To predict rainfall, our proposed model architecture combines the Convolutional Neural Network (CNN), which uses the ResNet-152 pre-training model, with the Recurrent Neural Network (RNN), which uses the Long Short-term Memory Network (LSTM) layer, for model training. By encoding the cloud images through CNN, we extract the image feature vectors in the training process and train the vectors and meteorological data as the input of RNN. After training, the accuracy of the prediction model can reach up to 82%. The result has proven not only the outperformance of our proposed rainfall prediction method in terms of cost and prediction time, but also its accuracy and feasibility compared with general prediction methods.

Named Data Network (NDN) can cache data in the router to reduce the data transmission cost and hence can be used in the resource limited Internet of Things systems. However, there are still some problems. Particularly for periodic data, if the cached data can't be updated in real time, the data content will be outdated. However, if each cache node updates the data frequently, it will cost a lot of unnecessary network traffic. In this paper, we improve the traditional NDN routing procedure and propose an active updating strategy for periodic data to further reduce the network traffic. First, we create an Interest Record Table and modify the Content Store to store the periodic information. Then, we modify the router work-flow to allow the router node to actively update the cache data. Finally, we propose an algorithm based on user's access regularity to calculate the update time point. Experimental results indicate that the proposed approach can reduce the network traffic by about 33.8% on average compared to the existing competing methods.

In the context of mobile crowd sensing, incentive mechanism is crucial to recruit mobile users to participate in the sensing task and ensure participants to provide high-quality sensing data. In this paper, we investigate a staged incentive mechanism for mobile crowd sensing. We firstly divide the incentive process into two stages: recruiting stage and sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a time-space correlation algorithm in the interaction and the winners can be screened after the sensing task. Finally, Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our staged incentive mechanism (SIM) can effectively improve participants' motivation and achieve high-quality sensing data from both space dimension and time dimension by extending the motivation from the recruitment stage to the sensing process.

With the introduction of computer programming in schools around the world, Scratch has risen in prominence for its thinkable, meaningful and social. Aiming to assessing the Computational Thinking skills of a Scratch program, we design and implement a new Scratch program analysis tool based on ANTLR. To solve some flaws (e.g., high failure rate and low efficiency) in Dr. Scratch which is the most relevant tool to assess Computational Thinking skills of Scratch programs, we choose the recognition tool ANTLR to design the system module and the assessing flow. And then, we customize more than 200 lexical and syntax parser rules in ANTLR. Furthermore, we expand the grading standard of assessing Computational Thinking skills in Dr. Scratch. Some fundamental concepts in Computer Science, such as stack, queue and recursion method, are involved in our grading standard. Experiment results show the performance (e.g., success rate, execution time) of SAT is superior to that of Dr. Scratch.

E-Health Systems are transforming the whole healthcare process to become more efficient and less expensive. QoS is usually used as a key criterion for E-Health service. The study shows that QoS of data supply chain are highly related to operating mode of nodes. However, existing optimization methods seldom took this observation into account, which shall decrease the optimal performance. In this paper, we propose an operating mode optimization strategy for nodes of data supply chain environments including E-Health Systems. First, a QoS mathematical model is developed. Then, we put forward the objective function to balance the data freshness, hit rate and cost from the aspects of users and service providers. To this end, we present a novel operating mode selection algorithm. It selects appropriate candidate operating modes for each node when generating composite operating mode of nodes with optimal QoS values. Experimental results indicate that the effectiveness of the proposed approach outperforms the existing methods by at least 20%.

Nowadays the integration of IoT and SDN has been a research hotspot which attracts significant attention. However, as resources are relatively limited in IoT, direct application of SDN will cause some challenges, one of them is that IoT forwarding nodes cannot store massive complex FlowTables like a traditional OpenFlow switch. To solve the problem, this paper proposes a hash-based distributed storage strategy. Specifically, we present a multi-dimension selection method to decide the optimum distributed storage location. And then a hash space is formed by using the content of data flow in IoT, it is the basis of FlowTables deployment and data forwarding in distributed storage. Moreover, we introduce a new FlowTables search mechanism which is built on the principle of the binary tree. Experimental results demonstrate that our strategy efficiently improves the FlowTables storage capacity with small performance loss in IoT.

