We are interested in the sensor networks for scientific applications to cover and measure statistics on the sea surface. Due to flows and waves, the sensor nodes may gradually lose their positions; leaving the points of interest uncovered. Manual readjustment is costly and cannot be performed in time. We argue that a network of mobile sensor nodes which can perform self-adjustment is the best candidate to maintain the coverage of the surface area. In our application, we face a unique double mobility coverage problem. That is, there is an uncontrollable mobility, U-Mobility, by the flows which breaks the coverage of the sensor network. Moreover, there is also a controllable mobility, C-Mobility, by the mobile nodes which we can utilize to reinstall the coverage. Our objective is to build an energy efficient scheme for the sensor network coverage issue with this double mobility behavior. A key observation of our scheme is that the motion of the flow is not only a curse but should also be considered as a fortune. The sensor nodes can be pushed to some locations by the U-Mobility that potentially help to improve the overall coverage. With that taken into consideration, more efficient movement decision can be made. To this end, we present a dominating set maintenance scheme to maximally exploit the U-Mobility and balance the energy consumption among all the sensor nodes. We prove that the coverage is guaranteed in our scheme. We further propose a fully distributed protocol that addresses a set of practical issues. Through extensive simulation, we demonstrate that the network lifetime can be significantly extended, compared to a straightforward back-to-original reposition scheme.

Existing works in wireless sensor networks exploit many theoretical assumptions of transmission links. However, in practical system, these assumptions are more complicated and even could not hold for the unexpected impact from the extraneous factors. In this article, we conduct a comprehensive measurement study in the real application to observe, verify, discover and model the feature of transmission link with outdoor environment factors in wireless sensor networks. We design a large scale measurement system to continuously monitor the link transmission in a forest. According to more than 10 million records [2], we analyze and obtain several observations such as the RSSI and its correlation model on pair-link, bursty link in bursty mode, RSSI with environment factors, etc. These observations provides a guideline for better design of new upper-layer algorithms and protocols in sensor applications.

A critical issue in wireless sensor networks (WSNs) is represented by the network throughput. To meet the throughput requirement, researchers propose multi-channel design in 802.15.4 networks to better utilize the wireless medium and avoid the co-channel interference. However, traditional orthogonal channel design restricts the number of channels and limits the throughput performance. We argue that the orthogonality is not necessary for multi-channel design in WSNs. In this paper, we investigate the feasibility of non-orthogonal channel design. In our experiment, we observe that with nonorthogonal transmission, the effect of interference comes from co-channel and inter-channel is different. More specifically, the inter-channel interference is tolerable with certain channel center frequency distance (CFD). According to that, we propose a novel scheme DCN (Dynamic CCA-threshold for Non-orthogonal transmission) which adjusts the CCA-threshold to enable the concurrent transmissions on adjacent non-orthogonal channels and thus improve the overall network throughput performance. Through comprehensive experiments on our testbed, we verify that our DCN achieves about 38.4% ~ 55.7% throughput improvement in general network configurations comparing to the default ZigBee design.

We are interested in event collection in a 2D region where sensors are deployed to detect and collect interested events. Using traditional multi-hop routing in wireless sensor networks to report events to a sink node or base station, will result in severe imbalanced energy consumption of static sensors. In addition, full connectivity among all the static sensors may not be possible in some cases since generally the sensors are randomly deployed in the target region. In this paper, we exploit a mobile sensor as the sink node to assist the event collection by controlling the movement of the mobile sink to collect static sensor readings. A key observation of our work is that an event has spatial-temporal correlation. Specifically, the same event can be detected by multiple nearby sensors within a period of time. Thus, it is more energy-efficient if the mobile sink can selectively communicate with only a portion of static sensors, while still collecting all the interested events. In this paper, we discuss the event collection problem by leveraging the mobility of the sink node and the spatial-temporal correlation of the event, in favor of maximizing the network lifetime with a guaranteed event collection rate. We first model the problem as sensor selection problem and show that it could be solved in polynomial time, if global knowledge of events is available and there is no velocity constraints on mobile sink. We also analyze the design of a feasible movement route for mobile sink to minimize the velocity requirements for a practical system. An online scheme is then proposed to relax the assumption about global knowledge of events and we prove that the expected event collection rate can be guaranteed in theory. Through comprehensive simulation on real trace data, we demonstrate that the network lifetime can be significantly extended, comparing to some other schemes.

