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Problem statement: In Mobile Ad hoc Network (MANET), both the routing layer and the Medium Access Control (MAC) layer are vulnerable to several attacks. There are very few techniques to detect and isolate the attacks of both these layers simultaneously. In this study, we developed a combined solution for routing and MAC layer attacks. Approach: Our approach, makes use of three techniques simultaneously which consists of a cumulative frequency based detection technique for detecting MAC layers attacks, data forwarding behavior based detection technique for detecting packet drops and message authentication code based technique for packet modification. Results: Our combined solution presents a reputation value for detecting the malicious nodes and isolates them from further network participation till its revocation. Our approach periodically checks all nodes, including the isolated nodes, at regular time period λ. A node which recovers from its misbehaving condition is revoked to its normal condition after the time period λ. Conclusion/Recommendations: By simulation results, we show that our combined solution provides more security by increased packet delivery ratio and reduced packet drops. We also shown that our approach has less overhead compared to the existing technique.

A mobile ad hoc network (MANET) is a self-organizing, self-configuring confederation of wireless systems. MANET devices join and leave the network asynchronously at will, and there are no predefined clients or server. The dynamic topologies, mobile communications structure, decentralized control, and anonymity creates many challenges to the security of systems and network infrastructure in a MANET environment. Consequently, this extreme form of dynamic and distributed model requires a revaluation of conventional approaches to security enforcements. In this paper, we propose a new routing mechanism to combat the common selective packet dropping attack. Associations between nodes are used to identify and isolate the malicious nodes. Simulation results show the effectiveness of our scheme compared with conventional scheme.

Lensless fluorescence imaging (LFI) is the imaging of fluorescence from cells or microspheres using an image sensor with no external lenses or filters. The simplicity of the hardware makes it well suited to replace fluorescence microscopes and flow cytometers in lab-on-a-chip applications, but the images captured by LFI are highly dependent on the distance between the sample and the sensor. This work demonstrates that not only can samples be accurately detected across a range of sample-sensor separations using LFI, but also that the separation can be accurately estimated based on the shape of fluorescence in the LFI image. First, a theoretical model that accurately predicts LFI images of microspheres is presented. Then, the experimental results are compared to the model and an image processing method for accurately predicting sample-sensor separation from LFI images is presented. Finally, LFI images of microspheres and cells passing through a microfluidic channel are presented.