Sensor Lifetime Research Articles

Improving inkjet printed flexible angle sensor performance by compression–tension sensor pair compensation

This paper introduces a new method for improved integrated angle sensing in the foldable robot joints (hinges), using low-cost inkjet printed sensors and a novel physical intelligence based compensation approach. Silver nanoparticle angle sensors are inkjet printed on a flexible PET substrate as a hinge material and implemented as pairs of compression and tension side folding individuals into a rigid experimental setup. Resulting combined signals improved the individual sensor responses due to their compensating physical characteristics, and gave the far best performances in the existing literature in terms of linearity, sensor life-time, static and cyclic drift, hysteresis and dynamic dependency. Proposed approach is promising for eliminating the major limitations on the printed sensor use in flexible hinges and paving the way to reliable, fully soft and easy-to-fabricate all-integrated foldable robots.

(Nanocarbons Division SES Young Investigator Award) Synthetic Biology Approaches for Overcoming Bottlenecks in Optical Nanocarbon Technologies

Bioengineering is the synthetic biologist’s approach to engineering new materials. It has allowed researchers to overcome billions of years of evolution to create unnatural biomolecules that have been programmed to interface with synthetic materials or optimized to bind analytes through interactions unfounded in nature. Biomolecules offer unparalleled molecular recognition that can be tuned by engineers to create highly specific sensors. Unfortunately, biology has its limits; many biological optical sensors rely on fluorophores that photobleach, which limit sensor lifetime, at visible emission wavelengths that overlap with tissue absorption. Unlike these fluorophores, semiconducting single-walled carbon nanotubes (SWCNTs) benefit from fluorescent emissions that are indefinitely photostable, demonstrating sensitivities that can detect analytes down to the single molecule. Their near-infrared fluorescence wavelengths are also transparent to tissue absorption, allowing for continuous in vivo sensing. Unfortunately, these nanomaterials lack the molecular recognition biology has to offer.In a sense, the advantages and disadvantages posed by the fields of bio- and nano-materials engineering are highly complementary. This seminar focuses on the development of a new generation of NanoBiOptic devices – devices that exploit the synergy of nano-bio hybrids – for sensing applications. We apply bioengineering techniques, such as directed evolution and artificial nucleic acid design, to circumvent current approaches used to engineer SWCNT-based sensors. In demonstrating these techniques, we realize several previously intractable platforms for bioanalyte detection and for optically monitoring various biomolecular interactions. Thus, by reprogramming biological materials to behave in an otherwise non-natural manner, synthetic biology has the potential to complement the physical sciences in the engineering of new synthetic optical platforms.

Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm

One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used. For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs).

Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

PurposeMinimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.Design/methodology/approachIn this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.FindingsEnergy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.Originality/valueThis paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Pd-decorated PdO nanoparticle nanonetworks: A low-cost eye-readable H2 indicator with reactivation ability

Developing low-cost optical hydrogen sensors for applications in the fields related to hydrogen economy has attracted great attentions, but still remains challenging. Herein, a new type of optical indicator based on Pd-decorated PdO nanoparticle nanonetworks (Pd-PdO NNs) is reported. Pd-PdO NNs have the capability to indicate the presence of hydrogen in concentration range of 0.5% to 100 vol% H2 in air background by a tunable color change. Detailed investigations show the reduction of PdO in H2 is an autocatalytic reaction, and decorated Pd species act as catalysts to accelerate the rate-determining step, so the response time can be dramatically shortened by more than 400 times. The as-prepared H2 indicator does not require expensive detectors, and the discoloration process can be observed conveniently by naked eyes or quantitively analyzed by cameras on smartphones. Moreover, this work highlights the used indicator can be recycled and reactivated repeatedly due to the interconvertibility between Pd and PdO, which opens a new strategy toward extending the sensor lifetime and reducing the cost. As such, Pd-PdO NNs hold the promise to be used as low-cost eye-readable H2 indicators that provide basic sensing services for a broad application scenarios.

671-P: Patterns of Sensor-Detected Hypoglycemia in Long-Duration CGM Data from a Diabetes Registry

Compared to time below range less is known about the frequency and temporal distribution of hypoglycemic episodes in CGM data. Pooled analysis of long-duration CGM data from a diabetes registry allows for large-scale exploration of sensor-detected hypoglycemic episodes (SDHE) . 66 patients with T1D used CGM over 365.7 (340.6-392.5) days (24.530 days total) . In addition to time below range (TBR) , we explored frequency of SDHE >15 minutes at <70, <54 and 40 mg/dl by time of day and sensor runtime (30 days) as well as glycemic control by pooled individual days (12AM-12AM) . TBR was 5.4% (<70) , 1.9% (<54) and 0.7% (=40) . 24.7% of SDHE<70 occurred at nighttime (0-6am = 25% of hours/day) . SDHE<54 and SDHE=40 had a nightly surplus with 34.4% and 41.5%, respectively. Number of daily SDHE<70 slightly decreased over the first month and was more uniform across subjects. Numbers of SHDE<54 and SHDE=40 are more varied, and the latter increased over time. Pooled CGM days showed a decrease and greater variance in glucose levels during the night, a surplus of nightly readings at 40mg/dl and a drop in glucose around noon. Focus on SHDE and individual days showed similar patterns to TBR regarding the surplus and variability of nightly low readings. The greater variability and the increase of SHDE=40 over 30 days while SDHE<70 remained stable suggest that sensor lifetime may be a confounder. Overall, SDHE is a valuable addition to TBR. Disclosure P.M.Baumann: None. D.A.Hochfellner: None. M.Cigler: None. R.Siegl: None. T.Poettler: None. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. Funding "This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement 777460. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF, International Diabetes Federation (IDF) , The Leona M. and Harry B. Helmsley Charitable Trust."

A weighted Markov-clustering routing protocol for optimizing energy use in wireless sensor networks

Interest has increased in wireless sensor networks (WSNs) in fields such as healthcare, industrial control and environmental monitoring. More recently, WSNs have been widely deployed in Internet of Things (IoT) based applications. They are considered as an essential part of IoT networks. The expanded use of WSNs raises daunting technical challenges, not the least of which is sensor battery energy conservation. The design of efficient sensor clustering strategies to reduce energy consumption by data transmission throughout the WSN has become crucial. Several applications of WSNs induce a random deployment of sensors such as the monitoring of conflict zones, the study of natural phenomena in hostile zones or rescue operations. In this context, energy conservation should be ensured according to the non-uniformity of the resulting sensor distribution in different areas of the network. In this study, a novel weighted Markov clustering protocol that considers sensor abundance for cluster-head and queried sensor selections is presented. The new protocol aims to decrease intra-cluster energy consumption by reducing the sending of redundant data in sensor-dense regions. In addition, it attempts to prolong sensor-sparse regions lifetime by limiting the number of queried sensors. This protocol combines a Markov clustering of sensors with a sensor weighting based on residual energy and sensor abundance in the network. The proposed protocol is a significant improvement of an existing unweighted Markov clustering protocol. The unweighted Markov clustering protocol is based on sensor residual energy and sensor location without taking into account the sensor abundance in different areas of the network. Simulations affirm that the new protocol handles more appropriately the non-uniformity of sensor distribution and enhance the durability of wireless sensor networks. Indeed, simulation results show that the proposed clustering protocol outperforms its unweighted ancestor and other well-known clustering protocols in terms of energy conservation and network lifetime. The number of expired sensors and the average dissipated energy are reduced, whereas, the average sensor lifetime is prolonged compared to the unweighted ancestor, or HEED, LEACH, PEGASIS, and TEEN.

Physiological reconstruction of blood glucose level using CGMS-signals only

Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor’s lifetime, while reducing the associated discomfort, would considerably improve patient’s quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method.

