Hui Tian

RandomWalk Routing for Wireless Sensor Networks

Topology is important for any type of networks because it has great impact on the performance of the network. For wireless sensor networks (WSN), regular topologies, which can help to efficiently save energy and achieve long networking lifetime, have been well studied in [1, 4, 5, 7, 9]. However, little work is focused on routing in patterned WSNs except the shortest path routing with the knowledge of global location information. In this paper, we propose a routing protocol based on random walk. It doesn’t require global location information. Moreover, the random walk routing achieves load balancing property inherently for WSNs which is difficult to achieve for other routing protocols. We also prove that the random walk routing consumes the same amount of energy as the shortest path routing in the scenarios where the message required to be sent to the base station is in comparatively small size with the inquiry message among neighboring nodes. Since in many applications of WSNs, sensor nodes often send only beeplike small messages to the base station to report their status, our proposed random walk routing is a viable scheme. Though the random walk routing provides load balancing in the WSN, the nodes near to the base station (BS) are inevitably under heavier burden than the nodes far from the base station. Therefore we further propose a density-aware deployment scheme to guarantee that the heavy-load nodes do not affect the network lifetime even if they are exhausted.

An optimal coverage scheme for wireless sensor network

The coverage problem is one of the most fundamental issues in a wireless sensor network, which directly affects the capability and efficiency of the sensor network. In this paper, we formulate this problem as a construction problem to find a topology that covers the required sensing area with high reliability. Deploying a good topology is also beneficial to management and energy saving. We propose an optimal coverage scheme for wireless sensor networks that can maintain sufficient sensing area as well as provide high reliability and long system lifetime, which is the main design challenge in sensor networks. With the same number of sensors, our scheme compares favorably with the existing schemes.

Developing energy-efficient topologies and routing for wireless sensor networks

The performance of wireless sensor networks (WSNs) is greatly influenced by their topology. WSNs with patterned topologies can efficiently save energy and achieve long networking lifetime. In this paper, we discuss different patterned topologies for constructing WSNs which can provide the required coverage area and the connectivity of all sensor nodes. We compare different performance measures among all patterned topologies, and find that WSNs in strip-based topology can provide the greatest coverage area with the same number of sensor nodes as used for WSNs in other patterned topologies. Strip-based topology also consumes least energy in the routing protocol of flooding. We show that triangle-based topology can provide the highest reliability among all patterned topologies. We also propose several routing protocols for WSNs in patterned topologies, which are based on different parameters when selecting next-hop neighbor. Our protocols require only local information and work in a simple and effective way to achieve energy efficiency.

Hamming distance and hop count based classification for multicast network topology inference

Topology information of a multicast network benefits significantly to many applications such as resource management, loss and congestion recovery. In this paper we propose a new algorithm, namely binary hamming distance and hop count based classification algorithm (BHC), to infer multicast network topology from end-to-end measurements. The BHC algorithm identifies multicast network topology using hamming distance of the sequences on receipt/loss of probe packets maintained at each pair of nodes and incorporating the hop count available at each node. We analyze the inference accuracy of the algorithm and prove that the algorithm can obtain accurate inference at higher probability than previous algorithms for a finite number of probe packets. We implement the algorithm in a simulated network and validate the algorithms performance in accuracy and efficiency.

Analysis on binary loss tree classification with hop count for multicast topology discovery

The use of multicast inference on end-to-end measurement has recently been proposed as a means of obtaining the underlying multicast topology. We analyze the algorithm of binary loss tree classification with hop count (HBLT). We compare it with the binary loss tree classification algorithm (BLT) and show that the probability of misclassification of HBLT decreases more quickly than that of BLT as the number of probing packets increases. The inference accuracy of HBLT is always 1 (the inferred tree is identical to the physical tree) in the case of correct classification, whereas that of BLT is dependent on the shape of the physical tree and inversely proportional to the number of internal nodes with a single child. Our analytical result shows that HBLT is superior to BLT, not only on time complexity, but also on misclassification probability and inference accuracy.

