Fault-tolerant wireless sensor networks using evolutionary games

This dissertation proposes an approach to creating robust communication systems in wireless sensor networks, inspired by biological and ecological systems, particularly by evolutionary game theory. In this approach, a virtual community of agents live inside the network nodes and carry out network functions. The agents use different strategies to execute their functions, and these strategies are tested and selected by playing evolutionary games. Over time, agents with the best strategies survive, while others die. The strategies and the game rules provide the network with an adaptive behavior that allows it to react to changes in environmental conditions by adapting and improving network behavior. To evaluate the viability of this approach, this dissertation also describes a micro-component framework for implementing agent-based wireless sensor network services, an evolutionary data collection protocol built using this framework, ECP, and experiments evaluating the performance of this protocol in a faulty environment. The framework addresses many of the programming challenges in writing network software for wireless sensor networks, while the protocol built using the framework provides a means of evaluating the general viability of the agent-based approach. The results of this evaluation show that an evolutionary approach to designing wireless sensor networks can improve the performance of wireless sensor network protocols in the presence of node failures. In particular, we compared the performance of ECP with a non-evolutionary rule-based variant of ECP. While the purely-evolutionary version of ECP has more routing timeouts than the rule-based approach in failure-free networks, it sends significantly fewer beacon packets and incurs statistically fewer routing timeouts in both simple fault and periodic fault scenarios

The holistic perspective of the INCISIVE Project: artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.This research received funding mainly from the European Union’s Horizon 2020 research and innovation program under grant agreement no 952179. It was also partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under contracts PID2019-107255GB and 2017-SGR-1414.Peer ReviewedArticle signat per 30 autors/es: Ivan Lazic (1), Ferran Agullo (2), Susanna Ausso (3), Bruno Alves (4), Caroline Barelle (4), Josep Ll. Berral (2), Paschalis Bizopoulos (5), Oana Bunduc (6), Ioanna Chouvarda (7), Didier Dominguez (3), Dimitrios Filos (7), Alberto Gutierrez-Torre (2), Iman Hesso (8), Nikša Jakovljević (1), Reem Kayyali (8), Magdalena Kogut-Czarkowska (9), Alexandra Kosvyra (7), Antonios Lalas (5) , Maria Lavdaniti (10,11), Tatjana Loncar-Turukalo (1),Sara Martinez-Alabart (3), Nassos Michas (4,12), Shereen Nabhani-Gebara (8), Andreas Raptopoulos (6), Yiannis Roussakis (13), Evangelia Stalika (7,11), Chrysostomos Symvoulidis (6,14), Olga Tsave (7), Konstantinos Votis (5) Andreas Charalambous (15) / (1) Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; (2) Barcelona Supercomputing Center, 08034 Barcelona, Spain; (3) Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain; (4) European Dynamics, 1466 Luxembourg, Luxembourg; (5) Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece; (6) Telesto IoT Solutions, London N7 7PX, UK: (7) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (8) Department of Pharmacy, Kingston University London, London KT1 2EE, UK; (9) Timelex BV/SRL, 1000 Brussels, Belgium; (10) Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece; (11) Hellenic Cancer Society, 11521 Athens, Greece; (12) European Dynamics, 15124 Athens, Greece; (13) German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus; (14) Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece; (15) Department of Nursing, Cyprus University of Technology, Limassol 3036, CyprusPostprint (published version

Food and environmental risk assessment of herbicide-tolerant genetically modified maize NK603 for food uses, import and processing under Directive 2001/18 /EC (Notification C/ES/00/01)

Source at https://vkm.no/In preparation for a legal implementation of EU-regulation 1829/2003, the Norwegian Scientific Committee for Food Safety (VKM) has been requested by the Norwegian Environment Agency (former Norwegian Directorate for Nature Management) and the Norwegian Food Safety Authority (NFSA) to conduct final food/feed and environmental risk assessments for all genetically modified organisms (GMOs) and products containing or consisting of GMOs that are authorised in the European Union under Directive 2001/18/EC or Regulation 1829/2003/EC. The request covers scope(s) relevant to the Gene Technology Act. The request does not cover GMOs that VKM already has conducted its final risk assessments on. However, the Agency and NFSA requests VKM to consider whether updates or other changes to earlier submitted assessments are necessary.I forbindelse med forberedelse til implementering av EU-forordning 1829/2003 i norsk rett, er Vitenskapskomiteen for mattrygghet (VKM) bedt av Miljødirektoratet (tidligere Direktoratet for naturforvalting [DN]) og Mattilsynet om å utarbeide endelige helse- og miljørisikovurderinger av alle genmodifiserte organismer (GMOer) og avledete produkter som inneholder eller består av GMOer som er godkjent i EU under forordning 1829/2003 eller direktiv 2001/18, og som er godkjent for ett eller flere bruksområder som omfattes av genteknologiloven. Miljødirektoratet og Mattilsynet har bedt VKM om endelige risikovurderinger for de EU-godkjente søknader hvor VKM ikke har avgitt endelige risikovurderinger. I tillegg er VKM bedt om å vurdere hvorvidt det er nødvendig med oppdatering eller annen endring av de endelige helse- og miljørisikovurderingene som VKM tidligere har levert

Development of Application Specific Clustering Protocols for Wireless Sensor Networks

