PROJECTS

Along with the increasing awareness of environmental protection and growing demand for green and pollution-free agricultural products, it has a great need to explore new ways to apply greener pest control methods in agricultural production. Researching on Solar Insecticidal Lamps (SILs) has continuously received incremental attentions from both the academia and industry, which brings a new mode for the preventing and controlling of agricultural migratory pests with phototaxis feature, and now is becoming to a hot research topic. Towards the fast development of "precision agriculture" and "smart agriculture" as well as the increasing demands for agricultural informatization, Wireless Sensor Networks (WSNs) have been widely used for agricultural information collection and intelligent control of agricultural equipment. WSNs are suitable for large-scale deployment and regional monitoring, which can be easily combined with SIL nodes. Based on the combination, a new type of agricultural Internet of things - Solar Insecticidal Lamps Internet of Things (SIL-IoTs) was proposed and the technology of WSNs for the prevention and control of phototactic migratory pests in agricultural applications were surveyed.

The rapid development of industrialization and urbanization process leads to high demand of energy.However, energy production by burning fossil fuels has serious impact on climate change. Solar photovoltaic power, which harvests energy from sunlight and generates electricity power, is the fastest-growing renewable energy technology. It is considered as a key approach for low carbon economic development. Although solar photovoltaic power has many advantages, such as cleanness, silentness and availability at almost anywhere, one limitation is that it needs to occupy a lot of space. To solve this problem, photovoltaic agriculture is intro duced where electricity production of solar power system and agricultural production activities can work simultane ously in the same area.A new paradigm of smart farming is proposed for the fifirst time. It is named as Photovoltaic Agricultural Internet of Things (PAIoT).We envision the scenarios of PAIoT in terms of energy supply, communication method and computing models. In addition, PAIoT’s advantages are presented and the open research issues are discussed for the PAIoT.

A new paradigm of mobile crowd sensing towards smart agriculture is termed Agricultural Mobile Crowd Sensing (AMCS). The AMCS is intended to improve the existing agricultural data collection system and facilitate the realization of smart agriculture. The comparative analysis between Space-Air-Ground Integrated Network and the AMCS is pre sented, regarding the cost, range of data collection, scalability, data granularity and flexibility.

Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which makes these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. We classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories.

With the evolution of mobile phone sensing and wireless networking technologies, mobile crowd sensing (MCS) has become a promising paradigm for large-scale sensing applications. MCS is a type of multi-participant sensing that has been widely used by many sensing applications because of its inherent capabilities, e.g., high mobility, scalability, and cost effectiveness. We reviews the existing works of MCS and clarifies the operability of MCS in sensing applications. With wide use and operability of MCS, MCS’s industrial applications are investigated based on the clarifications of: 1) the evolution of industrial sensing and 2) the benefits MCS can provide to current industrial sensing. As a feasible industrial sensing paradigm, MCS opens up a new field that provides a flexible, scalable, and cost-effective solution for addressing sensing problems in industrial spaces.

With the rapid development of modern society, the negative effects of noise pollution on public health, animal and plants growth are exacerbated, that seriously hinder the sustainable development of ecological environment. To obtain detailed noise distribution information, many countries have launched the noise mapping strategic action. The academics have made great contributions for real-time noise data acquisition in terms of low cost sensor node design, system architecture and binary noise determination. However, there are still many open research issues that need to be addressed, such as high sensing energy consumption, resource constraint on sensor nodes and lossy wireless networks. This project focuses on the challenges of environmental sampling, processing and wireless transmission during noise data acquisition with low cost wireless sensor networks to achieve adaptive noise sampling, noise sources recognition on resource-constrained embedded platforms, and reliability improvement of wireless sound data delivery.

