks. Specifically, we want to maximise throughput under the constraint of satisfying both the desired individual station data rate and the transmit power within regulatory limits. For this optimisation, we employ Pearl's algorithm, a Bayesian belief propagation implementation, which is informed by statistics drawn from network trials on Isle of Tiree with 100 households. The method confirms results obtained with an earlier deterministic approach

Managing Vulnerabilities of Tactical Wireless RF Network Systems: A Case Study

Organisations and individuals benefit when wireless networks are protected. After assessing the risks associated with wireless technologies, organisations can reduce the risks by applying countermeasures to address specific threats and vulnerabilities. These countermeasures include management, operational and technical controls. While these countermeasures will not prevent all penetrations and adverse events, they can be effective in reducing many of the common risks associated with wireless RF networks. Among engineers dealing with different scaled and interconnected engineering systems, such as tactical wireless RF communication systems, there is a growing need for a means of analysing complex adaptive systems. We propose a methodology based on the systematic resolution of complex issues to manage the vulnerabilities of tactical wireless RF systems. There are is a need to assemble and balance the results of any successful measure, showing how well each solution meets the system’s objectives. The uncertain arguments used and other test results are combined using a form of mathematical theory for their analysis. Systems engineering thinking supports design decisions and enables decision‐makers to manage and assess the support for each solution. In these circumstances, complexity management arises from the many interacting and conflicting requirements of an increasing range of possible parameters. There may not be a single ‘right’ solution, only a satisfactory set of resolutions which this system helps to facilitate. Smart and innovative performance matrixes are introduced using a mathematical Bayesian network to manage, model, calculate and analyse all the potential vulnerability paths in wireless RF networks

D11.2 Consolidated results on the performance limits of wireless communications

Deliverable D11.2 del projecte europeu NEWCOM#The report presents the Intermediate Results of N# JRAs on Performance Limits of Wireless Communications and highlights the fundamental issues that have been investigated by the WP1.1. The report illustrates the Joint Research Activities (JRAs) already identified during the first year of the project which are currently ongoing. For each activity there is a description, an illustration of the adherence and relevance with the identified fundamental open issues, a short presentation of the preliminary results, and a roadmap for the joint research work in the next year. Appendices for each JRA give technical details on the scientific activity in each JRA.Peer ReviewedPreprin

Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach

Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks

We consider a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by byzantine nodes, i.e. nodes which deliberately alter the result of the local decision to induce an error at the fusion center. When independent states are considered, the optimum fusion rule over a batch of observations has already been derived, however its complexity prevents its use in conjunction with large observation windows. In this paper, we propose a near-optimal algorithm based on message passing that greatly reduces the computational burden of the optimum fusion rule. In addition, the proposed algorithm retains very good performance also in the case of dependent system states. By first focusing on the case of small observation windows, we use numerical simulations to show that the proposed scheme introduces a negligible increase of the decision error probability compared to the optimum fusion rule. We then analyse the performance of the new scheme when the FC make its decision by relying on long observation windows. We do so by considering both the case of independent and Markovian system states and show that the obtained performance are superior to those obtained with prior suboptimal schemes. As an additional result, we confirm the previous finding that, in some cases, it is preferable for the byzantine nodes to minimise the mutual information between the sequence system states and the reports submitted to the FC, rather than always flipping the local decision