Predicting mobile tourists
- Authors: Matthew, Michael , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2009
- Type: Text , Conference proceedings
- Full Text: false
Diversified adaptive frequency rolling to mitigate self and static interferences
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: Increase in the number of coexisting networks in Industrial, Scientific and Medical (ISM) band cause interferences and demands for intelligent interference avoidance schemes. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets which has the tendency to mitigate both the self and static interferences and ensures sufficient frequency diversity. Simulation studies validate the prospects for the proposed scheme to be used for frequency hopping networks against already existing techniques, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR).
Energy efficient and hop constraint intra-cluster transmission for heterogeneous sensor networks
- Authors: Azad, Arman , Kamruzzaman, Joarder
- Date: 2008
- Type: Text , Conference proceedings
- Full Text: false
- Description: Although transmission policy is crucial in extending lifetime of sensor networks, most existing policies make simplified assumptions which include: i) circular cluster with cluster head (CH) at the center, ii) uniform periodic data generation model and iii) unrestricted transmission range for nodes. But, in practice, these assumptions are too restrictive for real-world deployment of heterogeneous sensor networks where clusters are usually polygonal. Moreover, in multi hop transmission energy consumption by sensors varies greatly with their distance from CH and even among sensors in the critical ring due to non-uniform relay traffic caused by asymmetric polygonal structure of cluster. In this paper, we propose a new transmission policy where sensors transmit at optimally determined hop sizes that varies over lifetime and a distributed hop selection algorithm that regulates each packet's arrival to CH within a given hop limit. Our formulation considers generic polygonal cluster, stochastic data generation model where data generation rate by sensors vary with events and limited transmission range for sensors. Performance analysis shows significant improvement in lifetime and better uniformity in energy usage among sensors in the proposed policy irrespective of cluster size, hop limit and maximum allowable transmission range of nodes
Asynchronous variable hop size transmission with stochastic data model for sensor networks
- Authors: Azad, Arman , Kamruzzaman, Joarder
- Date: 2008
- Type: Text , Conference proceedings
- Full Text: false
- Description: Most existing data models and transmission policies for sensor network assume uniform periodic data generation and unconstrained transmission range for sensor nodes, both assumptions being too restrictive to capture and analyze real- world operation for practical deployment. In this paper, we consider these two practical aspects and present a new transmission policy formulated after (i) stochastic data model where a set of events occur with certain probabilities and rate of data generation by a sensor varies based on sensed event and (ii) limited transmission range of sensors. Assuming co-centric rings around the base station, located at a generic location (internal or external to the network area), ring thickness and hope sizes over lifetime is determined by formatting an optimization problem where nodes in each ring may transmit data at different hop sizes at a given instant and also vary hop sizes over lifetime. Performance analysis shows significant improvement in network lifetime and better uniformity in energy usage distribution in the proposed policy irrespective of network size and maximum allowable transmission range of nodes.
An adaptive approach to opportunistic data forwarding in underwater acoustic sensor networks
- Authors: Nowsheen, Nusrat , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference proceedings
- Full Text:
- Description: Reliable data transfer for underwater acoustic sensor networks (UASNs) is a major research challenge in applications such as pollution monitoring, oceanic data collection, and surveillance due to the long propagation delay and high error rate of the acoustic channel. To address this issue, an opportunistic data forwarding protocol was proposed which achieves high packet delivery success ratio with less routing overhead and energy consumption by selecting the next hop forwarder among a set of candidates based on its link reliability and data transfer reach ability. However, the protocol relies on fixed data hold time approach, i.e., Each node holds data packets for a fixed amount of time before a forwarder discovery process is initiated. Depending on the value of the fixed hold time and deployment contextual scenario, this may incur large end-to-end delay. Moreover, lack of consideration of network condition in hold time limits its performance. In this paper, we propose an adaptive technique to improve its performance. The adaptive approach calculates data hold time at each node dynamically considering a number of 'node and network' metrics including current buffer occupancy, delay experienced by stored data packets, arrival and service rate, neighbors' data transmissions and reach ability. Simulation results show that compared with fixed hold time approach, our adaptive technique reduces end-to-end delay significantly, achieves considerably higher data delivery and less energy consumption per successful packet delivery.
