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For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-path and multi-hop wireless powered sensor network in case of hardware impairment, constituting an energy node, one source node, single sink node, and a series of distributed relay sensor nodes, where the energy node transmits wireless energy to all terminals in the first stage, and the relay sensor nodes relay the information of the source node to the sink node in the second stage. There exists M available paths between the source node and sink node, one of which is chosen for serving source-sink communication. To enhance the minimum achievable data rate, we propose a multi-hop communication protocol based on time-division-multiple-access and an optimal throughput path algorithm. We formulate the time allocation optimization problem about energy and information transmission of the proposed multi-hop cooperation, and confirm through abundant simulation experiments that the proposed scheme can availably improve user unfairness and spectral efficiency, and thus enhance its throughput performance. [ABSTRACT FROM AUTHOR]

In the process of fulfilling k-coverage over target nodes, a large quantity of redundant data may be produced, which will cause network congestion, reduce communication efficiency, impair coverage quality, and exhaust network energy quickly. To solve this problem, the paper proposes an Energy-Efficient k-Coverage Algorithm (EEKCA), which attempts to construct a network coverage model by utilizing the relative positions of nodes. Through analyzing the model, the coverage expectations for nodes in the monitored area and the minimum number of nodes required for full coverage are computed. As for power consumption, the paper presents an energy shifting function between working nodes and neighboring nodes, by use of which the scheduling for low-energy node is completed, balancing the energy consumption over the entire network and optimizing network resources. Finally, simulation results suggest that the proposed algorithm not only can improve the coverage quality of network but also can prevent the rapid depletion of node energy, thus achieving the goal of extending network lifetime. [ABSTRACT FROM AUTHOR]

Multipath routing is an alternative routing technique, which uses redundant paths to deliver data from source to destination. Compared to single path routing protocols, it can address reliability, delay, and energy consumption issues. Thus, multipath routing is a potential technique to overcome the long propagation delay and adverse link condition in underwater environment. However, there are still some problems in multipath routing. For example, the multiple paths may interfere with each other and arouse large end-to-end delay difference amongst multiple paths. This paper proposes a novel multipath routing structure and a conflict-free algorithm based on TDMA scheme. The forwarding nodes are selected based on the propagation delay and location information. This special multiple routing structure not only can ensure parallel multiple transmission without collision, but also can get a small end-to-end delay difference amongst multiple paths. Simulation results show that the multipath routing scheme proposed in this paper outperforms the traditional strategy. [ABSTRACT FROM AUTHOR]

Urban road travel time is an important parameter to reflect the traffic flow state. Besides, it is one of the important parameters for the traffic management department to formulate guidance measures, provide traffic information service, and improve the efficiency of the detectors group. Therefore, it is crucial to improve the forecast accuracy of travel time in traffic management practice. Based on the analysis of the change-point and the ARIMA model, this paper constructs a model for the massive data collected by loop detectors to forecast travel time parameters. Firstly, the preprocessing algorithm for the data of loop detectors is given, and the calculating model of the travel time is studied. Secondly, a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns. Then, this paper establishes a forecast model to forecast travel time in different patterns using the improved ARIMA model. At last, the model is verified by simulation and the verification results of several groups of examples show that the model has high accuracy and practicality. [ABSTRACT FROM AUTHOR]

Localization is one of the most important problems in wireless sensor networks. In this paper, we investigate the convergence rate estimate problem of a distributed localization algorithm which approximately formulates the localization problem as the convex feasibility problem including the consistent case and the inconsistent case. Although existing works established optimal consensus convergence analysis for this algorithm, they did not provide the convergence rate estimate. In this paper, we mainly show that for the consistent case the convergence rate of the optimal consensus will be exponential under some basic conditions, while for the inconsistent case we provide a necessary condition for the optimal consensus and a convergence rate estimate inequality. Furthermore, numerical examples are also provided to validate the established convergence and convergence rate results. [ABSTRACT FROM AUTHOR]

