Guangmin Hu

Energy-aware geographic routing in wireless sensor networks with anchor nodes

Energy efficiency has become an important design consideration in geographic routing protocols for wireless sensor networks because the sensor nodes are energy constrained and battery recharging is usually not feasible. However, numerous existing energy-aware geographic routing protocols are energy-inefficient when the detouring mode is involved in the routing. Furthermore, most of them rarely or at most implicitly take into account the energy efficiency in the advance. In this paper, we present a novel energy-aware geographic routing (EAGR) protocol that attempts to minimize the energy consumption for end-to-end data delivery. EAGR adaptively uses an existing geographic routing protocol to find an anchor list based on the projection distance of nodes for guiding packet forwarding. Each node holding the message utilizes geographic information, the characteristics of energy consumption, and the metric of advanced energy cost to make forwarding decisions, and dynamically adjusts its transmission power to just reach the selected node. Simulation results demonstrate that our scheme exhibits higher energy efficiency, smaller end-to-end delay, and better packet delivery ratio compared to other geographic routing protocols. Copyright

Energy-aware interference-sensitive geographic routing in wireless sensor networks

Energy conservation and interference reduction are the two ultimate goals in the design of network protocols for wireless sensor networks (WSNs). Energy-aware geographic routing has been considered as an attractive routing scheme for energy conservation in WSNs owing to its desirable scalability and simplicity. However, most energy-aware geographic routing protocols seldom consider interference reduction. The authors present an energy-aware interference-sensitive geographic routing (EIGR) protocol, which focuses on minimising the total network energy consumption and reducing interference. EIGR adaptively uses an anchor list to guide data delivery, and selects the minimum-interference link from energy-optimal relay region for data delivery. To further reduce the energy consumption and interference, EIGR adjusts the transmission power of each forwarding node so as just to reach the selected next forwarding node. Simulation results demonstrate that the proposed approach exhibits noticeably higher energy efficiency, shorter end-to-end delay and higher packet delivery ratio compared with other geographic routing protocols.

Energy-aware multipath geographic routing for detouring mode in wireless sensor networks

For energy-constrained wireless sensor networks, we present an Energy-aware Multipath Geographic Routing (EMGR) protocol. EMGR utilizes geographic information, the characteristics of energy consumption and the metric of advanced energy cost to select the next forwarding node, and uses a dynamic anchor list to shift routing path for load balance. Simulation results show that EMGR is superior to other protocols in terms of energy efficiency, network lifetime, end-to-end delay, and packet delivery ratio. Copyright

Hole plastic scheme for geographic routing in wireless sensor networks

Geographic routing in wireless sensor networks suffers from the local minimum problem when data packets encounter the concave boundary of a hole (void area), i.e., no neighbor node are closer to the destination than the current node. In this paper, we propose a hole plastic scheme which adaptively fill the concave area of a hole with potential stuck nodes according to 1-hop information of neighbors, to relieve the local minimum problem faced by geographic routing. The basic idea is To mark the nodes located in the concave area of the hole as potential stuck nodes which do not participate in data delivery unless a source/destination is located on the concave area of the hole. Once the hole plastic process is achieved, subsequently arriving data flows will be prevented from entering the concave area of the hole by potential stuck nodes. The proposed hole plastic scheme is achieved in a local self-organized manner, i.e., potential stuck node marking is based on the information of 1-hop neighbors, and traditional multi-hop cooperation methods such as hole detection, hole boundary tracing, hole modeling are not required.

End-to-End Measurements for Network Tomography under Multipath Routing

In most works of network tomography, end-to-end measurements are conducted based on the assumption of single-path routing. However, multipath routing caused by load balancing is increasingly common in todays Internet and makes it hard to tell which end-to-end path is measured by the current probing flow. In this letter, we propose a tomographic scheme able to reveal the corresponding relationship between end-to-end paths and probing flows. After that, one can explicitly probe each end-to-end path with a specific five-tuple flow. Simulation results demonstrate that our proposed scheme could recover the relationship accurately using around 200 packets per flow.