Detecting intrusion in the traffic signals of an intelligent traffic system
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Saha, Tapash
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 20th International Conference on Information and Communications Security, ICICS 2018; Lille, France; 29th-31st October 2018; published in Lecure Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11149 LNCS, p. 696-707
- Full Text: false
- Reviewed:
- Description: Traffic systems and signals are used to improve traffic flow, reduce congestion, increase travel time consistency and ensure safety of road users. Malicious interruption or manipulation of traffic signals may cause disastrous instants including huge delays, financial loss and loss of lives. Intrusion into traffic signals by hackers can create such interruption whose consequences will only increase with the introduction of driverless vehicles. Recently, many traffic signals across the world are reported to have intruded, highlighting the importance of accurate detection. To reduce the impact of an intrusion, in this paper, we introduce an intrusion detection technique using the flow rate and phase time of a traffic signal as evidential information to detect the presence of an intrusion. The information received from flow rate and phase time are fused with the Dempster Shaffer (DS) theory. Historical data are used to create the probability mass functions for both flow rate and phase time. We also developed a simulation model using a traffic simulator, namely SUMO for many types of real traffic situations including intrusion. The performance of the proposed Intrusion Detection System (IDS) is appraised with normal traffic condition and induced intrusions. Simulated results show our proposed system can successfully detect intruded traffic signals from normal signals with significantly high accuracy (above 91%).
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A contender-aware backoff algorithm for CSMA based MAC protocol for wireless sensor network
- Authors: Miraz Al-Mamun, Miraz , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: Existing contention based nonpersistent medium access control protocols in Wireless Sensor Network (WSN) do not perform well in high contention. Their performances are affected by occurrence of collision due to uniform probability distribution in choosing Time Slot (TS) during backoff period. To address this issue nonuniform probability distribution was proposed. However success rate still drops for higher number of contenders. In this paper we propose CSMA/s (Collision Sense Multiple Access /per Slot based), a new approach in nonuniform contender-aware probability distribution for choosing TS in the backoff period. Rather than taking a premeditated fixed value for contender population size, our proposed scheme embeds neighborhood population size into its bedrock to automatically converge to the actual number of contenders which enables the contender to adaptively choose TS in the backoff period for reducing collision. This method produces better success rate and lower latency for even very high number of contenders.
QoS-centric collision window shaping for CSMA-CA MAC protocol
- Authors: Miraz Al-Mamun, Miraz , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: Collision Sense Multiple Access (CSMA) has been preferred to Time Division Multiple Access (TDMA) as medium access scheme for Wireless Multimedia Sensor Network (WMSN) in the scenarios where the traffic is bursty in nature and multiple consecutive and contiguous packets generated from the same collision neighborhood need to be sent. Protocols based on nonuniform probability distribution do not perform well in high contention and heterogeneous traffic scenarios due to nonadaptive nature to contention neighborhood. In this paper we have proposed a scheme to adapt the Contention Window (CW) size according to the collision neighborhood population complying with the application specific latency and success probability constraints. This scheme shows improved performance compared with SIFT, a stereotype of non-uniform probability based CSMA protocol and can be deployed with any CSMA-CA (CSMA with Collision Avoidance) based backoff algorithm
An intelligent model to control preemption rate of instantaneous request calls in networks with book- ahead reservation
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush , Islam, Farzana
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 Australasian Telecommunication Networks and Applications Conference, ATNAC, Adelaide 2008. Published in Proceedings of Australasian Telecommunication Networks and Applications conference , IEEE (pp.344-34)
- Full Text: false
- Reviewed:
- Description: Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rat
Mobile malware detection - An analysis of the impact of feature categories
- Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 486-498
- Full Text: false
- Reviewed:
- Description: The use of smartphones and hand-held devices continues to increase with rapid development in underlying technology and widespread deployment of numerous applications including social network, email and financial transactions. Inevitably, malware attacks are shifting towards these devices. To detect mobile malware, features representing the characteristics of applications play a crucial role. In this work, we systematically studied the impact of all categories of features (i.e., permission, application programmers interface calls, inter component communication and dynamic features) of android applications in classifying a malware from benign applications. We identified the best combination of feature categories that yield better performance in terms of widely used metrics than blindly using all feature categories. We proposed a new technique to include contextual information in API calls into feature values and the study reveals that embedding such information enhances malware detection capability by a good margin. Information gain analysis shows that a significant number of features in ICC category is not relevant to malware prediction and hence, least effective. This study will be useful in designing better mobile malware detection system.
