Ai Chen

Designing localized algorithms for barrier coverage

Global barrier coverage that requires much fewer sensors than full coverage, is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. However, it has been proved that given a sensor deployment, sensors can not locally determine whether the deployment provides global barrier coverage, making it impossible to develop localized algorithms, thus limiting its use in practice. In this paper, we introduce the concept of local barrier coverage to address this limitation. Motivated by the observation that movements are likely to follow a shorter path in crossing a belt region, local barrier coverage guarantees the detection of all movements whose trajectory is confined to a slice of the belt region of deployment. We prove that it is possible for individual sensors to locally determine the existence of local barrier coverage, even when the region of deployment is arbitrarily curved. Although local barrier coverage does not always guarantee global barrier coverage, we show that for thin belt regions, local barrier coverage almost always provides global barrier coverage. To demonstrate that local barrier coverage can be used to design localized algorithms, we develop a novel sleep-wakeup algorithm for maximizing the network lifetime, called Localized Barrier Coverage Protocol (LBCP). We show that LBCP provides close to optimalenhancement in network lifetime, while providing global barrier coverage most of the time. It outperforms an existing algorithm called Randomized Independent Sleeping (RIS) by up to 6 times.

Local Barrier Coverage in Wireless Sensor Networks

Global barrier coverage, which requires much fewer sensors than full coverage, is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. However, it has been proved that given a sensor deployment, sensors can not locally determine whether the deployment provides global barrier coverage, making it impossible to develop localized algorithms, thus limiting its use in practice. In this paper, we introduce the concept of local barrier coverage to address this limitation. Motivated by the observation that movements are likely to follow a shorter path in crossing a belt region, local barrier coverage guarantees the detection of all movements whose trajectory is confined to a slice of the belt region of deployment. We prove that it is possible for individual sensors to locally determine the existence of local barrier coverage, even when the region of deployment is arbitrarily curved. Although local barrier coverage does not deterministically guarantee global barrier coverage, we show that for thin belt regions, local barrier coverage almost always provides global barrier coverage. To demonstrate that local barrier coverage can be used to design localized algorithms, we develop a novel sleep-wakeup algorithm for maximizing the network lifetime, called localized barrier coverage protocol (LBCP). We prove that LBCP guarantees local barrier coverage and show that LBCP provides close to optimal enhancement in the network lifetime, while providing global barrier coverage most of the time. They outperform an existing algorithm called randomized independent sleeping (RIS) by up to six times.

Measuring and guaranteeing quality of barrier-coverage in wireless sensor networks

Sensors may fail due to various reasons such as heat, malicious activity, environmental hazards, extended use, and lack of power. As more and more sensors fail, certain desired properties such as barrier coverage will diminish and eventually fall below a desired level. In such a case, the network will have to be repaired. It is therefore desirable to have mechanisms to monitor network properties. In this paper, we are interested in measuring the quality of barrier coverage. In the literature, researchers only consider whether or not a sensor network provides barrier coverage. This is equivalent to measuring its quality as either 0 or 1. We believe quality of barrier coverage is not binary and propose a metric for measuring it. If the measured quality is short of a desired value, we further identify all local regions that need to be repaired. The identified regions are minimum in the sense that if one of them is not repaired then the resulting network will still be short of quality. We also discuss how to actually repair a region.

HIMAC: High Throughput MAC Layer Multicasting in Wireless Networks

Efficient, scalable and robust multicasting support from the MAC layer is needed for meeting the demands of multicast based applications over WiFi and mesh networks. However, the IEEE 802.11 protocol has no specific mechanism for multicasting. It implements multicasting using broadcasting at the base transmission rate. We identify two fundamental reasons for performance limitations of this approach: (a) Channel-state indifference: irrespective of the current quality of the channel to the receivers, the transmission always uses the base transmission rate; (b) Demand ignorance: packets are transmitted by a node even if children in the multicast tree have received those packets by virtue of overhearing. We propose a solution for MAC layer multicasting called HIMAC that uses the following two mechanisms: unary channel feedback (UCF) and unary negative feedback (UNF) to respectively address the shortcomings of 802.11. Our study is supported by measurements in a testbed, and simulations. We observe that the end-to-end throughput of multicast sessions using MAODV can be increased by up to 74% while reducing the end-to-end latency by up to a factor of 56

One-way barrier coverage with wireless sensors

One of the extensively studied coverage models in wireless sensor networks is barrier coverage, which guarantees that any movement crossing the given belt must be detected, while the direction of the movement is not required. For some intrusion detection applications, it may be the case that only one direction of crossing (the belt) is illegal such as border guarding. Therefore, we introduce a new coverage model called one-way barrier coverage, which requires that the network reports illegal intruders while ignores legal intruders. We propose an appropriate definition for one-way barrier coverage. We deeply investigate one-way barrier coverage with binary sensors. Our research illustrates that it is not straightforward to provide oneway barrier coverage even though there is only one intruder. When there are multiple intruders, we introduce the concept of neighboring barriers and design different protocols to provide one-way barrier coverage for different sensor models based on neighboring barriers.

