Conference Papers

TSF3: Computer and Information Systems

Lightweight Security Protocol for A Biosensor

Hussam AlHamadi (Khalifa University & Information Security Research Center, United Arab Emirates (UAE)); Amjad Gawanmeh and Mahmoud AlQutayri (Khalifa University, United Arab Emirates (UAE))


Security is one of the major challenges that affect the deployment of the biosensors that form WBSNs. The implementation of any security protocol comes along with the additional overhead of an extra power consumption from the limited resources of devices like biosensors. Nevertheless, the additional security operations may come with a delay that affects the realtime objective of the biosensors application. In this paper, a lightweight security protocol is presented to secure the medical information which is transmitted from the biosensor to the gateway. The proposed security protocol relies on a counter method at the biosensor side to save the biosensor's power. The security protocol shouldn't include unnecessary computational processes to has an acceptable cost of computational delay. Therefore, the performance of the proposed protocol is compared with that of other existing techniques.

Survey of Incentive Mechanisms for Crowd Sensing

Ahmed Suliman (Khalifa University, United Arab Emirates (UAE)); Hadi Otrok (Khalifa University, United Arab Emirates (UAE) & CIISE, Concordia University, Canada)


With the wide spread of smart phones, the paradigm of crowd sensing is gaining immense popularity. Crowd sensing is the act of collecting certain kind of data from people in a specific area of interest. Since the users involved in this activity incur a cost for performing the task, i.e. the cost of uploading the data or the energy to sense, they need to be compensated for their work or else they will not be inclined to participate in the sensing task. Many papers have been published related to user selection and incentive mechanisms in crowd sensing. In this paper we review a range of approaches proposed in both areas, provide comparison between them and outline areas for future work.

Automatic arabic Text Summarization Based on Noun Extraction

Lamees Al Qassem and Hassan Barada (Khalifa University, United Arab Emirates (UAE)); Di Wang (Khalifa University & EBTIC, United Arab Emirates (UAE)); Ahmad Alrubaie and Nawaf Almoosa (Khalifa University, United Arab Emirates (UAE))


This paper focuses on discussing the challenges faced in summarizing Arabic documents and the literature in this field, which is fairly limited and very recent. The reviewed systems are classified based on the methodology followed in building the system. Finally, a new system architecture is proposed and discussed. The system relies on the extracting nouns and clustering them based on their semantic.

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