M-Dimension : multi-characteristics based routing protocol in human associated delay-tolerant networks with improved performance over one dimensional classic models

Human associated delay-tolerant network (HDTN) is a new delay-tolerant network where mobile devices are associated with humans. It can be viewed from both their geographic and social dimensions. The combination of these different dimensions can enable us to more accurately comprehend a delay-tolerant network and consequently use this multi-dimensional information to improve overall network efficiency. Alongside the geographic dimension of the network which is concerned with geographic topology of routing, social dimensions such as social hierarchy can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network.

We propose a multi-dimensional routing protocol (M-Dimension) for the human associated delay-tolerant network which uses the local information derived from multiple dimensions to identify a mobile node more accurately. Each dimension has a weight factor and is organized by the Distance Function to select an intermediary and applies multi-cast routing. We compare M-Dimension to existing benchmark routing protocols using the MIT Reality Dataset, a well-known benchmark dataset based on a human associated mobile network trace file. The results of our simulations show that M-Dimension has a significant increase in the average success ratio and is very competitive when End-to-End Delay of packet delivery is used in comparison to other multi-cast DTN routing protocols.

Privacy protected data forwarding in human associated delay tolerant networks

Human associated delay-tolerant networks (HDTNs) are new networks for DTNs, where mobile devices are associated with humans and demonstrate social related communication characteristics. As most of recent works use real social trace files to study the date forwarding in HDTNs, the privacy protection becomes a serious issue. Traditional privacy protections need to keep the attributes semantics, such as data mining and information retrieval. However, in HDTNs, it is not necessary to keep these meaningful semantics. In this paper, instead, we propose to anonymize the original data by coding to preserve individual's privacy and apply Privacy Protected Data Forwarding (PPDF) model to select the top N nodes to perform the multicast. We use both MIT Reality and Infocom 06 datasets, which are human associated mobile network trace file, to simulate our model. The results of our simulations show that this method can achieve a high data forwarding performance while protect the nodes' privacy as well.

Anonymous data forwarding in human associated delay tolerant networks

Human associated delay-tolerant networks (HDTNs) are new networks for DTNs, where mobile devices are associated with humans and demonstrate social-related communication characteristics. As most of recent works use the social attributes to study the date forwarding in HDTNs and these attributes are critical for the data provider, how to use the anonymous attributes becomes a serious issue. In this paper, we propose a three-dimensional coordinate model by using the anonymous attributes to perform the data forwarding. We use MIT reality dataset, which is a human associated mobile network trace file, to simulate our model. The results of simulations show that the proposed model can use the anonymous attributes to achieve a high data forwarding performance.

Node localization using mobile robots in delay-tolerant sensor networks

We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.

A simple motion heuristic for controlling mobile sinks in delay tolerant sensor networks

Optimising energy consumption in wireless sensor networks is of dominant importance. Sink mobility is introduced to deal with this problem by approaching the sensor nodes and collecting their data buffers using the less energy demanding single-hop communication. The sink route is very crucial for the data collection operation performed in the network especially when the collection requests generated by the sensors are revealed dynamically to the sink and not known ahead. This paper presents a practical motion heuristic for constructing the sink route based on the dynamic arrival of the collection requests. Three control schemes are proposed for coordinating the interaction of multiple mobile sinks collectively performing the data collection in the network. The main objective is maximising the data collected by each mobile sink while minimising the sleeping time of each sensor awaiting the collection service. Simulation results show the performance of the mobile sinks under the proposed control schemes and the impact of the motion heuristic on the sensors' sleeping time in the network.

Delay tolerant networks

The delay-tolerant networks (DTNs) are emerging research topics that have attracted keen research efforts from both academia and industry. Different from the traditional communication networks, DTNs consider an extreme network condition where a complete end-to-end path between the data source and destination may not exist, and the network is subject to dynamic node connections and unstable topologies. With the above features, DTNs find broad applications in the situations where legacynetworks cannot work effectively, such as data communications in rural areas, where stable communications infrastructure is not available or costly, and crucial areas, e.g., disaster rescue and battlefield communications. To summarize, the DTNs, as an important technology complementary to traditional networkings, can be widely applied to national welfare and the people’s livelihood.

