Junling Lu

A novel contact prediction-based routing scheme for DTNs

Delay/disruption tolerant networks (DTNs) make opportunistic communications by utilising the mobility of nodes. The characteristics of high mobility of nodes and high dynamicity of network topology result in an absence of instantaneous end-to-end path from any source to a destination and thus make routing a challenge in DTNs. To deal with this issue, researchers have investigated a variety of routing schemes for DTNs based on the prediction of future contacts, in which node mobility is explored and used. However, the previous works did not consider the instant contact information such as the last contact duration time and the instant separation time since the last contact whilst making routing decisions, leading to less prediction accuracy of future contacts and thus worse routing performance. In this paper, a novel contact prediction-based routing scheme is proposed for DTNs to increase delivery ratio by considering the instant contact information. Specifically, to predict the contact probability of two nodes accurately, the statistical contact information, the instant contact information and the contact transitivity are comprehensively considered. The simulation evaluations show that the proposed contact prediction-based routing substantially improves delivery ratio and reduces delivery latency and delivery overhead compared with traditional DTN routing schemes. Copyright

Mobility-aware routing in delay tolerant networks

Delay tolerant networks (DTNs) make use of opportunistic encounters of nodes for communication. The characteristics of high mobility of nodes, frequent link variation and long communication delays in DTNs result in an absence of an instantaneous end-to-end path from any source to a destination, making routing a challenge in DTNs. To deal with this issue, a lot of routing schemes have been proposed, in which future contacts of nodes are predicted based on node mobility traces and contact information. However, the previous works did not consider the spatial information of nodes, such as dwelling time at a location, and the transitivity of contacts in the prediction process of future encounter opportunities of nodes. In this paper, a novel mobility prediction-based routing (MPR) scheme is proposed for DTNs, in which the spatial information of nodes and contact transitivity are both taken into account. Specifically, a time-homogeneous semi-Markov process model is proposed to describe node mobility. By employing the semi-Markov model, we formulate the probability of a node destined to an area subject to the remaining time period constraint. The simulation results show that the proposed MPR scheme substantially improves delivery ratio and reduces delivery latency compared with traditional DTN routing schemes.

Fuzzy random multi-objective optimization based routing for wireless sensor networks

Wireless sensor networks are deployed in complex and uncertain environments, and multiple objectives of routing algorithms are expected to be optimal. However, routing algorithms based on deterministic single objective optimization may not flexibly meet the above needs of applications. This paper adopts fuzzy random optimization and multi-objective optimization, introduces fuzzy random variables to describe both fuzziness and randomness of link delay, link reliability and nodes’ residual energy, and proposes a routing model based on fuzzy random expected value and standard deviation model. A hybrid routing algorithm based on fuzzy random multi-objective optimization is designed, which embeds fuzzy random simulation into genetic algorithm with Pareto optimal solution. Simulation results show that the presented algorithm, by adjusting the parameters of fuzzy random variables for depicting both fuzziness and randomness, achieves a longer lifetime and wider performances of delay, latency jitter, reliability, communication interference, energy and balanced energy distribution. Therefore, the presented algorithm can meet different application needs of the cluster head network in the two-tiered wireless sensor networks.

Signal power random fading based interference-aware routing for wireless sensor networks

Power loss and interference coexist in wireless transmissions where random uncertainty is aggravated due to the mobility of sensor nodes. A probability interference model was proposed, based on the physical model and random fading of the received signal power, to depict the uncertainty of wireless interference. In addition, an interference-aware routing metric was designed, in which interference, routing convergence and residual energies of nodes were integrated. Furthermore, an interference-aware probabilistic routing algorithm was proposed for mobile wireless sensor networks, and its correctness and time and space complexities were proved. The NS-2 simulation experiments showed that the proposed algorithm can achieve higher packet delivery ratio than Greedy Perimeter Stateless Routing in various cases like the pause time and maximum moving speed. Simultaneously, the energy consumption of a packet and average delay were taken into consideration to better meet the needs of mobile scenarios with higher reliability.

Spacial Mobility Prediction Based Routing Scheme in Delay/Disruption-Tolerant Networks

Routing is a challenging issue in Delay/Disruption Tolerant Networks (DTNs) due to the characteristics of high mobility of nodes and high dynamicity of network topology. The previous works did not consider the spacial information of nodes, such as dwelling time at a location when predicting the future contact of two nodes. In this paper, a novel Spacial Mobility Prediction based Routing (SMPR) scheme is proposed for DTNs, in which the spacial information of nodes and contact transitivity are both taken into account. Specifically, a time homogeneous semi-Markov process model is proposed to describe the mobility of nodes. The simulation results show that the proposed SMPR scheme substantially improves delivery ratio and reduces delivery latency compared with traditional DTN routing schemes.

User social activity-based routing for cognitive radio networks

The social activities of Primary Users (PUs) and Secondary Users (SUs) affect actual accessible whitespace in Cognitive Radio Networks (CRNs). However, the impacts of primary activities on available whitespace have been extensively investigated due to the dominating priority of PUs, while the impacts of secondary activities on actual accessible whitespace have been ignored. Therefore, we propose to incorporate the primary and secondary activities in the analysis and decision of the accessible whitespace, namely, both the dominance of PUs over SUs and the competitions among SUs are simultaneously taken into account. Specifically, we first approximate primary activity probability based on the real datasets of mobile phone usage records, then the spectrum opportunity between a pair of communication SUs is deduced based on primary activities. Next, we infer the access probability limit of SUs successfully accessing the whitespace according to the primary activity probability, and depict the secondary activity probability from the views of social activity patterns and social networks respectively. Furthermore, the actual accessible probability of whitespace is given by introducing the competitions among SUs. Finally, a greedy routing algorithm, considering the accessible whitespace and the distance to the destination, is proposed to verify our idea. The experiment results based on the real datasets demonstrate the correctness of our analysis and the advantages of the proposed algorithm.

An Efficient Context-Aware Privacy Preserving Approach for Smartphones

With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.

An Edge Correlation Based Differentially Private Network Data Release Method

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

Higher-Load Data Transmitting in Opportunistic Networks Based on Probability Analysis of Communicating Capabilities

Opportunistic Network has become an international hotspot in according with the popularity of various mobile intelligent terminals. But the nodes with limited communication capabilities cannot satisfy the growing of large content data transmission. Aim to these issues, we propose a novel communication capacity calculation model of Opportunistic Network based on the classical Random Direction mobile model. We observe that different size of fragments in large content represents different delivery delays. Considering that the large content are always the multimedia data, it would necessarily produce additional head-part of every fragment, and this will affect the whole overhead of network. We define the restrain facts model of overhead and put forward an optimal fragment size algorithm. Finally, we designed and evaluated the methods and algorithms in simulate circumstance of Opportunistic Networks. The experiments results show that the average value of contact duration and total times of contacts analyzed from logs are all in according with the contact duration expectation and contact times calculated from the model. The optimal size of fragment is located at the well position in the proper range of fragment size.