Since Apple introduced the iBeacons in Worldwide Developers Conference (WWDC) 2013, the iBeacon has been rapidly accepted and generalized in the market. For the deployed iBeacons, it is necessary to monitor their status. In this paper, we design a crowd sensing based monitoring framework which combines the moving and static schemas of participants to monitor the real status of iBeacons. In such a system, the inaccuracy and conflict of the collected signal information, commonly caused by the error rate of participants or the differences of sensing context, have received more and more attention. Estimating the real status of iBeacons according to the uploaded signal information becomes a big challenge for our monitoring system. Towards this end, we propose a context-aware estimation approach in this paper. We first model the effects of sensing context, and then propose an iterative method to infer the error rate of participants and estimate the real status of iBeacons with high precision. Our method is tested via extensive simulations, and verified by our monitoring system which has been applied in the teaching building. The results demonstrate that the proposed estimation approach outperforms recent popular three-estimates algorithm and OtO EM algorithm. At last, we develop the review mechanism, which ensures the efficiency of our monitoring system.

Due to the execution paradigm may be different at different invocation time, users obtain different QoS when interacting with the same Data Supply Chain (DSC). However, existing QoS prediction methods seldom took this observation into consideration, which shall decrease the prediction accuracy. In this paper, we propose a context-based QoS prediction method for data supply chain. First, a QoS mathematical model is developed for considering the mass data transmission across elementary sub-chains. Then, two execution paradigms of data supply chain are discussed. Besides, we explored several special context factors of data supply chain (such as invocation time, data source update period and execution paradigm) which influence QoS. By processing such context information, we can obtain the part of data supply chain which is need to execute when the user query occurs and leverage them to predict QoS. Experimental results indicate that our approach improves the prediction accuracy and efficiency of QoS when compared to previous methods.

With the development of social network, there are more and more people to share pictures on social platforms. Since the information contained in picture has the different requirements for confidentiality, it makes the selective presentation of secret information to be an urgent problem. Estimating user's privilege of gaining some regions based on his/her attributes is a novel solution. But there are few perfect solutions aiming at the strategy of adaptive calculation for user's privilege in the existing literatures, especially for the scenario in which the real values of some attributes have priorities. In this work, based on the cipher text-policy attribute-based encryption (CP-ABE), we propose an adaptive and safe presenting scheme for the information contained in a picture. This scheme firstly embeds the confidential data outside the secret region, and generates the image mosaic in the secret region; when someone requesting the original version of image, it adaptively calculates the recovery privilege of requestor with the strategy proposed in this paper, then precisely present some regions based on the privilege level of requestor. Moreover, we firstly propose the vote-attribute which facilitates the attribute revocation. The experiments demonstrate that, based on the privilege level, the proposed scheme can safely present the original version of the corresponding image region, and expediently achieve the attribute revocation. Compared with other algorithms, our scheme can restore the original version of image with only 1/2 secret data, and spend little time over the attribute revocation. Besides, the average of peak signal to noise ratio (PSNR) is 4dB more than the algorithms available, and the standard variance of PSNR is less than 0.4.

For crowd sensing applications, the inaccuracy and conflict of the reported samples, commonly caused by untrained participants and heterogeneous mobile devices, have received more and more attention. Given this, constructing a high-quality radio map of iBeacons via crowd sensing is very challenging. In this paper, we resort to the family of robust estimation and design a robust radio map construction scheme, named by RrMCS, to enhance the quality of iBeacon map construction via crowd sensing. For heterogeneous devices, we use a linear robust estimation technique to map the signal strength scanned by different users into a uniform space. Moreover, we alleviate the negative influence of outliers and detect the abnormal participants by utilizing the multivariate robust statistics on the signal information uploaded by different users. Finally, we implement an Android-based system for constructing the iBeacon radio map and conduct extensive experiments under indoor environment. The experimental results validate the enhancement of the constructed iBeacon radio map by the proposed RrMCS.

Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing to address reliable and accurate indoor localization. The location of landmark can calibrate the location of user and inertial navigation can optimize the location of landmark in turn. In this work, we define the landmark as certain characteristic structure with Beacon. To tackle the challenges of misjudgment of landmarks and variability in user walking profiles, we have developed algorithms for reliable detection of landmarks and personalization of step length with the aid of crowd sensing. We have built an indoor localization system integrating these modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and easy to deploy. Extensive experiments on users with smartphone devices, with over 37 subjects walking over an aggregate distance of over 20 kilometers were carried out. Evaluation results show that our system can achieve a mean accuracy of 2m initially and 1m with the calibration of landmarks in a 39m × 21m testing area.

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car-following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Researchers' attempts to monitor and collect environmental data have led to the emergence of Wireless Sensor Networks (WSNs). In light of WSN, the monitoring target has been changed recently from the environment to the human or animal body: sensor nodes are placed on, in, or around the body to gather biosignals, which will be stored by a wearable sink node. But, WSN has an important problem, that is, the coverage problem, which may generate interference signals and influence system reliability during data transmission. Some biosignals are very important. The reduction of system reliability due to high latency or packet loss may result in significant loss of important data and can be life‐threatening (A patient's heartbeat or breathing stops but no staff receives the medical alerts). For this reason, this study makes use of a cooperative non‐zero‐sum game model for controlling the transmit power of the system as well as reducing the obstructions between simultaneous transmissions and avoiding contention between messages. In our simulated scenario, every WBAN is a regarded as a node. To avoid the situation that any one of the sensors stops running, this study further includes a global optimization technique to cover all nodes in the game and improve the time difference of sensors' lifetime. Copyright © 2017 John Wiley & Sons, Ltd.

In participatory sensing applications, the inaccuracy and conflict of the reported data, commonly caused by the error rate of participants or the differences of observation context, have received more and more attention. Estimating the real status of facilities according to observations becomes a big challenge for participatory sensing. Towards this end, we propose a context-aware estimation approach based on participatory sensing in this paper. We first model the effects of observation context, and then propose an iterative method to infer the error rate of participants and estimate the real status with high precision. Our method is verified using a public facilities monitoring application in our campus, and tested via extensive simulations. The results demonstrate that the proposed method outperforms recent popular three-estimates algorithm and OtO EM algorithm.

There are more and more systems using mobile devices to perform sensing tasks, but these increase the risk of leakage of personal privacy and data. Data hiding is one of the important ways for information security. Even though many data hiding algorithms have worked on providing more hiding capacity or higher PSNR, there are few algorithms that can control PSNR effectively while ensuring hiding capacity. In this paper, with controllable PSNR based on LSBs substitution- PSNR-Controllable Data Hiding (PCDH), we first propose a novel encoding plan for data hiding. In PCDH, we use the remainder algorithm to calculate the hidden information, and hide the secret information in the last x LSBs of every pixel. Theoretical proof shows that this method can control the variation of stego image from cover image, and control PSNR by adjusting parameters in the remainder calculation. Then, we design the encoding and decoding algorithms with low computation complexity. Experimental results show that PCDH can control the PSNR in a given range while ensuring high hiding capacity. In addition, it can resist well some steganalysis. Compared to other algorithms, PCDH achieves better tradeoff among PSNR, hiding capacity, and computation complexity.

