Energy efficient organization and modeling of wireless sensor networks

With their focus on applications requiring tight coupling with the physical world, as opposed to the personal communication focus of conventional wireless networks, wireless sensor networks pose significantly different design, implementation and deployment challenges. Wireless sensor networks can be used for environmental parameter monitoring, boundary surveillance, target detection and classification, and the facilitation of the decision making process. Multiple sensors provide better monitoring capabilities about parameters that present both spatial and temporal variances, and can deliver valuable inferences about the physical world to the end user. In this dissertation, the problem of the energy efficient organization and modeling of dynamic wireless sensor networks is investigated and analyzed. First, a connectivity distribution model that characterizes the corresponding sensor connectivity distribution for a multi-hop sensor networking system is introduced. Based on this model, the impact of node connectivity on system reliability is analyzed, and several tradeoffs among various sleeping strategies, node connectivity and power consumption, are evaluated. Motivated by the commonality encountered in the mobile sensor wireless networks, their self-organizing and random nature, and some concepts developed by the continuum theory, a model is introduced that gives a more realistic description of the various processes and their effects on a large-scale topology as the mobile wireless sensor network evolves. Furthermore, the issue of developing an energy-efficient organization and operation of a randomly deployed multi-hop sensor network, by extending the lifetime of the communication critical nodes and as a result the overall network\u27s operation, is considered and studied. Based on the data-centric characteristic of wireless sensor networks, an efficient Quality of Service (QoS)-constrained data aggregation and processing approach for distributed wireless sensor networks is investigated and analyzed. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion, based on the availability of local only information. Data aggregation is performed on the fly at intermediate sensor nodes, while at the same time the end-to-end latency constraints are satisfied. An analytical model to represent the data aggregation and report delivery process in sensor networks, with specific delivery quality requirements in terms of the achievable end-to-end delay and the successful report delivery probability, is also presented. Based on this model, some insights about the impact on the achievable system performance, of the various designs parameters and the tradeoffs involved in the process of data aggregation and the proposed strategy, are gained. Furthermore, a localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy and buffer overflow, for given QoS requirements. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as energy efficiency, network lifetime, end-to-end latency, and data loss are also evaluated and discussed

Towards a Queueing-Based Framework for In-Network Function Computation

We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and scheduling algorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and k th -order statistics. For such functions we exactly characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In acyclic wireline networks, we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks, we provide a MaxWeight-like algorithm with dynamic flow splitting, which is shown to be throughput-optimal

Data aggregation in wireless sensor networks using the SOAP protocol

Wireless sensor networks (WSN) offer an increasingly attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have physical and resource limitations, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. In this paper, a data aggregation framework using SOAP (Simple Object Access Protocol) on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. The aim of the study is to assess the suitability of the protocol in such environments where resources are limited compared to traditional networks

Adaptive scheme to Control Power Aware for PDR in Wireless Sensor Networks

Nowadays Wireless sensor networks playing vital role in all area. Which is used to sense the environmental monitoring, Temperature, Soil erosin etc. Low data delivery efficiency and high energy consumption are the inherent problems in Wireless Sensor Networks. Finding accurate data is more difficult and also it will leads to more expensive to collect all sensor readings. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data, provide opportunities for reducing the energy consumption of continuous sensor data collection and to achieve network energy efficiency and stability. So as we propose Dynamic scheme for energy consumption and data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme, sleep/awake method with dynamic scheme. Our framework is clustering based. A cluster head represents all sensor nodes within the region and collects data values from them. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical dynamic algorithm for data aggregation, it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme with dynamic scheme for clustering-based continuous data collection in sensor networks. When a cluster node fails because of energy depletion we need to choose alternative cluster head for that particular region. It will help to achieve less energy consumption. Our proposed models, analysis, and framework are validated via simulation and comparison with Static Cluster method in order to achieve better energy efficiency and PDR

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Analysis of Structured and Un-Structured Network Protocols for Data Aggregation Over Distributed Wireless Sensor Networks

The focus of this thesis is on design and evaluation of one-shot data aggregation protocols for static and mobile wireless sensor networks (WSNs). The goal in one-shot data aggregation is to compute a statistical summary of sensor data such as max, average, sum, count and min, when initiated by a special node such as the base station. WSNs have wide range of applications in both static and mobile/dynamic systems. Static sensor networks are especially useful when monitoring is required in harsh, inaccessible environments and when the region to be monitored is really large. Examples of static sensor network applications include environmental monitoring systems, monitoring of industrial control systems, monitoring of degradation in slagging gasifiers, distributed object detection and tracking. Example of mobile applications include vehicular ad-hoc networks and networks of personal radios used in emergency dispatch and battlefields.;For data aggregation in static networks with stable links, structured approaches such as spanning trees are generally preferred. This is because, once a data aggregation structure has been established, link topologies remain fixed and there is minimal need to actively maintain and change the routing structures. In this thesis, one such tree based data aggregation protocol has been designed and evaluated using simulations in networks ranging from 100-1000 nodes. The protocol has also been implemented at a smaller scale in the context of a smart refractory environment, where slag penetration in gasifiers is remotely monitored using smart bricks that are embedded with sensors. In mobile networks and networks with frequent link changes, topology driven structures are likely to be unstable and to incur a high communication overhead. Therefore, self-repelling random walks have been recently proposed as an attractive alternative for data aggregation in mobile systems. In this thesis, a brief overview of random walk based data aggregation has been presented and systematic evaluation of tree based and random walk based data aggregation protocols in networks ranging from 100-1000 nodes under varying degrees of node mobility has been done. The conditions under which unstructured protocols become more attractive in terms of convergence time and messaging efficiency as compared to tree based structured approaches have been quantified