Room-temperature sensing performance of Pt nanoparticles modified In2O3@ZnS core-shell hollow nanospheres to n-butanol

Room-temperature detection is a great pursuit of goal for semiconductor sensors, aiming to lower energy consumption, prolong the lifetime of sensors, and mitigate safety risks. In this study, room-temperature sensing materials, made of novel Pt nanoparticles modified In2O3@ZnS core-shell hollow nanospheres (Pt-In2O3@ZnS CSHNs), were synthesized by simple hydrothermal and in-situ reduction methods without using any surfactants or toxic organic solvents. The effects of the morphology and composition on gas sensing performance were systematically investigated by adjusting the content of Pt nanoparticles. These materials exhibited excellent sensing performance towards n-butanol at (near) room temperature (~25 °C). The optimal working temperature of Pt-In2O3@ZnS CSHNs (50 °C) was decreased by 210 °C than that of In2O3@ZnS (1:1) CSHNs (260 °C) towards 100 ppm of n-butanol. In particular, the 5% Pt-In2O3@ZnS CSHNs showed an excellent sensing response towards different concentrations of n-butanol at room temperature. The highest sensing response of Pt-In2O3@ZnS CSHNs was up to 7.1 (at 50 °C) and 6.2 (at 25 °C) for 100 ppm n-butanol with a short response time. Further studies demonstrated that such enhanced performances were possibly owing to the “catalytic sensitization” effect driven by Pt NPs and the “electronic sensitization” effect triggered through the formation of Pt-ZnS Schottky junction and In2O3/ZnS n-n heterojunction. This study may shed light on promising gas sensing materials to achieve the room-temperature detection of n-butanol in practical application.

Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform.

In industrial wireless sensors networks (IWSNs), the sensor lifetime predictability is critical for ensuring continuous system availability, cost efficiency and suitability for safety applications. When deployed in a real-world dynamic and centralised network, the sensor lifetime is highly dependent on the network topology, deployment configuration and application requirements. (In the absence of an energy-aware mechanism, there is no guarantee for the sensor lifetime). This research defines a conceptual model for enhancing the energy predictability and efficiency of IWSNs. A particularization of this model is the predictive energy-aware routing (PEAR) solution that assures network lifetime predictability through energy-aware routing, energy balancing and profiling. The PEAR solution considers the requirements and constraints of the industrial ISA100.11a communication standard and the VR950 IIoT Gateway hardware platform. The results demonstrate the PEAR ability to ensure predictable energy consumption for one or multiple network clusters. The PEAR solution is capable of intracluster energy balancing, reducing the overconsumption 10.4 times after 210 routing changes as well as intercluster energy balancing, increasing the cluster lifetime 2.3 times on average and up to 3.2 times, while reducing the average consumption by 23.6%. The PEAR solution validates the feasibility and effectiveness of the energy-aware conceptual indicating its suitability within IWSNs having real world applications and requirements.

Energy-Aware Hybrid MAC Protocol for IoT Enabled WBAN Systems

Energy efficiency is an important quality-of-service requirement that needs to be considered when designing an efficient MAC protocol for a WBAN system due to the limited power resources of biomedical sensor devices. To address this, an energy-aware multi-group hybrid MAC (MG-HYMAC) protocol is proposed in this work to improve energy efficiency as well as the lifetime of the biomedical sensor devices in a personalized healthcare system. The proposed protocol combines both the advantages of the CSMA/CA and the TDMA schemes to enable the biomedical sensors to efficiently contend for transmission opportunities and to allow them to efficiently transmit health data. The MG-HYMAC protocol is combined with a transmission scheduling technique to duty cycle the operations of the biomedical devices with less critical data to determine when and how the biomedical sensor devices will transmit their health data packets in order to reduce collisions to save energy and prolong the battery lifetime of the biomedical sensor devices so as to improve the overall network lifetime. Also, a stochastic probability model and a heuristic-based power control scheme are developed to solve time allocation and power control problems to improve energy efficiency and the biomedical sensor devices lifetime. To validate the MG-HYMAC protocol, it was compared with other related protocols (including HyMAC and CPMAC) and simulated in MATLAB. The simulation results proved that the proposed MG-HYMAC protocol outperformed the existing MAC protocols using standard metrics like energy efficiency, biomedical sensor devices lifetime, and convergence speed.

