This paper presents an authentication and key agreement protocol to streamline communication activities for a group of mobile stations (MSs) roaming from the same home network (HN) to a serving network (SN). In such a roaming scenario, conventional schemes require the SN to interact with the HN for authenticating respective MSs, at the cost of repeated message exchanges and communication delay. Instead, in our design, when the first MS of a group visits, the SN performs full authentication with the concerned HN and thereby obtains authentication information for the MS and other members. Thus when any other MS of the same group visits, the SN can authenticate locally without subsequent involvement of the HN, so as to simplify protocol operations. We will show that our scheme does not trade performance for security and robustness to the extent that security requirements are unduly weakened. Both qualitative and quantitative discussions indicate that our proposed scheme lends itself to pragmatic settings.

In the past few years, Peer-to-Peer lending (P2P lending) has grown rapidly in the world. The main idea of P2P lending is disintermediation and removing the intermediaries like banks. For a small business and some individuals without enough credit or credit history, P2P lending is a good way to apply for a loan. However, the fundamental problem of P2P lending is information asymmetry in this model, which may not correctly estimate the default risk of lending. Lenders only determine whether or not to fund the loan by the information provided by borrowers, causing P2P lending data to be imbalanced datasets which contain unequal fully paid and default loans. Imbalanced datasets are quite common in the real worlds, such as credit card fraud in transactions, bad products in the plant and so on. Unfortunately, the imbalanced data are unfriendly to the normal machine learning schemes. In our scenario, models without any adaptive methods would focus on learning the normal repayment. However, the characteristic of the minority class is critical in the loaning business. In this study, we utilize not only several machine learning schemes for predicting the default risk of P2P lending but also re-sampling and cost-sensitive mechanisms to process imbalanced datasets. Furthermore, we use the datasets from Lending Club to validate our proposed scheme. The experiment results show that our proposed scheme can effectively raise the prediction accuracy for default risk.

A NEtwork that is MObile (NEMO) usually consists of at least one Mobile Router (MR) attached to the infrastructure to manage all external communication for of all nodes inside a NEMO. Because a NEMO moves as a whole, previous mobile ReSource reserVation Protocols have two problems in supporting quality of services (QoS) for NEMOs; that is, mobility unawareness and excessive signal overhead. In this paper, we first address these two problems and then propose a Mobile Bandwidth-Aggregation (MBA) reservation scheme to support QoS guaranteed services for NEMOs. In order to resolve these two problems, MBA makes an MR the proxy of all nodes insides a NEMO and has the MR aggregates and reserve the bandwidth required for all node inside a NEMO. Mathematical analysis and simulation results show that the proposed MBA scheme can significantly reduce the signal overhead for reservation maintenance. Furthermore we also present three hypothetic policies of tunnel reservations for NEMOs, and conduct simulation to evaluate these policies in terms of blocking probabilities and bandwidth utilizations.Keywords NEMO · Mobile router · QoS · RSVP · MRSVP · HMRSVP A preliminary version of this work presented at

In a tree-structured ZigBee wireless sensor network, nodes close to the root of the tree (i.e., hot-spot nodes) may exhaust their power earlier than those distant from the root due to heavy loads on packet forwarding. This hot-spot problem is inherent in tree-structured networks and may demand extra energy to recover from failures of hot-spot nodes. In this paper, the backbone-aware topology formation (BATF) scheme is proposed to alleviate the hot-spot problem. BATF utilizes power-rich nodes to form a backbone tree that does not suffer from the hot-spot problem. Each power-rich node independently initiates a ZigBee tree network that attracts associations from ZigBee-compliant devices in order to distribute packet-forwarding loads over a larger set of nodes. Issues of BATF such as the partition of address space and ZigBee-compliant routing are discussed in detail. Simulation results confirm that BATF does alleviate the hot-spot problem as it improves network lifetime as well as data collection capability. Comparisons with native ZigBee protocols show that the improvement comes from our protocol design rather than simply introducing power-rich nodes.

Abstract-In this article, we first depict the call-role sensitivity problem in Network Address Translation (NAT) traversal, and then propose an approach to resolving the problem. The problem is whether a direct connection can be found between two peers across NATs mainly depends on the NAT type at the caller's side. We propose the extra-candidate connectivity check where both peers initiate a direct connectivity check to eliminate the effect of the call role. We have implemented the extra-candidate connectivity check and conducted experiments with 18 different NATs. Experimental results show that our approach can indeed resolve the call-role sensitivity problem, and maximize the direct connectivity rate (DCR) which is improved by 18.71% from the original scheme.