Applications in wireless sensor networks (WSNs) span over various areas like weather forecasting to measuring soil parameters in agriculture, and from battle_eld to health monitoring. Constrained battery power of sensor nodes make the network design a challenging task. Amongst several research areas in WSN, designing energy e_cient protocols is a prominent area. Clustering is a proven solution to enhance the network lifetime by utilizing the availablebattery power e_ciently. In this thesis, a hypothetical overview has been done to study the strengths and weaknesses of existing clustering algorithms that inspired the design of distributed and energy e_cient clustering in WSN. Distributed Dynamic Clustering Protocol (DDCP) has been proposed to allow all the nodes to take part in the cluster formation scheme and data transmission process. This protocol consists of a cluster-head selection algorithm, a cluster formation scheme and a routing algorithm for the data transmission between cluster-heads and the base station. All the sensor nodes present in the network takes part in the cluster-head selection process. Staggered Clustering Protocol (SCP) has been proposed to develop a new energy e_cient clustering protocol for WSN. This algorithm is aiming at choosing cluster-heads that ensure both the intra-cluster data transmission and inter-cluster data transmission are energy-e_cient. The cluster formation scheme is accomplished by exchanging messages between non-cluster-head nodes and the cluster-head to ensure a balanced energy loadamong cluster-heads. An energy e_cient clustering algorithm for wireless sensor networks using particle swarm optimization (EEC-PSO) has been proposed to ensure energy e_ciency by creating optimized number of clusters. It also improves the link quality among the cluster-heads with the cluster member nodes. Finding a set of suitable cluster-heads from N sensor nodes is considered as non-deterministic polynomial (NP)-hard optimization problem. The application of WSN in brain computer interface (BCI) has been proposed to detect the drowsiness of a driver on wheels. The sensors placed in a braincap worn by the driver are divided into small clusters. Then the sensed data, known as EEG signal, are transferred towards the base station through the cluster-heads. The base station may be placed at a nearby location of the driver. The received data is processed to take a decision when to trigger the warning tone

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

Proponemos un marco conceptual para acoplar técnicas de auto-organización y técnicas de autocuración. A este marco se le llama de auto-curación y es capaz de hacer frente a enlaces inalámbricos inestables y nodos defectuosos. Dividimos el marco en dos componentes principales: la auto-organización y auto-curación. En el componente de auto-organización, nosotros construimos una topología de árbol que determine las rutas hacia el sumidero. En el componente de auto-curación, la topología del árbol se adapta a ambos tipos de fallas siguiendo tres pasos: recopilación de información, detección de fallas, y la recuperación de fallos. En el paso de recopilación de información, los nodos determinan el estado actual de la red mediante la recopilación de información de la capa MAC. En el paso de detección de fallas, los nodos analizan la información recopilada y detectan nodos/enlaces defectuosos. En el paso de recuperación de fallos, los nodos recuperan la topología del árbol mediante la sustitución de componentes defectuosos con redundantes (es decir, componentes de respaldo). Este marco permite una red con resiliencia que se recupera sin agotar los recursos de la red.We propose a conceptual framework for putting together self-organizing and self-healing techniques. This framework is called the self-healing framework and it is capable of coping with unstable wireless links and faulty nodes. We divide the framework into two major components: selforganization and self-healing. In the self-organization component, we build a tree topology that determines routing paths towards the sink. In the self-healing component, the tree topology copes with both types of failures by following three steps: information collection, fault detection, and fault recovery. In the information collection step, the nodes determine the current status of the network by gathering information from the MAC layer. In the fault detection step, the nodes analyze the collected information and detect faulty nodes/links. In the fault recovery step, the nodes recover the tree topology by replacing the faulty components with redundant ones (i.e., backup components). This framework allows a resilient network that recovers itself without depleting the network resources.Doctor en IngenieríaDoctorad

The holistic perspective of the INCISIVE project : artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions

Generic Adaptation Support for Wireless Sensor Networks

Wireless Sensor Networks are used in various and expanding application scenarios and are also considered to be important elements of the Internet of Things. They monitor and deliver data, which is not only used for research but to an increasing degree also in business environments. With the increasing complexity of these scenarios and the increasing dependency on the availability of the sensor network data, the requirements to a Wireless Sensor Network increase at the same pace. Since Wireless Sensor Networks are typically implemented using resource-constrained platforms, sensor network algorithms are typically optimised for specific operating conditions such as static or mobile networks, high or low traffic etc. However, due to scenario complexity and dynamic real-world conditions a static configuration of a Wireless Sensor Network software cannot always meet the requirements. Moreover, these requirements of the sensor network's user can change over time, for example concerning accuracy. Therefore, the sensor network software has to adapt itself to cope with dynamic system conditions and user requirements. This thesis presents the TinyAdapt and TinySwitch frameworks to solve the aforementioned problems. TinyAdapt, our generic adaptation framework for Wireless Sensor Networks, allows for the autonomous adaptation of arbitrary sensor network algorithms based on explicit and intuitively defined user preferences and on automatically monitored network conditions. Due to a two-phase approach, run-time adaptation is executed completely and efficiently on standard sensor node hardware and does not need support from, e.g., the base station. The creation of adaptive applications is guided by a complete workflow, which is presented as well. When changing parameters of an algorithm is not enough to achieve the desired adaptation results, the algorithm has to be exchanged completely. However, several limitations of TinyOS and the sensor node hardware limit the use of simple code exchange by node reprogramming for efficient adaptation. TinySwitch, our generic switching framework, allows to switch between alternative algorithms that are already installed in parallel. TinySwitch analyses these algorithms, determines their dependencies and creates all code to enable one of the algorithms while isolating all others. Due to its minimal overhead, TinySwitch is perfectly suited for run-time adaptation in TinyAdapt