Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview

Biology has often been used as a source of inspiration in computer science and engineering. Bioinspired principles have found their way into network node design and research due to the appealing analogies between biological systems and large networks of small sensors. This paper provides an overview of bioinspired principles and methods such as swarm intelligence, natural time synchronization, artificial immune system and intercellular information exchange applicable for sensor network design. Bioinspired principles and methods are discussed in the context of routing, clustering, time synchronization, optimal node deployment, localization and security and privacy

Cooperative agent-based SANET architecture for personalised healthcare monitoring

The application of an software agent-based computational technique that implements Extended Kohonen Maps (EKMs) for the management of Sensor-Actuator networks (SANETs) in health-care facilities. The agent-based model incorporates the BDI (Belief-Desire-Intention) Agent paradigms by Georgeff et al. EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize. Current results show a combinatorial approach to optimization with EKMs provides an improvement in event trajectory estimation compared to standalone cooperative EKM processes to allow responsive event detection for patient monitoring scenarios. This will allow healthcare professionals to focus less on administrative tasks, and more on improving patient needs, particularly with people who are in need for dedicated care and round-the-clock monitoring. ©2010 IEEE

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Deployment of DeepTech AI Models in Engineering Solutions

Ponencia presentada en ICRAMAE-2k21, International Conference on Recent Advances in Mechanical and Automation Engineering, Vivekananda Global University, Jaipur, India, 29-30th November 2021[EN]Industrial Engineering is a branch of engineering that focuses on the design and operation of industrial processes. It involves the application of science to the construction of production systems. This field has undergone significant advancements over the last decades. In the last centuries, the emergence of different technologies has led to breakthroughs in engineering, making it possible to automate processes in industries. Steam, electricity, the internet, and now Artificial Intelligence technologies have all brought with them greater levels of automation to machinery, gradually decreasing human involvement in processes such as procurement, raw material handling, manufacturing and quality control

AI models for recommendation

Ponencia presentada en EMAI2021, West Bengal, India, 4/4/2021[EN]Today, the industries of all European countries face common challenges: improving resource efficiency, becoming more environmentally friendly, mitigating climate change, improving the digitization in all segments of the value chain and improving transparency and safety, providing consumers with detailed information and ensuring the safety and quality of the final product. Growing concerns about environmental and social issues are pushing the demands of stakeholders (customers, workers, shareholders, consumers, etc.) and the public towards more sustainable processes and products. Sustainability is closely linked to climate change: the introduction of sustainable measures, both by consumers and producers, is inherently a measure against climate change

Recommendation AI models: case studies

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN] The targeted consumers can be not only individuals sensitive to environmental and sustainable consumption issues, but also communities, small businesses (e.g., local coffee shop, school, sports club) that share the same concerns as their customers or are just trying to better address their needs. In addition, this tool is designed to assist decision-makers in companies (e.g., supply chain and purchasing managers) as well as policy makers in assessing the overall sustainability of products. Likewise, the tool can provide valuable information to manufacturers who, based on the "sustainable market momentum" gained, could innovate their products and their approach to improving sustainability, thus differentiating themselves from the competitio

Intelligent models for recommendation

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN]Information tools are one of the types of tools available in an effort to change consumers' perceptions, motivations, knowledge and standards. Accordingly, it is increasingly important for consumers to be able to make informed choices about the products they buy, especially in terms of sustainability. Together with the commitment of businesses and organizations to more responsible and sustainable processes and production, the implementation of the European Green Deal and the Sustainable Development Goals is an urgent challenge to all actors in society to contribute to changing the way we meet our needs

Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms

Behavioural adaptation is a pervasive component in a myriad of animal societies. A well-known strategy, known as Levy Walk, has been commonly linked to such adaptation in foraging animals, where the motion of individuals couples periods of localized search and long straight forward motions. Despite the vast number of studies on Levy Walks in computational ecology, it was only in the past decade that the first studies applied this concept to robotics tasks. Therefore, this Thesis draws inspiration from the Levy Walk behaviour, and its recent applications to robotics, to design biologically inspired models for two swarm robotics tasks, aiming at increasing the performance with respect to the state of the art. The first task is cooperative surveillance, where the aim is to deploy a swarm so that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm that augments the concept of Levy Walk to include the Reynolds’ flocking rules and achieve both exploration and coordination in a swarm of unmanned aerial vehicles. The second task is adaptive foraging in environments of clustered rewards. In such environments behavioural adaptation is of paramount importance to modulate the transition between exploitation and exploration. Nature enables these adaptive changes by coupling the behaviour to the fluctuation of hormones that are mostly regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010), in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found. The Endocrine Levy Walk is then augmented to consider social interactions between members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model, the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013) and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also made both in simulated and physical experiments and assessed in terms of search efficiency, number of rewards found and cluster search efficiency, strengthening the argument in favour of the Endocrine Levy Firefly as a promising approach to tackle collaborative foragin

Delay-Constrained Mobile Energy Charging in Wireless Sensor Networks

为了延长无线传感网的生存期,基于可充电的移动设备,研究设计了一种无线传感网中移动式能量补充的方法,移动节点可以在为传感器节点补充能量的同时收集数; 据.首先,通过将无线传感器网络监测区域分割为大小相同的子区域,该子区域内的节点组成一个簇;其次,以一个簇内的总能量为计算依据,设计移动节点的路径; 生成算法以确定能量高效的移动路线;最后,使用10种不同的随机网络拓扑图进行了仿真实验,以节点移动速度和时延为限制条件分别得到了对比数据.结果表明; ,本文提出的算法与NJNP( nearest-job-next with preemption)算法相比在时延相同的条件下( 800; s),生存期提升了6 000 s左右,在节点速度5 m/s条件下生存期提升了将近14 000; s.证明本文所提方法有效地提高了充电效率,延长了网络的生存期,可用于大规模的无线传感器网络.In order to prolong the lifetime of wireless sensor networks by using; energy-rechargeable mobile devices,this paper designs a mobile energy; replenishment method wherein a mobile element gathers data and recharges; sensors simultaneously. Firstly,the whole sensor network is divided into; several sub-regions equally and the sensors in each sub-region are; formed into a cluster. Secondly, considering the energy in a whole; cluster,the mobility path is designed to find the energy-efficient; mobile trace of the mobile element. Finally,in the simulation; experiment,we used ten different random network topologies to show the; comparisons with extensive simulation experiments under different; velocities and deadlines. The results indicate that the proposed; algorithm increases lifetime by approximately 6 000 s compared with; Nearest-Job-Next with Pre-emption( NJNP) under the deadline of 800 s.; Moreover,the proposed algorithm increases lifetime by approximately 14; 000 s compared with NJNP at velocity of 5 m/s. Thus,the proposed; algorithm can improve recharging efficiency and prolong the lifetime of; wireless sensor networks,which can be used in large-scale sensor; networks.国家自然科学基金资助项目; 福建省高等学校杰出青年科研人才培育计划资助项