Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

The design of a Bayesian Network for mobility management in Wireless Sensor Networks

Mobility in Wireless Sensor Networks (WSNs) is achieved by attaching sensors to mobile objects such as animals (Juang et al. 2002), people (Campbell et al. 2008), and robots (Dantu et al. 2005). Currently, the research about WSN management is mainly focused on energy management functions to control how sensors should use their power; fault management functions to solve sensor problems; quality of services (QoS) management functions to quantify and control the performance; and mobility management functions to detect the sensor movement so that the network wireless connectivity is always maintained (Wang et al. 2010; Ruiz et al. 2003). However, the sensor mobility has not only an impact on the network connectivity, but also on the network spatial coverage. In mobile WSNs, the extension of the spatial coverage is often changing, and as a result, the region of interest might be inaccurately sensed by the mobile sensors. Therefore, the representation of a movement context is important to avoid making interpretations and decisions outside of the situation in which the WSN is capturing information; and make possible to decide where, when and how the sensing is performed in order to obtain the most suitable spatial coverage of a region of interest. This paper proposes a Bayesian network (BN) approach for making explicit the structural and parametric components of a movement context using WSN metadata. The aim is to infer mobility management requirements when a spatial coverage is incorrectly covering a Region of Interest (ROI), regardless the network connectivity. The BN approach provides several advantages regarding to the probabilistic representation of a movement context, the inference of mobility management requirements based on such a context, and the dynamic updating of the movement context every time new metadata are retrieved from the WSN. Previous research works in WSNs have used a similar approach focusing on energy management (Elnahrawy and Nath 2004) and prediction of sensor movement directions (Coles et al. 2009). The main contribution of our work is the analysis of how well a ROI is being covered by mobile sensors, and what are the requirements to improve that coverage given a movement context. A controlled experiment was carried out and the results show that, when the ROI is not being sufficiently covered by a WSN, the BN can probabilistically infer different mobility management requirements, based on a given movement context. Two movement contexts have been used to illustrate this approach. They are related to whether the sensing is being carried out in an emergency situation or not

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

A Bio-inspired Framework for Highly Efficient Structural Health Monitoring and Vibration Analysis

Civil engineering structures are continuously exposed to the risk of damage whether due to ageing effects, excessive live loads or extreme events, such as earthquakes, blasts and cyclones. If not readily identified, damage will inevitably compromise the structural integrity, leading the system to stop operating and undergo in-depth interventions. The economic and social impacts associated with such an adverse condition can be significant, therefore effective methods able to early identify structural vulnerabilities are needed for these systems to keep meeting the required life-safety standards and avoid the impairment of their normal function. In this context, vibration-based analysis approaches play a leading role as they allow to detect structural faults which lie beneath the surface of the structure by identifying and quantifying anomalous changes in the system’s inherent vibration characteristics. However, although the considerable degree of maturity attained within the fields of experimental vibration analysis (EVA) and structural health monitoring (SHM), several technical issues still need to be addressed in order to ensure the successful implementation of these powerful tools for damage identification purposes. The scope of this paper is to present a bio-inspired framework for optimal structural health monitoring and vibration analysis. After a critical overview on current methods and tools, three main sources of bio-inspiration are described along with the relative algorithms derived for SHM applications. It is shown how uncovering the general principles behind the functioning of selected biological systems can foster the development of efficient solutions to the technical conflicts of actual SHM architectures and lead to new sensing paradigms for optimal network topology and sensors location. A compatibility-matrix is proposed to help compare biological and SHM systems and discriminate desired from unwanted features. Such a framework will ultimately assist in seeking for the most suitable nature-inspired solutions for more accurate condition screening and robust vibration analysis.FEDER funds through the Competitiveness Factors Operational Programme - COMPETE and by national funds through FCT – Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio

A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE

L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN), caratterizzate da bassi costi e bassa intrusività. Tuttavia, sebbene in grado di effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacità di elaborazione necessarie a supportare un sistema intelligente; d’altra parte senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui. Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione. Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire ad un sistema centralizzato intelligente di effettuare ragionamenti di alto livello. L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura è stata arricchita con un protocollo di gestione distribuita della reputazione. È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche del testbed, con l’obiettivo di controllare la temperatura in un ambiente lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive equipments for monitoring and modifying the environment according to users’ needs, and to globally defined constraints. Furthermore, such systems cannot ignore requirements about ubiquity, scalability, and transparency to the user. An enabling technology capable of accomplishing these goals is represented by Wireless Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However, although provided of in-network processing capabilities, WSNs do not exhibit processing features able to support comprehensive intelligent systems; on the other hand, without this pre-processing activities the wealth of sensory data may easily overwhelm a centralized AmI system, clogging it with superfluous details. This work proposes a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part, based on a new approach to knowledge extraction from raw data, that addresses this issue at different abstraction levels. WSNs are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts in order to carry on symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users’ desires, taking into account both implicit and explicit feedbacks from the users, while considering global system-driven goals, such as energy saving. The proposed conceptual architecture was exploited to develop a testbed providing the hardware and software tools for the development and management of AmI applications based on WSNs, whose main goal is energy saving for global sustainability. In order to make the AmI system able to communicate with the external world in a reliable way, when some services are required to external agents, the architecture was enriched with a distributed reputation management protocol. A sample application exploiting the testbed features was implemented for addressing temperature control in a work environment. Knowledge about the user’s presence is obtained through a multi-sensor data fusion module based on Bayesian networks, and this information is exploited by a multi-objective fuzzy controller that operates on actuators taking into account users’ preference and energy consumption constraints