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Impact of friendly jammers on secrecy multicast capacity in presence of adaptive eavesdroppers

  • Authors: Giti, Jishan , Srinivasan, Bala , Kamruzzaman, Joarder
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 IEEE Globecom Workshops, 36th IEEE Global Communications Conference; Singapore, Singapore; 4th-8th December 2017
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  • Description: We consider the problem of security in wireless multicasting for a multiple-input multiple-output (MIMO) relay-aided system. The network suffers from a group of adaptive eavesdroppers who can act as both simple eavesdroppers and hostile jammers. This paper formulates the impact of friendly jammers to improve secured communication. We derived the expressions for secrecy multicast capacities considering the absence and presence of friendly jammers. The best relay for transmission is chosen from a group of relays that aids to achieve the maximum secrecy capacity while the best jammer is selected based on competitive interference price. Numerical results show that the achievable secrecy multicast capacity increases significantly in the presence of jammer to nullify the effect of adversaries. Results under different scenarios of varying jamming and relay powers demonstrate the efficacy of friendly jammers in providing physical layer security.
  • Description: We consider the problem of security in wireless multicasting for a

Periodic associated sensor patterns mining from wireless sensor networks

  • Authors: Rashid, Mamunur , Kamruzzaman, Joarder , Gondal, Iqbal , Hassan, Rafiul
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017); Guangzhou, China; 14/11/2017-18/11/2017 p. 247-255
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  • Description: Mining interesting knowledge from the massive amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature all-confidence measure based associated sensor patterns can captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. However, when the user given all-confidence threshold is low, a huge amount of patterns are generated and mining these patterns may not be space and time efficient. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of associated patterns in WSNs. Associated sensor patterns that occur after regular intervals is called periodic associated sensor patterns. Even though mining periodic associated sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a compact tree structure called Periodic Associated Sensor Pattern-tree (PASP-tree) and an efficient mining approach for finding periodic associated sensor patterns (PASPs) from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding periodic associated sensor patterns.

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
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  • 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.

Survey of recent cyber security attacks on robotic systems and their mitigation approaches

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2017
  • Type: Text , Book chapter
  • Relation: Detecting and mitigating robotic cyber security Risks p. 284-299
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  • Description: With the rapid expansion of digital media and the advancement of the artificial intelligence, robotics has drawn the attention of cyber security research community. Robotics systems use many Internet of Things (IoT) devices, web interface, internal and external wireless sensor networks and cellular networks for better communication and smart services. Individuals, industries and governments organisations are facing financial loses, losing time and sensitive data due these cyber attacks. The use these different devices and networks in robotics systems are creating new vulnerabilities and potential risk for cyber attacks. This chapter discusses about the possible cyber attacks and economics losses due to these attacks in robotics systems. In this chapter, we analyse the increasing uses of public and private robots, which has created possibility of having more cyber-crimes. Finally, contemporary and important mitigation approaches for these cyber attacks in robotic systems have been discussed in this chapter. © 2017, IGI Global. All rights reserved.

A data mining approach for machine fault diagnosis based on associated frequency patterns

  • Authors: Rashid, Md. Mamunur , Amar, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Applied Intelligence Vol. 45, no. 3 (2016), p. 638-651
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  • Description: Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis. © 2016, Springer Science+Business Media New York.

An efficient data extraction framework for mining wireless sensor networks

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 23rd International Conference, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing, Part III (Lecture Notes in Computer Science series) Vol. 9949, p. 491-498
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  • Description: Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor's activities over time from the sensor database that attached with a well-equipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associated-correlated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

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
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  • 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.

Modeling multiuser spectrum allocation for cognitive radio networks

  • Authors: Bin Shahid, Mohammad , Kamruzzaman, Joarder , Hassan, Md Rafiul
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Computers & Electrical Engineering Vol. 52, no. (2016), p. 266-283
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  • Description: Spectrum allocation scheme in cognitive radio networks (CRNs) becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns. Most existing schemes focus on the gain of individual users, ignoring the effect of an allocation on other users and rely on the 'periodic sensing and transmission' cycle which reduces spectrum utilization. This paper introduces a scheme that exploits collaboration among users to detect PU's return which relieves active CR users from the sensing task, and thereby improves spectrum utilization. It defines a Capacity of Service (CoS) metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users. The proposed scheme significantly improves capacity of service, reduces interference loss and collision, and hence, enhances dynamic spectrum access capabilities. (C) 2015 Elsevier Ltd. All rights reserved.

