Rahat Iqbal

User-centred design and evaluation of ubiquitous services

Theoretical and technological progress has revived the interest in the design of services for the support of co-located human-human communication and collaboration, witnessing the start of several large-scale projects over the last few years. Most of these projects focus on meetings and/or lecture situations. However, user-centred design and evaluation frameworks for co-located communication and collaboration are a major concern. In this paper, we summarise the prevalent approaches towards user-centred design and evaluation, and we develop two different services. In one service, participants in a small-group meeting receive real-time feedback about observable properties of the meeting that are directly related to the social dynamics, such as individual amount of speaking time or eye-gaze patterns. In the other service, teachers in a classroom receive real-time feedback about the activities and attention level of participants in the lecture. We also propose ways to address the different dimensions that are relevant to the design and evaluation of these services (the individual, the social and the organisational dimension), bringing together methods from different disciplines.

Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud

The exponential growth in the demands of users to access various resources during mobility has led to the popularity of Vehicular Mobile Cloud. Vehicular users may access various resources on road from the cloud which acts as a service provider for them. Most of the existing proposals on vehicular cloud use unicast sender-based data forwarding, which results in an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay, and reliable data transmission. Most of the applications for vehicular cloud have tight upper bounds with respect to reliable transmission. In view of the above, in this paper, we formulate the problem of reliable data forwarding as a Bayesian Coalition Game (BCG) using Learning Automata concepts. Learning Automata (LA) are assumed as the players in the game stationed on the vehicles. For taking adaptive decisions about reliable data forwarding, each player observes the moves of the other players in the game. For this purpose, a coalition game is formulated among the players of the game for taking adaptive decisions. For each action taken by a player in the game, it gets a reward or a penalty from the environment, and accordingly, it updates its action probability vector. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) is also proposed. The proposed scheme is evaluated in different network scenarios with respect to parameters such as message overhead, throughput, and delay by varying the density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics. A Bayesian Coalition game-based reliable data transmission is proposed for vehicular cloud.Learning Automata (LA) are assumed to be the players in the game.For each action taken by the players in the game, they may get a reward or a penalty based upon which all the future actions to be taken are decided.The results obtained are convincing as compared to other approaches of its category.

An intelligent approach for building a secure decentralized public key infrastructure in VANET

This paper proposed an efficient decentralized public key infrastructure (PKI) using the concepts of Bayesian Coalition Game (BCG) and Learning Automata (LA). LA are assumed as the players in the game, which coordinate among one another for information sharing. To preserve the confidentiality and integrity of the messages, dynamic coalition among the players of the game is formulated using symmetric key encryption and hash-based message authentication. Also, privacy preservation and certificate revocation are included in the proposed scheme to defend against the misbehaving vehicles. For each action taken by an automaton, its action may be rewarded/penalized by the environment in which it operates. LA update their actions probability matrices by getting the reinforcement signal from the environment. The performance of the proposed scheme is evaluated with respect to various metrics in comparison to the other state-of-the-art existing schemes. The results obtained prove the superiority of the proposed scheme. Learning automata-based secure PKI infrastructure is proposed for VANETs.A dynamic coalition among the players of the game is formed using Bayes Theory.The proposed scheme is resilient with respect to various types of attacks.

A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment

Abstract With the widespread popularity and usage of Internet-enabled devices, Internet of things has become one of the most popular techniques of the modern era. However, data generated from various smart devices in IoT environment is one of the biggest concerns. To process such a large database repository generated from different types of devices in IoT environment, Cloud Computing (CC) has emerged as a key technology. But, the private information from IoT devices is stored in distributed private cloud server so that only legitimate users are allowed to access the sensitive information from the cloud server. Keeping focus on all these points, this article first shows security vulnerabilities of the multi-server cloud environment of the protocols proposed by Xue et al. and Chuang et al. Then, we propose an architecture which is applicable for distributed cloud environment and based on it, an authentication protocol using smartcard has been proposed, where the registered user can access all private information securely from all the private cloud servers. To strengthen the proposed protocol, we have used AVISPA tool and BAN logic model in this article. Moreover, an informal cryptanalysis confirms that the protocol is protected against all possible security threats. The performance analysis and comparison confirm that the proposed protocol is superior than its counterparts with respect to various parameters.

Cloud enabled data analytics and visualization framework for health-shocks prediction

In this paper, we present a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services (AWS) integrated with geographical information systems (GIS) to facilitate big data capture, storage, index and visualization of data through smart devices for different stakeholders. In order to develop a predictive model for health-shocks, we have collected a unique data from 1000 households, in rural and remotely accessible regions of Pakistan, focusing on factors like health, social, economic, environment and accessibility to healthcare facilities. We have used the collected data to generate a predictive model of health-shock using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting health-shocks. The evaluation of the proposed system in terms of the interpret-ability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold cross-validation of the data samples shows above 89% performance in predicting health-shocks based on the given factors. Cloud enabled framework for interactive data visualization, gathering, & analysis.Analysis of iron triangle under socioeconomic, cultural, and geographical norms.First publicly available data-set to understand health-shocks and its causes.

