Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs). In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS) approach is proposed and implemented in Disk Based Data Centric Storage (DBDCS) architecture. DBDCS is an adaptation of magnetic disk storage platter consisting tracks and sectors. In DBDCS, the disc track and sector analogy is used to map data locations. A distance based indexing method is used for storing and querying multi-dimensional similar data. EDDS applies a threshold based hierarchical approach, which uses temporal correlation among sectors and sector segments to calculate a dynamic skyline. The efficiency and effectiveness of EDDS has been evaluated in terms of latency, energy consumption and accuracy through a simulation model developed in Castalia

Wireless network architecture for future smart grid machine to machine communications

Transformation of the conventional power grid into an efficient power delivery network is an important advance that will benefit consumers, business and the environment by providing improved integration of renewable energy, including solar and wind. A reliable, low latency communication system is a fundamental requirement for smart power grids. To achieve bidirectional energy distribution capability and to support diverse Smart Grid (SG) applications, the modern SG requires the capacity to handle the traffic generated by machine to machine (M2M) communication infrastructure. Successful integration of numerous SG applications, renewable energy sources and Electric Vehicles (EVs) into a conventional power grid would not be possible without a communication network that has been designed to support the needs of the new and innovative renewable power generation, distribution and storage technologies. While the legacy communication infrastructure, utilized to support the existing power network, fails to support all of the SG functionalities, Software Defined Networking (SDN), based on wireless communication systems, has the potential to provide an effective solution. SDN offers a range of features that fulfill the unique requirements of the SG applications. Being a new networking paradigm, SDN remains to be implemented for SG M2M communication scenarios and there remain a number of challenges that need to be overcome. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. The aim of this research was to carry out an in-depth study on the future SG communication networks and to propose solutions to identified limitations of existing communication networks. Keeping this intention in mind, the study first focuses on the SG application modeling techniques based on the traffic requirements and power supply load profiles. To address the dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. SG application models were developed and evaluated using a range of scenarios reflecting typical usage. Heterogenous network architectures and efficient traffic models were developed to identify an appropriate wireless communication technology and to maximize the network performance for major SG applications. However, a careful observation of the communication networks ability to manage and control the diverse M2M communications reveals that the inadequate dynamic communication network configuration capability would be a problem for future SG applications. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. To realize the full potential of the SGs and deployment scenarios it is essential to analyze the major applications and key requirements to develop those applications. Also, it might be necessary to select an appropriate communication technology for each of the power system domains. The study first focuses on the SG application modeling techniques based on the traffic requirement and load supply profiles of the power system. To address dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. The developed SG application models were further evaluated using simulation scenarios and a test bed model. The challenge of selecting an appropriate wireless communication technology and maximizing network performance for major SG applications was handled by developing multiple heterogenous network architectures and efficient traffic models. A comprehensive literature review of the state of the art of SG applications and standards was carried out to develop robust network models utilizing diverse communication technologies. The literature survey immensely helped to develop two novel SG application models, Zigbee based Pilot protection scheme for a smart distribution grid and Vehicle to Grid (V2G) smart load management scheme. Application modelling included detail traffic modelling, developing smart algorithms, analytical models, user load profile analysis, simulation models and test bed setups. Furthermore, a novel WiMax Ranging scheme is presented to improve the random-access mechanism for various periodic M2M applications supported by extensive simulation based performance analysis. Future SGs will be overwhelmed by an excessive number of sensor devices that collect various data related to the power system. In a SG Neighborhood Area Network (NAN), wireless sensor networks (WSNs) will play a key role in the development of major SG applications. The application centric WSNs require complex configurations such as well-defined access techniques, transmission and security protocols. Challenges also include development of appropriate routing protocols to tackle resource limitations and delay caused by decentralized WSNs and ad hoc based packet forwarding techniques. A careful observation of manageability and controllability of the diverse M2M network reveals that the inadequate dynamic network configuration capability of the existing SG communication network would be a key bottleneck for future SG. Thus, a novel WSN based communication framework is presented exploiting the emerging SDN networking paradigm. SDN would be beneficial for SGs in many ways. By decoupling the control plane and data forwarding plane, SDN facilitates real-time control and integration of network services and applications that can reach down into the network through the controller hierarchy. A higher degree of control over the overall SG communication network would be achievable via the dynamic programmability provided by SDN. The SDN based WSN network must be robust enough to support the adaptive energy dispatching capacity of the modern power system. The proposed communication framework incorporates novel communication features to separate the control plane and data forwarding plane within the SG communication network. This includes detailed modeling of the control and data plane communication parameters to support both delay sensitive and delay tolerant SG applications. The unique SDN features offers a platform to accommodate maximum number of SG applications with highest controllability and manageability. The performance of the SDN based future SG network is evaluated using a simulation scenario that considers realistic user load profiles, wireless standards, the SG premises geographical area and the state of the art of the SG standards. Although the control plane enables a global view of the data plane and provides a centralized platform to control and deploy new services, physically a single controller in the controller would not be practical for SG networks. The challenges arise in terms of scalability, security and reliability, particularly in a SG environment. To increase the efficiency of the proposed SDN based WSNs for the SG NAN, the study proposed distributed controllers with a comprehensive analytical model that optimizes the number of distributed controllers to enhance performance of the proposed communication framework in the NAN domain. The proposed framework along with the analytical model derive several solutions, such as the minimum number of controllers to support the switches and M2M devices, accommodate SG applications and a differentiated flow processing technique to support all traffic types within the network. Lastly, the study focuses on developing SDN-based application specific traffic models for the smart distribution grid. The thesis focuses on three major issues while developing a future SG communication system. Firstly, its identifies major applications and their traffic requirements at different domains of the SG. Appropriate traffic models were developed by designing robust wireless communication network models. Also, application centric smart optimization techniques are adopted to achieve maximum performance and presented with simulation results, statistical analysis and a test bed result analysis. Secondly, to facilitate the centralized controllability and programmability for supporting diverse SG applications within the SG, a novel WSNs communication framework is presented exploiting the next generation SDN paradigm. Both delay sensitive and delay tolerant SG applications were considered based on the traffic requirement to develop the SDN based WSN communication framework in the SG NAN. Smart algorithms were developed at the SDN based WSN application layer to accommodate a large number of SG applications. The framework feasibility is demonstrated by the simulations carried out to verify the model and provide a statistical analysis. Thirdly, the thesis focuses on developing a novel analytical model that can be used to determine the optimal number of distributed controllers and switches in a SG NAN domain. The proposed application centric traffic modelling techniques, SDN based wireless communication framework and analytical models in this thesis can be adapted for research into other communication networks, particularly those that are begin developed for the Internet of Things and other forms of M2M communications. Also, due to the technology agonistic characteristics of the analytical and traffic models, they can be used in the development of various wireless networks, particularly those that focus on wireless sensor networks, more generally than the broader Internet of Things