Mining associated sensor patterns for data stream of wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Conference proceedings
- Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
- Full Text: false
- Reviewed:
- Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.
A rule based inference model to establish strategy-process relationship
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
- Full Text: false
- Reviewed:
- Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.
Dynamic content distribution for decentralized sharing in tourist spots using demand and supply
- Authors: Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal , Kaisar, Shahriar
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017; Valencia, Spain; 26th-30th June 2016 p. 2121-2126
- Full Text: false
- Reviewed:
- Description: Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach. © 2017 IEEE.
- Description: 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble
- Authors: Haque, Md Sarwar , Hassan, Md Rafiul , BinMakhashen, Galal , Owaidh, Abdullah , Kamruzzaman, Joarder
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017; Delhi, India; 14th-16th December 2017; published in Intelligent Systems Design and Applications (part of the Advances in Intelligent Systems and Computing book series) Vol. 736, p. 702-711
- Full Text:
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- Description: It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Who are convincing? An experience based opinion formation dynamics in online social networks
- Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 30th European Simulation and Modelling Conference, ESM 2016; Las Palmas, Spain; 26th-28th October 2016 p. 167-173
- Full Text: false
- Reviewed:
- Description: Online social network (OSN) is one of the major platforms where our opinions are formed now-a-days and increasing so. Opinion formation dynamics captures the ways public opinions are formed, mainly from two different sources, (i) neighbours' opinions, (ii) external opinions from sources other than the neighbours. In this paper, we formulate an opinion formation model by considering two very important factors, that were ignored or a very little explored in the literature. First, we model the convincing power of the opinions encountered from the two sources. Second, we incorporate the experience of users' previous interactions with the two opinion sources. The problem is formulated as an agent based model where each member of an OSN is represented with an agent and their relationships with a graph. Finally through simulation, we create various scenarios, and apply our model to observe the steady state outcomes of the dynamics. This helps us to study the nature of the public opinions under various influences of our model parameters.
- Description: European Simulation and Modelling Conference 2016, ESM 2016
Influence of clustering on the opinion formation dynamics in online social networks
- Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap; Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11306 LNCS, p. 144-155
- Full Text: false
- Reviewed:
- Description: With the advent of Online Social Networks (OSNs), opinion formation dynamics continuously evolves, mainly because of the widespread use of OSNs as a platform of social interactions and our growing exposure to others’ opinions instantly. When presented with neighbours’ opinions in OSNs, the natural clustering ability of human agents enables them to perceive the grouping of opinions formed in the neighbourhood. A group with similar opinions exhibits stronger influence on an agent than the individual group members. Distance-based opinion formation models only consider the influence of neighbours who are within a confidence bound threshold in the opinion space. However, a bigger group formed outside this distance threshold can exhibit stronger influence than a group within the bound, especially when that group contains influential or popular agents like leaders. To the knowledge of the authors, the proposed model is the first to consider the impact of clustering capability of agent and incorporates the influence of opinion clusters (groups) formed outside the confidence bound. Simulation results show that our model can capture several characteristics of real-world opinion dynamics. © Springer Nature Switzerland AG 2018.