An intelligent RFID-enabled authentication scheme for healthcare applications in vehicular mobile cloud

Recently, vehicular cloud computing (VCC) has emerged as the one of the fast growing technologies with an aim to provide uninterrupted services to the moving clients even on-the-fly. One of the services provided by VCC is the mobile healthcare in which patient can be provided diagonosis from anywhere during their mobility. This paper proposes an intelligent RFID-enabled authentication scheme for healthcare applications in VCC environment. In the proposed scheme, a Petri Nets-based authentication model is used for authentication of tags, and readers. Both server, and tag authentications are protected by strong elliptical curve cryptography (ECC)-based key generation mechanism. The proposed scheme is found to be secure with respect to mutual authentication of servers and tags, replay attack, tracking attack, users anonymity, eavesdropping, and cloning with forward secrecy. To evaluate the effectiveness of the proposed scheme, it is evaluated with respect to the overhead generated, computation complexity, and % service delivery, where its performance is found better than the case where it is not applied.

Designing with ethnography: An integrative approach to CSCW design

This paper presents a part of wider research endeavor within the field of Computer Supported Cooperative Work (CSCW) to leverage the use of ethnography for systems design. It investigates the role of ethnography in the development of CSCW systems and its relevance to real world problems, particularly, to uncover the social organisation of work practices. The usage of ethnography to inform CSCW design constitutes many challenges to systems design. The most significant of these design challenges is the inevitable need for well-established methods for applying the ethnography within CSCW to inform systems design. We have developed an integrative approach based on ethnography and meta-modelling for use in the context of CSCW research and practice. We have also applied this approach to the analysis of cooperative work in the academic domain of investigation.

A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients

In this paper, we discuss the development of an ambient intelligent-based system for the monitoring of dementia patients living in their own homes. Within this system groups of unobtrusive wireless sensor devices can be deployed at specific locations within a patient’s home and accessed via standardized interfaces provided through an open middleware platform. For each sensor group intelligent agents are used to learn fuzzy rules, which model the patient’s habitual behaviours in the environment. An online rule adaptation technique is applied to facilitate short-term tuning of the learnt behaviours, and long-term tracking of behaviour changes which could be due to the effects of cognitive decline caused from dementia. The proposed system reports macro level behaviour changes and micro level perception drift to care providers to enable them to make better-informed assessments of the patient’s cognitive abilities and changing care needs. We demonstrate experiments in a real pervasive computing environment, in which our intelligent agent approach can learn to model the user’s behaviours and allow online adaptation of its model to better approximate the learnt behaviours and identify long-term macro-level behaviour changes, which could be attributed to cognitive decline. We also show an example of how the user’s perceptions for thermal comfort may be captured and visualised to provide a means by which micro-level perception changes can be monitored.

Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia

Type-2 self-organizing fuzzy logic controllers for automatic anesthesia control.Type-2 SOFLC use type-2 fuzzy sets to handle anesthesia control uncertainties.Data capturing inter and intra-patient variability used to define type-2 fuzzy sets.Simulations show effectiveness of type-2 SOFLC in control of anesthetic infusion under noisy and uncertain surgical conditions.Type-2 SOFLC are able to outperform the existing type-1 SOFLC. In this paper, novel interval and general type-2 self-organizing fuzzy logic controllers (SOFLCs) are proposed for the automatic control of anesthesia during surgical procedures. The type-2 SOFLC is a hierarchical adaptive fuzzy controller able to generate and modify its rule-base in response to the controllers performance. The type-2 SOFLC uses type-2 fuzzy sets derived from real surgical data capturing patient variability in monitored physiological parameters during anesthetic sedation, which are used to define the footprint of uncertainty (FOU) of the type-2 fuzzy sets. Experimental simulations were carried out to evaluate the performance of the type-2 SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for anesthesia (muscle relaxation and blood pressure) under signal and patient noise. Results show that the type-2 SOFLCs can perform well and outperform previous type-1 SOFLC and comparative approaches for anesthesia control producing lower performance errors while using better defined rules in regulating anesthesia set points while handling the control uncertainties. The results are further supported by statistical analysis which also show that zSlices general type-2 SOFLCs are able to outperform interval type-2 SOFLC in terms of their steady state performance.

An ant based multi constraints QoS aware service selection algorithm in Wireless Mesh Networks

Abstract Over the years, multihop Wireless Mesh Networks (WMNs) are providing cost effective services to the end users such as Video on demand (VoD), Voice over IP (VoIP), and Video conferencing in an efficient manner. Most of these services require an efficient service selection mechanism that can provide Quality of Service (QoS) in presence of various constraints such as delay, jitter, and service availability. In presence of these constraints, multi-constraint QoS aware service selection in WMNs is an NP-hard problem. In this paper, we propose an ant colony based multi constraints QoS aware service selection (QSS) algorithm. In the proposed algorithm, ants are launched from source node. The best path is chosen based upon the defined cost effective (CE) metric in presence of constraints. The constraints are chosen both from network and user perspectives. The goodness of the chosen path is determined by the CE metric. Moreover, ants are not launched randomly from the source node. They are launched based upon the guided search evaluation (GSE) criterion. This criterion is also used if the two paths have the same CE metric. The simulation results show that the proposed algorithm is more effective than the earlier improved ant colony QoS routing algorithm (IAQR) algorithm with respect to convergence, end-to-end delay (jitter), and service availability. Specifically, the service availability increases by 35–50% in the proposed QoS aware scheme compared to IAQR algorithm.