Abstract:Radio frequency (RF) energy harvesting is one of the effective methods to deal with the energy limitation of wireless network nodes. The deployment positions and transmit power setting of RF energy sources (ESs) determine the energy harvesting rate of each node. Most of the existing research work considers scenarios where no candidate ES deployment positions are given. However, in the practical application scenario, there are often many areas inside the network region where the ESs cannot be placed. The ESs can only be arranged in some reasonable candidate locations. So far, almost no work has been done to study how to select appropriate deployment positions among candidate deployment positions of ESs. Given the nodes' locations, nodes' energy energy-harvesting-rate demand, the number of ESs and the candidate deployment positions of ESs, design the ES deployment schemes which minimize the total network power consumption. Firstly, the problem is modeled as a mixed integer programming problem. Then a low-complexity approximation heuristic scheme and a genetic algorithm based deployment scheme with lower total network power consumption are proposed, respectively. Simulation results show that the proposed two schemes reduce the total network power consumption by about 90% as compared to the scheme of randomly selecting the deployment locations and the total network power consumption of genetic scheme can be 35% lower than that of heuristic algorithm. Therefore, the deployment scheme based on genetic scheme can be used for the small and medium-sized ES deployment scenarios, while the heuristic scheme can be used for large-scale ES deployment scenarios.

Abstract:This paper investigates a fog-assisted wireless energy harvesting network, where the UAV acts as the mobile wireless energy source and the fog server to charge and provide computation service to the sensors simultaneously. With the harvested energy, the sensors complete their computation tasks locally or offload them to the UAV. For such a system, a total energy consumption minimization problem for the UAV by jointly optimizing the UAV's flying trajectory is formulated, the task offloading and CPU frequency subject to the tasks computing requirements and the energy harvesting requirements being satisfied. Since the problem is non-convex and with no known solution, an efficient solution method is designed on the basis of Successive convex approximation (SCA) method. Simulation results show that the UAV energy consumption can be greatly reduced by using our proposed design, and the trajectory plays a dominant factor on the energy consumption of the UAV. Moreover, the longer the given time, the longer the trajectory length of the UAV. Additionally, with the increasing of the sensors' energy harvesting threshold or the decreasing of the energy conversion efficiency, the trajectory shifts toward the sensors more obviously. Compared with the uniform distribution of sensors, when the sensors are distributed concentrated, the UAV should fly closer to the sensors.

Abstract:Without the need for the third party and key sharing, the dummy-based privacy protection scheme enables the users to obtain precise query results while protecting their location privacy. However, when the adversary has certain background knowledge, e.g., the spatiotemporal reachability information, the location semantics, the users' historic query statistics, the probability of dummies being inferred will rise and the degree of privacy protection will be reduced. To solve this problem, a personalized dummy generation method based on spatiotemporal correlations and location semantics is proposed. Dummies are first generated based on the continuous reachability with previous request locations, and then filtered through the check of location semantic similarity and finally filtered by accessibility to user's historic query statistics. Experiments based on real datasets show that the proposed dummy generation method can effectively reduce the risk of privacy disclosure compared with current two dummy generation methods, especially when the adversary has related background knowledge.

Abstract:Most of the existing UWSNs clock synchronization algorithms use nodes to exchange data frequently, but neglect the synchronization information received by neighbor nodes within the scope of node-based communication, so there are some problems such as high energy consumption and low synchronization efficiency in synchronization communication, which affect the accuracy of network clock synchronization. Therefore, based on the analysis of the influence of UWSNs multi-objective optimization network topology evolution process on clock synchronization, this paper uses the theory of group consistency and Markov chain to establish the synchronization cycle regulation mechanism and clock synchronization model without too much increase of the average transmission radius and communication energy consumption. Then, using linear regression fitting method, the inter-cluster synchronization and intra-cluster synchronization are established. Finally, the performance of UWSNs clock synchronization algorithm is verified by simulation.

Abstract:Sensor-cloud is a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-cloud greatly enhances the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems that need to be solved in sensor-cloud, such as the limitations of WSNs in terms of communication and energy, the high latency and privacy security issues due to applying cloud platform as the data processing and control center. The core of edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives edge computing great potential in solving the shortcomings of sensor-cloud. After a lot of research, the latest research status of sensor-cloud was analyzed, the characteristics of existing sensor-cloud were summarized, the problems of existing sensor-cloud solution were revealed and the implementation scheme of sensor-cloud based on edge computing was proposed. Finally, the challenges and future research directions of the research in sensor-cloud were discussed.

