Junaid Ahsenali Chaudhry

Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: a Comprehensive Study

In this paper, we have presented a comprehensive study on designing an aware energy architecture of Wireless Sensor Networks. A summary and modelling of various major techniques in designing the constituents of clustered wireless sensor network architecture are given in detail. In continuation of it, we have also analysed our proposed scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. These incorporations in MCDA result in minimising transmissions, suppressing the unneeded response of transmissions and near equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and un-clustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC concerning parameters above.

Threats to critical infrastructure from AI and human intelligence

Without critical infrastructure (CI), the society shall come to a stand still. Today, our life on earth is very much dependent on electricity, global freight of food and goods, telecommunication, healthcare, etc. Except a few human societies living in the forests and remote areas, any community around the globe needs the basic modern facilities. Many industries today, where technology has taken root and made things easier for human workforce increasing their profit margins, are having discussions to replace human workforce with technology—technologies that can offer and use human-like intelligence to mitigate risks and keep processes running, with machine or artificial intelligence. This notion ha s not only created insecurity among human workforce but also has created conundrum for intelligence researchers to develop a product (artificial intelligence) that is derived from discarded (human) intelligence. Advent of big data analytics, machine learning, and data mining is linked to this “replacement–movement” where historical data are used to predict statistical trends along with the confidence factors to make decisions for future. This approachmight be sufficiently successful in repetitive, mundane tasks of even nth degree of breadth but cannot classify creativity, innovation, out-of-the-box actions, emotional intelligence, etc., which is a classic argument against artificial intelligence in general. Some might argue against indulging into smart environments where, according to Marc Weiser, “Technology is so pervasive that if disappears from the forefront”, is not a smart idea after all. We would like to argue against this notion purely in the ground that deeper penetration of technology into our lives is evident and logical; hence, bridges rather than dams need to be built for smoother transition facilitated by more secure and privacy-centric strategies.

ECB4CI: an enhanced cancelable biometric system for securing critical infrastructures

Physical access control is an indispensable component of a critical infrastructure. Traditional password-based methods for access control used in the critical infrastructure security systems have limitations. With the advance of new biometric recognition technologies, security control for critical infrastructures can be improved by the use of biometrics. In this paper, we propose an enhanced cancelable biometric system, which contains two layers, a core layer and an expendable layer, to provide reliable access control for critical infrastructures. The core layer applies random projection-based non-invertible transformation to the fingerprint feature set, so as to provide template protection and revocability. The expendable layer is used to protect the transformation key, which is the main weakness contributing to attacks via record multiplicity. This improvement enhances the overall system security, and undoubtedly, this extra security is an advantage over the existing cancelable biometric systems.

A security review of local government using NIST CSF: a case study

Evaluating cyber security risk is a challenging task regardless of an organisation’s nature of business or size, however, an essential activity. This paper uses the National Institute of Standards and Technology (NIST) cyber security framework (CSF) to assess the cyber security posture of a local government organisation in Western Australia. Our approach enabled the quantification of risks for specific NIST CSF core functions and respective categories and allowed making recommendations to address the gaps discovered to attain the desired level of compliance. This has led the organisation to strategically target areas related to their people, processes, and technologies, thus mitigating current and future threats.

A Critical Analysis of Mobility Management Related Issues of Wireless Sensor Networks in Cyber Physical Systems

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area.

A validated fuzzy logic inspired driver distraction evaluation system for road safety using artificial human driver emotion

Abstract This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aware of distractions when they take over and they continue to drive. With advancements in autonomous vehicles technologies, it is possible to have an onboard driver assistance systems. However, research is yet to be reported on this issue whether onboard driver assistance program will be effective or not. The Emotion Enabled Cognitive Driver Assistance Model is a system based on an encapsulated Emotion Enabled Cognitive Driver Assistant (EECDA), which computes the effects of external factors at the distraction level of the subject and generates algorithmically generated fear emotion. During experiments, the EECDA intervenes when the fear intensity of the driver crosses a threshold by sending two sound alerts to the driver to take appropriate action. To demonstrate the effectiveness of the proposed approach as a road safety system, a Cognitive Agent-Based Computing (CABC) framework has been utilized to validate the results of the EECDAM. Algorithms are utilized using fuzzy sets to compute distraction of the drivers. We also present an Agent-Based Model (ABM) to validate the implementation of the proposed scheme. Extensive experiments demonstrate the proficiency of the proposed model for robust collision avoidance.

Autonomic Fault Identification for Ubiquitous Self Healing Systems

In this paper we report the results of our experiments conducted for fault identification in self healing systems. The proposed scheme is designed to identify faults in a large-scale system where frequent faults can increase downtime. The experiments show that the scheme proposed has linear computational complexity and highly portable.