Cluster Aware Mobility Encounter Dataset Enlargement

The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset.Comment: 5 pages, 4 figures. In 2019 International Wireless Communications and Mobile Computing Conference (IWCMC), June 201

A 3G/WiFi-enabled 6LoWPAN-based u-healthcare system for ubiquitous real-time monitoring and data logging

Ubiquitous healthcare (U-healthcare) systems are expected to offer flexible and resilient high-end technological solutions enabling remote monitoring of patients health status in real-time and provisioning of feedback and remote actions by healthcare providers. In this paper, we present a 6LowPAN based U-healthcare platform that contributes to the realization of the above expectation. The proposed system comprises two sensor nodes sending temperature data and ECG signals to a remote processing unit. These sensors are being assigned an IPv6 address to enable the Internet-of-Things (IoT) functionality. A 6LowPAN-enabled edge router, connected to a PC, is serving as a base station through a serial interface, to collect data from the sensor nodes. Furthermore, a program interfacing through a Serial-Line-Internet-Protocol (SLIP) and running on the PC provides a network interface that receives IPv6 packets from the edge router. The above system is enhanced by having the application save readings from the sensors into a file that can be downloaded by a remote server using a free Cloud service such as UbuntuOne. This enhancement makes the system robust against data loss especially for outdoor healthcare services, where the 3G/4G connectivity may get lost because of signal quality fluctuations. The system provided a proof of concept of successful remote U-healthcare monitoring illustrating the IoT functionality and involving 3G/4G connectivity while being enhanced by a cloud-based backup

Wavelet-based encoding scheme for controlling size of compressed ECG segments in telecardiology systems

One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption

Embedded gateway services for Internet of Things applications in ubiquitous healthcare

The continuous advancement in computer and communication technologies has made personalized healthcare monitoring a rapidly growing area of interest. New features and services are envisaged, raising users' expectations in healthcare services. The emergence of Internet of Things brings people closer to connect the physical world to the Internet. In this paper, we present embedded services that are part of a ubiquitous healthcare system that allows automated and intelligent monitoring. The system uses IP connectivity and the Internet for end-to-end communication, from each 6LoWPAN sensor nodes to the web user interface on the Internet. The proposed algorithm in the Gateway performs multithreaded processing on the gathered medical signals for conversion to real data, feature extraction and wireless display. The user interface at the server allows users to access and view the medical data from mobile and portable devices. The ubiquitous system is exploring possibilities in connecting Internet with things and people for health services

On the Study of the WiMAX Security Threats

This paper examines threats to the security of the WiMAX broadband wireless access technology. Threats associated with the Network layer and Application layer are reviewed in detail and countermeasures are studied. Threats are listed according to the layer in which they operate. Security has always been a key issue with wireless networks since they have no physical boundaries. Experience with the 802.11 standard has shown numerous vulnerabilities to a variety of attacks even when security measures are in place. More recent standards such as the IEEE 802.16 WiMAX Mesh standard are being developed with a strong focus on security to counter many of the shortcomings of the previous wireless standards. There are still many existing and evolving threats which must be considered and addressed to ensure this new standard is able to meet the security requirements of the environments for which it is expected to be deployed. In this paper, we identify the major threats to WiMAX 802.16 networks and discuss the various ways of addressing their corresponding vulnerabilities and we propose recommendations for potential security enhancements based on some of the characteristics of the physical layer (coding and antenna systems)

Adaptive Probabilistic Proactive Routing for Dense MANETs

Conventional proactive routing protocols, due to their inherent nature based on shortest paths, select longer links which are amenable to rapid breakages as nodes move around. In this paper, we propose a novel adaptive probabilistic approach to handle routing information in dense mobile ad hoc networks in a way to improve the proactive routing pertinence as a function of network dynamics. We first propose a new proactive routing framework based on probabilistic decisions and a generic model to compute the existence probabilities of nodes and links. Then, we present a distributed algorithm to collect the cartography of the network. This cartography is used to instantiate the existence probabilities. Conducted simulations show that our proposal yields substantially better routing validity. Nonetheless, it amounts to much longer routes. We proposed then a bounding technique to adapt and overcome this side effect and defined two probabilistic proactive routing variants. Conducted simulations show that our proposed bounded probabilistic proactive routing schemes outperform conventional routing protocols and yield up to 66 percent increase in throughput

AI-based Energy Model for Adaptive Duty Cycle Scheduling in Wireless Networks

The vast distribution of low-power devices in IoT applications requires robust communication technologies that ensure high-performance level in terms of QoS, light-weight computation, and security. Advanced wireless technologies (i.e. 5G and 6G) are playing an increasing role in facilitating the deployment of IoT applications. To prolong the network lifetime, energy harvesting is an essential technology in wireless networks. Nevertheless, maintaining energy sustainability is difficult when considering high QoS requirements in IoT. Therefore, an energy management technique that ensures energy efficiency and meets QoS is needed. Energy efficiency in duty cycling solutions needs novel energy management techniques to address these challenges and achieve a trade-off between energy efficiency and delay. Predictive models (i.e., based on AI and ML techniques) represent useful tools that encapsulate the stochastic nature of harvested energy in duty cycle scheduling. The conventional predictive model relies on environmental parameters to estimate the harvested energy. Instead, Artificial Intelligence (AI) allows for recursive prediction models that rely on past behavior of harvested and consumed energy. This is useful to achieve better precision in energy estimation and extend the limit beyond predictive models directed solely for energy sources that exhibit periodic behavior. In this paper, we explore the usage of a ML model to enhance the performance of duty cycle scheduling. The aim is to improve the QoS performance of the proposed solution. To assess the performance of the proposed model, it was simulated using the INET framework of the OMNet++ simulation environment. The results are compared to an enhanced IEEE 802.15.4 MAC protocol from the literature. The results of the comparative study show clear superiority of the proposed AI-based protocol that testified to better use of energy estimation for better management of the duty cycling at the MAC sublayer