In smart building systems, the automatic control of devices relies on matching the sensed environment information to customized rules. With the development of wireless sensor and actuator networks (WSANs), low-cost and self-organized wireless sensors and actuators can enhance smart building systems, but produce abundant sensing data. Therefore, a rule engine with ability of efficient rule matching is the foundation of WSANs based smart building systems. However, traditional rule engines mainly focus on the complex processing mechanism and omit the amount of sensing data, which are not suitable for large scale WSANs based smart building systems. To address these issues, we build an efficient rule engine. Specifically, we design an atomic event extraction module for extracting atomic event from data messages, and then build a β-network to acquire the atomic conditions for parsing the atomic trigger events. Taking the atomic trigger events as the key set of MPHF, we construct the minimal perfect hash table which can filter the majority of the unused atomic event with O(1) time overhead. Moreover, a rule engine adaption scheme is proposed to minimize the rule matching overhead. We implement the proposed rule engine in a practical smart building system. The experimental results show that the rule engine can perform efficiently and flexibly with high data throughput and large rule set.

ABSTRACT There is an increasing consensus that the locator/identifier separation of IP address is necessary to resolve the scalability issues of current Internet routing architecture. After identifiers are separated from locators, an identifier‐to‐locator mapping service must be employed to map identifiers onto locators. From this point, this paper proposes an anomaly detection and response approach based on mapping requests. By using the cumulative sum algorithm for change point detection, this approach introduces the anomalous traffic detection of mapping requests to diagnose the aberrant network behaviors. Once alarming, two effective response methods can be chosen to control the anomalous attack traffic in real time. Furthermore, in order to decouple the mapping request traffic from the mapping cache, this approach not only takes into account the mapping cache timeout but also puts forward a practical mapping request threshold algorithm. In particular, our simulation results show that, compared with the anomaly detection approach based on network traffic, the proposed approach is more advantageous and efficient. In addition, we also discuss the possible false positive and false negative problems, which may be caused by some accidental phenomena. Copyright © 2014 John Wiley & Sons, Ltd.

In recent years new research studies have appeared that concern the issue of network planning in LTE-Advanced. In this paper, the coverage problem in LTE-Advanced relay networks is formulated based on integer linear programming (ILP). We propose the Enhanced tree (E-Tree) algorithm to place the evolved Node B (eNB) and relay station (RS) at the location which has the lowest construction cost. The goal is not only satisfy the minimum requirement but also meet the two hop relaying limitation in LTE-Advanced networks. The E-Tree algorithm is proposed based on graph theoretic technique, and analyzed with the simulation results. The simulation results show that the proposed algorithm provides a rapid planning method and the lowest construction cost with various network environment.

SUMMARY The Media‐Independent Handover (MIH) standard proposed by IEEE 802.21 can reduce handoff delay when mobile nodes switch between heterogeneous access networks and help Mobile Nodes (MNs) roam seamlessly. The MIH standard is suitable for not only heterogeneous networks but also homogeneous networks. According to the Fast Mobile IPv6 (FMIPv6), during handover initiation, for MNs to get new Access Router (nAR's) prefixes, using Router Solicitation for Proxy Advertisement (RtSolPr)/Proxy Router Advertisement (PrRtAdv) may be time consuming. If there are additional quality of service requirements, the FMIPv6 would require much more time. Based on FMIPv6, this paper proposes a new IS‐FMIPv6, which combines MIH services. We also propose a new mechanism for discovering the access route. This scheme reduces the time spent for finding the route to the nAR and increases the probability that FMIPv6 operates in the predictive mode by reducing initiation handoff delay. Published in 2010 by John Wiley & Sons, Ltd.

Abstract In recent years, thanks to the development and popularization of wireless network technologies, the issue of vehicularad hocnetwork (VANET) has received great attention, and more and more VANET‐related researches have been brought up. Generally speaking, the biggest difference between VANET and traditionalad hocnetwork is the velocity of carriers because in VANET, the velocity of vehicles, the carriers, is much higher than those in traditionalad hoc. Therefore, it would be a great challenge to forward data efficiently in VANETs and many researches proposed have focused on the development of routing protocols. The current proposed routing protocols are all assumed to simulate in a distributed and ideal environment. As for the complex geographic environments, such as urban scenarios, extra amendments must be needed to improve the efficiency of the routing protocols. Thus, the main purpose of this paper is to design a suitable routing protocol for urban scenarios with better performance and adaptability. For this reason, greedy on straight roads and predictive at the intersections (GSPI) routing protocol is proposed to use greedy mode on straight roads and to use predictive mode at the intersections. In greedy mode, we choose the next hop according to the weight value that combines the distances and multi‐rate. In predictive mode, we predict the directions of the vehicles to determine the next hop. The simulation results reveal that our proposed algorithm indeed proves its feasibility. Copyright © 2010 John Wiley & Sons, Ltd.