An improved algorithm for multicast topology discovery from end‐to‐end measurements

We present a new multicast topology inference algorithm called binary loss tree classification with hop count (HBLT). HBLT improves the previous algorithm of binary loss tree classification (BLT) not only in time complexity but also in misclassification probability and inference accuracy. The time complexity of HBLT is O(l2) instead of O(l3) required by BLT in the worst case, and O(l · log l) instead of O(l3) by BLT in the expected case, where l is the number of receivers in the multicast network. The misclassification probability of HBLT decreases more quickly than that of BLT as the number of probe packets increases. For correct classification, the inference accuracy of HBLT is always 1, i.e. the inferred tree is identical to the physical tree, whereas that of BLT is dependent on the shape of the physical tree and inversely proportional to the number of internal nodes with single child. We also show through simulation that HBLT requires fewer probe packets to infer the correct topology and hence has a lower misclassification probability and higher inference accuracy than BLT. Copyright

Multicast-based inference for topology and network-internal loss performance from end-to-end measurements

The use of multicast traffic as measurement probes is effective to infer network-internal characteristics. In this paper, we propose novel approaches to infer multicast network topology and link loss performance from end-to-end measurements. First, we present a new algorithm, binary hamming distance classification algorithm (BHC), that identifies multicast network topology based on the hamming distance of the sequences on receipt/loss of probe packets maintained at each pair of nodes. It is proved by analysis and simulation that BHC can infer the topology at a higher accuracy and efficiency than the previous algorithms with a finite number of probe packets. We also propose a new statistical approach to infer network-internal link loss performance based on the inferred topology. The inferred link loss rate is proved to be consistent with the real loss rate as the number of probe packets tends to infinity. Our new approach makes it possible to infer multicast network topology and loss performance simultaneously. We extend our algorithms for both multicast topology and loss performance inference in binary trees to general trees, and present a new method of loss rate-based scheme for general tree topology inference so that the inferred topology can correctly converge to the true topology which was difficult to achieve previously.

An Improved Algorithm of Multicast Topology Inference from End-to-End Measurements

Multicast topology inference from end-to-end measurements has been widely used recently. Algorithms of inference on loss distribution show good performance in inference accuracy and time complexity. However, to our knowledge, the existing results produce logical topology structures that are only in the complete binary tree form, which differ in most cases significantly from the actual network topology. To solve this problem, we propose an algorithm that makes use of an additional measure of hop count. The improved algorithm of incorporating hop count in binary tree topology inference is helpful to reduce time complexity and improve inference accuracy. Through comparison and analysis, it is obtained that the time complexity of our algorithm in the worst case is O (l 2) that is much better than O (l 3) required by the previous algorithm. The expected time complexity of the algorithm is estimated at O (l·log2 l), while that of the previous algorithm is O (l 3).

Mobile agents based topology discovery algorithms and modelling

Mobile agents have been successfully developed/or collecting and processing network management information in the Internet, telecommunications network and other type of networks. Topological information of a network is important to the network administrator and users. Mobile agent based topology discovery algorithms are proposed for both Internet and multicast networks in this paper. Statistical models are built for analyzing the behavior of the mobile agents performing topology discovery task. The dwell time distribution at a host, life span distribution of a mobile agent, inter-report time distribution of a mobile agent and reports inter-arrival time distribution at the management station are analyzed. These analytical results show great significance to describe the behavior of the mobile agents performing the given task. A clear insight is given into the performance of mobile agents based topology discovery algorithms and modelling the mobile agents with given task.

Discover multicast network internal characteristics based on Hamming distance

One of the important techniques to monitor and control large-scale networks today is to implement only at the end. However end-based control needs to have the knowledge of network internal characteristics. The paper proposes a novel approach to discover network internal characteristics from end-to-end multicast traffic measurements, which requires no support from internal routers. Our approach is based on Hamming distance of sequences on receipt/loss of probe packets maintained at each pair of nodes. As we discuss in this paper, our approach mainly focuses on identification of network internal characteristics of routing topology and loss performance. The simulation shows that the Hamming distance-based approach can discover the routing topology which is more accurate and efficient with a finite number of probe packets than before. The Hamming distance matrix proposed in this paper can also effectively discover the loss performance of the network.