An accurate localization algorithm tailored for anisotropic wireless sensors networks (WSNs) is proposed in this paper. Using the proposed algorithm, each regular or position-unaware node estimates its distances only to reliable anchors or position-aware nodes. The latter are properly chosen following a new reliable anchor selection strategy that ensures an accurate distance estimation making thereby our localization algorithm more precise. It is shown that the proposed algorithm is implementable in both 2-dimensional (2D) and 3-dimensional (3D) scenarios. A power saving mechanism aiming to enhance the WSN lifetime is also envisaged in this paper. It is proven that the proposed algorithm could easily incorporate such a mechanism. Simulations show that our algorithm, whether combined or not with the power saving mechanism, consistently outperforms the best representative localization algorithms currently available in the literature in terms of accuracy, even with the presence of nonuniform node distribution or radiation irregularities. [ABSTRACT FROM AUTHOR]

Sensor web systems, cyber-physical systems, and the so-called Internet of Things are concepts that share a set of common characteristics. The nature of such systems is highly dynamic and very heterogeneous and issues such as interoperability, energy consumption, or resource management must be properly managed to ensure the operation of the applications within the required quality of service level. In this context, base technologies such as component based software engineering or Service Oriented Architecture can play a central role. Model driven development and middleware technologies also aid in the design, development, and operation of such systems. This paper presents a middleware solution that provides runtime support for the complete lifecycle management of a system consisting of several concurrent applications running over a set of distributed infrastructure nodes. The middleware builds up on top of a general purpose component model and is driven by a quality of service aware self-configuration algorithm that provides stateful reconfiguration capabilities in face of both internal (application triggered) and external (application unaware) reconfiguration events. The platform has been deployed over an automated warehouse supervision system that serves as a case study. [ABSTRACT FROM AUTHOR]

The advent of the Internet of things (IoT) is changing the way how we interact with the physical world. However, the current Internet suffers from exponential increase in bandwidth demand. In order to resolve the bandwidth issue, we can consider aerial networks by unmanned aerial vehicles (UAV) or the so-called drones for establishing a three-dimensional mobile network in an ad hoc manner. By deploying a network from the sky, we can use the otherwise idle wireless medium and high mobility free from ground obstacles. Aerial networks are especially effective for supporting the temporary surge of population as well as disaster areas because building an additional network infrastructure requires extensive time. In this paper, we propose an efficient handover mechanism for aerial networks in the three-dimensional space, which significantly differs from the conventional two-dimensional schemes. The proposed scheme adjusts the height of a drone and the distance between the drones. To this end, we use the seamless handover success probability and the false handover initiation probability in order to evaluate the optimal coverage decision algorithm. To the best of our knowledge, the proposed scheme is the first attempt for resolving handover of net-drones in the three-dimensional space. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data collection with high efficiency is a key problem for the completion of crowdsensing tasks in Mobile Crowdsensing Networks. Much work has been done on the opportunistic routing problem, but they usually just focused on the sociality of node, and the attributes of node itself have always been ignored, such as energy and buffer, which are particularly important for the persistent crowdsensing tasks. In this paper, we now modify the spectral clustering algorithm and propose a novel and more practical routing protocol for MCSN, named EBRP, in which the sociality of node and the dynamic changes of the attributes of node itself are all fully considered when the relay nodes are selected. To evaluate the performance of our routing protocol, we have done extensive simulations and much analysis based on three real-life mobility traces. The results show that our EBRP can greatly outperform the existing protocols in terms of delivery ratio, cost, and node survival ratio. [ABSTRACT FROM AUTHOR]

Worker selection for many crowd-sensing tasks must consider various complex contexts to ensure high quality of data. Existing platforms and frameworks take only specific contexts into account to demonstrate motivating scenarios but do not provide general context models or frameworks in support of crowd-sensing at large. This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creator define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. In the first phase, it selects workers who satisfy predefined constraints. In the second phase, by leveraging the worker’s past participation history, it further selects those who are more likely to undertake a crowd-sensing task based on a case-based reasoning algorithm. We demonstrate the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluate the effectiveness of the case-based reasoning algorithm for willingness-based selection by using a questionnaire-generated dataset. Results show that our case-based reasoning algorithm outperforms the currently practiced baseline method. [ABSTRACT FROM AUTHOR]

The unique characteristics of wireless sensor networks make them susceptible to the attacks. The secure scheme is considered in designing the routing protocol and algorithm. The cryptography and authentication secure technology can defend the external attacks but have no way to protect the network when the internal attacks occur. However, the trust management scheme with attack-resistance and robustness can efficiently detect the internal attack. On the condition of defending the attacks, it is also an issue that the trust management scheme aids routing algorithm to improve the security and performance. In this paper, we proposed a Trusted Tree-based trust management for secure routing in wireless sensor networks. The common attacks are firstly analyzed. And then the dynamic time window is designed in the distributed trust model with the detection of direct trust and indirect trust. Based on the trusted nodes, Trusted Tree is constructed. And the path quality of nodes in Trusted Tree is used as the gradient for routing. Finally, we add Trusted Tree-based trust management into the QoS based routing protocol and algorithm. Experimental results show that the QoS based routing algorithm with Trusted Tree has better performance and security. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) model by analyzing the limitations of existing localization algorithm is proposed in this paper. Specifically, neither the anchor nor the ID is required in the proposed model, and the node function is limited. The hardware function of the node in our model is independent of the MAC protocol. Moreover, based on the proposed WSN model, we design an energy efficient anchor-free localization algorithm for no-identity WSN. Instead of multiple measurements, mathematical calculation is adopted to acquire the position information. Subsequently, the effectiveness of the algorithm is confirmed by rigid theory. The simulation results show that the proposed algorithm can improve the performance of localization and energy efficiency in WSN significantly. [ABSTRACT FROM AUTHOR]

Heterogeneous networks with dense deployment of small cells can employ cognitive features to efficiently utilize the available spectrum resources. Spectrum sensing is the key enabler for cognitive radio to detect the unoccupied channels for data transmission. In order to deal with shadowing and multipath fading in sensing channels, cooperative spectrum sensing is designed to increase the accuracy of the sensed signal. In this paper, an optimized local decision rule is implemented for the case that the received data from primary users are possibly correlated due to the sensing channel impairments. Since the prior information is unavailable in the real systems, Neyman-Pearson criterion is used as the cost function. Then, a discrete iterative algorithm based on Gauss-Seidel process is applied to optimize the local cognitive user decision rules under a fixed fusion rule. This method with low complexity can minimize the cost using the golden section search method in a finite number of iterations. ROC curves are depicted using the achieved probability of detection and false alarm by numerical examples to illustrate the efficiency of the proposed algorithm. Simulation results also confirm the superiority of the proposed method compared to the conventional topologies and decision rules. [ABSTRACT FROM AUTHOR]

In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements. [ABSTRACT FROM AUTHOR]

Accurate measurement of hand forces in motorbike riding is highly desirable for studies of safe riding. In this paper, we implement a force-sensing glove system for measuring real-time hand forces during motorbike riding with the aim of giving feedback to the riders. It consists of a pair of gloves with tactile sensors suitably mounted and configured for data acquisition via a wireless smartphone. A novel calibration method is developed for dynamic calibration considering force measurement in natural operation and environments. Consequently, a series of data classification algorithms ensure accurate hand performance feedbacks for motorbike riders. The feedback data could potentially alert the riders to predict and prevent accidents. Validation tests demonstrate that this force-sensing glove system has a strong potential as a tool for hand performance monitoring in real environment. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) is a solution for automated inventory and object detection applications. However, if RFID tags are attached to metal objects, detection errors may occur due to Foucault currents and interferences caused by multiple simultaneous reflections. Errors may increase if metal objects are moving. The paper presents a novel algorithm using RFID low-level reader variables, such as RSSI (Received Signal Strength Indicator), phase angle, and Doppler shift, to detect and trace metal objects. The algorithm was designed to identify if a tag is static or moving and, in the latter case, to compute its speed and direction. The algorithm differs from previous approaches since it uses a simple setup with one commercial portal reader coupled with one single element antenna. Experiments employed one tag located on one metal moving object and 12 static interferer tags, in both outdoor and indoor locations. Results show that the algorithm identifies static tags with no errors. For moving tags, the algorithm shows a maximum 12% error. The algorithm correctly estimates direction and computes object speed. Test conditions emulate fork lift speeds when carrying objects in an industrial warehouse. [ABSTRACT FROM AUTHOR]

Backpressure based scheduling has revealed remarkable performance in wireless multihop networks as reported in a lot of previous work. However, its lack of consideration on energy use efficiency is still an obstacle for backpressure based algorithms to be deployed in resource-constrained wireless sensor networks (WSNs). In this paper, we focus on studying the design of energy efficient backpressure based algorithm. For this purpose, we propose a gradient-assisted energy-efficient backpressure scheduling algorithm (GRAPE) for WSNs. GRAPE introduces a new link-weight calculation method, based on which gradient information and nodal residual energy are taken into account when making decisions on backpressure based transmission scheduling. According to the decisions made by this new method, packets are encouraged to be forwarded to nodes with more residual energy. We theoretically prove the throughput-optimality of GRAPE. Simulation results demonstrate that GRAPE can achieve significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work. [ABSTRACT FROM AUTHOR]

Gas source localization (GSL) is a critical component in wireless sensor network (WSN) which can be used to estimate the position of gas leaks through the gas concentration measured by the monitoring nodes. In this paper, we first derive a gas diffusion model under windy conditions based on the gas diffusion theory. Because of the nonlinearity of the gas diffusion model under windy conditions, the traditional weighted centroid algorithm for gas source localization produces very inaccurate estimation of the source location. To address this problem, we propose a novel gas source localization algorithm which combines the weighted centroid method and particle filter. Simulation results validate the effectiveness of our algorithm under unknown gas source strength. Compared to weighted centroid method alone, our algorithm shows a higher accuracy in location. Moreover, its convergence rate is improved by the preestimation of initial value. [ABSTRACT FROM AUTHOR]

Context-awareness in wireless sensor networks (WSNs) relies mainly on the position of objects and humans. The provision of this positional information becomes challenging in the harsh environmental conditions where WSNs are commonly deployed. With an antagonistic philosophy of design, fingerprinting and ranging have emerged as the key technologies underpinning wireless localization in harsh environments. Fingerprinting primarily focuses on accurate estimation at the expense of exhaustive calibration. Ranging mainly pursues an easy-to-deploy solution at the expense of moderate performance. In this paper, we present a resilient framework for sustained localization based on accurate fingerprinting in critical areas and light ranging in noncritical spaces. Such framework is conceived from the Bayesian perspective that facilitates the specification of recursive algorithms for real-time operation. In comparison to conventional implementations, we assessed the proposed framework in an indoor scenario with measurements gathered by commercial devices. The presented techniques noticeably outperform current approaches, enabling a flexible adaptation to the fluctuating conditions of harsh environments. [ABSTRACT FROM AUTHOR]

Malware remains a major threat to nowadays Internet. In this paper, we propose a DNS graph mining-based malware detection approach. A DNS graph is composed of DNS nodes, which represent server IPs, client IPs, and queried domain names in the process of DNS resolution. After the graph construction, we next transform the problem of malware detection to the graph mining task of inferring graph nodes’ reputation scores using the belief propagation algorithm. The nodes with lower reputation scores are inferred as those infected by malwares with higher probability. For demonstration, we evaluate the proposed malware detection approach with real-world dataset. Our real-world dataset is collected from campus DNS servers for three months and we built a DNS graph consisting of 19,340,820 vertices and 24,277,564 edges. On the graph, we achieve a true positive rate 80.63% with a false positive rate 0.023%. With a false positive of 1.20%, the true positive rate was improved to 95.66%. We detected 88,592 hosts infected by malware or C&C servers, accounting for the percentage of 5.47% among all hosts. Meanwhile, 117,971 domains are considered to be related to malicious activities, accounting for 1.5% among all domains. The results indicate that our method is efficient and effective in detecting malwares. [ABSTRACT FROM AUTHOR]

The proposed idea in this paper is the constructive ramification of cluster designing mechanism floated in recently published work, multilayer cluster designing algorithm (MCDA) to ameliorate the performance in network lifetime. Novel algorithms for time slot allocation, minimizing the cluster head completion candidates, and cluster member selection∖node affiliation to cluster head play underpinning roles to achieve the target. These incorporations in MCDA result in minimizing transmissions, suppressing unfavorable response of transmissions and near-equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, number of designed clusters, number of nodes per cluster, and unclustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCD and EADUC. [ABSTRACT FROM AUTHOR]

This paper develops a novel augmented filtering framework based on information weighted consensus fusion, to achieve the simultaneous localization and tracking (SLAT) via wireless sensor networks (WSNs). By integrating augmented transition and observation models, we formulate a dynamical system that encodes both the target moving manners and coarse sensor locations in an augmented state. We then conduct augmented filtering based on augmented extended Kalman filters to estimate the augmented state. We further refine our target estimate according to information weighted consensus filtering which fuses the target information obtained from neighboring sensors. The fused information is fed back as the target estimate to the augmented filter. Our framework is computationally efficient because it only requires neighboring sensor communications. Experiments on SLAT problem validate the effectiveness of the proposed algorithm in terms of tracking accuracy and localization precision in limited ranging conditions. [ABSTRACT FROM AUTHOR]

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), connecting disjoint segments is significant for network restoration, especially in some mission critical applications. However, the variability of distances between disjoint segments has tremendous influence on relay nodes deployment. In fact, finding the optimal solution for connecting disjoint segments in terms of the number and positions of relay nodes is NP-hard. To address this issue, plenty of heuristics, such as STP-MSP (Cheng et al., 2008), MST-1tRN (Lloyd et al., 2007), and CORP (Lee and Younis, 2010) are deeply pursued. In this paper, we propose a distributed restoration algorithm based on optimal relay node placement (simply, ORNP). It aims at federating separated segments by populating the minimum number of relay nodes in a WSN that has suffered a significant damage. In addition, both of complexity and upper bound of the relay count for ORNP are explored. The simulation results show that ORNP performs better than STP-MSP, MST-1tRN, and CORP in terms of relay count and the connectivity of resulting topology. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime. [ABSTRACT FROM AUTHOR]

This paper presents a novel matrix completion algorithm to enable energy efficient data gathering in wireless sensor networks. The algorithm takes full advantage of both the low-rankness and the DCT compactness features in the sensory data to improve the recovery accuracy. The time complexity of the algorithm is analyzed, which indicates it has a low computational cost. Moreover, the recovery error is carefully analyzed and a theoretical upper bound is derived. The error bound is then validated by experimental results. Extensive experiments are conducted on three datasets collected from two testbeds. Experimental results show that the proposed algorithm outperforms state-of-the-art methods for low sampling rate and achieves a good recovery accuracy even if the sampling rate is very low. [ABSTRACT FROM AUTHOR]

More and more network cameras are now working over distributed networks, offering the capability of remote intelligent video surveillance. In this paper, we bring forward an original particle filter tracking algorithm named labelled particle filter which describes each image patch with a binary label. Based on the imaging theory of thermography, moving objects, such as pedestrians and automobiles, usually have higher intensities compared with the background in a gray-level pseudocolor mode. Thus an image patch can be classified into two categories according to its intensity distribution, and we can use a one-bit binary label, positive or negative, to describe the attribute of image patch. Therefore, the candidate target template is established only if the label of candidate target matches the label of reference target, and the computational complexity is reduced consequently. Experiments are conducted to show that the proposed algorithm can handle real-time object tracking with less time cost while maintaining high tracking accuracy. [ABSTRACT FROM AUTHOR]

Nodes in underwater wireless sensor networks (UWSNs) keep moving and dispersing due to force of water flow and aquatic creatures touching, and thus some isolated unknown nodes emerge. This type of isolated unknown nodes cannot directly communicate with enough beacons in their neighborhoods, which makes localizations for them disabled or the localization error unbearable. To this end, a multihops fitting localization approach is proposed in this paper. Firstly, some intermediate nodes between beacons and unknown nodes are set as routers to construct paths via a greedy method; then, the multihop paths are approximately fitted into straight lines; finally, the positions of unknown nodes can be estimated by trilateration. The proposed algorithm is analyzed and simulated in terms of localization error and error variance, and the results are proven preferable. [ABSTRACT FROM AUTHOR]

We present an approach for the modelling and simulating of the modem section of underwater sensor networks. The proposal is based on a specially designed modem architecture and the use of simulation tools and models that represent each of the communication elements: the water medium, physical transducers, electronics, and coding/decoding software. The algorithms can be simulated in the modelling environment; this framework does not require recoding and allows the combination of real and modelled elements. In physical terms, the modem engine provides a decoupled pipelined design of the processing path for the algorithms which allows users to run complex algorithms without requiring a highly demanding specific hardware. The proposal includes a methodology that has allowed us to significantly reduce the effort required in the process, from algorithm development to the effective deployment of the system. As a case study, this paper shows its application and results in the evaluation of a multipath and Doppler-shift correction algorithms. [ABSTRACT FROM AUTHOR]

