The past few years have witnessed increased interest among researchers in cluster-based protocols for homogeneous networks because of their better scalability and higher energy efficiency than other routing protocols. Given the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster-based protocols should be compatible with these constraints in either the setup state or steady data transmission state. With focus on these constraints, we classify routing protocols according to their objectives and methods towards addressing the shortcomings of clustering process on each stage of cluster head selection, cluster formation, data aggregation and data communication. We summarize the techniques and methods used in these categories, while the weakness and strength of each protocol is pointed out in details. Furthermore, taxonomy of the protocols in each phase is given to provide a deeper understanding of current clustering approaches. Ultimately based on the existing research, a summary of the issues and solutions of the attributes and characteristics of clustering approaches and some open research areas in cluster-based routing protocols that can be further pursued are provided.
In this work, the dielectrophoretic force (F(DEP)) response of Aluminium Microelectrode Arrays with tapered profile is investigated through experimental measurements and numerical simulations. A standard CMOS processing technique with a step for the formation of a tapered profile resist is implemented in the fabrication of Tapered Aluminium Microelectrode Arrays (TAMA). The F(DEP) is investigated through analysis of the Clausius-Mossotti factor (CMF) and cross-over frequency (f(xo)). The performance of TAMA with various side wall angles is compared to that of microelectrodes with a straight cut sidewall profile over a wide range of frequencies through FEM numerical simulations. Additionally, electric field measurement (EFM) is performed through scanning probe microscopy (SPM) in order to obtain the region of force focus in both platforms. Results showed that the tapered profile microelectrodes with angles between 60° and 70° produce the highest electric field gradient on the particles. Also, the region of the strongest electric field in TAMA is located at the bottom and top edge of microelectrode while the strongest electric field in microelectrodes with straight cut profile is found at the top corner of the microelectrode. The latter property of microelectrodes improves the probability of capturing/repelling the particles at the microelectrode's side wall.
Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches.
Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.
We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people's presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people's presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people's effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people's presence and multipath effects were considered.
Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples' presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples' presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples' presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.
Magnetic Induction Tomography (MIT), which is also known as Electromagnetic Tomography (EMT) or Mutual Inductance Tomography, is among the imaging modalities of interest to many researchers around the world. This noninvasive modality applies an electromagnetic field and is sensitive to all three passive electromagnetic properties of a material that are conductivity, permittivity and permeability. MIT is categorized under the passive imaging family with an electrodeless technique through the use of excitation coils to induce an electromagnetic field in the material, which is then measured at the receiving side by sensors. The aim of this review is to discuss the challenges of the MIT technique and summarize the recent advancements in the transmitters and sensors, with a focus on applications in biological tissue imaging. It is hoped that this review will provide some valuable information on the MIT for those who have interest in this modality. The need of this knowledge may speed up the process of adopted of MIT as a medical imaging technology.
Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept "Internet of Things" has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.
Wake-up radio is a promising approach to mitigate the problem of idle listening, which incurs additional power consumption for the Internet of Things (IoT) wireless transmission. Radio frequency (RF) energy harvesting technique allows the wake-up radio to remain in a deep sleep and only become active after receiving an external RF signal to 'wake-up' the radio, thus eliminating necessary hardware and signal processing to perform idle listening, resulting in higher energy efficiency. This review paper focuses on cross-layer; physical and media access control (PHY and MAC) approaches on passive wake-up radio based on the previous works from the literature. First, an explanation of the circuit design and system architecture of the passive wake-up radios is presented. Afterward, the previous works on RF energy harvesting techniques and the existing passive wake-up radio hardware architectures available in the literature are surveyed and classified. An evaluation of the various MAC protocols utilized for the novel passive wake-up radio technologies is presented. Finally, the paper highlights the potential research opportunities and practical challenges related to the practical implementation of wake-up technology for future IoT applications.
With the advancement of digital microfluidics technology, applications such as on-chip DNA analysis, point of care diagnosis and automated drug discovery are common nowadays. The use of Digital Microfluidics Biochips (DMFBs) in disease assessment and recognition of target molecules had become popular during the past few years. The reliability of these DMFBs is crucial when they are used in various medical applications. Errors found in these biochips are mainly due to the defects developed during droplet manipulation, chip degradation and inaccuracies in the bio-assay experiments. The recently proposed Micro-electrode-dot Array (MEDA)-based DMFBs involve both fluidic and electronic domains in the micro-electrode cell. Thus, the testing techniques for these biochips should be revised in order to ensure proper functionality. This paper describes recent advances in the testing technologies for digital microfluidics biochips, which would serve as a useful platform for developing revised/new testing techniques for MEDA-based biochips. Therefore, the relevancy of these techniques with respect to testing of MEDA-based biochips is analyzed in order to exploit the full potential of these biochips.
