A low-power opportunistic communication protocol for wearable applications

© 2015 IEEE.Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment

Framework for collecting data from IoT Device

The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. All these phases need to be secure by following security protocol to ensure data integrity. This work presents the design of a lightweight and easy-to-use data collection framework for IoT devices. This framework consists of collecting data from sensors and sending them to Cloud storage securely and in real-time for further processing and visualization. Our main objective is to make a data-collecting platform that will be plug-and-play and secure so that any organization or research team can use it to collect data from any IoT device for further data analysis. This framework is expected to help with the data collection from a variety of different IoT devices

Reliable Data Collection from Mobile Users for Real-Time Clinical Monitoring

Real-time patient monitoring is critical to early detection of clinical patient deterioration in general hospital wards. A key challenge in such applications is to reliably deliver sensor data from mobile patients. We present an empirical analysis on the reliability of data collection from wireless pulse oximeters attached to users. We observe that most packet loss occur from mobile users to their first-hop relays. Based on this insight we developed the Dynamic Relay Association Protocol (DRAP), a simple and effective mechanism for dynamically discovering the right relays for wireless sensors attached to mobile users. DRAP enables highly reliable data collection from mobile users without requiring any change to complex routing protocols. We have implemented DRAP on the TinyOS platform and a prototype clinical monitoring system. Empirical evaluation showed DRAP delivered at least 96% of pulse oximetry data from multiple users, while maintaining a radio duty cycle below 2.8% and reducing the RAM footprint by 65% when compared to CTP. Our results demonstrates the feasibility and efficacy of wireless sensor network technology for real-time clinical monitoring

An Improved User Authentication Protocol for Hierarchical Wireless Sensor Networks using Elliptic Curve Cryptography

In wireless sensor network, most of the queries are issued at the base station or gateway node the network. However, there are some critical WSN applications where real-time data are needed. But data at base station may not be real-time because of communication delay or periodic nature of data collection. So, real-time data can be accessed from the sensor nodes directly on demand. Before allowing the user to access real-time data from the sensor node, authentication of user must be ensured. But user authentication in case of wireless sensor network is a very critical task, as sensor nodes are deployed in unattached environment and are prone to possible hostile network attacks. Any authentication protocol in WSN must be designed keeping the fact that sensor nodes have limited computing power, memory, energy and communication capabilities. In this thesis, an improved user authentication protocol based on Elliptic Curve Cryptography (ECC) has been introduced for hierarchical wireless sensor networks (HWSN). This thesis shows that the ECC based protocol is suitable for wireless sensor networks, where higher security is demanded. Besides this the proposed scheme provides mutual authentication and a secret session key for communication between the user and the cluster head. It also provides an option for addition or replacement of cluster head in the network whenever there is a need. Then a comparative study of the proposed scheme with various existing is presented

GR-45 Framework for Collecting Data from specialized IoT devices.

The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. All these phases need to be secure by following security protocol to ensure data integrity. In this paper, we present the design of a lightweight and easy-to-use data collection framework for IoT devices. This framework consists of collecting data from sensors and sending them to Cloud storage securely and in real-time for further processing and visualization. Our main objective is to make a data-collecting platform that will be plug-and-play and secure so that any organization or research team can use it to collect data from any IoT device for further data analysis. This framework is expected to help with the data collection from a variety of different IoT devices.Advisors(s): Dr. Maria Valero, Dr. Hossain ShahriarTopic(s): IoT/Cloud/Networkin

Changes of data sampling procedure to avoid energy and data losses during microclimates monitoring with wireless sensor networks.