PRADD : A path reliability-aware data delivery protocol for underwater acoustic sensor networks

  • Authors: Nowsheen, Nusrat , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 75, no. (2016), p. 385-397
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  • Description: Underwater Acoustic Sensor Networks (UASNs) are becoming increasingly promising to monitor aquatic environment. However, reliable data delivery remains challenging due to long propagation delay and high error-rate of underwater acoustic channel, limited energy and inherent mobility of sensor nodes. To address these issues, we propose a protocol called Path Reliability-Aware Data Delivery (PRADD) to improve data transfer reliability for delay tolerant underwater traffic. Data delivery reliability is significantly improved by selecting the next hop forwarder on-the-fly based on its link reliability, reachability to gateways and coverage probability through probabilistic estimation. Data forwarding solution is coupled with delay tolerant networking paradigm to improve delivery with reduced overhead. PRADD does not require active localization technique to estimate the updated location of a sensor node except its initial coarse location. The movement of an anchored node is exploited to estimate its coverage probability. Mobile message ferries are used to collect stored data from one or more nodes, called gateways. A strategy for gateway selection is devised to maximize their lifetime. Simulation results show that PRADD achieves significant performance improvement over competing protocols using low overhead and less energy.

Search and tracking algorithms for swarms of robots: A survey

  • Authors: Senanayake, Madhubhashi , Senthooran, Ilankaikaone , Barca, Jan , Chung, Hoam , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Robotics and Autonomous Systems Vol. 75, no. Part B (2016), p. 422-434
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  • Description: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. © 2015 Elsevier B.V.

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
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  • 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

A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements

  • Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
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  • Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.

A mapreduce based technique for mining behavioral patterns from sensor data

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
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  • Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.

A new convergence rate estimation of general artificial immune algorithm

  • Authors: Hong, Lu , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Journal of Intelligent and Fuzzy Systems Vol. 28, no. 6 (2015), p. 2793-2800
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  • Description: Artificial immune algorithm has been used widely and successfully in many computational optimization areas, but the theoretical research exploring the convergence rate characteristics of artificial immune algorithm is yet inadequate. In this paper, instead of the traditional eigenvalue estimation of state transition matrix, stochastic processes theory is introduced to study the convergence rate of general artificial immune algorithm. The method begins by analyzing the necessary condition for convergence of artificial immune algorithm and takes it as the sufficient condition for a class of general artificial immune algorithm. Through the definition of Markov chain convergence rate, a probability strong convergence rate estimation method of general artificial immune algorithm is proposed. This method is judged by the final convergence of the best antibody, which overcomes the conservative defect of traditional estimation methods. The simulation results show the correctness of the proposed estimation method, and the estimation method can be used to judge the convergence and convergence rate of a class of artificial immune algorithms. This research has a certain theoretical reference value to optimize the convergence rate in the practical application of artificial immune algorithm.

An efficient pose estimation for limited-resourced MAVs using sufficient statistics

  • Authors: Senthooran, Ilankaikone , Barca, Jan , Kamruzzaman, Joarder , Murhsed, Manzur , Chung, Hoam
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015; Hamburg; Germany; 28th September-2nd October 2015 Vol. 2015, p. 3735-3740
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  • Description: We present a computationally efficient RGB-D based pose estimation solution for less computationally resourced MAVs, which are ideally suited as members in a swarm. Our approach applies the sufficient statistics derived for a least-squares problem to our problem context. RANSAC-based outlier detection in aligning corresponding feature points is a time consuming operation in visual pose estimation. The additive nature of the used sufficient statistics significantly reduces the computation time of the RANSAC procedure since the pose estimation in each test loop can be computed by reusing previously computed sufficient statistics. This eliminates the need for recomputing estimates from scratch each time. A simpler hypotheses testing method gave similar performance in terms of speed but less accurate than our proposed method. We further increase the efficiency by reducing the problem size to four dimensions using attitude data from an Attitude and Heading Reference System (AHRS). Using a real-world dataset, we show that our algorithm saves up to 94% of computation time for the RANSAC-based procedure in pose estimation while improving the accuracy.

