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In wireless communication, wormhole attack is a crucial threat that deteriorates the normal functionality of the network. Invasion of wormholes destroys the network topology completely. However, most of the existing solutions require special hardware or synchronized clock or long processing time to defend against long path wormhole attacks. In this work, we propose a wormhole detection method using range-based topology comparison that exploits the local neighbourhood subgraph. The Round Trip Time (RTT) for each node pair is gathered to generate neighbour information. Then, the network is reconstructed by ordinal Multidimensional Scaling (MDS) followed by a suspicion phase that enlists the suspected wormholes based on the spatial reconstruction. Iterative computation of MDS helps to visualize the topology changes and can localize the potential wormholes. Finally, a verification phase is used to remove falsely accused nodes and identify real adversaries. The novelty of our algorithm is that it can detect both short path and long path wormhole links. Extensive simulations are executed to demonstrate the efficacy of our approach compared to existing ones.

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The rapid proliferation of Massive Open Online Courses (MOOC) has resulted in many-fold increase in sharing the global classrooms through customized online platforms, where a student can participate in the classes through her personal devices, such as personal computers, smartphones, tablets, etc. However, in the absence of direct interactions with the students during the delivery of the lectures, it becomes difficult to judge their involvements in the classroom. In academics, the degree of student's attention can indicate whether a course is efficacious in terms of clarity and information. An automated feedback can hence be generated to enhance the utility of the course. The precision of discernment in the context of human attention is a subject of surveillance. However, visual patterns indicating the magnitude of concentration can be deciphered by analyzing the visual emphasis and the way an individual visually gesticulates, while contemplating the object of interest. In this paper, we develop a methodology called Gestsatten which captures the learner's attentiveness from his visual gesture patterns. In this approach, the learner's visual gestures are tracked along with the region of focus. We consider two aspects in this approach -- first, we do not transfer learner's video outside her device, so we apply in-device computing to protect her privacy; second, considering the fact that a majority of the learners use handheld devices like smartphones to observe the MOOC videos, we develop a lightweight approach for in-device computation. A three level estimation of learner's attention is performed based on these information. We have implemented and tested Gestatten over 48 participants from different age groups, and we observe that the proposed technique can capture the attention level of a learner with high accuracy (average absolute error rate is 8.68%), which meets her ability to learn a topic as measured through a set of cognitive tests.