Industrial WSNs are evolving to become the key interconnection between management and factory products in large-scale petrochemical plants. Apart from improved manufacturing, asset tracking, and robotic applications, toxic gas detection is one of the major issues in petrochemical plants, since toxic gas leakage can severely threaten the safety of first-line working staff. Continuous object detection, one of the major applications in WSNs, has become an important research topic in large-scale industry. We overview continuous object detection techniques that have emerged in recent years. Most of the research focuses on the estimation of the toxic gas boundary. However, an accurate boundary is less likely to be detected due to the nature (e.g., invisibility, fast movement, and changing shape) of toxic gas. Thus, it is essential to ensure the boundary area rather than only the boundary of the toxic gas. We propose a novel boundary area detection technique with planarization algorithms like RNG and GG. Exhaustive simulation studies enable us to find an optimal trade-off point between the cost of a number of deployed sensor nodes and the accuracy of the estimated toxic gas boundary area size.

We propose a cross-layer optimization scheme named Adjusting the Transmission Radius (ATR), which is based on the Energy Consumed uniformly Connected K-Neighborhood (EC-CKN) sleep scheduling algorithm in wireless sensor networks (WSNs). In particular, we discovered two important problems, namely, the death acceleration problem and the network isolation problem, in EC-CKN-based WSNs. Furthermore, we solve these two problems in ATR, which creates sleeping opportunities for the nodes that cannot get a chance to sleep in the EC-CKN algorithm. Simulation and experimental results show that the network lifetime of ATR-Connected-K-Neighborhoodbased WSNs increases by 19%, on average, and the maximum increment is 41%.

Location information of nodes is the basis for many applications in wireless sensor networks (WSNs). However, most previous localization methods make the unrealistic assumptions: (i) all nodes in WSN are always awake and (ii) the radio range of nodes is an ideal circle. This overlooks the common scenario that sensor nodes are duty-cycled in order to save energy and the radio range of nodes is irregular . In this paper we revisit the Distance-V ector-based (DV-based) positioning algorithms, particularly, Hop-Count-Ratio based Localization (HCRL) algorithm and investigate the following problems: (i) how is the relationship between the number of sleeping neighbor sensor nodes and the localization accuracy and (ii) how is the relationship between the degree of irregularity (DOI, which is a parameter of radio range irregularity) and the localization accuracy.

Industrial Wireless Sensor Networks (IWSNs) are required to provide highly reliable and real-time transmission. Moreover, for Connected K-Neighborhood (CKN) sleep scheduling based duty-cycled IWSNs in which the network lifetime of IWSNs can be prolonged, the Two-Phase Geographic Greedy Forwarding (TPGF) geographic routing algorithm has attracted attention due to its unique transmission features: multi path, shortest path, and hole bypassing. However, the performance of TPGF in CKN based duty-cycled IWSNs with radio irregularity is not well investigated in the literature. We valuate the impact of radio irregularity on CKN based duty-cycled IWSNs. Further, we investigate the routing performance of TPGF in CKN based duty-cycled IWSNs with radio irregularity, in terms of the number of explored routing paths as well as the lengths of the average and shortest routing paths. Particularly, we establish the upper bound on the number of explored routing paths. The upper bound is slightly relaxed with radio irregularity compared to without radio irregularity, however, it is bounded by the number of average 1-hop neighbors in always-on IWSNs. With extensive simulations, we observe that the cross-layer optimized version of TPGF (i.e., TPFGPlus) finds reliable transmission paths with low end-to-end delay, even in CKN based duty-cycled IWSNs with radio irregularity.

Network simulators are necessary for testing algorithms of large scale wireless sensor net works (WSNs), but lack the accuracy of real-world deployments. Deploying real WSN test bed provides a more realistic test environment, and allows users to get more accurate test results. However, deploying real testbed is highly constrained by the available budget when the test needs a large scale WSN environment. By leveraging the advantages of both network simulator and real testbed, an approach that integrates simulation environment and testbed can effectively solve both scalability and accuracy issues. Hence, the simula tion of virtual WSN, the visualization of real testbed, and the interaction between simu lated WSN and testbed emerge as three key challenges. In this paper, we present an integrated framework called NetTopo for providing both simulation and visualization func tions to assist the investigation of algorithms in WSNs. NetTopo provides a common virtual WSN for the purpose of interaction between sensor devices and simulated virtual nodes. Two case studies are described to prove the effectiveness of NetTopo.