Xufei Mao

A Delay-Efficient Algorithm for Data Aggregation in Multihop Wireless Sensor Networks

Data aggregation is a key functionality in wireless sensor networks (WSNs). This paper focuses on data aggregation scheduling problem to minimize the delay (or latency). We propose an efficient distributed algorithm that produces a collision-free schedule for data aggregation in WSNs. We theoretically prove that the delay of the aggregation schedule generated by our algorithm is at most 16R + Δ - 14 time slots. Here, R is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our algorithm significantly improves the previously known best data aggregation algorithm with an upper bound of delay of 24D + 6Δ + 16 time slots, where D is the network diameter (note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation algorithm. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice. We prove that the overall lower bound of delay for data aggregation under any interference model is max{log n,R}, where n is the network size. We provide an example to show that the lower bound is (approximately) tight under the protocol interference model when rI = r, where rI is the interference range and r is the transmission range. We also derive the lower bound of delay under the protocol interference model when r <; rI <; 3r and rI ≥ 3r.

CitySee: Urban CO 2 monitoring with sensors

Motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2 management in cities, we present CitySee, a real-time CO2-monitoring system using sensor networks for an urban area (around 100 square kilometers). In order to conduct environment monitoring in a real-time and long-term manner, CitySee has to address the following challenges, including sensor deployment, data collection, data processing, and network management. In this discussion, we mainly focus on the sensor deployment problem so that necessary requirements like connectivity, coverage, data representability are satisfied. We also briefly go through the solutions for the remaining challenges. In CitySee, the sensor deployment problem can be abstracted as a relay node placement problem under hole-constraint. By carefully taking all constraints and real deployment situations into account, we propose efficient and effective approaches and prove that our scheme uses additional relay nodes at most twice of the minimum. We evaluate the performance of our approach through extensive simulations resembling realistic deployment. The results show that our approach outperforms previous strategies. We successfully apply this design into CitySee, a large-scale wireless sensor network consisting of 1096 relay nodes and 100 sensor nodes in Wuxi City, China.

Energy-Efficient Opportunistic Routing in Wireless Sensor Networks

Opportunistic routing, has been shown to improve the network throughput, by allowing nodes that overhear the transmission and closer to the destination to participate in forwarding packets, i.e., in forwarder list. The nodes in forwarder list are prioritized and the lower priority forwarder will discard the packet if the packet has been forwarded by a higher priority forwarder. One challenging problem is to select and prioritize forwarder list such that a certain network performance is optimized. In this paper, we focus on selecting and prioritizing forwarder list to minimize energy consumption by all nodes. We study both cases where the transmission power of each node is fixed or dynamically adjustable. We present an energy-efficient opportunistic routing strategy, denoted as EEOR. Our extensive simulations in TOSSIM show that our protocol EEOR performs better than the well-known ExOR protocol (when adapted in sensor networks) in terms of the energy consumption, the packet loss ratio, and the average delivery delay.

Incentives for Mobile Crowd Sensing: A Survey

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in peoples lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, its essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

Multicast capacity for hybrid wireless networks

We study the multicast capacity of a random wireless network consisting of ordinary wireless nodes and base stations, known as a hybrid network. Assume that <i>n</i> ordinary wireless nodes are randomly deployed in a square region and all nodes have the uniform transmission range <i>r</i> and uniform interference range <i>R</i>><i>r</i>. We further assume that each ordinary wireless node can transmit/receive at <i>W</i> bits/second over a common wireless channel. In addition, there are <i>m</i> additional base stations (neither source nodes nor receiver nodes) placed regularly in this square region and connected by a high-bandwidth wired network. For each ordinary node <i>v</i>, we randomly pick <i>k</i>-1 nodes from the other <i>n</i>-1 ordinary nodes as the receivers of the multicast session at node <i>v</i>. The aggregated multicast capacity is defined as the total data rate of all multicast sessions in this hybrid network. We derive asymptotic upper bounds and lower bounds on multicast capacity of the hybrid wireless networks. The total multicast capacity is <i>O</i>(√<i>n</i> /√log <i>n</i> · √<i>m</i>/<i>k</i> · <i>W</i>) when <i>k</i> = <i>O</i>(<i>n</i> / log <i>n</i>), <i>k</i> = <i>O</i>(<i>m</i>), <i>k</i> / √<i>m</i> → ∞ and <i>m</i> = <i>o</i>(<i>a</i>² / <i>r</i>²); the total multicast capacity is Θ(√<i>n</i> / √log <i>n</i> · <i>W</i> / √<i>k</i>) when <i>k</i> = <i>O</i>(<i>n</i>/log <i>n</i>), <i>k</i> = Ω(<i>m</i>) and <i>m</i>/<i>k</i> → <i>O</i>. When <i>k</i> = <i>O</i>(<i>n</i>/log <i>n</i>) and <i>k</i> = <i>O</i>(√<i>m</i>), the upper bound for minimum multicast capacity is at most <i>O</i>(<i>r</i>·<i>n</i>/<i>a</i> · √<i>m</i> · <i>W</i>/<i>k</i>) and is at least Ω(<i>W</i>) respectively. When <i>k</i> =Ω^α(<i>n</i>/log <i>n</i>), the multicast capacity is Θ(<i>W</i>).

