Biblio

Future wearable devices are expected to increasingly exchange their positioning information with various Location-Based Services (LBSs). Wearable applications can include activity-based health and fitness recommendations, location-based social networking, location-based gamification, among many others. With the growing opportunities for LBSs, it is expected that location privacy concerns will also increase significantly. Particularly, in opportunistic wireless networks based on device-to-device (D2D) connectivity, a user can request a higher level of control over own location privacy, which may result in more flexible permissions granted to wearable devices. This translates into the ability to perform location obfuscation to the desired degree when interacting with other wearables or service providers across the network. In this paper, we argue that specific errors in the disclosed location information feature two components: a measurement error inherent to the localization algorithm used by a wearable device and an intentional (or obfuscation) error that may be based on a trade-off between a particular LBS and a desired location privacy level. This work aims to study the trade-off between positioning accuracy and location information privacy in densely crowded scenarios by introducing two privacy-centric metrics.

Mobile data offloading using opportunistic network has recently gained its significance to increase mobile data needs. Such offloaders need to be properly incentivized to encourage more and more users to act as helpers in such networks. The extent of help offered by mobile data offloading alternatives using appropriate incentive mechanisms is significant in such scenarios. The limitation of existing incentive mechanisms is that they are partial in implementation while most of them use third party intervention based derivation. However, none of the papers considers trust as an essential factor for incentive distribution. Although few works contribute to the trust analysis, but the implementation is limited to offloading determination only while the incentive is independent of trust. We try to investigate if trust could be related to the Nash equilibrium based incentive evaluation. Our analysis results show that trust-based incentive distribution encourages more than 50% offloaders to act positively and contribute successfully towards efficient mobile data offloading. We compare the performance of our algorithm with literature based salary-bonus scheme implementation and get optimum incentive beyond 20% dependence over trust-based output.

The present work describes a remote environment monitoring system based on the paradigms of mesh networks and opportunistic networks, whereby a sensor node can explore “con-nectivity windows” to transmit information that will eventually reach another network participants. We discuss the threats to the system's security and propose security mechanisms for the system ensuring the integrity and availability of monitoring information, something identified as critical to its proper operation.

It is hard to set up an end-to-end connection between source and destination in Opportunistic Networks, due to dynamic network topology and the lack of infrastructure. Instead, the store-carry-forward mechanism is used to achieve communication. Namely, communication in Opportunistic Networks relies on the cooperation among nodes. Correspondingly, Opportunistic Networks have some issues like long delays, packet loss and so on, which lead to many challenges in Opportunistic Networks. However, malicious nodes do not follow the routing rules, or refuse to cooperate with benign nodes. Some misbehaviors like black-hole attack, gray-hole attack may arbitrarily bloat their delivery competency to intercept and drop data. Selfishness in Opportunistic Networks will also drop some data from other nodes. These misbehaviors will seriously affect network performance like the delivery success ratio. In this paper, we design a Trust-based Routing Protocol (TRP), combined with various utility algorithms, to more comprehensively evaluate the competency of a candidate node and effectively reduce negative effects by malicious nodes. In simulation, we compare TRP with other protocols, and shows that our protocol is effective for misbehaviors.

Opportunistic Networks are delay-tolerant mobile networks with intermittent node contacts in which data is transferred with the store-carry-forward principle. Owners of smartphones and smart objects form such networks due to their social behaviour. Opportunistic Networking can be used in remote areas with no access to the Internet, to establish communication after disasters, in emergency situations or to bypass censorship, but also in parallel to familiar networking. In this work, we create a mobile network application that connects Android devices over Wi-Fi, offers identification and encryption, and gathers information for routing in the network. The network application is constructed in such a way that third party applications can use the network application as network layer to send and receive data packets. We create secure and reliable connections while maintaining a high transmission speed, and with the gathered information about the network we offer knowledge for state of the art routing protocols. We conduct tests on connectivity, transmission range and speed, battery life and encryption speed and show a proof of concept for routing in the network.