The large interest in utilising fibre Bragg grating (FBG) strain sensors for minimally invasive surgery (MIS) applications to replace conventional electrical tactile sensors has grown in the past few years. FBG strain sensors offer the advantages of optical fibre sensors, such as high sensitivity, immunity to electromagnetic noise, electrical passivity and chemical inertness, but are not limited by phase discontinuity or intensity fluctuations. FBG sensors feature a wavelength-encoding sensing signal that enables distributed sensing that utilises fewer connections. In addition, their flexibility and lightness allow easy insertion into needles and catheters, thus enabling localised measurements inside tissues and blood. Two types of FBG tactile sensors have been emphasised in the literature: single-point and array FBG tactile sensors. This paper describes the current design, development and research of the optical fibre tactile techniques that are based on FBGs to enhance the performance of MIS procedures in general. Providing MIS or microsurgery surgeons with accurate and precise measurements and control of the contact forces during tissues manipulation will benefit both surgeons and patients.
Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.
The optical characteristics of graphene oxide (GO) were explored to design and fabricate a GO-based optical humidity sensor. GO film was coated onto a SU8 polymer channel waveguide using the drop-casting technique. The proposed sensor shows a high TE-mode absorption at 1550 nm. Due to the dependence of the dielectric properties of the GO film on water content, this high TE-mode absorption decreases when the ambient relative humidity increases. The proposed sensor shows a rapid response (<1 s) to periodically interrupted humid air flow. The transmission of the proposed sensor shows a linear response of 0.553 dB/% RH in the range of 60% to 100% RH.
Zinc oxide (ZnO) nanorods (NRs) have been synthesized via the hydrothermal process. The NRs were grown over a conductive glass substrate. A non-enzymatic electrochemical sensor for hydrogen peroxide (H₂O₂), based on the prepared ZnO NRs, was examined through the use of current-voltage measurements. The measured currents, as a function of H₂O₂ concentrations ranging from 10 μM to 700 μM, revealed two distinct behaviours and good performance, with a lower detection limit (LOD) of 42 μM for the low range of H₂O₂ concentrations (first region), and a LOD of 143.5 μM for the higher range of H₂O₂ concentrations (second region). The prepared ZnO NRs show excellent electrocatalytic activity. This enables a measurable and stable output current. The results were correlated with the oxidation process of the H₂O₂ and revealed a good performance for the ZnO NR non-enzymatic H₂O₂ sensor.
The front-end electronics (FEE) of the Compact Muon Solenoid (CMS) is needed very low power consumption and higher readout bandwidth to match the low power requirement of its Short Strip application-specific integrated circuits (ASIC) (SSA) and to handle a large number of pileup events in the High-Luminosity Large Hadron Collider (LHC). A low-noise, wide bandwidth, and ultra-low power FEE for the pixel-strip sensor of the CMS has been designed and simulated in a 0.35 µm Complementary Metal Oxide Semiconductor (CMOS) process. The design comprises a Charge Sensitive Amplifier (CSA) and a fast Capacitor-Resistor-Resistor-Capacitor (CR-RC) pulse shaper (PS). A compact structure of the CSA circuit has been analyzed and designed for high throughput purposes. Analytical calculations were performed to achieve at least 998 MHz gain bandwidth, and then overcome pileup issue in the High-Luminosity LHC. The spice simulations prove that the circuit can achieve 88 dB dc-gain while exhibiting up to 1 GHz gain-bandwidth product (GBP). The stability of the design was guaranteed with an 82-degree phase margin while 214 ns optimal shaping time was extracted for low-power purposes. The robustness of the design against radiations was performed and the amplitude resolution of the proposed front-end was controlled at 1.87% FWHM (full width half maximum). The circuit has been designed to handle up to 280 fC input charge pulses with 2 pF maximum sensor capacitance. In good agreement with the analytical calculations, simulations outcomes were validated by post-layout simulations results, which provided a baseline gain of 546.56 mV/MeV and 920.66 mV/MeV, respectively, for the CSA and the shaping module while the ENC (Equivalent Noise Charge) of the device was controlled at 37.6 e- at 0 pF with a noise slope of 16.32 e-/pF. Moreover, the proposed circuit dissipates very low power which is only 8.72 µW from a 3.3 V supply and the compact layout occupied just 0.0205 mm2 die area.
In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.
Cyberattacks in the modern world are sophisticated and can be undetected in a dispersed setting. In a distributed setting, DoS and DDoS attacks cause resource unavailability. This has motivated the scientific community to suggest effective approaches in distributed contexts as a means of mitigating such attacks. Syn Flood is the most common sort of DDoS assault, up from 76% to 81% in Q2, according to Kaspersky's Q3 report. Direct and indirect approaches are also available for launching DDoS attacks. While in a DDoS attack, controlled traffic is transmitted indirectly through zombies to reflectors to compromise the target host, in a direct attack, controlled traffic is sent directly to zombies in order to assault the victim host. Reflectors are uncompromised systems that only send replies in response to a request. To mitigate such assaults, traffic shaping and pushback methods are utilised. The SYN Flood Attack Detection and Mitigation Technique (SFaDMT) is an adaptive heuristic-based method we employ to identify DDoS SYN flood assaults. This study suggested an effective strategy to identify and resist the SYN assault. A decision support mechanism served as the foundation for the suggested (SFaDMT) approach. The suggested model was simulated, analysed, and compared to the most recent method using the OMNET simulator. The outcome demonstrates how the suggested fix improved detection.
In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application 'MQTT dashboard' is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.