An Improved Gateway-Based Energy-Aware Multi-Hop Routing Protocol for Enhancing Lifetime and Throughput in Heterogeneous WSNs

In this work, an improved version of the gateway based multi-hop routing protocol was studied. The MGEAR protocol is mainly used for prolonging network lifetime in homogeneous wireless sensor network. Herein, the proposed approach aims to prolong network lifetime and enhance the throughput of this protocol in the case of heterogeneous wireless sensor networks (HWSNs). In the MGEAR, the network is divided into several fields; sensor nodes in the first field communicate directly with the base station. Sensors in the center of the network send their data to the gateway which perform data fusion and spread to the base station. The rest of nodes are divided into two equal regions, in each region sensor nodes are grouped into clusters with a leading node as cluster-head. The central point of our approach is the selection of cluster-heads which is based on a ratio between the residual energy of each sensor node and the average energy of the region which it belongs. In order to equalize the load and prolong the lifetime of sensors, the cluster-head election probability is computed in each round according to the residual energy of each sensor node. Finally, the simulation results showed that this model had higher throughput and increased the lifetime by 130%, 151%, 167%, 171%, and 215% compared to HCR, ERP, ModLEACH, D-MSEP and DDEEC protocols respectively in the case of 2-level heterogeneity. In case of 3-level heterogeneity, the network lifetime is increased by 123%, 150%, 163% and 218% compared to HCP, ModLEACH, hetSEP and hetDEEC.

Smart facemask for wireless CO2 monitoring

The use of facemasks by the general population is recommended worldwide to prevent the spread of SARS-CoV-2. Despite the evidence in favour of facemasks to reduce community transmission, there is also agreement on the potential adverse effects of their prolonged usage, mainly caused by CO2 rebreathing. Herein we report the development of a sensing platform for gaseous CO2 real-time determination inside FFP2 facemasks. The system consists of an opto-chemical sensor combined with a flexible, battery-less, near-field-enabled tag with resolution and limit of detection of 103 and 140 ppm respectively, and sensor lifetime of 8 h, which is comparable with recommended FFP2 facemask usage times. We include a custom smartphone application for wireless powering, data processing, alert management, results displaying and sharing. Through performance tests during daily activity and exercise monitoring, we demonstrate its utility for non-invasive, wearable health assessment and its potential applicability for preclinical research and diagnostics.

A Novel Multi-Objective Optimization Based Evolutionary Algorithm for Optimize the Services of Internet of Everything

In the new era, the Internet of Everything (IoE) provides distributed services like data, processes, people, and things, etc. The services to the connected IoE significantly increase the time of service, workload, energy consumption, and delay. These objectives conflict with each other. To address the issue, a novel multi-objective based evolutionary algorithm is proposed. In the proposed method, a new rapid mutation operator is incorporated with multi-objective differential evolution (MODE) to overcome the stagnation of the local optimum. The proposed method to maintain the diversity and enhance the convergence speed of the existing MODE algorithm is described. The proposed method provides more diversity and convergence speed for choosing better candidate solutions. The addition of the proposed method is evaluated with the application of IoE services. We have designed the two objective and three objective-based IoE services scenarios. Furthermore, the proposed method optimizes services like service cost, service delay, and the lifetime of sensors. It is interesting to observe that the proposed approach better performs the most recent state-of-the-art multiobjective evolutionary algorithms.

Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method then forms Zigbee as well as LoRaWAN networking clusters. Furthermore, depending on energy usage and data packet size, the fitness of each sensor node is assessed using the Dynamic Intelligent Reasoning Based Neural (DIRN) approach. MATLAB software is used to implement and execute this study. When the Zigbee network’s transmission distance is 650 m and the LoRaWAN network’s transmission range is 3.5 km, the system can function with a packet loss rate of less than 0.04 percent. This study shows significant gains in the performance of the system when compared to traditional approaches based on digital findings obtained on software solutions.