Interference plays an important role in the communication of wireless sensor networks. In this paper, we for the first time propose the concept of tolerable interference and investigate the tolerability of interference in multi-channel sensor networks with empirical measurements. We find that the transmission in sensor networks can tolerate certain level interference from the adjacent channels, which are not orthogonal to the current transmission channel. That is to say, the center frequency distance (CFD) for concurrent transmissions could be smaller than the default setting in sensor networks (i.e., 5 MHz in ZigBee). Concretely, with comprehensive experiments, we make two interesting observations: first, the throughput could be improved by relaxing CCA-threshold with the tolerable neighboring-channel interference; second, most of the corrupted packets caused by tolerable interference only have a small portion of error bits which could be corrected by introducing packet recovery scheme. Based on the above observations, we further have some discussions for better design of WSNs protocols, e.g., the system could enable some adaptive CCA-threshold scheme and packet recovery protocol to improve the overall throughput significantly.

We are interested in the sensor networks for scientific applications to cover and measure statistics on the sea surface. Due to flows and waves, the sensor nodes may gradually lose their positions; leaving the points of interest uncovered. Manual readjustment is costly and cannot be performed in time. We argue that a network of mobile sensor nodes which can perform self-adjustment is the best candidate to maintain the coverage of the surface area. In our application, we face a unique double mobility coverage problem. That is, there is an uncontrollable mobility, U-Mobility, by the flows which breaks the coverage of the sensor network. Moreover, there is also a controllable mobility, C-Mobility, by the mobile nodes which we can utilize to reinstall the coverage. Our objective is to build an energy efficient scheme for the sensor network coverage issue with this double mobility behavior. A key observation of our scheme is that the motion of the flow is not only a curse but should also be considered as a fortune. The sensor nodes can be pushed by free to some locations that potentially help to improve the overall coverage. With that taken into consideration, more efficient movement decision can be made. To this end, we present a dominating set maintenance scheme to maximally exploit the U-Mobility and balance the energy consumption among all the sensor nodes. We prove that the coverage is guaranteed in our scheme. We further propose a fully distributed protocol that addresses a set of practical issues. Through extensive simulation, we demonstrate that the network lifetime can be significantly extended, compared to a straight forward back-to-original reposition scheme.

There is a growing interest in using wireless sensor networks for security monitoring in the underground coal mines. In such applications, the sensor nodes are deployed to detect interested events, e.g., the density of certain gas at some locations is higher than the predefined threshold. These events are then reported to the base station outside. Using conventional multi-hop routing for data reporting, however, will result in imbalance of energy consumption among the sensors. Even worse, the unfriendly communication condition underground makes the multi-hop data transmission challenging, if not impossible. In this paper, we thus propose to leverage tramcars as mobile sinks to assist event collection and delivery. We further observe that the sensor readings have spatial and temporal correlation. More precisely, the same event may be observed by multiple neighboring sensor nodes and/or at different time. Obviously, it can be more energy-efficient if the data are selectively reported. As such, we first provide a general, yet realistic definition on the events. We then transform the event collection problem into a set coverage problem; and our objective is to maximize the system lifetime with the coverage rate of events guaranteed. We show that the problem is NP-hard even when all the events are known in advance. We present an online scheme which leverages the spatial-temporal correlation of the events to balance the communication energy of the static sensor nodes. We prove that the expected event coverage rate can be guaranteed in theory. Through extensive simulation, we demonstrate that our scheme can significantly extend system lifetime, as compared to a stochastic collection scheme.

Mobile sensor has enabled many new applications in wireless sensor networks where coverage is one of the most important problems to support those applications. Previous research works of coverage issue mainly focus on the static sensor network, which can not handle the significant challenge brought by the node mobility. In this paper, we consider the coverage problem in mobile sensor networks under a very general framework, in which all the sensors are moving all the time with any specified movement model. We propose a scheme that can provide a Real-time Coverage-Map According to Probability (RC- MAP) of the mobile sensor networks. By introducing probability into coverage, RC-MAP provides three different types of coverage map in real-time, including probability to be covered, expected number of sensors to cover, and expected waiting time before being covered. Extensive simulation results demonstrate the high accuracy and effectiveness of the proposed scheme.

Many sensor network applications exploit the mobility of sensor nodes and the location-awareness plays an important role in these applications. However, it is too expensive to equip special hardware in each sensor node for localization, thus a distributed range-free localization scheme in mobile sensor network is highly desired. Several such localization techniques have been developed but few of them consider leveraging the feature of node mobility. Leveraging node mobility, in this paper, both static constrain set by transmission range and velocity constrain set by node movement are introduced. Based on these two types of constrain, we propose a range-free mobile inequality localization (MIL) algorithm, which uses ring inequalities to restrict and estimate the possible location in numerical method. Our approach is distributed, quickly re-localizable and works well when all sensors are moving uncontrollably. We analyze the theoretical bound for the accuracy of the position estimation and the comprehensive simulations demonstrate that our algorithm performs much better than the existing works.