PEAM: A polymorphic, energy-aware MAC protocol for WBAN

In remote healthcare monitoring and care delivery, obtaining the right information at the right time is critical. For these systems, designing an efficient MAC protocol that is able to meet the challenges on network quality such as throughput, latency, data reliability and energy saving is an imperative task. This paper presents a polymorphic, multi-behavioral MAC protocol called PEAM (Polymorphic, Energy-Aware MAC Protocol) designed to support WBANs used in ubiquitous healthcare monitoring systems. The adaptability feature characterizing the protocol is essential in handling different patients' health states. PEAM was designed based on cross-layer constructs that allow across the layers synchronization and coordination orchestrated by the application layer and targeting the Data link layer services. PEAM has also been enabled to interplay with IPv6 networks through interaction with the 6LowPAN middleware. This protocol was evaluated and compared to the well-known IEEE 802.15.4 standard using the network simulator OMNET++, and exhibited clear superiority. 2016 IEEE.Scopu

Environmentally Powered Smart Sensor Nodes in a Mesh Network Topology for Air Quality Diagnostic

Background & Objective: Air pollution is a major problem that has been recognised throughout the world for hundreds of years. In the middle ages, the burning of coal in cities released increasing amounts of smoke and sulphur dioxide to the atmosphere. In the late 18th century, the Industrial Revolution, beginning in the UK, led to escalation in pollutant emissions based around the use of coal by both homes and industry.Pollutant emissions continued to grow through the 19th and early 20th centuries leading to poor Air Quality (AQ). In more recent times, pollution from motor vehicles has become the most recognised AQ issue. Present pollution monitoring is revealing that if we don't act swiftly then vehicle pollution could harm our environment and reduce the quality of life for future generations. The number of cars, both in Qatar and in most countries around the world, is currently steadily increasing, and a speed up in technological development is required to combat the pollution problem. Poor AQ has negative effects on the environment and thus on our wellbeing. Air pollution from transport vehicles includes emissions of carbon monoxide, particulates, nitrogen oxides and dioxide, hydrocarbons and so on. Whilst much attention has been directed towards poor outdoors AQ we sometimes forget that we spend up to 90% of our time indoors. Consequently, keeping the air which we breathe at home clean is crucial, particularly for vulnerable people including babies, children, pregnant women and the unborn babies, the elderly, and those suffering from respiratory or allergic diseases such as asthma. Although in the majority of homes the indoor AQ (IAQ) is fairly good, CO, SO2, NO2 etc... are just some of the indoor air pollutants which can give cause for concern. The level of indoor air pollutants depends on factors such as outdoor air pollutants, construction materials and interior finishing, poorly-designed air conditioning and ventilation systems, and households and furniture (via outgassing from fabrics, paints, pets, etc.). Over-ventilation, as often thought, does not solve the problem. Nevertheless, this illusory solution is not adequate in the hot climate of the Gulf region as it leads to unacceptable levels in power demand. Sustaining economic and social growth is impossible without a holistic environmental vision that places environmental preservation for Qatar's future generations at the forefront. The QNV2030 aim to strike a balance between developmental needs (human, society, economy) and the protection of its natural environment, whether land, sea or air. Echoing international initiatives, Qatar has recently undertaken relentless intensive steps in an attempt to address the problem of AQ. The QNV2030 associated Qatar National Development Strategy 2011-2016 (QNDS 2011-2016), through a set of well-defined recommendations, stresses on the need to address the issues of environment and air pollution. To meet these needs, in this extended abstract we present SERENO (SEnsor and REceiver Node), a renewable energy-harvested sensor node that intelligently monitors air quality continuously without human intervention. This paper discusses the challenges of designing an autonomous system powered by ambient energy harvesting. Preliminary results demonstrate that, the presented platform could effectively report and trace air quality levels in a sort of set and forget scenario using a mesh network topology to cover as large as possible area deploying from tens to thousand nodes. Methods: The advances in low power electronics and in electrochemical sensors have enabled the development of low cost and power efficient air quality monitoring systems (dedicated to specific target gas and analysis) suitable for even stringent environments and disruptive locations, where air quality assessment is required without human intervention. Environmental energy harvesting technologies has been developed to maximize wireless sensor systems' lifetime and minimize energy consumption by adopting ultra-low power electronics (i.e. ultra-low power controller, low-power AO and CMOS switches). The use of virtually no-power consumed sensors (e.g. electrochemical sensors connected to signal conditioning electronics with supply current around 1 uA) coupled with wireless communication, working on the principle duty cycling (i.e. the device remains in low power SLEEP mode for almost 100% of the time) helps in maximizing the power efficiency and lifetime of the system. It makes the system operative only to perform designated tasks i.e. sensor warm-up, sampling, data processing and wireless data transmission or communication. The top view diagram of the renewable energy harvested system for air quality monitoring named SERENO is given below. Figure 1 shows the 6 sensors operative on the PCB. The general description of the proposed system is described below.qscienc