Wireless sensor networks are gaining importance in agricultural applications, such as monitoring crops microclimates. Precision agriculture is one of the areas that can most benefit from this technology in the sense that wireless sensors networks allow data collection with high resolution, enabling better decision making. Such networks have restrictions on their deployment in a real environment, for example, on energy. Thus, several studies have been conducted in order to optimize the use of this technology. Depending on the application, it is desirable that the available energy on sensor nodes batteries allows operation for months or even years. One proposed solution to extend the lifetime of sensor nodes, so as to avoid unnecessary data collection, is the implementation of a routing protocol that allows a differentiated data sampling. An application that can benefit from this approach is vineyard microclimates monitoring, which is very important to monitor temperature and reIative humidity, and can apply precision agriculture techniques to the crop. Thus, in the program to be installed into sensor nodes, rules for data collection are defined, so that the value collected by the sensor at a given time is in the rule that defines normal conditions, the rate of sampling data used will be higher; however, when the value collected by the sensor is out ofthis rule, the sampling rate will automatically be reprogrammed to a higher value. This differentiated data collection allows savings in power consumption under normal conditions, and generates less data to be analyzed. Keywords: Wireless sensor network, microclimates monitoring, vineyards differentiated data samplin

Design and Evaluation of IoT-Enabled Instrumentation for a Soil-Bentonite Slurry Trench Cutoff Wall

In this work, we describe our approach and experiences bringing an instrumented soil-bentonite slurry trench cutoff wall into a modern IoT data collection and visualization pipeline. Soil-bentonite slurry trench cutoff walls have long been used to control ground water flow and contaminant transport. A Raspberry Pi computer on site periodically downloads the sensor data over a serial interface from an industrial datalogger and transmits the data wirelessly to a gateway computer located 1.3 km away using a reliable transmission protocol. The resulting time-series data is stored in a MongoDB database and data is visualized in real-time by a custom web application. The system has been in operation for over two years achieving 99.42% reliability and no data loss from the collection, transport, or storage of data. This project demonstrates the successful bridging of legacy scientific instrumentation with modern IoT technologies and approaches to gain timely web-based data visualization facilitating rapid data analysis without negatively impacting data integrity or reliability. The instrumentation system has proven extremely useful in understanding the changes in the stress state over time and could be deployed elsewhere as a means of on-demand slurry trench cutoff wall structural health monitoring for real-time stress detection linked to hydraulic conductivity or adapted for other infrastructure monitoring applications

A Cloud Based Disaster Management System

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.info:eu-repo/semantics/publishedVersio

Towards Real-time Wireless Sensor Networks

Wireless sensor networks are poised to change the way computer systems interact with the physical world. We plan on entrusting sensor systems to collect medical data from patients, monitor the safety of our infrastructure, and control manufacturing processes in our factories. To date, the focus of the sensor network community has been on developing best-effort services. This approach is insufficient for many applications since it does not enable developers to determine if a system\u27s requirements in terms of communication latency, bandwidth utilization, reliability, or energy consumption are met. The focus of this thesis is to develop real-time network support for such critical applications. The first part of the thesis focuses on developing a power management solution for the radio subsystem which addresses both the problem of idle-listening and power control. In contrast to traditional power management solutions which focus solely on reducing energy consumption, the distinguishing feature of our approach is that it achieves both energy efficiency and real-time communication. A solution to the idle-listening problem is proposed in Energy Efficient Sleep Scheduling based on Application Semantics: ESSAT). The novelty of ESSAT lies in that it takes advantage of the common features of data collection applications to determine when to turn on and off a node\u27s radio without affecting real-time performance. A solution to the power control problem is proposed in Real-time Power Aware-Routing: RPAR). RPAR tunes the transmission power for each packet based on its deadline such that energy is saved without missing packet deadlines. The main theoretical contribution of this thesis is the development of novel transmission scheduling techniques optimized for data collection applications. This work bridges the gap between wireless sensor networks and real-time scheduling theory, which have traditionally been applied to processor scheduling. The proposed approach has significant advantages over existing design methodologies:: 1) it provides predictable performance allowing for the performance of a system to be estimated upon its deployment,: 2) it is possible to detect and handle overload conditions through simple rate control mechanisms, and: 3) it easily accommodates workload changes. I developed this framework under a realistic interference model by coordinating the activities at the MAC, link, and routing layers. The last component of this thesis focuses on the development of a real-time patient monitoring system for general hospital units. The system is designed to facilitate the detection of clinical deterioration, which is a key factor in saving lives and reducing healthcare costs. Since patients in general hospital wards are often ambulatory, a key challenge is to achieve high reliability even in the presence of mobility. To support patient mobility, I developed the Dynamic Relay Association Protocol -- a simple and effective mechanism for dynamically discovering the right relays for forwarding patient data -- and a Radio Mapping Tool -- a practical tool for ensuring network coverage in 802.15.4 networks. We show that it is feasible to use low-power and low-cost wireless sensor networks for clinical monitoring through an in-depth clinical study. The study was performed in a step-down cardiac care unit at Barnes-Jewish Hospital. This is the first long-term study of such a patient monitoring system

