Reza Malekian

Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System

A system capable of mobile health monitoring was designed and implemented from the first principles. The system is capable of measuring vital physiological parameters, interpret the measured signals, and provide a sense of monitoring and biofeedback to the user. In the case, a medical emergency is detected notifications are sent to a medical tedam for the analysis. The system was designed through the investigation of various biosignal extraction methods. An optical pulse oximeter sensor was designed along with the required software algorithms. The photoplethysmographic signals were extracted and used to calculate heart rate, saturation of oxygen, and pulse transits time. The pulse transit time was utilized in the estimation of continuous blood pressure measurement. The pulse oximetry implementation applies reflectance-based sensing on the users fingertip and palm. Skin temperature is also measurable through the use of a digital temperature sensor. The system is capable of providing feedback to the user by means of a smartphone application receiving data from the device through Bluetooth. The measurement of the proposed physiological parameters were successfully measured with the appropriate degree of accuracy required by medical standards. The system is capable of health monitoring through accurate measurements, providing the user with feedback and successfully identifying medical stress resulting in the sending of a notification to a medical doctor.

Accurate Vehicle Location System Using RFID, an Internet of Things Approach

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.

Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing

Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject’s trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.

Software defined wireless sensor networks application opportunities for efficient network management: A survey

Abstract Wireless Sensor Networks (WSNs) are commonly used information technologies of modern networking and computing platforms. Todays network computing applications are faced with a high demand of powerful network functionalities. Functional network reach is central to customer satisfaction such as in mobile networks and cloud computing environments. However, efficient management of WSNs remains a challenge, due to problems supplemental to them. Recent technology shift proposes Software Defined Networking (SDN) for improving computing networks. This review paper highlights application challenges faced by WSNs for monitored environments and those faced by the proposed approaches, as well as opportunities that can be realized on applications of WSNs using SDN. We also highlight Implementation considerations by focusing on critical aspects that should not be disregarded when attempting to improve network functionalities. We then propose a strategy for Software Defined Wireless Sensor Network (SDWSN) as an effort for application improvement in monitored environments.

Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks

Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks (WSN), secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either sides secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol (PPSPP) for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol (HEEDP) without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.

A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres

Diabetes, blood pressure, heart, and kidney, some of the diseases common across the world, are termed ’silent killers’. More than 50 % of the world’s population are affected by these diseases. If suitable steps are not taken during the early stages then severe complications occur from these diseases. In the work proposed, we have discussed the manner in which the Internet-of-Things based Cloud centric architecture is used for predictive analysis of physical activities of the users in sustainable health centers. The architecture proposed is based on the embedded sensors of the equipment rather than using wearable sensors or Smartphone sensors to store the value of the basic health-related parameters. Cloud centric architecture is composed of a Cloud data center, Public cloud, Private cloud, and uses the XML Web services for secure and fast communication of information. The architecture proposed here is evaluated for its adoption, prediction analysis of physical activities, efficiency, and security. From the results obtained it can be seen that the overall response between the local database server and Cloud data center remains almost constant with the rise in the number of users. For prediction analysis, If the results collected in real time for the analysis of physical activities exceed any of the parameter limits of the defined threshold value then an alert is sent to the health care personnel. Security analysis also shows the effective encryption and decryption of information. The architecture presented is effective and reduces the proliferation of information. It is also suggested, that a person suffering from any of the diseases mentioned above can defer the onset of complications by doing regular physical activities.

The Study of Handover in Mobile IP Networks

Mobile IP protocol (MIP) is a standard protocol that allows users to maintain non-stop connectivity with their home address regardless of physical movement. In this paper we study handoff in mobile IP networks and mobile IP protocol extensions for Handoff latency minimization.

Molecular Communication Model for Targeted Drug Delivery in Multiple Disease Sites With Diversely Expressed Enzymes

Targeted drug delivery (TDD) for disease therapy using liposomes as nanocarriers has received extensive attention in the literature. The liposomes ability to incorporate capabilities such as long circulation, stimuli responsiveness, and targeting characteristics, makes it a versatile nanocarrier. Timely drug release at the targeted site requires that trigger stimuli such as pH, light, and enzymes be uniquely overexpressed at the targeted site. However, in some cases, the targeted sites may not express trigger stimuli significantly, hence, achieving effective TDD at those sites is challenging. In this paper, we present a molecular communication-based TDD model for the delivery of therapeutic drugs to multiple sites that may or may not express trigger stimuli. The nanotransmitter and nanoreceiver models for the molecular communication system are presented. Here, the nanotransmitter and nanoreceiver are injected into the targeted body systems blood network. The compartmental pharmacokinetics model is employed to model the transportation of these therapeutic nanocarriers to the targeted sites where they are meant to anchor before the delivery process commences. We also provide analytical expressions for the delivered drug concentration. The effectiveness of the proposed model is investigated for drug delivery on tissue surfaces. Results show that the effectiveness of the proposed molecular communication-based TDD depends on parameters such as the total transmitter volume capacity, the receiver radius, the diffusion characteristic of the microenvironment of the targeted sites, and the concentration of the enzymes associated with the nanotransmitter and the nanoreceiver designs.

Design of Smart Sensors for Real-Time Water Quality Monitoring

This paper describes work that has been done on design and development of a water quality monitoring system, with the objective of notifying the user of the real-time water quality parameters. The system is able to measure the physiochemical parameters of water quality, such as flow, temperature, pH, conductivity, and the oxidation reduction potential. These physiochemical parameters are used to detect water contaminants. The sensors, which are designed from first principles and implemented with signal conditioning circuits, are connected to a microcontroller-based measuring node, which processes and analyzes the data. In this design, ZigBee receiver and transmitter modules are used for communication between the measuring and notification nodes. The notification node presents the reading of the sensors and outputs an audio alert when water quality parameters reach unsafe levels. Various qualification tests are run to validate each aspect of the monitoring system. The sensors are shown to work within their intended accuracy ranges. The measurement node is able to transmit data by ZigBee to the notification node for audio and visual display. The results demonstrate that the system is capable of reading physiochemical parameters, and can successfully process, transmit, and display the readings.

A Method for Driving Route Predictions Based on Hidden Markov Model

We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means