Improving the accuracy of boolean tomography by exploiting path congestion degrees

Boolean tomography is based on exploiting performance level correlations of end-to-end measurements to identify the congested links. Most work to date attempts to find the congested links according to the observed pattern of congested paths and the prior link congestion probabilities. In their work, the prior link congestion probabilities are either assumed to be unrealistically equal or estimated by a computationally complex algorithm. Furthermore, all congested paths are mapped down to the same “bad” state regardless of their congestion degrees, then separate causes of congestion may be identified as a common cause. In this paper, we propose a fast Bottom-Up Approach named BUA to estimate the prior probabilities based on a small number of measurement snapshots. BUA is computationally simpler than the existing approaches since it computes the congestion probability of each individual link through an explicit function of the measurements. We then extract the subsets of congested paths that might traverse the same congested links in current measurement snapshot according to their congestion degrees. The links that cause the congestion of each subset of paths are identified with the aid of the learnt probabilities. Simulations in different network scenarios demonstrate that our approach is able to improve the accuracy of the identification procedure.

Improving maximum-likelihood-based topology inference by sequentially inserting leaf nodes

Understanding the topology of a network is very important for network control and management. There have been several methods designed for estimating network topology from end-to-end measurements. Among these methods, the maximum-likelihood-based topology inference method is superior to suboptimal and pair-merging approaches, because it is capable of finding the global optimal topology. However, the existing method which searches the maximum likelihood tree directly is time-consuming, and may not be able to obtain the accurate topology of a larger-scale network. To overcome these issues, this study presents a maximum-likelihood-based leaf nodes inserting topology inference method. The method first builds a binary tree with two leaf nodes, and then inserts the remaining nodes into the tree one by one according to the maximum-likelihood criterion. When compared with the previous methods, the proposed method has the advantages of less computational cost and higher estimate precision. The analytical and simulation results show good performances by the proposed method.

Identification of congestion links under multipath routing with end-to-end measurements

Congestion links can not only introduce great packet losses, but also cause significant delay flutters to paths that traverse them. However, most of current approaches just try to identify congestion links that meet end-to-end loss observations. Whats worse, most of them also take no consideration of multipath routing, while which will introduce more than a single routing path between two end-hosts and can make a single-source network own a non-tree topology instead of the tree one. In this paper, we employ both end-to-end loss and delay observations to identify congestion links in a single-source network where multipath routing is enabled. We first prove that under certain topology conditions, the link delay variances in such non-tree topology can be inferred solely from end-to-end delay measurements. Then, we propose an algorithm to identify as congested a set of links, which can not only account for end-to-end path losses but also demonstrate great delay variances at the meantime. Simulation results validate the desirable performance of our proposed scheme.

Identify Congested Links with Network Tomography Under Multipath Routing

Identifying congested links accurately to ensure the Service Level Agreements is an important but challenging task, since it is costly or even practically unfeasible to monitor massive interior links directly for large networks. Network tomography has been proposed to overcome this problem by using end-to-end (path) measurements. However, most of existing tomographic methods only focus on the loss performance degradation, while paying much less attention the fact that network congestion will also greatly worsen the delay performance. Nevertheless, most of them normally work under single-path routing, which may also get violated in today’s Internet as multipath routing is increasingly common. In this paper, we consider the problem of using end-to-end measurements to identify congested links when multipath routing is employed in a non-tree network. Firstly, we use both link delay variances and link loss rates to model the system constraints between end- to-end paths and the interior links, and transfer the issue of congested link identification as an optimization problem. By theoretically demonstrating that the link delay variances are identifiable from the end-to-end delay measurements with certain topology conditions, we further prove that the above optimization problem is a Non-deterministic Polynomial-time hard (NP-hard) problem. Then in order to solve such an NP-hard problem, two greedy algorithms based on bool and additive congestion statuses are proposed. Lastly, simulation studies show that with extra delay constraints, our proposed algorithms are able to achieve better identification performances than existing methods under multipath routing.

Two-Type Information Fusion Based IP-to-AS Mapping Table Refining

The Internet topology at the autonomous system (AS) level is of great importance, and traceroute has been known to be a potential tool to obtain a complete AS topology. The original IP-to-AS mapping table maps the IP addresses in traceroute paths to their origin ASes, which may cause false AS links. The existing methods refine the original mapping table based on traceroute-BGP path pairs or alias resolution data. However, the information extracted from either of them is inaccurate and incomplete. In this paper, we present a two-type information fusion based method to refine the original mapping table. We extract four kinds of information from path pair and alias resolution data. Based on these information, we build a candidate AS set for each router. Then we choose the AS that is consistent with the existing information to be the owner AS of each router and map all of the IP addresses on the router to it. We validate the result with the ground truth from PeeringDB and Looking Glass severs. Compared with the existing methods, our method produces a more accurate mapping table. In addition, we discuss the coverage of our method and show that our method is convergent and more robust against the reduction of information or the increase of incorrect information.