Real-Time Cross-Layer Routing Protocol for Ad Hoc Wireless Sensor Networks

Reliable and energy efficient routing is a critical issue in Wireless Sensor Networks (WSNs) deployments. Many approaches have been proposed for WSN routing, but sensor field implementations, compared to computer simulations and fully-controlled testbeds, tend to be lacking in the literature and not fully documented. Typically, WSNs provide the ability to gather information cheaply, accurately and reliably over both small and vast physical regions. Unlike other large data network forms, where the ultimate input/output interface is a human being, WSNs are about collecting data from unattended physical environments. Although WSNs are being studied on a global scale, the major current research is still focusing on simulations experiments. In particular for sensor networks, which have to deal with very stringent resource limitations and that are exposed to severe physical conditions, real experiments with real applications are essential. In addition, the effectiveness of simulation studies is severely limited in terms of the difficulty in modeling the complexities of the radio environment, power consumption on sensor devices, and the interactions between the physical, network and application layers. The routing problem in ad hoc WSNs is nontrivial issue because of sensor node failures due to restricted recourses. Thus, the routing protocols of WSNs encounter two conflicting issue: on the one hand, in order to optimise routes, frequent topology updates are required, while on the other hand, frequent topology updates result in imbalanced energy dissipation and higher message overhead. In the literature, such as in (Rahul et al., 2002), (Woo et al., 2003), (TinyOS, 2004), (Gnawali et al., 2009) and (Burri et al., 2007) several authors have presented routing algorithms for WSNs that consider purely one or two metrics at most in attempting to optimise routes while attempting to keep small message overhead and balanced energy dissipation. Recent studies on energy efficient routing in multihop WSNs have shown a great reliance on radio link quality in the path selection process. If sensor nodes along the routing path and closer to the base station advertise a high quality link to forwarding upstream packets, these sensor nodes will experience a faster depletion rate in their residual energy. This results in a topological routing hole or network partitioning as stated and resolved in and (Daabaj 2010). This chapter presents an empirical study on how to improve energy efficiency for reliable multihop communication by developing a real-time cross-layer lifetime-oriented routing protocol and integrating useful routing information from different layers to examine their joint benefit on the lifetime of individual sensor nodes and the entire sensor network. The proposed approach aims to balance the workload and energy usage among relay nodes to achieve balanced energy dissipation, thereby maximizing the functional network lifetime. The obtained experimental results are presented from prototype real-network experiments based on Crossbow’s sensor motes (Crossbow, 2010), i.e., Mica2 low-power wireless sensor platforms (Crossbow, 2010). The distributed real-time routing protocol which is proposed In this chapter aims to face the dynamics of the real world sensor networks and also to discover multiple paths between the base station and source sensor nodes. The proposed routing protocol is compared experimentally with a reliability-oriented collection-tree protocol, i.e., the TinyOS MintRoute protocol (Woo et al., 2003). The experimental results show that our proposed protocol has a higher node energy efficiency, lower control overhead, and fair average delay