Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

Power saving and energy optimization techniques for Wireless Sensor Networks

Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, we present a survey of power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols.Sendra Compte, S.; Lloret, J.; García Pineda, M.; Toledo Alarcón, JF. (2011). Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications. 6(6):439-459. doi:10.4304/jcm.6.6.439-459S4394596

Incremental Processing and Optimization of Update Streams

Over the recent years, we have seen an increasing number of applications in networking, sensor networks, cloud computing, and environmental monitoring, which monitor, plan, control, and make decisions over data streams from multiple sources. We are interested in extending traditional stream processing techniques to meet the new challenges of these applications. Generally, in order to support genuine continuous query optimization and processing over data streams, we need to systematically understand how to address incremental optimization and processing of update streams for a rich class of queries commonly used in the applications. Our general thesis is that efficient incremental processing and re-optimization of update streams can be achieved by various incremental view maintenance techniques if we cast the problems as incremental view maintenance problems over data streams. We focus on two incremental processing of update streams challenges currently not addressed in existing work on stream query processing: incremental processing of transitive closure queries over data streams, and incremental re-optimization of queries. In addition to addressing these specific challenges, we also develop a working prototype system Aspen, which serves as an end-to-end stream processing system that has been deployed as the foundation for a case study of our SmartCIS application. We validate our solutions both analytically and empirically on top of our prototype system Aspen, over a variety of benchmark workloads such as TPC-H and LinearRoad Benchmarks

A hierarchical group model for programming sensor networks

A hierarchical group model that decouples computation from hardware can characterize and aid in the construction of sensor network software with minimal overhead. Future sensor network applications will move beyond static, homogeneous deployments to include dynamic, heterogeneous elements. These sensor networks will also gain new users, including casual users who will expect intuitive interfaces to interact with sensor networks. To address these challenges, a new computational model and a system implementing the model are presented. This model ensures that computations can be readily reassigned as sensor nodes are introduced or removed. The model includes methods for communication to accommodate these dynamic elements. This dissertation presents a detailed description and design of a computational model that resolves these challenges using a hierarchical group mechanism. In this model, computation is tasked to logical groups and split into collective and local components that communicate hierarchically. Local computation is primarily used for data production and publishes data to the collective computation. Similarly, collective computation is primarily used for data aggregation and pushes results back to the local computation. Finally, the model includes data-processing functions interposed between local and collective functions that are responsible for data conversion. This dissertation also presents implementations and applications of the model. Implementations include Kensho, a C-based implementation of the hierarchical group model, that can be used for a variety of user applications. Another implementation, Tables, presents a spreadsheet-inspired view of the sensor network that takes advantage of hierarchical groups for both computation and communication. Users are able to specify both local and collective functions that execute on the sensor network via the spreadsheet interface. Applications of the model are also explored. One application, FUSN, provides a set of methods for constructing filesystem-based interfaces for sensor networks. This demonstrates the general applicability of the model as applied to sensor network programming and management interfaces. Finally, the model is applied to a novel privacy algorithm to demonstrate that the model isn\u27t strictly limited to programming interfaces

Computational intelligent sensor-rank consolidation approach for Industrial Internet of Things (IIoT).

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches