Nazrul M. Ahmad

Cloudlet-based cyber foraging framework for distributed video surveillance provisioning

Continuous monitoring activities in surveillance system generate massive amount of data to be transferred from Internet-of-Things (IoT) devices (i.e., cameras and sensors) to a centralized processing unit for analysis. However, several issues need to be addressed, including data migration over bandwidth limited and high latency communication networks, and the heterogeneous nature of data obtained from the surveillance system. The aim of this paper is to develop a scalable and lightweight intelligent distributed surveillance system that make use of an integrated framework of Internet-of-Things (IoT) and cloud computing. It leverages on the pervasiveness in IoT and ubiquitous property of cloud computing technology. This paper introduces the concept of bringing cloud closer to the IoT in order to address the resource poverty of IoT and the dependency for massive data transmission to distant cloud. For providing real-time on-site object detection, this paper instantiates a cloudlet on resource-rich nearby infrastructure which is connected to the IoT devices for distributed retrieval and processing of critical and sensitive data.

Hadoop in OpenStack: Data-location-aware cluster provisioning

Nowadays, cloud based analytics platforms are replacing traditional physical clusters due to the high efficiency it provides. Such cloud platforms runs Hadoop on virtual clusters with remotely attached storage. In cloud architecture with multiple geographically separated regions, virtual machines (VMs) belonging to a virtual cluster are placed randomly. In order to run MapReduce jobs, data have to be moved to the regions where the VMs reside to achieve data locality. In this paper, we propose a data-location aware virtual cluster provisioning strategy to identify the data location and provision the cluster near to the storage. The use of bio-inspired optimization algorithms are considered for optimizing the placements of VMs. Data location aware cluster provisioning reduces the network distance between storage and the virtual cluster, resulting in faster job completion times.

End to End Ipsec Support across Ipv4/Ipv6 Translation Gateway

The presence of IPv4/IPv6 translation gateway provides transparent routing mechanism to IPv4-only nodes and IPv6-only nodes which trying to establish communication from disparate address realms. However, the mechanism breaks TCP/IP intrinsic functionalities that results in IPSec cannot be applied in this environment. The existing solutions to address the compatibility issues between translation gateway and IPSec are either to enhance the translation gateway operation or to modify IPSec architecture especially on IKE negotiation process. By realizing the fact that most of the intermediate networking devices such as translation gateway are beyond the end nodes administration, this paper discusses the existing solutions to improve IKE negotiation in order to ensure end to end IPSec interoperability across translation gateway. Inspired by this solution, this paper proposes new IKE authentication by using Address Based Keys with certificateless signature to alleviate the limitation of traditional pre-shared keys and Public Key Infrastructure (PKI).

Performance analysis of MapReduce on OpenStack-based hadoop virtual cluster

With the emergence of big data phenomenon, MapReduce and Hadoop distributed processing infrastructure have been commonly applied for large-scale data analytics. Hadoop distributed filesystem (HDFS) usually being deployed on physical clusters. With the advent of cloud computing platform such as OpenStack, a number of works have been carried out in implementing Hadoop virtual cluster on cloud computing infrastructure. This paper presents a performance analysis of MapReduce implementations on OpenStack-based Hadoop virtual cluster. The results of the analysis show that the MapReduce implementations are performing in a scalable manner towards an increase in the size of the Hadoop virtual cluster being deployed.

IKE authentication using certificateless signature

Aiming at the problems of implementing the conventional IKE authentications such as pre-shared key and certificate-based Public Key Infrastructure (PKI), this paper proposes a new certificateless IKE authentication scheme. This scheme uses bilinear pairing to structure the framework of certificateless IKE authentication, solves the end to end IPSec security by removing the requirement of both nodes either to be manually configured with the common shared key or to exchange the certificates and the necessity to be enrolled to certain Certificate Authority (CA). Furthermore, this paper provides a set of specifications for implementing IKE authentication using certificateless signature that can be used to verify the validity of the scheme in a single trust domain infrastructure.

