A Model of Data Forwarding in MANETs for Lightweight Detection of Malicious Packet Dropping

This work introduces a model of data forwarding in MANETs which is used for recognizing malicious packet dropping behaviors. First, different legitimate packet discard situations are modeled, such as those generated by collisions, channel errors or mobility related droppings. Second, we propose an anomaly-based IDS system based on an enhanced windowing method to carry out the collection and analysis of selected crosslayer features. Third, a real deployment of the IDS is also considered by suggesting a methodology for the collection of the selected features in a distributed manner. We evaluate our proposal in a simulation framework and the experimental results show a considerable enhancement in detection results when compared with other approaches in the literature. For instance, our scheme shows a 22% improvement in terms of true positives rate and a remarkable 83% improvement in terms of false positives rate when compared to previous well-known statistical solutions. Finally, it is notable the simplicity and lightweightness of the proposal

A Hybrid Algorithm for Reliable and Energy-efficient Data Gathering in Wireless Sensor Networks

Reliability and energy efficiency are two important requirements of the data gathering process in wireless sensor networks. Accordingly, we propose a novel data gathering algorithm which meets these requirements. The proposed scheme categorizes the sensed data into valuable and normal data and handles each type of data based on its demands. The main requirement of valuable data is reliability. Thus, the adopted strategy to gather this type of data is to send several copies of data packets toward the sink. The rise of energy exhaustion in this scheme is tolerable. This is due to that, the valuable data is generated at a low rate. On the other hand, our main concern in gathering normal data is energy efficiency. As most of the sensed data is normal, an energy-efficient approach to gather normal data results in considerable energy conserving. Thus, we exploit clustering technique for normal data gathering. We also propose a lightweight intrusion detection system to detect malicious nodes. Simulation results and theoretical analysis confirm that our proposed algorithm provides reliability and energy efficiency at an acceptable level