AMDD : Exploring entropy based anonymous multi-dimensional data detection for network optimization in human associated DTNs

Human associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and demonstrate social-related communication characteristics. Most of recent works use real social trace file to analyse its social characteristics, however social-related data is sensitive and has concern of privacy issues. In this paper, we propose an anonymous method that anonymize the original data by coding to preserve individual's privacy. The Shannon entropy is applied to the anonymous data to keep rich useful social characteristics for network optimization, e.g. routing optimization. We use an existing MIT reality dataset and Infocom 06 dataset, which are human associated mobile network trace files, to simulate our method. The results of our simulations show that this method can make data anonymously while achieving network optimization.

Routing and privacy protection in human associated delay tolerant networks

This thesis proposes Human Associated Delay Tolerant Networks, where data communications among mobile nodes are determined by human social behaviours. Three models are proposed to handle the social attributes effect on data forwarding, the time impact on nodes’ movement and the privacy protection issue when social attributes are introduced.

Opportunistic offloading of deadline-constrained bulk cellular traffic in vehicular DTNs

The ever-growing cellular traffic demand has laid a heavy burden on cellular networks. The recent rapid development in vehicle-to-vehicle communication techniques makes vehicular delay-tolerant network (VDTN) an attractive candidate for traffic offloading from cellular networks. In this paper, we study a bulk traffic offloading problem with the goal of minimizing the cellular communication cost under the constraint that all the subscribers receive their desired whole content before it expires. It needs to determine the initial offloading points and the dissemination scheme for offloaded traffic in a VDTN. By novelly describing the content delivery process via a contact-based flow model, we formulate the problem in a linear programming (LP) form, based on which an online offloading scheme is proposed to deal with the network dynamics (e.g., vehicle arrival/departure). Furthermore, an offline LP-based
analysis is derived to obtain the optimal solution. The high efficiency of our online algorithm is extensively validated by simulation results.

An improved stochastic modeling of opportunistic routing in vehicular CPS

Vehicular Cyber-Physical System (VCPS) provides CPS services via exploring the sensing, computing and communication capabilities on vehicles. VCPS is deeply influenced by the performance of the underlying vehicular network with intermittent connections, which make existing routing solutions hardly to be applied directly. Epidemic routing, especially the one using random linear network coding, has been studied and proved as an efficient way in the consideration of delivery performance. Much pioneering work has tried to figure out how epidemic routing using network coding (ERNC) performs in VCPS, either by simulation or by analysis. However, none of them has been able to expose the potential of ERNC accurately. In this paper, we present a stochastic analytical framework to study the performance of ERNC in VCPS with intermittent connections. By novelly modeling ERNC in VCPS using a token-bucket model, our framework can provide a much more accurate results than any existing work on the unicast delivery performance analysis of ERNC in VCPS. The correctness of our analytical results has also been confirmed by our extensive simulations.

A new physarum network based genetic algorithm for bandwidth-delay constrained least-cost multicast routing

Bandwidth-delay constrained least-cost multicast routing is a typical NP-complete problem. Although some swarm-based intelligent algorithms (e.g., genetic algorithm (GA)) are proposed to solve this problem, the shortcomings of local search affect the computational effectiveness. Taking the ability of building a robust network of Physarum network model (PN), a new hybrid algorithm, Physarum network-based genetic algorithm (named as PNGA), is proposed in this paper. In PNGA, an updating strategy based on PN is used for improving the crossover operator of traditional GA, in which the same parts of parent chromosomes are reserved and the new offspring by the Physarum network model is generated. In order to estimate the effectiveness of our proposed optimized strategy, some typical genetic algorithms and the proposed PNGA are compared for solving multicast routing. The experiments show that PNGA has more efficient than original GA. More importantly, the PNGA is more robustness that is very important for solving the multicast routing problem.