Pervasive Sensor Networks Research Articles

The Future of Clean Computing May Be Dirty

The emergence of the Internet of Things and pervasive sensor networks have generated a surge of research in energy scavenging techniques. We know well that harvesting RF, solar, or kinetic energy enables the creation of battery-free devices that can be used where frequent battery changes or dedicated power lines are impractical. One unusual yet ubiquitous source of power is soil (earth itself) - or more accurately, bacterial communities in soil. Microbial fuel cells (MFCs) are electrochemical cells that harness the activities of microbes that naturally occur in soil, wetlands, and wastewater. MFCs have been a topic of research in environmental engineering and microbiology for decades, but are a relatively new topic in electronics design and research. Most low-power electronics have traditionally opted for batteries, RF energy, or solar cells. This is changing, however, as the limitations and costs of these energy sources hamper our ability to deploy useful systems that last for decades in challenging environments. If large-scale, long-term applications like underground infrastructure monitoring, smart farming, and sensing for conservation are to be possible, we must rethink the energy source.

On Learning From Inaccurate and Incomplete Traffic Flow Data

Today, we live in an era where pervasive sensor networks both collect and broadcast rich digital footprints about the human mobility. However, most of this data often comes in an incomplete and/or inaccurate fashion. In this paper, we propose a knowledge discovery framework to handle such issues in the context of automatic incident detection systems fed with traffic flow data. This framework operates in three steps: 1) it clusters sensors with a novel multi-criteria distance metric tailored for this purpose, followed by a heuristic rule that labels the abnormal groups; 2) then, a spatial cross-correlation framework identifies seasonal and individual abnormal readings to perform a more fine-grained filtering; and 3) finally, we propose a novel fundamental diagram that discovers the critical density of a given road section/spot on a data-driven fashion that is resistant to both outliers and noise within the input data. Large-scale experiments were conducted over traffic flow data provided by a major Asian highway operator. The obtained results illustrate well the contributions of this framework: it drastically reduces the noise within the raw data, and it also allows determining reliable definitions of traffic states (congestion/no congestion) on a completely automated way.

Automated process recognition architecture for cyber-physical systems

ABSTRACTAdvances in pervasive computing, sensor networks and activity recognition have opened several paths to achieve novel solutions for applying model-driven data-intensive techniques. Complex system integrating computers, communication and control systems, generate new challenges for Enterprise Systems in the area of data modelling and processing. This paper is focused on the analysis of data intensive streams using process mining techniques, in the context of Enterprise Information Systems. An automated process recognition method based on data acquired from sensor networks, able to generate a process model describing dynamic aspects of the monitored environment, is proposed and analyzed.

Energy Harvesting for Self-Sustainable Wireless Body Area Networks

Energy is regarded as one of the most critical challenges for static and mobile pervasive sensor networks. It not only limits a network's operating lifetime but also causes a performance bottleneck. Traditional techniques for overcoming energy constraints include replacing batteries or using a constant electrical source for wired recharging. These might not always be feasible solutions, however, and in some cases, could even prove prohibitive. Fortunately, due to recent technological advancements, several prominent techniques now exist to viably address such energy constraints. Among these, energy harvesting is gaining significant attention in academia and industry due to its potential to enable perpetual operation without the need for external power cables or periodic battery replacements. Energy can be harnessed from a diverse set of nonelectrical ubiquitous sources whenever required. This article presents a review of several potential energy-harvesting sources for wireless body area networks (WBAN). The authors examine sources' characteristics, usefulness in enhancing service availability and minimizing performance bottleneck, current challenges, and possible future research directions. They discuss how recent developments in energy-harvesting technologies have raveled self-sustainable operation for low-power body sensor networks.

An energy-efficient data gathering method based on compressive sensing for pervasive sensor networks

This paper proposes an energy-efficient data gathering method called CN-MSTP (Combining Minimum Spanning Tree with Interest Nodes) for pervasive wireless sensor networks, basing on Compressive sensing (CS) and data aggregation. The proposed CN-MSTP protocol selects different nodes at random as projection nodes, and sets each projection node as a root to construct a minimum spanning tree by combining with interest nodes. Projection node aggregates sensor reading from sensor nodes using compressive sensing. We extend our method by letting the sink node participate in the process of building a minimum tree and introduce eCN-MSTP. We compare our methods with the other methods. Simulation results indicate that our two methods outperform the other methods in overall energy consumption saving and load balance and hence prolong the lifetime of the network.