Exploiting constructive interference for scalable flooding in wireless networks

Constructive interference-based flooding (CIBF) is a latency-optimal flooding protocol, which can realize millisecond network flooding latency and submicrosecond time synchronization accuracy, require no network state information, and be adapted to topology changes. However, constructive interference (CI) has a precondition to function, i.e., the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold (e.g., 0.5 μs, for the IEEE 802.15.4 radio). In this paper, we derive the closed-form packet reception ratio (PRR) formula for CIBF and theoretically disclose that CIBF suffers the scalability problem. The packet reception performance of intermediate nodes degrades significantly as the density or the size of the network increases. We analytically show that CIBF has a PRR lower bound (94.5%) in the grid topology. Based on this observation, we propose the spine constructive interference-based flooding (SCIF) protocol for an arbitrary uniformly distributed topology. Extensive simulations show that SCIF floods the entire network much more reliably than the state-of- the-art Glossy protocol does in high-density or large-scale networks. We further explain the root cause of CI with waveform analysis, which is mainly examined in simulations and experiments.

An improved approximation algorithm for data aggregation in multi-hop wireless sensor networks

Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency. We propose an efficient distributed method that produces a collision-free schedule for data aggregation in WSNs. We prove that the latency of the aggregation schedule generated by our algorithm is at most 16<i>R</i>+Δ--14 time-slots. Here <i>R</i> is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our method significantly improves the previously known best data aggregation algorithm [3], that has a latency bound of 24<i>D</i>+6Δ+16 time-slots, where <i>D</i> is the network diameter (Note that <i>D</i> can be as large as 2<i>R</i>). We conduct extensive simulations to study the practical performances of our proposed data aggregation method. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice. We prove that the overall lower-bound of latency of data aggregation under any interference model is max{log <i>n</i>, <i>R</i>} where <i>n</i> is the network size. We provide an example to show that the lower-bound is (approximately) tight under protocol interference model when <i>r_I</i>=<i>r</i>, where <i>r_I</i> is the interference range and <i>r</i> is the transmission range. We also derive the lower-bound of latency under protocol interference model when <i>r</i> < <i>r_I</i> < 3<i>r</i> and <i>r_I</i> ≥ 3<i>r</i>.

CitySee: not only a wireless sensor network

CitySee, an environment monitoring system with 1196 sensor nodes and 4 mesh nodes in an urban area, is mainly motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2 management in cities. Being one of the largest working wireless sensor networks, CitySee faces several challenges such as hardware design, software development, platforms, network protocols, and, most important, satisfactory services to users. We share some early lessons learned from this project, illustrate the potential benefits and risks of current solutions, and discuss the possible extensions of CitySee applications.

Relationship classification in large scale online social networks and its impact on information propagation

In this paper, we study two tightly coupled topics in online social networks (OSN): relationship classification and information propagation. The links in a social network often reflect social relationships among users. In this work, we first investigate identifying the relationships among social network users based on certain social network property and limited pre-known information. Social networks have been widely used for online marketing. A critical step is the propagation maximization by choosing a small set of seeds for marketing. Based on the social relationships learned in the first step, we show how to exploit these relationships to maximize the marketing efficacy. We evaluate our approach on large scale real-world data from Renren network, showing that the performances of our relationship classification and propagation maximization algorithm are pretty good in practice.

SmartLoc: push the limit of the inertial sensor based metropolitan localization using smartphone

We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.