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
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  • 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

Condition monitoring through mining fault frequency from machine vibration data

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12th-17th July 2015 p. 1-8
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  • Description: In machine health monitoring, fault frequency identification of potential bearing faults is very important and necessary when it comes to reliable operation of a given system. In this paper, we proposed a data mining based scheme for fault frequency identification from the bearing data. In this scheme, we propose a compact tree called SAP-tree (sliding window associated frequency pattern tree) which is built upon the analysis of frequency domain characteristics of machine vibration data. Using this tree we devised a sliding window-based associated frequency pattern mining technique, called SAP algorithm, that mines for the frequencies relevant to machine fault. Our SAP algorithm can mine associated frequency patterns in the current window with frequent pattern (FP)-growth like pattern-growth method and used these patterns to identify the fault frequency. Extensive experimental analyses show that our technique is very efficient in identifying fault frequency over vibration data stream.

Consistency driven opinion formation modelling in presence of external sources

  • Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: International Joint Conference on Neural Networks, IJCNN 2015; Killarney, Ireland; 12th-17th July 2015
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  • Description: Opinion formation in social networks has changed in a more rigorous way due to the inception of Online Social Networks (OSNs) as a platform of generating and sharing huge amount of contents as well as easy and ubiquitous access to varied information sources. Our opinions are not only updated through interactions with our neighbours in OSNs, but also shaped by the opinions received from information sources external to the native OSNs. Current models only consider the neighbours' influence in opinion evolution, thus lack the impact of other information sources, e.g., news media, web search, bulletin board, discussion forum on opinion formation. They consider individual opinion distances to model the influence among interactive neighbours, but fail to capture the influence of majority supported opinions and its possible impact in opinion evolution. Our model explicitly captures the effect of external sources on opinion formation in an OSN. We combine the implication of most perceived opinions in terms of consistency along with opinion distance to emulate the influence of different opinion sources. Consistency is measured by the entropy of opinions derived from a particular source type. Simulation results show that our model properly captures the consensus, polarization and fragmentation properties of opinion evolution. Finally, we investigate the influence of stubborn agents on opinion formation and compare it with a contemporary model. © 2015 IEEE.

Content exchange among mobile tourists using users' interest and place-centric activities

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 2015 10th International Conference on Information, Communications and Signal Processing (Icics); Singapore, Singapore; 2nd-4th December 2015 p. 1-5
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  • Description: In this work we investigate decentralized content exchange among tourists who are mostly strangers, depicts irregular movement patterns and most likely not to have any prior social relationship or difficult to establish any in a tourist spot. We incorporate user's interest, trustworthy online recommendations, and place-centric information to facilitate content exchange in such tourist destinations. The proposed administrator selection policy considers stay probability in activities, connectivity among nodes and their available resources. We have done extensive simulation using network simulator NS3 on a popular tourist spot in Australia that provides a number of activities. Our proposed approach shows promising results in exchanging contents among users measured in terms of content hit and delivery success rate as well as latency. The success rate is comparable to those reported in the literature for cases where social relationship exist and nodes follow regular predictable movement patterns.

Content sharing among visitors with irregular movement patterns in visiting hotspots

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 2015 IEEE 14th International Symposium on Network Computing and Applications (NCA); Cambridge, United States; 28th - 30th September 2015; published in Proceedings - 2015 IEEE 14th International Symposium on Network Computing and Applications, NCA 2015 p. 230-234
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  • Description: Smart mobile devices have become immensely popular among the people worldwide and provide a new platform for generating and sharing contents. The centralized and hybrid architectures for content sharing require constant Internet connection, increase traffic and incur costs. To address these issues several content sharing approaches have been proposed using the decentralized architecture. Most of the proposed approaches use spatio-temporal regularity and pre-existing social relationships of the users to predict their movements and facilitate content sharing. However, there are scenarios such as visiting hotspots where regular movement patterns or established social relationships among people might not exist. Content sharing in such scenarios has not been addressed yet in literature and existing prediction based approaches are ineffectual. This study focuses on facilitating content sharing in the afore-mentioned scenarios. We take account of user interests, recommendations from on-line social networks, hotspot specific activities and other relevant information to construct communities which facilitate content sharing. For each community an administrator, who maintains content and member lists and render directory services, is selected based on stay probability, interest score, battery lifetime and device configuration. Simulation results show that our proposed approach attains high content hit and success rate and low latency in delivery which is nearly comparable to those proposed for scenarios with regular predictable movement patterns reported in literature.