Sensor Clustering and Sensing Technology for Optimal Throughput of Sensor-Aided Cognitive Radio Networks Supporting Multiple Licensed Channels

In cognitive radio networks, secondary users may not sense licensed channels efficiently due to problems of fading channel, shadowing, unfamiliar environment, and so forth. To cope with the limitation, a pervasive sensor network can cooperate with cognitive radio network, which is called sensor-aided cognitive radio network. In the paper, we investigate sensor clustering and sensing time of each sensor cluster with aiming at achieving optimal throughput of sensor-aided cognitive radio network supporting multiple licensed channels. Moreover, the minimum throughput requirement of cognitive radio user is also guaranteed in the sensor clustering problem. To do this, we formulate the throughput maximization problem as a mixed-integer nonlinear programming and utilize the Branch and Bound algorithm to solve it. We also propose an heuristic algorithm which can provide similar performance to that of the Branch and Bound algorithm while reducing computation complexity significantly.

Mobile augmented reality for environmental monitoring

In response to dramatic changes in the environment, and supported by advances in wireless networking, pervasive sensor networks have become a common tool for environmental monitoring. However, tools for on-site visualization and interactive exploration of environmental data are still inadequate for domain experts. Current solutions are generally limited to tabular data, basic 2D plots, or standard 2D GIS tools designed for the desktop and not adapted to mobile use. In this paper, we introduce a novel augmented reality platform for 3D mobile visualization of environmental data. Following a user-centered design approach, we analyze processes, tasks, and requirements of on-site visualization tools for environmental experts. We present our multilayer infrastructure and the mobile augmented reality platform that leverages visualization of georeferenced sensor measurement and simulation data in a seamless integrated view of the environment.

Novel wireless pervasive sensor network to improve the understanding of noise in street canyons

This paper presents the first results of the application of an array of up to 50 novel wireless pervasive sensors, referred to as motes, developed at Newcastle University in the project, MESSAGE (Mobile Environmental Sensing System Across Grid Environments). The motes measure noise, carbon monoxide, nitrogen dioxide, temperature, humidity and traffic occupancy/flow. The system has wireless communication (using ZigBee standard) to allow daisy-chaining of data from mote to mote (either static or dynamic) to router technology which forwards the data into a gateway. This passes the data through GPRS (or hard wired web link) to be captured in real-time into an Urban Traffic Management and Control, compliant database. Following a brief discussion of the health impacts associated with noise exposure and the policy context, the validation and proof of concept of the use of a pervasive sensor array to measure noise in an urban area is demonstrated through separate deployments in Leicester (UK) and Palermo (Italy). The motes provide real-time minute by minute, noise data [Leq in dB(A)] and their low cost and small size means that they can be pervasively deployed. From the two applications the results clearly show the potential of these motes to measure noise levels and to improve the understanding of the effect of traffic flow characteristics on the minute by minute variation in noise levels in busy urban streets. The distribution of one minute LAeq noise levels measured by the standard deviation were shown to exhibit consistent but different characteristics depending which of three measurement clusters, primary congested road and well ventilated secondary roads with busy traffic, tertiary roads without acoustic screening from busy roads and finally residential streets. Predictive relationships which allow the magnitude of the standard deviation to be expected depending on the noise level are presented. The results demonstrated a measurement system and predictive method that offers potential to explore more suitable noise parameters that correlate the human response to traffic related noise.

Self-organizing virtual macro sensors

The future large-scale deployment of pervasive sensor network infrastructures calls for mechanisms enabling the extraction of general-purpose data at limited energy costs. The approach presented in this article relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence to spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. The result of this process is that a sensor network can be modeled as a collection of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data about its region and is able to act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and evaluated in both a simulation environment and a real test bed, and quantitatively compared with related works in the area. Current limitations and areas of future development are also discussed.

Execution and Time Models for Pervasive Sensor Networks

Sensor-actuator networks and interactive ubiquitous environments are distributed systems in which the sensor-actuators communicate with each other by message-passing. This paper makes three contributions. First, it gives a general system and execution model for such sensor-actuator networks in pervasive environments. Second, it examines the range of time models that are useful for specifying properties, and for implementation, in such distributed networks, and places approaches and limitations in perspective. Third, it shows that although the partial order time model has not been seen to be useful as a specification tool in real applications of sensornets, yet, it is useful for real applications in pervasive sensornets because (under certain conditions) it can serve as a viable alternative to physically synchronized clocks that provide the linear order time model.

Gallery: A Self-healing Topology Model for Pervasive Sensor Networks

Pervasive Sensor Network(PSN) is identified as rapid dynamic network. PSN environment is viable only through Sensor networks. Fault-tolerance is expected high when handling speedy movements of sensor nodes. Henceforth, we propose a novel self-healing topology named Gallery; a placement model for Pervasive WSNs with dynamic topology control functions. The topology model ensures the localization of sensor nodes and guarantees the scalability by increasing its tracks and slices. The 2D and 3D deployment methods are Gallery is discussed. A wait-n-relay method is introduced for mobility nodes. An informal clustering of nodes within a region is identified as track region. The careful design of track region and relevant parameters are done. A covering map of mobility enabled relay sensor is calculated. The simulation is done with omnetpp simulator. Self-healing is promising by exact data aggregation by relay sensor if an desirable event raised within the region of interest.

Automatic configuration of pervasive sensor networks for augmented reality

The ubiquitous tracking (Ubitrack) approach uses spatial relationship graphs and patterns to support a distributed software architecture for augmented reality (AR) systems in which clients can produce, transform, transmit, and consume tracking data.

RFID: From Supply Chains to Sensor Nets

The next generation internet will be the internet of things (and not just of computing devices like PCs, PDAs); this is presumed to be enabled by integrating simple computing plus communications capabilities into common objects of everyday use. Radio-frequency identification (RFID) is a compelling technology for creation of such pervasive sensor networks due to its potential for ubiquitous, low-cost/low-maintenance use. However, the current drivers for RFID deployment emphasize supply chain management using passive tags, implying that RFID sensor nets require advances beyond the components and system designs aimed at supply chain applications. This work provides a glimpse of how this may be achieved.

Support for context-aware monitoring in home healthcare

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we introduce a logic-based context model for situation assessment combined with high level declarative feedback policy specification, and we use logic programming techniques to reason about different pieces of knowledge for prevention.

Fault perturbations in building sensor network data streams

The Critter temperature sensor device has been developed to understand the full nature of pervasive sensor networks. Off the shelf integrated sensor devices incorporate some amount of adaptation to make the devices more reliable. The Critter provides raw instantaneous readings including outlier data that may be considered anomalies or perturbations. We have deployed Critter data sensors pervasively through one academic building for almost 18 months. This paper explores the causes of temperature data perturbations, defined as two temperature data readings taken within seconds of each other that differ by several degrees. Temperature sensor data perturbations are actually the effects of user activity within buildings. By capturing the raw data without automatic processing, we are able to show a correlation between time of the work day and the frequency of data perturbation.

Imputation of missing sensor data values using in-exact replicas

Sensor network systems attempt to exploit redundancy which is assumed to be inherent in a pervasive sensor network due to the density of the sensor nodes. However, there is a lack of sensor data stream correlation between seemingly redundant sensors. The approach presented the uses and prediction techniques that have been used to anticipate a sensor value using its own history, to allow one sensor to be able to anticipate the data from another different sensor, given a historical stream of data from the sensor to be anticipated. The specific mechanisms explored are artificial neural networks, pattern recognition and multivariate polynomial regression.

On the Use of IEEE Std. 802.15.4 to Enable Wireless Sensor Networks in Building Automation

IEEE 802.15.4 is a new standard that addresses the need of low-rate wireless personal area networks or LR-WPAN with a focus on enabling pervasive wireless sensor networks for residential, commercial and industrial applications. The standard is characterized by maintaining a high level of simplicity, allowing for low cost and low power implementations. This work presents a brief technical introduction of the IEEE 802.15.4 standard and analyzes its applicability for building automation applications.