Data location aware scheduling for virtual Hadoop cluster deployment on private cloud computing environment

With the advancements of Internet-of-Things (IoT) and Machine-to-Machine Communications (M2M), the ability to generate massive amount of streaming data from sensory devices in distributed environment is inevitable. A common practice nowadays is to process these data in a high-performance computing infrastructure, such as cloud. Cloud platform has the ability to deploy Hadoop ecosystem on virtual clusters. In cloud configuration with different geographical regions, virtual machines (VMs) that are part of virtual cluster are placed randomly. Prior to processing, data have to be transferred to the regional sites with VMs for data locality purposes. In this paper, a provisioning strategy with data-location aware deployment for virtual cluster will be proposed, as to localize and provision the cluster near to the storage. The proposed mechanism reduces the network distance between virtual cluster and storage, resulting in reduced job completion times.

An empirical investigation of RSSI-based distance estimation for wireless indoor positioning system

RSSI-based distance estimation techniques for wireless indoor positioning system require extensive offline calibration to construct propagation model in order to describe the relationship between received signal strength and distance. This paper investigates the accuracy of the well-known propagation models against the measured data at indoor building. From the results, the dual slope model exhibits the best propagation model and it is chosen as the reference for further investigation. The accuracy of dual slope model in distance estimation suffers from the degradation due to the presence of Non Line of Sight NLOS condition between mobile station and access point. Therefore, to further improve the accuracy, this paper studies the effect of breakpoint distance and evaluates two simple techniques, running variance and kurtosis index, to identify the NLOS condition. Once the NLOS condition is identified, the best dual slope model can be selected for accurate distance estimation.

Learning above-and-below relationship for vision-based robot navigation system using Distributed Hierarchical Graph Neuron (DHGN) algorithm

Obstacle avoidance is one of the important considerations in developing a vision-based robot navigation system. For flying robots, the ability to learn the above-and-below relationship for obstacle avoidance is necessary. This paper presents a conceptual work in developing a learning mechanism to identify the above-and-below relationship for obstacle avoidance in vision-based robot navigation system using a pattern recognition algorithm known as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a bio-inspired pattern recognition algorithm that implements learning and memorization through a distributed and hierarchical processing. Preliminary results of simple above-and-below navigation with binary images using DHGN indicate that the scheme is able to produce high recall accuracy for obstacle detection. In addition, the proposed scheme implements a one-shot learning approach that is suitable for realtime deployment in robot navigation system.

A RSSI-based rogue access point detection framework for Wi-Fi hotspots

The prevalence of Wi-Fi hotspot poses an unprecedented number of threats that can compromise a clients identity, personal data, and network integrity. Even though most of the Wi-Fi hotspots are unmanaged, unmonitored, and lack of security measures, the urge for the clients to connect to the network is very strong. Thus, this will expose the clients to the adversaries who set up Wi-Fi hotspots Rogue Access Point (RAP) intentionally to pilfer data. To address the threat of RAP, most of existing RAP detections are either by placing sensors to monitor anomalies, or by performing a centralized monitoring at the gateway router. Such infrastructural commitments are limited, expensive and rarely to be implemented in Wi-Fi hotspots. Hence, this work proposes a framework of client-centric RAP detection for Wi-Fi hotspots which exploits Received Signal Strength Indicator (RSSI) values measured over time between client and APs. Unlike existing solutions, the key idea is to piggyback AP-sensitive information in IEEE 802.11 beacon frame that enables the client to manipulate this information together with the measured RSSI values in order to perform the detection without authentication and association to any AP.

Detecting Access Point Spoofing Attacks Using Partitioning-based Clustering

The impersonation of wireless Access Point (AP) poses an unprecedented number of threats that can compromise a wireless client’s identity, personal data, and network integrity. The AP impersonation attack is conducted by establishing rogue AP with spoofed Service Set Identifier (SSID) and MAC address same as the target legitimate AP. Since these identities can be easily forged, there is no identifier can be used to identify the legitimate AP. Due to strong correlation between the AP signal strength and the distance, in this paper, we propose a client-centric AP spoofing detection framework by exploiting the statistical relationship of signal strength from the legitimate and rogue APs. We show the relationship between the signals can be determined by using two classical partitioning-based clustering methods, K-means and K-medoids analysis. The experimental results show that both analysis methods can achieve over 90% detection rate