The packet-switched NGN (Next Generation Network) architecture allows users to access multimedia services via the Internet anytime and anywhere. Through the core IP Multimedia Subsystem (IMS), NGN can provide multimedia services, such as voice, video, data and messages over heterogeneous networks. However, for high-quality services in practical environment, the performance and QoS (Quality of Service) of NGN must be guaranteed. Thus, based on the Testing and Test Control Notation version 3 (TTCN-3) specifications, this paper develops a test system utilizing Open IMS Core for conformance test of IMS session initiation for fixed and mobile networks. We also verify the QoS and analyze the performance of SUT (System Under Test) from the experimental results.

In Wireless Access in Vehicular Environments (WAVE), the terminal devices are usually highly mobile vehicles or transportation tools, which handoff much more frequently than those in wireless networks. However, the quality of network services, like VoIP and multimedia streaming, will be seriously influenced by too frequent handoff or too long handoff latency. As a result, traditional handoff mechanisms no longer satisfy the need of vehicular communications. At present, most existing fast handoff mechanisms are based on the signal strength of the APs, which does not suit vehicular communications fully. Generally speaking, the regional traffic in Vehicular Ad-hoc Networks (VANETs) keep changing and varying. For example, on busy roads or in rush hours, the increase of number of vehicles may lead to the network congestion and degrade the performance of vehicular communications substantially. Therefore, this paper presents the QualityScan scheme, an efficient pre-scanning method to improve the handoff performance in VANETs. With the attempt to reduce the handoff latency and maintain the load balance among the APs, our proposed scheme regularly collects the loading states of the nodes by the pre-established AP controller (APC), and predicts the network traffic of the next moment. Consequently, Mobile Nodes (MNs) can select the most suitable AP for the best QoS according to the parameters received by passive scanning, while more QoS parameters should participate in cooperation. The simulation result proves that our proposed scheme not only maintains the QoS and the load balance among the nodes, but also further enhances the handoff performance in VANETs.

Among the present wireless network technologies, wireless sensor is one of the most extensively applied devices. In addition, with the rapid advancement of wireless communication in recent years, Wireless Sensor Network (WSN) has become very popular research issue and how to realize indoor localization by using sensors is the most common topic. For the time being, localization in wireless sensor networks can be divided into two major categories: Range-based and Range-free localization algorithms. The Range-based algorithms mainly use ranging techniques (ToA, TDoA, AoA, RSS, for example) as the tools to measure the distances between the nodes. From the measured information, the estimated position can be figured out. On the other hand, instead of utilizing ranging techniques, the Range-free algorithms estimate the positions by making use of reference nodes. However, indoor environment exist many interference factors that easily lead to localization inaccuracy. In order to solve the problems that indoor localization may encounter, some algorithms use RF-mapping method to avoid the influence of indoor environment to the localization result. Nevertheless, to make the localization precise needs more data about reference nodes and this greatly increases both the time to set up and the volumes of calculation. Thus, this paper proposes a GA-based (Genetic Algorithms, GA) localization algorithm to estimate the location of unknown nodes and avoid the influence of environmental factors by pre-establishing the pattern. Moreover, the proposed algorithm does not need too much information about reference nodes to pre-establish the pattern.

