Urban forests can maintain urban ecological balance and improve environmental quality, but it is difficult to identify such forests accurately due to its complex and fragmented features. This study aims to develop a deep-learning network to extract the urban forest spatial distribution from high-spatial resolution image, like from Chinese Gaofen-2 (GF-2) image. Based on the GF-2 surface reflectance image and urban forest samples known in prior, this study firstly create a U-Net network to train and generate a predictive model to identify the urban forest, and then used the trained model to get the spatial distribution of urban forest in the Beibei district. Results showed that the U-Net Network can predict urban forests distribution accurately and rapidly.

Taking the Dexing Copper Mine in Jiangxi Province, China as an study area, we used the long-term sequence summer Landsat images in 2002-2019 to investigate the variation of vegetation growth status and their ecological restoration effects. According to the specific situation of the study area, the Green-Red Normalized Difference Vegetation Index (GRNDVI) calculated from remote sensing data was used to analyse the growth of mine vegetation and the dynamic change in the whole mining area. Moreover, the annual growth changes were compared with normal vegetation growth. The CV method, Hurst method, and Sen+Mann-Kendall method were combined used to evaluate the intensity of vegetation growth, change patterns and change sustainability analysis to obtain the overall growth and change of vegetation and then predict the vegetation growth trend in the study area. The results show that this method can assess accurately the vegetation growth trend in the study area.

Recently, the concept of fog-cloud storage is attracting lots of attentions to overcome the limit of the central cloud storage. A storage audit scheme aims to ensure user that his/her data on the storage is sound. So far, various audit schemes have been introduced for cloud storages. However, compared to a central cloud storage, a distributed fog-cloud storage consists of multiple local fog storages in addition to a global cloud storage and therefore it is not straightforward to directly apply an existing audit scheme for a cloud storage to a fog-cloud storage. To address this issue, this paper introduces a new fog-cloud storage architecture which can achieve much higher throughput compared to the traditional central cloud storage architecture by reducing the traffics at the routers nearby the cloud storage. The proposed architecture provides transparency such that an end user device does not know the existence of fog storages, and only needs to upload its request toward the central cloud. This means that there is no need to make a modification on the existing end user devices. Our system provides a stronger audit scheme which is naturally coupled with the initial data upload process and does not suffer from the replay attack using old proof of data soundness.

Over years, many efforts are made for the problem of constructing quality fault-tolerant virtual backbones in wireless network. In case that a wireless network consists of physically equivalent nodes, e.g., with the same communication range, unit disk graph (UDG) is widely used to abstract the wireless network and the problem is formulated as the minimum k-connected m-dominating set problem on the UDG. So far, most results are focused on designing a constant factor approximation algorithm for this NP-hard problem under two positive integers k and m satisfying m ≥ k ≥ 1 and k ≤ 3. This paper introduces an approximation algorithm for the problem with m ≥ k ≥ 1. This algorithm is simple to implement; it connects the components by adding a bounded number of paths, which first computes a 1-connected m-dominating set D and repeats the following steps: (a) search the separators arbitrarily in (i - 1, m)-CDS with i = 2, 3, ⋯ , k, (b) add a bounded number of paths connecting the components separated by separators in (i-1, m)-CDS to improve the connectivity of (i-1, m)-CDS, until it becomes k-connected, and (c) remove redundant paths if there exist at every iteration. We provide a rigorous theoretical analysis to prove that the proposed algorithm is correct and its approximation ratio is a constant, for any fixed k.

Recently, the concept of barrier-coverage of wireless sensor network has been introduced for various civilian and military defense applications. This paper studies the problem of how to organize hybrid sensor network, which consists of a number of energy-scarce ground sensors with homogenous initial battery level and energy-plentiful mobile sensors, to maximum the lifetime of barrier-coverage. Two key observations are (a) as the lifetime of each mobile sensor is much longer than that of the static ground sensors, each mobile sensor is capable of contributing multiple sensor barrier formations, and (b) no mobile sensor node can join two hybrid barriers which will be successively used to continuously protect the area of interest due to the moving delay. Based on these, we introduce a new maximum lifetime barrier-coverage problem in hybrid sensor network. We first propose a simple heuristic algorithm by combining existing ideas along with our own. Then, we design another efficient algorithm for the problem and prove that the lifetime of hybrid barrier constructed by this algorithm is at least three times greater than the existing one on average. Our simulation result shows that the second algorithm outperforms the first algorithm at least 33 percent and up to 100 percent.

