Task reduction using regression-based missing data imputation in sparse mobile crowdsensing

The Journal of Supercomputing - Mobile Crowd Sensing (MCS) involves allocation of sensing tasks associated with an area of interest to a crowd of participants over time. Consequently, the...     

Collaborative techniques for intrusion detection in mobile ad-hoc networks

In this paper, we present two intrusion detection techniques for mobile ad-hoc networks, which use collaborative efforts of nodes in a neighborhood to detect a malicious node in that neighborhood. The first technique is designed for detection of malicious nodes in a neighborhood of nodes in which each pair of nodes in the neighborhood are within radio range of each other. Such a neighborhood of nodes is known as a clique [12]. The second technique is designed for detection of malicious nodes in a neighborhood of nodes, in which each pair of nodes may not be in radio range of each other but where there is a node among them which has all the other nodes in its one-hop vicinity. This neighborhood is identical to a cluster as mentioned in [12]. Both techniques use message passing between the nodes. A node called the monitor node initiates the detection process. Based on the messages that it receives during the detection process, each node determines the nodes it suspects to be malicious and send votes to the monitor node. The monitor node upon inspecting the votes determines the malicious nodes from among the suspected nodes. Our intrusion detection system is independent of any routing protocol. We give the proof of correctness of the first algorithm, which shows that it correctly detects the malicious nodes always when there is no message loss. We also show with the help of simulations that both the algorithms give good performance even when there are message losses arising due to unreliable channel.     

Binary decision-based collaborative spectrum sensing in infrastructure-less cognitive radio network

Collaborative spectrum sensing (CSS) plays a vital role in achieving accurate spectrum sensing results in a cognitive radio network (CRN). However, CSS in infrastructure-less CRNs is a challenging task as there is no central authority which aggregates individual sensing reports to take a final decision. Hence, it becomes the responsibility of each node to aggregate sensing reports of other nodes to take the final decision. Towards this end, we present two collaborative spectrum sensing techniques for infrastructure-less CRN based on message passing. The first technique is distributed binary decision-based CSS (DBCSS), in which a node broadcasts its sensing report and uses the binary decisions of its h-hop neighbors for making its final sensing decision. The second one is distributed cluster-based CSS (DCCSS) in which a cluster head consolidates the sensing reports in its cluster into a message and broadcast this message in the network. We support the validity of the proposed schemes through extensive simulation results.     

Detecting Byzantine attack in cognitive radio networks using machine learning

Wireless Networks - One primary function in a cognitive radio network (CRN) is spectrum sensing. In an infrastructure-based CRN, instead of individual nodes independently sensing the presence of...     

Mitigating spectrum sensing data falsification attack in ad hoc cognitive radio networks

In a cognitive radio ad hoc network, there is no central authority. Hence, distributed collaborative spectrum sensing (CSS) plays a major role in achieving an accurate spectrum sensing result. However, CSS is sensitive to spectrum sensing data falsification (SSDF) attack, in which a malicious user falsifies its local sensing report before disseminating it into the network. To capture such abnormal behavior of a node, we present an approach for detecting SSDF attack based on dissimilarity score. A secondary user (SU) computes the dissimilarity score of its neighbors from the messages received from its h‐hop neighbors. Further, we also present how the proposed scheme can be used on the sequence of sensing reports to detect and isolate the malicious SUs on the fly.