LTE networking : extending the reach for sensors in mHealth applications

Sensor and electronic-health networks are widely utilized at home and in industry/research applications. In a local sense, a sensor-to-sensor network can have a range of a few meters to a couple of hundred meters (ZigBee Pro can extend this range up to 2000 m). With the deployment of mobile technology in the healthcare space (mobile-Health ‘m-Health’) and using cellular coverage, the range can virtually be unbounded. However, supporting bounded delay (end-to-end delay), class of service, and quality of service for critical sensor-mHealth applications may become challenging. This challenge can be alarmingly extended when thousands of users run their sensor-mHealth applications simultaneously and depend on limited coverage of the cell tower to transmit their health-related data across. In this paper we will discuss how the 3rd Generation Partnership Project–Long Term Evolution networks can address such aggregation issues, and discuss the challenges and provide recommendations.

Sensor fusion based localization of a mobile user in a wireless network

This paper applies sensor fusion to the localization problem of a mobile user. We propose that the use of direction of arrival (DOA) estimations along with received signal strength measurements can increase the accuracy and robustness of location estimations. The DOA estimations are incapable of providing multi-dimensional positioning alone, while signal strength methods are prone to high uncertainties. A Robust Extended Kalman Filter (REKF) is used to derive the state estimate of the mobile user's position, and successfully track the mobile users with less system complexity, as it requires measurements from only one base station. Therefore, localization of mobile users can be performed at the single base station. Furthermore, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Location based power control for mobile devices in a cellular network

This paper provides location estimation based power control strategy for cellular radio systems via a location based interference management scheme. Our approach considers the carrier-to-interference as dependent on the transmitter and receiver separation distance and therefore an accurate estimation of the precise locations can provide the power critical mobile user to control the transition power accordingly. In this fully
distributed algorithms, we propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user’s closest mobile base station from the user’s location, heading and altitude. Our analysis demonstrates that this algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations and hence enable the user to transmit at the rate that is sufficient for the interference management. Our power control
algorithms based on this estimation converges to the desired power trajectory. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impact the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network, and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impacted the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network; and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

Clustering versus evenly distributing energy dissipation in wireless sensor routing for prolong network lifetime

A novel Cluster Heads (CH) choosing algorithm based on both Minimal Spanning Tree and Maximum Energy resource on sensors, named MSTME, is provided for prolonging lifetime of wireless sensor networks. MSTME can satisfy three principles of optimal CHs: to have the most energy resource among sensors in local clusters, to group approximately the same number of closer sensors into clusters, and to distribute evenly in the networks in terms of location. Simulation shows the network lifetime in MSTME excels its counterparts in two-hop and multi-hop wireless sensor networks.

Network lifetime of application-specific randomly deployed wireless sensor networks in arbitrary sensor density

In many cases, sensors are randomly deployed in Wireless Sensor Networks (WSN), called Sensor-Randomly-Deployed WSN (SRD WSN). Several cluster-based routing protocols are provided to maximize network lifetime of SRD WSN in different sensor densities. LEACH performs better than direct routing in the density of 0.01. BCDCP excels LEACH in the density of 0.05. DMSTRP outperforms LEACH and BCDCP in the density of. However, simulation results under one or two kinds of sensor densities are not strong enough to prove the optimum of the routing protocols. In this paper, we give the general formulas to compute the network lifetimes of the above three routing protocols, discuss their optimal number of clusters, and compare their optimal network lifetime in arbitrary sensor densities. These formulas can provide more general design guidelines applicable to SRD WSN than simulation results under only one or two kinds of sensor densities. © 2007 IEEE.

Integrating network analysis, sensor tags, and observation to understand shark ecology and behavior

Group living in animals is a well-studied phenomenon, having been documented extensively in a wide range of terrestrial, freshwater, and marine species. Although social dynamics are complex across space and time, recent technological and analytical advances enable deeper understanding of their nature and ecological implications. While for some taxa, a great deal of information is known regarding the mechanistic underpinnings of these social processes, knowledge of these mechanisms in elasmobranchs is lacking. Here, we used an integrative and novel combination of direct observation, accelerometer biologgers, and recent advances in network analysis to better understand the mechanistic bases of individual-level differences in sociality (leadership, network attributes) and diel patterns of locomotor activity in a widespread marine predator, the lemon shark (Negaprion brevirostris). We found that dynamic models of interaction based on Markov chains can accurately predict juvenile lemon shark social behavior and that lemon sharks did not occupy consistent positions within their network. Lemon sharks did however preferentially associate with specific group members, by sex as well as by similarity or nonsimilarity for a number of behavioral (nonsimilarity: leadership) and locomotor traits (similarity: proportion of time swimming "fast," mean swim duration; nonsimilarity: proportion of swimming bursts/transitions between activity states). Our study provides some of the first information on the mechanistic bases of group living and personality in sharks and further, a potential experimental approach for studying fine-scale differences in behavior and locomotor patterns in difficult-to-study organisms.

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.

Network coding based converge-cast scheme in wireless sensor networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.

Terminal approach navigation for autonomous robots in a sensory network

In this paper we present a technique based on precision guidance approach for the sensor delivery and reception problem between two mobile robots. A slave robot is employed to collect sensors and slack them on a tray carried by the mobile master robot. We define the terminal attitude of the slave robot with respect to the master and present a LQR control approach to solving the problem of achieving a desired terminal approach angle necessary for the appropriate sensor delivery. The approach criteria is defined in terms of both minimizing the miss distance and controlling the slave robot's body attitude with respect to the master robot at the terminal point.

Reliable event transfer in wireless sensor networks deployed for emergency response

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find application in battlefield awareness to sense chemicals and other gases used in chemical and biological warfare. An application that has become increasingly attractive in the post 9/11 era is the use of wireless sensor networks for emergency (first) response in mass casualty incidents. Reliability of data and event transfer is of critical importance to emergency response applications. In this work we propose a reliable event transfer mechanism making use of an overlay network of relay nodes. The overlay network removes the burden of data relaying from the sensor nodes and results in increasing the lifetime of the network. Simulation results prove the benefits of such an architecture. Reliability is increased 10-30% with reduction in event traffic of 60-80%.