Following the recent advances in drone technologies, various algorithmic optimization problems related to the effective operation of drones are drawing lots of attentions. This paper considers two interesting multiple-drone-assisted search-and-reconnaissance scenarios, in each of which, the trajectory optimization of multiple drones is of great significance to minimize the latency in the system. In the first scenario, multiple drones, whose moments of mobilization are not necessarily the same, are trying to urgently collect intelligence from a given point of interest, and we would like to minimize the task completion time, i.e., the time period between the moment that the first drone commences its operation to the moment that the intelligence from all of the points are collected, by optimizing their trajectories. In the second scenario, multiple drones with different speeds, are hovering around the same routes to regularly collect intelligence from highly geographically-diversified points of interest over an extended time period, and we would like to minimize the worst-case data refreshment rate, the largest time gap between two consecutive observations over the same point of interest. In this paper, we formally define each problem, prove its NP-hardness, and propose an approximation algorithm for it. We also conduct a simulation to study the performance of our result.

This paper proposes a new polynomial time constant factor approximation algorithm for a more-a-decade-long open NP-hard problem, the minimum four-connected m-dominating set problem in unit disk graph (UDG) with any positive integer m ≥ 1 for the first time in the literature. We observe that it is difficult to modify the existing constant factor approximation algorithm for the minimum three-connected m-dominating set problem to solve the minimum four-connected m-dominating set problem in UDG due to the structural limitation of Tutte decomposition, which is the main graph theory tool used by Wang et al. to design their algorithm. To resolve this issue, we first reinvent a new constant factor approximation algorithm for the minimum three-connected m-dominating set problem in UDG and later use this algorithm to design a new constant factor approximation algorithm for the minimum four-connected m-dominating set problem in UDG.

Over years, many efforts are made for the problem of constructing quality fault-tolerant virtual backbones in wireless network. In case that a wireless network consists of physically equivalent nodes, e.g. with the same communication range, unit disk graph (UDG) is widely used to abstract the wireless network and the problem is formulated as the minimum k-connected mdominating set problem on the UDG. So far, most results are focused on designing a constant factor approximation algorithm for this NP-hard problem under two positive integers k and m satisfying m ≥ k ≥ 1 and k ≤ 3. Very recently, Shi et. al. and Fukunaga separately introduced constant factor approximation algorithms for the problem with m ≥ k ≥ 1. However, we found the structures of the algorithms are extremely complicated, and thus it would be difficult to implement and use them in practice. Motivated by such observation, this paper introduces a novel approximation algorithm for the problem with m ≥ k ≥ 1. This algorithm is based on our new technique which first computes a 1-connected m-dominating set D and repeatedly (a) decomposes D into an i-connected block tree, with i = 2, 3, ··· , k, and (b) use this graph structure to improve the connectivity of D, until D becomes k-connected. We provide a rigorous theoretical analysis to prove that the proposed algorithm is correct and its approximation ratio is a constant. We compare the structure of our algorithm against the existing ones and show our algorithm is much simpler to understand and implement.

Over years, virtual backbone has attracted lots of attention as a promising approach to deal with the broadcasting storm problem in wireless networks. Frequently, the problem of a quality virtual backbone is formulated as a variation of the minimum connected dominating set problem. However, a virtual backbone computed in this way is not resilient against topology change since the induced graph by the connected dominating set is one-vertex-connected. As a result, the minimum k-connected m-dominating set problem is introduced to construct a fault-tolerant virtual backbone. Currently, the best known approximation algorithm for the problem in unit disk graph by Wang assumes k ≤ 3 and m ≥ 1, and its performance ratio is 280 when k = m = 3. In this paper, we use a classical result from graph theory, Tutte decomposition, to design a new approximation algorithm for the problem in unit disk graph for k ≤ 3 and m ≥ 1. In particular, the algorithm features with (a) a drastically simple structure and (b) a much smaller performance ratio, which is nearly 62 when k = m = 3. We also conduct simulation to evaluate the performance of our algorithm.

Over years, virtual backbone has attracted lots of attentions as a promising approach to deal with the broadcasting storm problem in wireless networks. One popular way to construct a quality virtual backbone is to solve the minimum connected dominating set problem. However, a virtual backbone computed in this way is not resilient against topology change since the induced graph by the connected dominating set is one-vertex-connected. As a result, the minimum k-connected m-dominating set problem is introduced to construct a fault-tolerant virtual backbone. Currently, the best known approximation algorithm for the problem in unit disk graph assumes k ≤ 3 and m ≥ 1 and its performance ratio is 280 when k = m = 3. In this paper, we use a classical result from graph theory, Tutte decomposition, to design a new approximation algorithm for the problem in unit disk graph for k ≤ 3 and m ≥ 3. In particular, the algorithm features with much simpler structure and much smaller performance ratio, e.g. nearly 66 when k = m = 3. We also conduct simulation to evaluate the performance of our algorithm.

