Energy-efficient routing and secure communication in wireless sensor networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Wireless Sensor Networks (WSNs) consist of miniature sensor nodes deployed to gather vital information about an area of interest. The ability of these networks to monitor remote and hostile locations has attracted a significant amount of research over the past decade. As a result of this research, WSNs have found their presence in a variety of applications such as industrial automation, habitat monitoring, healthcare, military surveillance and transportation. These networks have the ability to operate in human-inaccessible terrains and collect data on an unprecedented scale. However, they experience various technical challenges at the time of deployment as well as operation. Most of these challenges emerge from the resource limitations such as battery power, storage, computation, and transmission range, imposed on the sensor nodes. Energy conservation is one of the key issues requiring proper consideration. The need for energy-efficient routing protocols to prolong the lifetime of these networks is very much required. Moreover, the operation of sensor nodes in an intimidating environment and the presence of error-prone communication links expose these networks to various security breaches. As a result, any designed routing protocol need to be robust and secure against one or more malicious attacks. This thesis aims to provide an effective solution for minimizing the energy consumption of the nodes. The energy utilization is reduced by using efficient techniques for cluster head selection. To achieve this objective, two different cluster-based hierarchical routing protocols are proposed. The selection of an optimal percentage of cluster heads reduces the energy consumption, enhances the quality of delivered data and prolongs the lifetime of a network. Apart from an optimal cluster head selection, energy consumption can also be reduced using efficient congestion detection and mitigation schemes. We propose an application-specific priority-based congestion control protocol for this purpose. The proposed protocol integrates mobility and heterogeneity of the nodes to detect congestion. Our proposed protocol uses a novel queue scheduling mechanism to achieve coverage fidelity, which ensures that the extra resources consumed by distant nodes are utilized effectively. Apart from energy conservation issue, this thesis also aims to provide a robust solution for Sybil attack detection in WSN. In Sybil attack, one or more malicious nodes forge multiple identities at a given time to exhaust network resources. These nodes are detected prior to cluster formation to prevent their forged identities from participating in cluster head selection. Only legitimate nodes are elected as cluster heads to enhance utilization of the resources. The proposed scheme requires collaboration of any two high energy nodes to analyse received signal strengths of neighbouring nodes. Moreover, the proposed scheme is applied to a forest wildfire monitoring application. It is crucial to detect Sybil attack in a wildfire monitoring application because these forged identities have the ability to transmit high false-negative alerts to an end user. The objective of these alerts is to divert the attention of an end user from those geographical regions which are highly vulnerable to a wildfire. Finally, we provide a lightweight and robust mutual authentication scheme for the real-world objects of an Internet of Thing. The presence of miniature sensor nodes at the core of each object literally means that lightweight, energy-efficient and highly secured schemes need to be designed for such objects. It is a payload-based encryption approach which uses a simple four way handshaking to verify the identities of the participating objects. Our scheme is computationally efficient, incurs less connection overhead and safeguard against various types of replay attacks

PAAL : a framework based on authentication, aggregation, and local differential privacy for internet of multimedia things

Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture to manage end and edge devices, preserve the privacy of end-devices and data, and protect the underlying network from external attacks. The proposed framework has three layers. In the first layer, the underlying network is partitioned into multiple clusters to manage end-devices and level-one edge devices (LOEDs). In the second layer, the LOEDs apply an efficient aggregation technique to reduce the volumes of generated data and preserve the privacy of end-devices. The privacy of sensitive information in aggregated data is protected through a local differential privacy-based technique. In the last layer, the mobile sinks are registered with a level-two edge device via a handshaking mechanism to protect the underlying network from external threats. Experimental results show that the proposed framework performs better as compared to existing frameworks in terms of managing the nodes, preserving the privacy of end-devices and sensitive information, and protecting the underlying network. © 2014 IEEE

RaSEC : an intelligent framework for reliable and secure multilevel edge computing in industrial environments

Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE

The Impact of Advertisement on Mutual Funds Awareness: A Case Study of Arif Habib Investment Limited

The critical role of advertising is to generate awareness about organization offerings regardless of the industry in which it is operating. Advertising and its effectiveness on mutual funds products in terms of awareness is studied in this paper. Arif Habib Investment limited was taken as case to investigate the impact of advertising on its funds awareness. Employees were interviewed for this purpose. Due to the investigative nature of the research qualitative research technique is used by the researchers. The studied showed that advertisement has positive impact on the attentiveness of products offered by mutual funds among patrons. The significance of advertising in generating brand awareness in financial sector is revealed during the course of the study

Plasma Modified Polycarbonate Nonwovens as Filtering Material for Liquid Aerosols

The filter materials commonly used in filtration processes consist of nonwoven fabrics made by melt blowing. In order to improve filtration properties they are subjected to various modifications. This paper presents the treatment of polycarbonate nonwovens with lowpressure cold plasma generated by a 13.56 MHz RF discharge using process gases such as Ar and O2. The effectiveness of such treatment was assessed on the basis of results of the penetration of nonwovens by paraffin oil mist as well as the air flow resistance. The effects of plasma on polycarbonate nonwovens, especially on their surface morphology and chemical structure, were evaluated by electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS). The results indicate that Ar plasma is a good tool for improving the filtration properties of polycarbonate filtering materials. According to these results, the surface roughness plays an important role in the high-efficiency filtration of liquid aerosols with a small increase in air flow resistance