In this paper, we propose a scalable transport scheme for delivering on-demand video streams over broadband wireless networks in next-generation network/IP multimedia subsystem (NGN/IMS) architecture. The proposed transport scheme makes use of fine-granular-scalability (FGS) encoded videos to accommodate high bandwidth variation of wireless networks. We formulate the bandwidth allocation to FGS-encoded streams as a resource allocation problem and develop a quality-aware bandwidth allocation scheme, called QABA. With QABA, the proposed transport scheme can dynamically adjust the bit rate allocated to different streams to maximize the overall perceptual quality when available network bandwidth varies with time. QABA is theoretically proven to be able to find the optimal bandwidth allocation for all on-demand streams. To validate the effectiveness of QABA, extensive trace-based simulations are performed.

In the vehicular environment, the transmissions of signals have to face the higher challenges then indoor wireless local area networks (WLAN). Multipath fading cause high bits error rate (BER), in such an unstable transmission environment. And high BER cause high dropping probability of packets. In order to reduce the dropping probability of handshaking packets without increase the capability of infrastructures, a mechanism is proposed in this paper. In our work, we altered the traditional cyclic prefix (CP) into a variable cyclic prefix (VCP) of request to send (RTS) frame and clear to send (CTS) frame, and reduce the handshaking packets error rate in vehicular wireless transmissions, hence we can improve the contending ability in Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) at vehicles to infrastructure (V2I) transmission., By analyzing environment parameters, we can predict the suitable VCP time slots and dynamic adjust VCPs. And we can competently eliminates the impacts of Intersymbol interference (ISI) caused by multipath fading in CSMA/CA.

Home media is getting more and more popular. For home users, a variety of applications are established based on HMWN (Home-Media Wireless Network). Nowadays, most of video streaming algorithm use extra bandwidth to keep high video quality. In fact, mobile users nowadays can participate in file sharing communities to download or upload files by different mobile devices on HMWN. Furthermore, to guarantee quality of video streaming when error occur the retransmission and selection drops method should be present. The receiver node decides a scheme in the end of frame and feedback to sender immediately before sender start encode next frame. The home media algorithm use MPEG-4 part2 visual video codec. MPEG-4 part 2 visual provide a simple reference coder tactic. It just code previous one frame not like multi reference frame tactic in H.264 or MPEG-4 part 10 AVC. In this paper, the main goal of this paper is to design a better mechanism called "A Robust End-to-End Delivery Scheme", we not only take simple coding method, but also consider the change the motion prediction coding method before coded next frames and send it. A novel video stream algorithm was proposed in this paper that can make usage of bandwidth more efficiency and also can be adaptive with error modules on HMWN.

Abstract Wireless equipment has become ubiquitous. However, in facing various software attacks, wireless linked networks are more vulnerable than wire linked networks. The general problem with Mobile IPv6 (MIPv6) (Table I) is the long handover latency period. To reduce the security latency, we propose early security key exchange for encryption in MIPv6 handoff. In our approach, two issues are addressed in dealing with the latency within the encryption technology during the handover. First, we extend the Early Binding Update (EBU) method to deal with the long security exchange negotiation time for the MIPv6 handoff. Second, we adopt the Security Access Gateway (SAG) to solve the limited computing and memory in the Mobile Node (MN). Copyright © 2008 John Wiley & Sons, Ltd.

Abstract The effective coverage distance of wireless LAN (WLAN) being small, users may leave the coverage area of the specific access point (AP) from time to time while roaming. However, the wireless network is a shared medium. The air is open for everyone. In general there is collision if a few users attempt to transmit with the same channel that is more rigorous during handoff period because of active scan mode. The active scan will perform requests for searching available AP. Unfortunately, this function consumes too much resource in wireless communication, and also affect total performance. We will propose an advanced active scan to improve it. In our proposal, we convert RF signal distribution to a simple classification problem, like as XOR classifier with artificial neural network (ANN). We combine ANN with active scan to achieve our goal. And the weight, which trained by ANN presents the connection character of geography. Moreover, the weight could be stored in AP for reusing and is called geographical fingerprint. The average enhancement of reducing the active scan area is about 62%. Copyright © 2006 John Wiley & Sons, Ltd.