This paper investigates two new groups of trajectory optimization problems which stem from networked multi-robotic systems. In particular, we study how to efficiently collect data from stationary sensor nodes using multiple robotic vehicles such as data ferries under different circumstance. The first group includes two new problems which aim to find the tours and the paths, respectively, of k robot vehicles with different mobilization conditions to collect data from ground sensor nodes with minimum latency. The second group consists of one new problem whose goal is to determine the quality tours of k robot vehicles with different speeds, where each of which follows its corresponding tour to repeatedly collect data from stationary sensors. We prove the three problems are NP-hard and propose constant factor approximation strategies for them. Through a simulation, an analytical study is conducted to evaluate the average performance of our core contribution.

This paper investigates the problem of computing the optimal trajectories of multiple data MULEs (e.g., robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks. By relying on a slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood ( k-TSPN) and the k-rooted path cover problem with neighborhood ( k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average.

In this paper, we study the problem of computing quality fault-tolerant virtual backbone in homogeneous wireless network, which is defined as the$k$-connected$m$-dominating set problem in a unit disk graph. This problem is NP-hard, and thus many efforts have been made to find a constant factor approximation algorithm for it, but never succeeded so far with arbitrary$k\geq 3$and$m\geq 1$pair. We propose a new strategy for computing a smaller-size 3-connected$m$-dominating set in a unit disk graph with any$m\geq 1$. We show the approximation ratio of our algorithm is constant and its running time is polynomial. We also conduct a simulation to examine the average performance of our algorithm. Our result implies that while there exists a constant factor approximation algorithm for the$k$-connected$m$-dominating set problem with arbitrary$k\leq 3$and$m\geq 1$pair, the$k$-connected$m$-dominating set problem is still open with$k>3$.

This paper considers the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks (WSNs). By relying on slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood (k-TSPN) and the k-rooted path cover problem with neighborhood (k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them. Our simulation results indicate our algorithms outperform their alternatives.

In this paper, we propose a new multiple-sink positioning problem in wireless sensor networks to best support real-time applications. We formally define this problem as thek-Sink Placement Problem (k-SPP) and prove that it is APX-complete. We show that an existing approximation algorithm for the well-knownk-center problem is a constant factor approximation ofk-SPP. Furthermore, we introduce a new greedy algorithm fork-SPP and prove its approximation ratio is very near to the best achievable, 2. Via simulations, we show our algorithm outperforms its competitor on average.

In this paper, we study the problem of constructing quality fault-tolerant Connected Dominating Sets (CDSs)in homogeneous wireless networks, which can be defined as minimum k-Connected m-Dominating Set ((k,m)-CDS) problem in Unit Disk Graphs (UDGs). We found that every existing approximation algorithm for this problem is incomplete for k ¿3 in a sense that it does not generate a feasible solution in some UDGs. Based on these observations, we propose a new polynomial time approximation algorithm for computing (3,m)-CDSs. We also show that our algorithm is correct and its approximation ratio is a constant.

A Virtual Backbone (VB) of a wireless network is a subset of nodes such that only VB nodes are responsible for routing-related tasks. Since a smaller VB causes less overhead, size is the primary quality factor of VB. Frequently, Unit Disk Graphs (UDGs) are used to model 2D homogeneous wireless networks, and the problem of finding minimum VBs in the networks is abstracted as Minimum Connected Dominating Set (MCDS) problem in UDGs. In some applications, the altitude of nodes can be hugely different and UDG cannot abstract the networks accurately. Then, Unit Ball Graph (UBG) can replace UDG. In this paper, we study how to construct quality CDSs in UBGs in distributed environments. We first give an improved upper bound of the number of independent nodes in a UBG, and use this result to analyze the Performance Ratio (PR) of our new centralized algorithm C-CDS-UBG, which computes CDSs in UBGs. Next, we propose a distributed algorithm D-CDS-UBG originated from C-CDS-UBG and analyze its message and time complexities. Our theoretical analysis shows that the PR of D-CDS-UBG is 14.937, which is better than current best, 22. Our simulations also show that D-CDS-UBG outperforms the competitor, on average.