An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. 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IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. 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A Co-evolutionary Algorithm-based Enhanced Grey Wolf Optimizer for the Routing of Wireless Sensor Networks

Wireless networks are frequently installed in arduous environments, heightening the importance of their consistent operation. To achieve this, effective strategies must be implemented to extend the lifespan of nodes. Energy-conserving routing protocols have emerged as the most prevalent methodology, as they strive to elongate the network\u27s lifetime while guaranteeing reliable data routing with minimal latency. In this paper, a plethora of studies have been done with the purpose of improving network routing, such as the integration of clustering techniques, heterogeneity, and swarm intelligence-inspired approaches. A comparative investigation was conducted on a variety of swarm-based protocols, including a new coevolutionary binary grey wolf optimizer (Co-BGWO), a BGWO, a binary whale optimization, and a binary Salp swarm algorithm. The objective was to optimize cluster heads (CHs) positions and their number during the initial stage of both two-level and three-level heterogeneous networks. The study concluded that these newly developed protocols are more reliable, stable, and energy-efficient than the standard SEP and EDEEC heterogeneous protocols. Specifically, in 150 m2 area of interest, the Co-BGWO and BGWO protocols of two levels were found the most efficient, with over than 33% increase in remaining energy percentage compared to SEP, and over 24% more than EDEEC in three-level networks

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches

Bio-Inspired Compressive Sensing based on Auditory Neural Circuits for Real-time Monitoring and Control of Civil Structures using Resource Constrained Sensor Networks.

Recent natural hazard disasters including Hurricane Sandy (2012) and the Tohoku Earthquake (2011) have called public attention to the vulnerability of civil infrastructure systems. To enhance the resiliency of urban communities, arrays of wireless sensors and actuators have been proposed to monitor and control infrastructure systems in order to limit damage, speed emergency response, and make post-disaster decisions more efficiently. While great advances in the use of wireless sensor networks (WSNs) for the purposes of monitoring and control of civil infrastructure have been made, significant technological barriers have hindered their ability to be reliably used in the field for long durations. Some of these limitations include: reliance on finite, portable power supplies, limited radio bandwidth for data communication, and limited computational capacity. To resolve current bottlenecks, paradigm-altering approaches to the design of wireless monitoring and control systems are required. Through the process of evolution, biological central nervous systems (CNS) have evolved into highly adaptive and robust systems whose sensing and actuation capabilities far surpass the current capabilities of engineered (i.e., man-made) monitoring and control systems. In this dissertation, the mechanisms employed by biological sensory systems serve as sources of inspiration for overcoming the current challenges faced by wireless nodes for structural monitoring and control. The basic, yet elegant, methods of signal processing and data transmission used by the CNS are mimicked in this thesis to enable highly compressed communication with real-time data processing for WSNs engaged in infrastructure monitoring. Specifically, the parallelized time-frequency decomposition of the mammalian cochlea is studied, modeled, and recreated in an ultra-low power analog circuit. In lieu of transmitting data, the cochlea-inspired wireless sensors emulate the neurons by encoding filtered outputs into binary electrical spike trains for highly efficient wireless transmission. These transmitted spike train signals are processed for pattern classification of sensor data to identify structural damage and to perform feedback control in real-time. A key contribution of this thesis is the development and experimental validation of a bio-inspired wireless sensor node that exhibits large energy savings while employing real-time processing techniques, thus overcoming many of the current challenges of traditional wireless sensor nodes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107302/1/cpeckens_1.pd

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware