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In recent years, the analysis of surface electromyography (sEMG) signals by feature engineering and machine learning has developed rapidly. However, when feature engineering is applied to feature extraction of sEMG signals, important feature information in the signals will inevitably be omitted, which will reduce the performance of signal analysis and recognition. Therefore, this paper proposes a method to complete classification of sEMG hand movements based on convolutional neural network (CNN) and stacking ensemble learning. In this method, a primary classifier based on CNN is designed to extract sEMG data features, which avoid omission of important feature information. A secondary classifier based on the stacking method is designed to integrate three primary classifiers trained with time domain, frequency domain and time-frequency domain data of the sEMG signal respectively. Then, several experiments on NinaPro DB5 dataset is performed to evaluate the proposed models. When the window length is 200ms, primary classifier is trained and tested with the sEMG signal data divided by the 80ms, 100ms, and 125ms sliding length. The best accuracy can reach 71%. The primary classifier and the secondary classifier trained and tested with sEMG signal data divided by window lengths of 200ms and 300ms in the case of a sliding length of 100ms. When the window length is 200ms, the best primary classifier accuracy and the best secondary classifier accuracy can be 70.92% and 72.09%, respectively. On the window length of 300ms, the best primary classifier accuracy and the best secondary classifier accuracy can reach 75.02% and 76.02%, respectively. Finally, the model designed is compared with Linear Discriminant Analysis (LDA), Long Short Term Memory-CNN (LCNN), Support Vector Machine (SVM), and Random Forests. Under the same conditions, the average accuracy of the secondary classifier is 11.5%, 13.6%. and 10.1% higher than LDA, SVM, and LCNN, respectively. Also, the average accuracy rate is 3.05% higher than SVM and Random Forests.

Based on multiple-attribute-decision-making and taint tracking,a sensitive-information leakage awareness method was proposed,some relative known vulnerabilities in big data platform was analyzed,target database was extracted and extended,multiple attribute model was built combined with operation semantic,a grey-correlation-analysis and technique for order preference by similarity to an ideal solution based sensitivity measurement was designed in combination of regular operation semantic for sensitive information.A prototype was built based on taint tracking,sensitive-information leakage vulnerabilities could be verified and discovered across big data platforms in this method.The experiment shows that verification for known bugs and discovery for unknown vulnerabilities can be accomplished based on leakage scenarios,which can be regarded as a support for protection in dynamic sensitive information data flow.

Aiming at dealing with prospect knowledge and complex combinatorial attack,a new location big data publishing mechanism under differential privacy technology was given.And innovative usability evaluation feedback mechanism was designed.It gave corresponding solution details for the sensitive attributes and the identity recognition to analyze the quality of service,aimed at privacy protecting for location based big data under situations like combination of location information and non-location information and attacker’s arbitrary background knowledge.Simulation results based on different spatial indexing technology proved that the new publishing model has a higher accuracy under specified privacy conditions for the location query service.

Resource discovery in delay-tolerant mobile social networks (MSN) continues to be challenging issue. An interests and behavior prediction-based dynamic resource discovery mechanism (IBRD) in 3-dimensional cartesian coor-dinate system was proposed. Firstly, IBRD extracted the interest vectors from the user's file resources and the profile table, and then the initial virtual interest communities through the cosine similarity computation between the nodes were con-structed. After mobile social networking data was anal the semi-Markov chain model was used to predict the beha-vior and movement trajectory of users. According to the prediction results of the Markov model, the dynamic ma en-ances of the virtual interest communities were realized. Next, an efficient resource discovery strategy based on the dynamic virtual interest communities was designed. Finally, proposed method was simulated on the platform of the opportunistic network environment simulator. Simulations results show that the proposed scheme consistently outperforms the state-of-the-art resource discovery schemes in terms of the searching efficiency the average delay and the communication cost.

According to the recommendation information processing problem in complex network environment,a trust model based on the recommendation chain classification was proposed.The classification method was based on honesty attribute of nodes,which could choose an effective recommendation chain on the basis of practical experience data.The recommendation information dissemination parameters were based on the information gain,which made recommendation information be more accurate.The factor of time was also considered in this model.The ability of interaction and the one of honesty were distinguished clearly.The concept of information entropy in information theory was used in the final ag-gregation calculation of direct trust and recommendation trust,which could get rid of the ambiguity of the previous sub-jective parameter settings.The main polymerization parameters could be continuously corrected with the interactions in order to achieve the situation being closest to the reality.Simulation results show the validity of recommendation chain classification and the rationality of the parameter settings in the proposed model.

To solve the information leakage problem resulting from several assertions of previous range query solutions,there is a secure range query scheme with one assertion (SRQSAE scheme),and the scheme is claimed to be secure against ciphertext-only attack.The security analysis on SRQSAE scheme is presented,and it shows SRQSAE scheme can not hide the size of search keyword.A new scheme of secure range query on encrypted data is proposed through introducing random numbers in the generation of search index and trapdoor.The new scheme can provide the privacy guarantee on search range and search keyword,and it achieves high level needs of security without losing efficiency.

Nowadays, anyone carrying a mobile device can enjoy the various location-based services provided by the Internet of Things (IoT). ‘Aggregate nearest neighbor query’ is a new type of location-based query which asks the question, ‘what is the best location for a given group of people to gather?’ There are numerous, promising applications for this type of query, but it needs to be done in a secure and private way. Therefore, a trajectory privacy-preserving scheme, based on a trusted anonymous server (TAS) is proposed. Specifically, in the snapshot queries, the TAS generates a group request that satisfies the spatial K-anonymity for the group of users—to prevent the location-based service provider (LSP) from an inference attack—and in continuous queries, the TAS determines whether the group request needs to be resent by detecting whether the users will leave their secure areas, so as to reduce the probability that the LSP reconstructs the users’ real trajectories. Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP. The response speed of the results is improved by narrowing the search scope of the points of interest (POIs) and speeding up the prune of the non-nearest neighbors. The security analysis and simulation results demonstrated that our proposed scheme could protect the users’ location and trajectory privacy, and the response speed and communication overhead of the service, were superior to other peer algorithms, both in the snapshot and continuous queries.

Considering the problem that cloud users will select a trusted cloud service provider, a dynamic trust evalua-tion model based on evaluation credibility was proposed.This model divides the ability of cloud service provider and the one of the user's requirments into many ranks, which can effectively solve the potential damage caused by the dynamic change in the ability of cloud service providers. A dynamic mechanism of trust changing about time-window was estab-lished. During the calculation of credibility, the user's evaluation credibility was used as the trust weight. The calculating accuracy of the recommended behavior credibility was improved by introducing the evaluation credibility and evaluation similarity. Simulation results show that the model results are closer to the cloud service provider's actual trust value, and can resist the attack of malicious cloud users effectively.

The existing trust evidence models cannot deal with malicious attacks in the distributed net quickly and effectively, and are lack of a trust normalization method that can measure relationship described by a triple set, so a trust model based on the improved D-S evidence theory was proposed. On this basis, basic trust value function based on continuous sequences and trust evaluation method based on evaluation function were also proposed to inhibit malicious nodes in a higher speed, and to enable the prediction results to be closer to the fact. Analysis and simulation show that this model has better effectiveness and robustness to deal th the aggregating trust attack, and trust evaluation method has better reasonableness and accuracy.

The introduction of renewal hash chain overcame resource-constrained defect in traditional hash chains,but the existing renewable schemes had still held unsatisfactory performance especially on security and complexity.The definitions of repetition,division and division-tree was proposed,and then a novel self-renewable hash chain construction scheme was put forward based on division and (,t n)-Mignotte's threshold Chinese remainder theorem secret sharing scheme.From three aspects of key space,twice authent ion and provable security,it theoretically proves that the proposed hash scheme could ensure the novel seed value regenerated safely and resisting the middle-man attack effectively.Simulation experiments demonstrate that the novel scheme obtains equal or more satisfactory performances on the costs of communication,computation and storage than typical schemes.

Some frame components, such as SYNC (frame synchronization) and RTS/CTS (Ready to Send/Clear to Send), are not taken into consideration when the traditional setting strategies conduct the optimization of SMAC (Sensor MAC) contention window size. This paper proposes mathematical models that allow the analysis of data packets forwarding delay within one SMAC virtual cluster. Simulation results in OMNeT++ show good agreements with the proposed mathematical models, validating the models' correctness. The curve analyses of the models confirm the existence of delay-optimization-oriented contention window size that is closely related to the number of simultaneously contending nodes. Afterwards, it is shown that SYNC, RTS/CTS and EIFS (Extended InterFrame Space) have impacts on the optimal contention window size and expected delivery delay to various degrees, as well as throughput and energy efficiency. One ideal setting strategy of delay-optimization-oriented SMAC contention window size requires the combination of the network scale, SYNC, RTS/CTS and EIFS. Additionally, it is demonstrated that the proposed setting strategy makes contributions to the improvement in the existing SMAC extensions when they are integrated with each other, in terms of the end-to-end delay, throughput and energy consumption.

For the purpose of balancing energy consumption of nodes in Wireless Sensor Networks (WSNs for short), a type of energy hole avoiding method based on synchronization of nodes in adjacent annuluses (SNAA for short) is proposed in this paper. The circular network is divided into virtual annuluses with the same width. Nodes are deployed nonuniformly and their number increases in geometric progression from the outer annuluses to the inner ones which could effectively reduce the work load on nodes near the center. Moreover, each node could find its optimal parent by considering the residual energy of each candidate as well as the distance between the two nodes in adjacent annuluses. And on the basis of synchronization of nodes between adjacent annuluses during their transmitting and receiving phases, a sleep scheduling strategy is also proposed to further reduce the energy consumption of nodes in idle listening mode. Simulation results show that SNAA has a superior performance on energy consumption balance compared to the algorithm proposed (Liu et al., 2013; Wu et al., 2008) and it could also mitigate the energy hole problem in WSNs.

The issue of data collection in wireless sensor networks was studied.A mobile sink-based data collection scheme called DCSR was proposed.DCSR consisted of two phases.Firstly,a number of collection sites were selected according to the sensor distribution.And then a quantum genetic algorithm was performed to calculate the shortest loop across the sites.The sink node would travel along this loop periodically and collect data at each site.Theoretical analysis and simulation results show that DCSR has better performance and collects more data.

The emergence of cloud security changed the major virus-defensive position from client to network and server group.From the view of the cloud security,a novel worm propagation model,SIR_C(SIR in cloud security),which drives from SIR model was proposed.Based on considering the traditional human countermeasures and the network congestion traffic which is caused by worms and will curb the spread of worms,SIR_C model focused on the analysis of the impact of the degree of cloud security deployment and the information gathering capability on worm propagation model.The simulation results show that SIR_C model was a meaningful attempt to research worm epidemics in cloud security network.

With the increasing number of ubiquitous terminals and the continuous expansion of network scale, the problem of unbalanced energy consumption in sensor networks has become increasingly prominent in recent years. However, a node scheduling strategy or an energy consumption optimization algorithm may be not enough to meet the requirements of large-scale application. To address this problem a type of Annulus-based Energy Balanced Data Collection (AEBDC) method is proposed in this paper. The circular network is divided into several annular sectors of different sizes. Nodes in the same annulus-sector form a cluster. Based on this model, a multi-hop data forwarding strategy with the help of the candidate cluster headers is proposed to balance energy consumption during transmission and to avoid buffer overflow. Meanwhile, in each annulus, there is a Wireless Charging Vehicle (WCV) that is responsible for periodically recharging the cluster headers as well as the candidate cluster headers. By minimizing the recharging cost, the energy efficiency is enhanced. Simulation results show that AEBDC can not only alleviate the “energy hole problem” in sensor networks, but also effectively prolong the network lifetime.

To reduce time delays during data collection and prolong the network lifetime in Wireless Rechargeable Sensor Networks (WRSNs), a type of high-efficiency data collection method based on Maximum Recharging Benefit (DCMRB) is proposed in this paper. According to the minimum number of the Mobile Data Collectors (MDCs), the network is firstly divided into several regions with the help of the Virtual Scan Line (VSL). Then, the MDCs and the Wireless Charging Vehicles (WCVs) are employed in each region for high efficient data collection and energy replenishment. In order to ensure the integrity of data collection and reduce the rate of packet loss, a speed adjustment scheme for MDC is also proposed. In addition, by calculating the adaptive threshold of the recharging request, those nodes with different energy consumption rates are recharged in a timely way that avoids their premature death. Finally, the limited battery capacity of WCVs and their energy consumption while moving are also taken into account, and an adaptive recharging scheme based on maximum benefit is proposed. Experimental results show that the energy consumption is effectively balanced in DCMRB. Furthermore, this can not only enhance the efficiency of data collection, but also prolong the network lifetime compared with the Energy Starvation Avoidance Online Charging scheme (ESAOC), Greedy Mobile Scheme based on Maximum Recharging Benefit (GMS-MRB) and First-Come First-Served (FCFS) methods.

With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.

Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of its l 1 -based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. The l 0 -based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF_CSOR). Furthermore, we study three different greedy algorithms under DAF_CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF_CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF_CSOR exhibits a certain advantage over conventional methods.

In view of the unbalanced energy consumption of traditional cluster-based sensor networks, this paper proposes a type of uneven clustering protocol in data collection. The network is divided into inner and outer regions according to the distance between nodes and the base station. The inner region is consisted of several layers and nodes in outer region are deployed in grids of different sizes. Sensor data is collected by nodes in outer region and then is be transmitted to the inner region. Nodes in the inner region do data fusion and forward data from the lower layer to the higher one. Simulation results show that, compared with MTP and CDFUD, the proposed algorithm performs well in balance of energy consumption and could effectively prolong the network lifetime.

Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.

We propose a set of distributed algorithms for improving the multicast throughput in wireless sensor networks. To this end, network coding is applied when exploiting path diversity with two disjoint paths to each multicast group receiver. We depart from the traditional wisdom that the multicast topology from source to receivers needs to be a tree and propose a novel and distributed algorithm to construct a 2-redundant multicast graph (a directed acyclic graph) as the multicast topology, on which network coding is applied. We conduct both analytical and simulation-based studies to evaluate the effectiveness and performance of our algorithm.

For the purpose of balancing node energy consumption in Wireless Sensor Networks and in the premise of considering network coverage, a kind of node broadcasting scheme at fixed intervals under variational acceleration straight-line movement model is proposed in this paper. Simulation results illustrate that the approach proposed in this paper has a superior performance on balance of energy consumption compared to IEA method and uniform broadcasting as well as random broadcasting methods. And the energy saving effect is close to that of the theoretical deployment model of data fusion and nondata fusion.

A new wireless sensing network paradigm is presented for face recognition applications. In addition to the flexibility the face recognition system gains by integrating into a wireless sensor network, we take it further by introducing mobility into the network to improve the sensing coverage area and cost efficiency. To implement these goals, a multilayered network structure and Gauss-Markov mobility model are proposed. Furthermore, analysis of the sensing coverage area is given. Besides, some of the potential application scenarios based on the proposed paradigm are also presented. According to the simulation, the whole system achieves high recognition rate and energy efficiency compared to stationary network.

A type of energy optimization localization method with mobile beacon is proposed in this paper. Traverse point of the mobile beacon is marked with the help of the optimum deployment model and the clockwise spiral-like moving path with fixed step size is also established. According to the moving path and the localization time, energy consumption of the network could be estimated and we could also give the sleep scheduling strategy for the node to be localized. Simulation results show that this method could not only promote the accuracy and success rate of localization, but also reduce the energy consumption.

To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.