A Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks using Cluster-Head Handover Mechanism

Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a modified Energy Efficient Clustering with Splitting and Merging (EECSM) for WSNs using Cluster-Head Handover Mechanism was implemented in this research. The modified model used information of the residual energy of sensor nodes to select backup Cluster Heads (CHs) while maintaining a suitable CH handover threshold to minimize energy consumption in the network. The backup CHs take over the responsibilities of the CHs once the handover threshold is reached. The modified model was validated in terms of network lifetime and residual energy ratio with EECSM using MATLAB R2013a. Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicates a significant reduction in energy consumption of the network nodes. Keywords— Clustering, Energy-Efficiency, Handover, Lifetime, Wireless Sensor Networ

CAREER: Data Management for Ad-Hoc Geosensor Networks

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important task of such geosensor networks is to collect, analyze and estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation. The project has a broad impact on the community involving undergraduate and graduate students in spatial database research at the University of Maine as well as being a key component of a current IGERT program in the areas of sensor materials, sensor devices and sensor. More information about this project, publications, simulation software, and empirical studies are available on the project\u27s web site (http://www.spatial.maine.edu/~nittel/career/)

Designing of Dynamic Re-clustering Leach Protocol for Calculating Total Residual Time and Performance

Wireless sensor networks (WSN), a plurality of sensors in an energy limited deliveries. the sensors are arranged randomly for many applications. Thus, the battery Or download a replacement would be practical. Therefore, the energy efficient routing protocol to expand the network. In this article we propose a new cluster based Re-Leach dynamic Protocol Dynamic Reclustering based Leach protocol (DR-Leach), the expansion of the lattice energy consumption and reduce the age. The idea is that the energy cluster leaders  next production cluster to balance In each round, the same number of nodes in the network of life. Make your first calculation It calculates the optimum amount of CHS in each round, and the optimum amount for each cluster. The results showed that the improvement of reliability protocols proposed frame and the total energy consumption than BCDCP Leach and protocols

A hybrid prediction model for energy-efficient data collection in wireless sensor networks

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions

Information management and security protection for internet of vehicles

Considering the huge number of vehicles on the roads, the Internet of Vehicles is envisioned to foster a variety of new applications ranging from road safety enhancement to mobile entertainment. These new applications all face critical challenges which are how to handle a large volume of data streams of various kinds and how the secure architecture enhances the security of the Internet of Vehicles systems. This dissertation proposes a comprehensive message routing solution to provide the fundamental support of information management for the Internet of Vehicles. The proposed approach delivers messages via a self-organized moving-zone-based architecture formed using pure vehicle-to-vehicle communication and integrates moving object modeling and indexing techniques to vehicle management. It can significantly reduce the communication overhead while providing higher delivery rates. To ensure the identity and location privacy of the vehicles on the Internet of Vehicles environment, a highly efficient randomized authentication protocol, RAU+ is proposed to leverage homomorphic encryption and enable individual vehicles to easily generate a new randomized identity for each newly established communication while each authentication server would not know their real identities. In this way, not any single party can track the user. To minimize the infrastructure reliance, this dissertation further proposes a secure and lightweight identity management mechanism in which vehicles only need to contact a central authority once to obtain a global identity. Vehicles take turns serving as the captain authentication unit in self-organized groups. The local identities are computed from the vehicle's global identity and do not reveal true identities. Extensive experiments are conducted under a variety of Internet of Vehicles environments. The experimental results demonstrate the practicality, effectiveness, and efficiency of the proposed protocols.Includes bibliographical references

Application Layer Architectures for Disaster Response Systems

Traditional disaster response methods face several issues such as limited situational awareness, lack of interoperability and reliance on voice-oriented communications. Disaster response systems (DRSs) aim to address these issues and assist responders by providing a wide range of services. Since the network infrastructure in disaster area may become non-operational, mobile ad-hoc networks (MANETs) are the only alternative to provide connectivity and other network services. Because of the dynamic nature of MANETs the applications/services provided by DRSs should be based on distributed architectures. These distributed application/services form overlays on top of MANETs. This thesis aims to improve three main aspect of DRSs: interoperability, automation, and prioritization. Interoperability enables the communication and collaboration between different rescue teams which improve the efficiency of rescue operations and avoid potential interferences between teams. Automation allows responders to focus more on their tasks by minimizing the required human interventions in DRSs. Automation also allows machines to operate in areas where human cannot because of safety issues. Prioritization ensures that emergency services (e.g. firefighter communications) in DRSs have higher priority to receive resources (e.g. network services) than non-emergency services (e.g. new reporters’ communications). Prioritizing vital services in disaster area can save lives. This thesis proposes application layer architectures that enable three important services in DRSs and contribute to the improvement of the three aforementioned aspects of DRSs: overlay interconnection, service discovery and differentiated quality of service (QoS). The overlay interconnection architecture provides a distributed and scalable mechanism to interconnect end-user application overlays and gateway overlays in MANETs. The service discovery architecture is a distributed directory-based service discovery mechanism based on the standard Domain Name System (DNS) protocol. Lastly, a differentiated QoS architecture is presented that provides admission control and policy enforcement functions based on a given prioritization scheme. For each of the provided services, a motivation scenario is presented, requirements are derived and related work is evaluated with respect to these requirements. Furthermore, performance evaluations are provided for each of the proposed architectures. For the overlay interconnection architecture, a prototype is presented along with performance measurements. The results show that our architecture achieves acceptable request-response delays and network load overhead. For the service discovery architecture, extensive simulations have been run to evaluate the performance of our architecture and to compare it with the Internet Engineering Task Force (IETF) directory-less service discovery proposal based on Multicast DNS. The results show that our architecture generates less overall network load and ensures successful discovery with higher probability. Finally, for the differentiated QoS architecture, simulations results show that our architecture not only enables differentiated QoS, it also improves overall QoS in terms of the number of successful overlay flows

SENOCLU, Energy Efficient Approach for Unsupervised Node Clustering in Sensor Networks

Acquisition and analysis of data from sensor networks, where nodes operate in unsupervised way, has become a ubiquitous issue. The biggest challenge in this process is related to limited energy, computational and memory capacity of sensor nodes. Therefore, the main goal of our work is to devise and evaluate the contribution of an energy efficient algorithm for data acquisition in sensor networks. The proposed SENOCLU algorithm considers specific requirements of sensor network application like energy efficiency, state change detection, load balancing, high-dimensions of the sensed data etc. By applying these techniques, this algorithm contributes in filling the gap between distributed clustering and high-dimensional clustering algorithms that are available in the literature. This work evaluates the contribution of this algorithm in comparison to other competing state-of-the-art techniques. The experiments show that by applying SENOCLU algorithm better life times of sensor networks are achieved and longer monitoring of different phenomena is provided