A Deadline Aware Real-time Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSN) find application in real-time events reporting and data gathering. When the sensor detects an event it is reported to the base stations, which then takes appropriate action. The course of action should have finite and bound delays defining a hard real-time constraint for time critical applications. This work proposes a network layer based, deadline aware real time routing protocol, which assumes a collision free known delay MAC (Medium Access Control) layer. The protocol works in three phases-the initialization phase, path establishment phase and the bandwidth division phase. This protocol ensures bounded delay in transmission of sensed data to the sink. It establishes a single path from each sensor node to the sink and allocates bandwidth for that path thereby reducing the time required for the sensed data to reach the sink

Traffic Adaptive Schedule-Based Mac Protocol For Wirelesssensor Networks

Wireless sensor networking is an emerging technology that has a wide range of potential applications inc1uding monitoring, medical systems, real-time, robotic exploration and etc. Energy is a critical resource in battery-powered sensor networks. Medium access control has an important role in minimizing energy consumption while it is responsible for successful data transferring in the network. Periodic data collection is the most comprehensive way of data gathering mechanism in wireless sensor network in which nodes report their samples in specific time interval s . It is possible to h ave some nodes with different update interval s in the network and therefore, finding a solution to accommodate nodes with different sampling intervals while maintaining the energy efficiency is the primary concern of this thesis

A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications

A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events

IJA: An Efficient Algorithm for Query Processing in Sensor Networks

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm

Decentralized Convex Optimization for Wireless Sensor Networks

Many real-world applications arising in domains such as large-scale machine learning, wired and wireless networks can be formulated as distributed linear least-squares over a large network. These problems often have their data naturally distributed. For instance applications such as seismic imaging, smart grid have the sensors geographically distributed and the current algorithms to analyze these data rely on centralized approach. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. To obtain the solution in real-time, we require decentralized algorithms that do not rely on a fusion center, cluster heads, or multi-hop communication. In this thesis, we propose several decentralized least squares optimization algorithm that are suitable for performing real-time seismic imaging in a sensor network. The algorithms are evaluated and tested using both synthetic and real-data traces. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes

Proposal of an architecture for sensor networks monitoring in Open Access Metropolitan Area Networks

Sensor networks have been used in a wide range of applications. In Digital Cities they play an important role in gathering real-time data in urban scale. However, the heterogeneous and complex technologies applied in such applications make it difficult to monitor and manage different sensor networks, and also prevents the interoperation between systems. Thus, this paper presents a proposal of a novel architecture based on service orientation for homogeneous interoperation among sensor networks used in a Digital City scenario. Based on the outlined architectural model, a case study took place in a Brazilian operational Digital City in the state of São Paulo. The objective of the study is to demonstrate that architecture can be used for monitoring heterogeneous environments in a unified way, promoting datasharing and interoperability.Keywords: Service-oriented architectures, Open Access Metropolitan Area Networks, ZigBee, sensor networks

Low cost color assessment of turbid liquids using supervised learning data analysis – proof of concept

This work reports the development of a low cost in-line color sensor for turbid liquids based on the transmission and scattering phenomena of light from RGB and IR LED sources, gathering multidimensional data. Three different methodologies to discriminate color from the turbidity influence are presented as a proof of concept approach. They are based in regression models, expectation maximization Gaussian mixtures and artificial neural networks applied to labeled measurements. Each methodology presents advantages and disadvantages which will depend on the intended implementation. Regression models revealed to be best suited for standard or occasional measurements, the EM Gaussian mixture will perform better for well-known controlled range of colors and turbidities and the neural networks have easy implementation and potential suited for real-time IoT platforms.publishe

On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks

We consider sensor networks that measure spatio-temporal correlated processes. An important task in such settings is the reconstruction at a certain node, called the sink, of the data at all points of the field. We consider scenarios where data is time critical, so delay results in distortion, or suboptimal estimation and control. For the reconstruction, the only data available to the sink are the values measured at the nodes of the sensor network, and knowledge of the correlation structure: this results in spatial distortion of reconstruction. Also, for the sake of power efficiency, sensor nodes need to transmit their data by relaying through the other network nodes: this results in delay, and thus temporal distortion of reconstruction if time critical data is concerned. We study data gathering for the case of Gaussian processes in one- and two-dimensional grid scenarios, where we are able to write explicit expressions for the spatial and time distortion, and combine them into a single total distortion measure. We prove that, for various standard correlation structures, there is an optimal finite density of the sensor network for which the total distortion is minimized. Thus, when power efficiency and delay are both considered in data gathering, it is useless from the point of view of accuracy of the reconstruction to increase the number of sensors above a certain threshold that depends on the correlation structure characteristics

Maximizing the capability of wireless sensor networks

Wireless micro-sensors introduce a new frontier in sensing devices and data acquisition capabilities. These sensors, capable of sensing, processing data, and short-range communication, can be spread over regions to form ad hoc wireless sensor networks (WSN) so as to deliver aggregate information from geographically diverse areas. This aggregate data gathering and processing induces a synergistic effect and enables a sensor network to complete sensing tasks that may never be feasible using a single, perhaps powerful, sensor. This new paradigm in sensing devices is not without many fundamental challenges, one being a constrained energy resource, which first need to be solved before the true capabilities of these networks may be realized. This thesis will discuss the models and techniques developed as an attempt to maximize the capability of a WSN. The premise used in the research is that the capability of a WSN can be maximize by developing a scheme that can duplicate the optimal energy efficient behavior of individual wireless sensors in a contention dominated, distributed decision-making, network environment. This optimal energy efficient behavior as determined by an analytically derived model and a mixed integer programming model will be presented. The analytical model enables the optimal sensor behavior to be calculated given a contention-less environment, and the integer programming model determines the optimal ON/OFF/transmission schedule for each sensor in a contention dominated network, over time. Finally, the optimal behavior found in the two models has been converted into a preliminary heuristic protocol that coordinates sensors in real time. The key aspects of this protocol along with its effectiveness, as compared to the optimal, are also presented