Optimizing Multicast Performance in Large-Scale WLANs

Support for efficient multicasting in WLANs can enable new services such as streaming TV channels, radio channels, and visitors information. With increasing deployments of large-scale WLANs, such services can have a significant impact. However, for a solution to be viable, the mutlicast services must minimally impact the existing unicast services which are currently the core services offered by most WLANs. This paper focuses on three objective functions motivated by different revenue functions and network scenarios: maximizing the number of users (MNU), balancing the load among APs (BLA), and minimizing the load of APs (MLA). We show that these problems are NP-hard and present centralized approximation algorithms and distributed approaches to solve them. Using simulations we evaluate the performance of these algorithms. We observe that the number of users can be increased by up to 36.9%, and the maximum AP load and the total load can be reduced by up to 52.9% and 31.1%, respectively.

Reliable broadcasting in wormhole-routed hypercube-connected networks using local safety information

This paper presents a method to cope with reliable broadcasting in faulty hypercubes using local safety information. A new definition, broadcast subcube, is introduced, with which various techniques are proposed to improve performance of the broadcast algorithm. Local safety information is well used in the fault-tolerant broadcast algorithm by considering only safety of the broadcast subcube. An unsafe hypercube can be split into a set of maximal safe subcubes. If these maximal safe subcubes meet certain requirements (listed in this paper), then broadcasting can still be done successfully and, in some cases, optimal broadcast is still possible. The sufficient condition for optimal broadcast of a message is presented in an unsafe hypercube. Extensive simulation results show that the proposed method outperforms previous methods, in all cases.

Using compressive sensing to reduce fingerprint collection for indoor localization

Many WLAN-based indoor localization techniques estimate a targets location by comparing received signal strength indicator (RSSI) with stored fingerprints. However, the collection of fingerprints is notoriously tedious and time-consuming. It is challenging to reduce the fingerprint collection and recover absent data without introducing errors. In this article, a new approach based on compressive sensing is presented for recovering absent fingerprints. The hidden structure and redundancy characteristics of fingerprints are revealed in the Merging Matrix. The spatial and temporal relativity of fingerprints leads the rank of the Merging Matrix to be small. But the multipath effect in indoor environments conceals the nature of the matrix. The algorithm Sparsity Rank Singular Value Decomposition (SRSVD) can clear away the interference. Experiment results show that using 10% of the data can recover all of the fingerprint information with error rate less than 16%. The localization accuracy with the recovered fingerprints is similar to the one with the original complete fingerprints.

Measuring and guaranteeing quality of barrier coverage for general belts with wireless sensors

Sensors may fail due to various reasons such as heat, malicious activity, environmental hazards, extended use, and lack of power. As more and more sensors fail, certain desired properties such as barrier coverage will diminish and eventually fall below a desired level. In such a case, the network will have to be repaired. It is therefore desirable to have mechanisms to monitor network properties. In this article, we are interested in measuring the quality of barrier coverage, which is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. In the literature, researchers only consider whether or not a sensor network provides barrier coverage. This is equivalent to measuring its quality as either 0 or 1. We believe quality of barrier coverage is not binary and propose a metric for measuring it. If the measured quality is short of a desired value, we further identify all local regions that need to be repaired. The identified regions are minimal in the sense that if one of them is not repaired then the resulting network will still be short of quality. We also discuss how to actually repair a region.

Reducing fingerprint collection for indoor localization

A typical WiFi-based indoor localization technique estimates a devices location by comparing received signal strength indicator (RSSI) against stored fingerprints and finding the closest matches. However, the collection of fingerprints is notoriously laborious and costly. It is challenging to reduce fingerprint collection and recover missing data without introducing significant errors. In this article, a novel approach based on compressive sensing is presented for recovering absent fingerprints. The hidden structure and redundancy characteristics of fingerprints are revealed in a merging matrix. The spatial and temporal correlations of fingerprints result in a small rank of the merging matrix. The Sparsity Rank Singular Value Decomposition (SRSVD) method is used to effectively reduce the interference caused by the multipath effect of the WiFi signal. We further propose to combine SRSVD with the K-Nearest Neighbor (KNN) algorithm to deal with missing columns or rows in the matrix. Experimental results show that with only half of the fingerprints, our approach can recover all the fingerprint information with error rate below 6.6%. Even with only 5% of the data, the approach can recover the information with error rate below 14%, without loss of localization accuracy.