Abstract:Wireless sensor network nodes are deployed in the places that are difficult to reach and maintain. Once a failure of sensor nodes occurs, debugging interaction depends on the network infrastructure. However, some wireless communication malfunctions very lead to sensor nodes lost from the network. Traditional debugging interactions based on the networks become invalid. In addition, due to the low-cost hardware structure of the sensor node, it is hard to provide additional interactions. To this end, this paper mines the potentialities of visible light sensor and light emitting unit and realizes a mixed-duplex debugging interaction system (DIS) based on visible light communication. Moreover, for the downlink of DIS, the sensing delay of ambient light sensor decreases the data transmitting rate. A compressed dual-header pulse interval modulation to the increase data transmitting rate is presented. And also, for the uplink of DIS, the light emitting unit of sensor node cannot match the image frame of camera generation, so a frame synchronization applied in camera rolling shutter mechanism is presented. The experiment results illustrates that the throughput of CDH-PIM is 11.09% higher than that of DH-PIM, and the energy consumption is reduced by 8.70%. The uplink frame synchronization scheme can achieve a data transfer rate of 600 b/s at 30 fps.

Abstract:Personnel counting are a method of counting or accurately estimating the population in a given area. It plays an important role in many applications, such as public safety, crowd control and marketing analysis. The traditional personnel counting method based on video stream and electronic tag has high hardware cost, and the accuracy and reliability of the personnel counting method based on video stream are low under the condition of insufficient light or occlusion. This paper presents a method of personnel counting based on Wi-Fi perception. This method reconstructs channel state information (CSI) in Wi-Fi. CSI of multi-subcarriers effectively reduces the influence of multipath effect. CSI is reconstructed by deconvolution phase and linear transformation, so that phase information can be centralized in the form of clusters. It avoids the problem of too large range of original phase distribution and too high randomness. Based on Hampel filter, the singular data of carrier amplitude is removed, the interference of environmental noise factors on the number of personnel characteristics is reduced, and the accuracy and stability of personnel counting using wireless signals are guaranteed. Finally, the numbers of people are classified by SVM. The experimental results show that the counting accuracy of the proposed method is about 95.8%, which can accurately identify the number of people in indoor environment.

Abstract:In the internet of things application, the data collected by the underlying sensor network is the basis of the upper decision and the foundation of all applications. If the collected data itself is problematic and untrustworthy, this will make the upper level of data protection and application a castle in the air. In order to solve the problem of untrustworthy data, a trustworthy data collection scheme based on mobile edge nodes is proposed. Through the evaluation of the node, the trust value of the node is used for path selection, and the mobile edge node is used as a mobile element to access the trustworthy cluster head node, thereby achieving efficient and reliable data collection. Theoretical analysis and extensive simulation experiments are carried out on the proposed trustworthy data collection algorithm based on utility value (UTDC). The experimental results show that the proposed trustworthy data collection algorithm based on utility value can avoid untrustworthy nodes, effectively reduce network delay and prolong the life cycle of the network.

Abstract:Aiming at the problems of UWSNs, such as unstable network topology control, unbalanced energy consumption caused by frequent changes and short network lifetime, this paper starts with the analysis of the evolution of topology caused by underwater uncertainties of sensor nodes, builds a state variable description model of distributed underwater sensor nodes, and concludes the multi-objective interaction and collaboration between nodes and environment. Topology control optimization problem for decision-making is mapped into game theory optimization problem. Then, potential game and Log-linear distributed learning rules are used to update the strategy behavior of nodes in the game. The non-homogeneous Markov chain theory is used to prove that the optimization problem of network topology control objective function converges to the solution of maximizing potential game function, so as to achieve guaranteeing. The purpose of maintaining network balance and prolonging network lifetime is to achieve the goal of maintaining network balance.

Abstract:The existing object detection methods for low-light image usually separate image restoration from object detection tasks. In addition, the quality and computing time of image restoration cannot meet the requirements of object detection task. To solve these problems, firstly, this study proposes an efficient image restoration convolutional neural network architecture, which aggregates feature information of multi-level contexts by combining feature maps of different scales, reduces information redundancy of convolutional layers, and improves the real-time performance of image restoration. In addition, a local-global attention block is designed to improve the ability of the recovery network to distinguish between noise and image content by calibrating the local information of each feature map and the relationship between feature channels. Secondly, this study designs a solution for collaborative processing of image restoration and target recognition tasks. The high-level semantic information of target recognition is used to guide the image recovery network learning, so as to highlight the feature information such as the structure and texture of the target, and make the recovery result more suitable for the target recognition task. Experimental results show that this method is superior to the existing methods in image restoration quality, computing time and object detection rate.

Abstract:Protecting privacy data is an important research content of IPv6 wireless sensor networks security. Since information hiding technology can achieve the invisibility of data, it plays an important role in privacy protection. In view of the characteristics of IPv6 wireless sensor networks and the security requirements of data concealment, combined with the theory of compressed sensing, the transmission overhead is concentrated on the convergence node. Using compressed sensing to effectively reduce the overhead of the sensing layer node, this paper proposes an information hiding method for IPv6 wireless sensor networks. The method mainly includes the hidden key management, the embedded algorithm and the extraction algorithm, which provides confidentiality for the transmission of sensitive data of the IPv6 wireless sensor networks to ensure the security and the reliability of sensitive data. The results show that the information hiding algorithm has less than 25% communication overhead with the increase of sensitive data.