J. Sens. Actuator Netw., Volume 8, Issue 3 (September 2019) – 13 articles

The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements. Full article

This study provides a mathematical model and theoretical analysis of an interference cancellation system combining an orthogonal matched filter (OMF) and adaptive array antenna that is called the extended OMF (EOMF). In recent years, an increase in the number of applications of mobile ad hoc networks (MANETs) is expected. To realize a highly reliable MANET, it is essential to introduce a method for cancelling the interference from other nodes. This research focuses on a scheme based on Code Division Multiple Access (CDMA) that enables simultaneous multiple access and low latency communication. However, there are problems with deteriorating performance due to the near–far problem and the increase in the amount of interference as the number of users increases. Additionally, another problem is that the spreading sequence of each user is unknown in a MANET. The OMF is expected to be a solution to these problems. The OMF performs interference cancellation by generating and subtracting a replica of the interference signal that is contained in the received signal. However, the OMF may generate an incorrect replica in the near–far problem. The EOMF compensates for the OMF’s weakness by combining the OMF with an adaptive array antenna. In this research, optimal parameters are derived from mathematical modelling and theoretical analysis of the EOMF. Specifically, the optimal weight vector and the minimum mean squared error that allow the adaptive algorithm to converge are derived and obtained from the numerical results. Full article

In the Internet of Things (IoT), a huge number of sensors, actuators and other equipment for data acquisition and processing will be interconnected by means of an omnipresent communication network able to efficiently support heterogeneous transmission technologies and applications. On the one hand, advanced optical communication systems, which already play a significant role in modern networks, are currently evolving to meet very high requirements of modern applications. On the other hand, there are already many ways to utilize optical components and effects for building precise, efficient, and reliable sensors. Thus, optical technologies have the potential to greatly help in realizing future smart infrastructures and systems. This paper gives an overview of currently available and emerging optical technologies for sensing and communication applications and reviews their possible application in the context of the IoT for realizing smart systems and infrastructures. Full article

Intrusion Detection Systems (IDS) are used to prevent attacks by detecting potential harmful intrusion attempts. Currently, there are a set of available Open Source IDS with different characteristics. The Open Source Host-based Intrusion Detection System (OSSEC) supports multiple features and its implementation consists of Agents that collect and send event logs to a Manager that analyzes and tests them against specific rules. In the Manager, if certain events match a specific rule, predefined actions are triggered in the Agents such as to block or unblock a particular IP address. However, once an action is triggered, the systems administrator is not able to centrally check and obtain detailed information of the past event logs. In addition, OSSEC may assume false positive or negative detections and their triggered actions: previously harmless but blocked IP addresses by OSSEC have to be unblocked in order to reestablish normal operation or potential harmful IP addresses not previously blocked by OSSEC should be blocked in order to increase protection levels. These operations to override OSSEC actions must be manually performed in every Agent, thus requiring time and human resources. Both these limitations have a higher impact on large scale OSSEC deployments assuming tens or hundreds of Agents. This paper proposes an extension to OSSEC that improves the administrator analysis capability by maintaining, organizing and presenting Agent logs in a central point, and it allows for blocking or unblocking IP addresses in order to override actions triggered by false detections. The proposed extension aims to increase efficiency of time and human resources management, mainly considering large scale OSSEC deployments. Full article

Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign. Full article

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