Maximizing Sensor Lifetime via Multi-node Partial-Charging on Sensors

In this paper, we study the employment of a mobile charger to charge lifetime-critical sensors under the multi-node partial-charging model, in which the charger can simultaneously charge the sensors within its charging range and each sensor may be partially charged each time. We notice that existing studies only scheduled the charger to minimize the number of dead sensors, but did not consider the charging scheduling for the sensors that have already run out of their energy, and the dead sensors will be last charged by the mobile charger. Then, their dead durations may be very long. In this paper, we consider not only how to minimize the number of dead sensors but also reduce the dead durations of sensors. To this end, we first formulate a sensor lifetime maximization problem, which is to find a charging tour for a mobile charger to charge sensors, such that the sum of sensor lifetimes is maximized. We then propose a novel <inline-formula><tex-math notation="LaTeX">$\frac{1}{3}$</tex-math></inline-formula>-approximation algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experiments. Experimental results show that both the average and maximum sensor dead durations by the proposed algorithm are up to 70&#x0025; shorter than those by existing algorithms.

AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system.

Artificial Intelligence (AI) driven adaptive techniques are viable to optimize the resources in the Internet of Things (IoT) enabled wearable healthcare devices. Due to the miniature size and ability of wireless data transfer, Body Sensor Networks (BSNs) have become the center of attention in current medical media technologies. For a long-term and reliable healthcare system, high energy efficiency, transmission reliability, and longer battery lifetime of wearable sensors devices are required. There is a dire need for empowering sensor-based wearable techniques in BSNs from every aspect i.e., data collection, healthcare monitoring, and diagnosis. The consideration of protocol layers, data routing, and energy optimization strategies improves the efficiency of healthcare delivery. Hence, this work presents some key contributions. Firstly, it proposes a novel avant-garde framework to simultaneously optimize the energy efficiency, battery lifetime, and reliability for smart and connected healthcare. Secondly, in this study, an Adaptive Transmission Data Rate (ATDR) mechanism is proposed, which works on the average constant energy consumption by varying the active time of the sensor node to optimize the energy over the dynamic wireless channel. Moreover, a Self-Adaptive Routing Algorithm (SARA) is developed to adopt a dynamic source routing mechanism with an energy-efficient and shortest possible path, unlike the conventional routing methods. Lastly, real-time datasets are adopted for intensive experimental setup for revealing pervasive and cost-effective healthcare through wearable devices. It is observed and analysed that proposed algorithms outperform in terms of high energy efficiency, better reliability, and longer battery lifetime of portable devices.

Solid-Contact pH Sensor with Covalent Attachment of Ionophores and Ionic Sites to a Poly(decyl methacrylate) Matrix.

With a view to improving the sensor lifetime, solid-contact ion-selective electrodes (ISEs) were prepared with a plasticizer-free and cross-linked poly(decyl methacrylate) matrix, to which only the ionic sites, only the ionophore, or both the ionic sites and ionophore were covalently attached. In earlier work with covalently attached ionophores or ionic sites, it was difficult to discount the presence of ionophores or ionic site impurities that were not covalently attached to the polymer backbone because the reagents used to introduce the ionophore or ionic sites had high hydrophobicities. In this work, we deliberately chose readily available hydrophilic reagents for the introduction of covalently attached H+ ionophores with tertiary amino groups and covalently attached sulfonate groups as ionic sites. This simplified the synthesis and made it possible to thoroughly remove ionophores and ionic sites not covalently attached to the polymer backbone. Our results confirm the expectation that hydrophobic ISE membranes with both covalently attached ionophores and ionic sites have impractically long response times. In contrast, ISEs with either covalently attached H+ ionophores or covalently attached ionic sites responded to pH with quick Nernstian responses and high selectivity. Both conventional plasticized poly(vinyl chloride) (PVC)-based ISEs and the new poly(decyl methacrylate) membranes were exposed to 90 °C heat for 2 h, 10% ethanol for 1 day, or undiluted blood serum for 5 days. In all three cases, the poly(decyl methacrylate) ISEs exhibited properties superior to conventional PVC-based ISEs, confirming the advantages of the covalent attachment.

Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review

The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.

Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN.

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.