Business context in big data analytics
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 10th International Conference on Information, Communications and Signal Processing, ICICS 2015; Singapore; 2nd-4th December 2015
- Full Text: false
- Reviewed:
- Description: Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such as the context of a query and its users, the context of a query-driven recommendation system, etc. However, these methods still have many challenges and none of them has considered the context of a business in either data collection or analysis process. To address this research gap, we introduce a big data analytic technique which embeds a business context in terms of the significance level of a query into the bedrock of its data collection and analysis process. We implemented our proposed model under the framework of Hadoop considering the context of a grocery shop. The results exhibit that our method substantially increases the amount of data collection and their deep insight with an increase of the significance level value. © 2015 IEEE.
- Description: 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
Significance level of a query for enterprise data
- Authors: Thi Ngoc Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew , Das, Rajkumar
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4494-4504
- Full Text: false
- Reviewed:
- Description: To operate enterprise activities, a large number of queries need to be processed every day through an enterprise system. Consequently, such a system frequently faces hugely overloaded information and incurs high delay in producing query responses for big data. This is because, traditional queries are normally treated with equal importance. With the advent of big data and its use in enterprise systems and the growth of process complexity, the traditional approach of query processing is no more suitable as it does not consider semantic information and captures all data irrespective of their relevance to a business organization, which eventually increases the computational time in both big data collection and analysis. The significance level of a query can make a trade-off between query response delay and the extent of data collection and analysis. This motivates us to concentrate on determining the significance level of a query considering its importance to an enterprise system. To our knowledge, no such approach is available in the literature. To bridge this research gap, this paper, for the first time, proposes an approach to determine the significance level of a query to prioritize them with the relevance to a business organization. As business processes play key roles in any enterprise system and all business processes are not equally important, this is done by determining the semantic similarity between a query and the processes of a business organization and the importance of a business process to that organization. With a case study on an enterprise system of a retail company, the results produced by our proposed approach have shown that significance level is higher for more important queries compared to the less important ones.
Carry me if you can : A utility based forwarding scheme for content sharing in tourist destinations
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 22nd Asia-Pacific Conference on Communications, APCC 2016; Yogyakarta, Indonesia; 25th-27th August 2016 p. 261-267
- Full Text:
- Reviewed:
- Description: Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships. © 2016 IEEE.
Exploiting evolving trust relationships in the modelling of opinion formation dynamics in online social networks
- Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017; Taipei, Taiwan; 27th-29th March 2017 p. 872-879
- Full Text: false
- Reviewed:
- Description: Mass participation of the members of a society in discussions to resolve issues related to a topic leads to forming public opinion. The timeline of the underlying dynamics goes through several distinguishable phases, and experiences transition from one to another. After initiated by concerned individuals, it draws active attention from almost everyone, and with time progression, people's participation starts declining as the issues are resolved or lost attraction. The existing works in the literature to capture the opinion formation process pay attention to model the dynamics in its active phase and thus ignore the other phases and the corresponding phase transitions. Trust relationships among the participants dynamically shape their interactions in different stages of the dynamics. Existing works fail to incorporate trust in defining the extent of influence one has on others, as they define the social relationships in the opinion space. To address this issue, we adopt simulated annealing to model the transitional behaviour of the dynamics, and then, amalgamate peoples relationships in the trust space with that in the opinion space to define the meta-heuristics of the algorithm for capturing the dynamical properties of the process. Finally, through simulation, we observe that our model is insightful in representing peoples' evolving behaviour in the different stages of opinion formation process, and consequently, can capture the various properties of the steady-state outcomes of the dynamics. © 2017 IEEE.
- Description: Proceedings - International Conference on Advanced Information Networking and Applications, AINA