Data centric storage framework for an intelligent wireless sensor network

In the last decade research into Wireless Sensor Networks (WSN) has triggered extensive growth in flexible and previously difficult to achieve scientific activities carried out in the most demanding and often remote areas of the world. This success has provoked research into new WSN related challenges including finding techniques for data management, analysis, and how to gather information from large, diverse, distributed and heterogeneous data sets. The shift in focus to research into a scalable, accessible and sustainable intelligent sensor networks reflects the ongoing improvements made in the design, development, deployment and operation of WSNs. However, one of the key and prime pre-requisites of an intelligent network is to have the ability of in-network data storage and processing which is referred to as Data Centric Storage (DCS). This research project has successfully proposed, developed and implemented a comprehensive DCS framework for WSN. Range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven the research focus. The architecture of the deployed network, referred to as Disk Based Data Centric Storage (DBDCS), was inspired by the magnetic disk storage platter consisting of tracks and sectors. The core contributions made in this research can be summarized as: a) An optimally synchronized routing algorithm, referred to Sector Based Distance (SBD) routing for the DBDCS architecture; b) DCS Metric based Similarity Searching (DCSMSS) with the realization of three exemplar queries – Range query, K-nearest neighbor query (KNN) and Skyline query; and c) A Decentralized Distributed Erasure Coding (DDEC) algorithm that achieves a similar level of reliability with less redundancy. SBD achieves high power efficiency whilst reducing updates and query traffic, end-to-end delay, and collisions. In order to guarantee reliability and minimizing end-to-end latency, a simple Grid Coloring Algorithm (GCA) is used to derive the time division multiple access (TDMA) schedules. The GCA uses a slot reuse concept to minimize the TDMA frame length. A performance evaluation was conducted with simulation results showing that SBD achieves a throughput enhancement by a factor of two, extension of network life time by 30%, and reduced end-to-end latency. DCSMSS takes advantage of a vector distance index, called iDistance, transforming the issue of similarity searching into the problem of an interval search in one dimension. DCSMSS balances the load across the network and provides efficient similarity searching in terms of three types of queries – range query, k-query and skyline query. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. DDEC encoded the acquired information into n fragments and disseminated across n nodes inside a sector so that the original source packets can be recovered from any k surviving nodes. A lost fragment can also be regenerated from any d helper nodes. DDEC was evaluated against 3-Way Replication using different performance matrices. The results have highlighted that the use of erasure encoding in network storage can provide the desired level of data availability at a smaller memory overhead when compared to replication