Journal of Sensor and Actuator Networks — Open Access Journal

Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management. Full article

Mobile health research field aims to provide access to healthcare anytime and anywhere through mobile computing technologies while using a cost-effective approach. Mobile health is closely related to ambient assisted living as both research fields address independence in elderly adults. Aging has become a relevant challenge, as it is anticipated that 20% of world population will be aged 60 years and older in 2050. Most people spend more than 90% of their time indoors, therefore the indoor environmental quality has a relevant impact on occupant’s health and well-being. We intended to provide real-time indoor quality monitoring for enhanced living environments and occupational health. This paper presents the AirPlus real-time indoor environmental quality monitoring system, which incorporates several advantages when compared to other systems, such as scalability, flexibility, modularity, easy installation, and configuration, as well as mobile computing software for data consulting and notifications. The results that were obtained are promising and present a significant contribution to the monitoring solutions available in the literature. AirPlus provides a rich dataset to plan interventions for enhanced indoor quality, but also to support clinical diagnostics and correlate occupant’s health problems with their living environment conditions. Full article

Security of IoT devices is getting a lot of attention from researchers as they are becoming prevalent everywhere. However, implementation of hardware security in these devices has been overlooked, and many researches have mainly focused on software, network, and cloud security. A deeper understanding of hardware Trojans (HTs) and protection against them is of utmost importance right now as they are the prime threat to the hardware. This paper emphasizes the need for a secure hardware-level foundation for security of these devices, as depending on software security alone is not adequate enough. These devices must be protected against sophisticated attacks, especially if the groundwork for the attacks is already laid in devices during design or manufacturing process, such as with HTs. This paper will discuss the stealthy nature of these HT, highlight HT taxonomy and insertion methods, and provide countermeasures. Full article

In underground (UG) multiple-input and multiple-output (MIMO), transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagate to reach the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes a three-path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three-path interference channel, and the degrees of freedom (multiplexing gain of this MIMO channel) requires careful modeling. Therefore, expressions are required for the degrees of freedom of the UG MIMO interference channel. The underground receiver needs to perfectly cancel the interference from the three different components of the EM waves propagating in the soil medium. This concept is based upon reducing the interference of the undesired components to a minimum level at the UG receiver using the receive beamforming. In this paper, underground environment-aware MIMO using transmit and receive beamforming has been developed. The optimal transmit and receive beamforming, combining vectors under minimal intercomponent interference constraints, are derived. It is shown that UG MIMO performs best when all three components of the wireless UG channel are leveraged for beamforming. The environment-aware UG MIMO technique leads to three-fold performance improvements and paves the way for design and development of next-generation sensor-guided irrigation systems in the field of digital agriculture. Based on the analysis of underground radio-wave propagation in subsurface radio channels, a phased-array antenna design is presented that uses water content information and beam-steering mechanisms to improve efficiency and communication range of wireless underground communications. It is shown that the subsurface beamforming using phased-array antennas improves wireless underground communications by using the array element optimization and soil–air interface refraction adjustment schemes. This design is useful for subsurface communication system where sophisticated sensors and software systems are used as data collection tools that measure, record, and manage spatial and temporal data in the field of digital agriculture. Full article

This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user’s position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users. Full article

Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored. Full article

One of the most common forgotten things of adults is that they go to the shops and completely forget what they went for. The solution to this problem is to carry a shopping list. In this project, a novel Internet of Things (IoT)-connected smart canister system is developed, which automatically senses the item quantity in the canisters using proximity sensor, sends the data to a hub using Bluetooth Low Energy, and then the hub sends a cloud message to the consumer’s smartphone app using the Internet. The hub and the smartphone app display the item quantities and automatically add the items that are about to finish to a digital shopping list. The automatic generation of the shopping list removes the burden of manually checking each item before going to the shops and gives peace of mind to the consumers. A prototype of the proposed system with three canister devices, one hub, and the smartphone app is developed and tested successfully. The canister device consumes ultra-low power and has a battery life of more than a year. Full article

Counterfeiting has always been a concern, costing a significant amount of money and causing losses in international trading markets. Radio frequency identification (RFID) tag Anti-counterfeiting is a conceptual solution that has received attention in the past few years. In this article, we present a survey study on the research topic of anti-counterfeiting products using RFID tags on merchandise. As this issue evolved in industry, there were several techniques used to address the problem; each technique uses a different concept and mechanism in resolving the issue. Each technique also has different pros and cons which we will address at the end of this paper with our findings. As we explore RFID technology and its implementation, we will discuss previous research before proceeding to the core of the topic of RFID Anti-counterfeiting based on the methods used. We compare the different techniques used at the end of the paper. Full article

Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost. Full article

A wireless Underground Sensor Network (WUSN) is a group of sensors that collectively communicate through the underground medium. Radio Frequency (RF) signal transmission of the sensors through the ground is the most challenging aspects of a WUSN due to the high attenuation of the electromagnetic (EM) signal in the soil. Signals are often required to travel through soils with a high density or water content and generally through a non-isotropic and non-homogenous soil mixture with different boundaries, both of which can attenuate the signal sharply. The variability of the these conditions and complexity of the behaviour of signal attenuation with respect to these parameters makes accurate estimation of EM signal attenuation in soil challenging. Two main EM signal attenuation models exist to estimate attenuation (modified-Friis and Complex Refractive Index Model-Fresnel (CRIM-Fresnel). These were reviewed and a methodology was developed in order to measure the attenuation of the EM signals in the laboratory. Results from the laboratory measurements were compared with the estimation values calculated from the attenuation models. These comparisons showed a large difference between the estimated values by the models. In addition, analysis of the comparison tests showed that the CRIM-Fresnel model provides a better estimation of attenuation in samples with lower permittivity values while the modified-Friis model had a higher accuracy in samples with higher clay/water content which have higher permittivity values. Full article

A service function chain (SFC) is an ordered virtual network function (VNF) chain for processing traffic flows to deliver end-to-end network services in a virtual networking environment. A challenging problem for an SFC in this context is to determine where to deploy VNFs and how to route traffic between VNFs of an SFC on a substrate network. In this paper, we formulate an SFC placement problem as an integer linear programing (ILP) model, and propose an availability-enhanced VNF placing scheme based on the layered graphs approach. To improve the availability of SFC deployment, our scheme distributes VNFs of an SFC to multiple substrate nodes to avoid a single point of failure. We conduct numerical analysis and computer simulation to validate the feasibility of our SFC scheme. The results show that the proposed scheme outperforms well in different network scenarios in terms of end-to-end delay of the SFC and computation time cost. Full article

Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings. Full article

The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost. Full article

This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration. Full article

The rabid growth of today’s technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost. This article is concerned with surveying the idea of SSNs, the difference between it and the traditional WSNs, the requirements for its construction, challenges facing it, and the opportunities that are provided by it, then describing our proposed architectures. As a result of using virtualization technology as a basic block in building SSNs, using different types of virtualization will produce different types of SSNs that will give different usages to it. This article proposes a novel approach of taxonomy for SSNs that is based on the used virtualization techniques, and it describes the needs and usages of each one. It presents a wide array of previously proposed solutions comparing them to each other and a brief description of the issues addressed by each category of that taxonomy. Additionally, the shared sensor architecture and shared network architecture were depicted. Finally, some of its applications in some daily life fields are listed. Full article

In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments. Full article

Thanks to their arising abilities to influence the human lifestyle, along with reducing the healthcare systems’ cost, wireless body area networks (WBANs) still form a strongly growing research field. Recent advances focus on the opportunities of coexistence and communication between a group of WBANs, that will forward the sensing data, using persons as network relays, until reaching a remote analysis server or cloud servers via the Internet, forming thus a body-to-body network (BBN). Such new-style networks support a range of innovative and promising applications, including ubiquitous healthcare (U-health), interactive games, and military, to cite a few. In this paper, we first present the evolution of the single WBAN concept to the cooperative network of multiple WBANs, giving rise to the BBN concept. A synopsis of the WBAN and BBN respective standards and applications is given, and the emerging BBN challenges are highlighted. Then, we present and discuss the existing WBAN proposals, especially the candidate WBAN protocols that could be adapted and used in BBNs, focusing on four intrinsically related axes of great importance for BBN design: energy efficiency, mobility prediction, quality of service (QoS) and security. Further BBN open issues are also investigated, namely, the wireless propagation between humans carrying wearable devices, the interference, storage and privacy issues as well as the heterogeneity of BBN devices and traffic. Full article

The increasing demand for location-dependent services in wireless sensor networks (WSNs) calls for solutions capable of handling the diversified demands and the unique challenges presented in WSNs. In most applications, nodes need to determine their locations in a reliable manner while operating under stringent constraints in computation, communication, and energy resources. This paper offers a novel solution to bridge the gap between the high accuracy demand and low resources available for range-based localization. We propose KickLoc, a fully distributed scheme, which considers the uncertainty of the distance measurements to minimize localization errors introduced from the range measurement, and leverages information from all neighboring nodes for better position estimations. Our work is evaluated via extensive simulations, with comparisons to other well-known localization schemes, and the Cramér-Rao lower bound (CRLB). In addition, we implement and evaluate the proposed system on sensor platforms with different range measurement mechanisms. The results show that this localization solution outperforms existing methods in various scenarios, while remains lightweight and suitable for small, low resources WSNs. Full article