The key to realizing the crowd sensing network is to overcome the resource restrictions of energy, bandwidth, computing, and so on. First of all, due to the number of users and sensor availability will be dynamic change over time, crowd sensing system is difficult to accurately predict and allocate resource to accomplish a specific task. Secondly, there is a need to consider how to choose an effective subset from a large number of users with different sensing ability, so as to allocate the sensing devices in communication resources under the constraint conditions. This paper proposes a profit maximization algorithm for resource allocation component in crowd sensing environment. The proposed algorithm not only considers the current profit of crowd sensing service request but also considers the long-term expected profits, so as to ensure long-term maximum profit. The objective function is no longer to minimize the completion time but rather to achieve the target profit maximization. The experimental results show that the new algorithm is feasible and superior to the traditional algorithms. [ABSTRACT FROM AUTHOR]

This paper mainly describes a fault diagnosis scheme for aircraft multibranches wiring networks. The background theory is based on the matching algorithm with theoretical reflection waveform and realistic reflection waveform. Firstly, a numerical model with a comprehensive database was built to create the theoretical waveform of the measured wiring network. On the other hand, the realistic reflection waveform of the measured wiring network can be collected by reflectometry detection system. Then, the waveform matching algorithm and fault diagnosis algorithm were applied to detect the fault type and fault location in the measured wiring network. With all the steps above, the fault diagnosis scheme was designed and realized. Finally, a multibranches wiring network, which includes two branches, and one of its branches contains two subbranches with different types of faults, was built to verify the fault diagnosis scheme and the results show that the scheme is an effective way to diagnose faults in multibranches wiring network. [ABSTRACT FROM AUTHOR]

How to cover a monitored area with the minimum number of sensor nodes is a fundamental in wireless sensor network (WSN). To address this problem, this paper proposes ECAPM, that is, Enhanced Coverage Algorithm Based on Probability Model. Given an area to monitor, ECAPM can calculate the minimum number of sensor nodes to cover that area. Firstly, ECAPM introduces the method to compute the expectation of probability that a sensor node is covered. Then ECAPM presents the procedure of expected value calculation when target node is covered by one sensor node and N sensor nodes. Finally ECAPM verifies the relation of proportional function when random variables are not independent. The simulation results show that ECAPM can cover the monitored area effectively with less sensor nodes and improve the quality of coverage. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), wireless links are unreliable and sensor nodes may be in sleep mode. Thus, many applications which require reliable broadcast cannot work properly if they lost some packets. In order to make sure every sensor node in the network receives all packets completely and correctly, retransmission of lost packets is indispensable. Many retransmission methods with network coding have been proposed but they do not catch coding opportunity. In this paper, real-time retransmission algorithm based on network coding (NCRR) is proposed to make the average number of transmissions as less as possible. During the transmission of original packets, we detect whether a certain time slot is suitable for retransmitting a coded packet based on recovery ratio. We analyze the number of transmissions with traditional retransmission and with network coding. Compared with existing approaches, simulation results show that our algorithm can effectively reduce the average number of transmissions and improve the transmission efficiency. [ABSTRACT FROM AUTHOR]

In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by the energy and bandwidth, a large-scale wireless sensor network displays the disadvantages of fusing the data collected by the sensor nodes and compressing them at the sensor nodes. Thus the goals of reduction of bandwidth and a high speed of data processing should be achieved at the second-level sink nodes. Traditional compression technology is unable to appropriately meet the demands of processing massive sensor data with a high compression rate and low energy cost. In this paper, Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm, making use of the CUDA framework at the second-level sink node is presented. The core idea of PMLZSS algorithm is parallel matrix matching. PMLZSS algorithm divides the data compression files into multiple compressed dictionary window strings and prereading window strings along the vertical and horizontal axes of the matrices, respectively. All of the matrices are parallel matched in the different thread blocks. Compared with LZSS and BZIP2 on the traditional serial CPU platforms, the compression speed of PMLZSS increases about 16 times while, for BZIP2, the compression speed increases about 12 times when the basic compression rate unchanged. [ABSTRACT FROM AUTHOR]

Several studies present methods to economize energy in wireless sensor networks (WSNs) which is one of the most confining resources in these systems. This paper presents a bioinspired, cellular automata (CA) based model for constructing data trees that connect all nodes with a sink node. Nonetheless, the proposed model takes into consideration not only the proximity between two nodes but also their remaining available energy. Consequently, by avoiding nodes with nearly depleted energy sources, the life time of the network can be prolonged. The plasmodium of Physarum polycephalum is the inspiration for the proposed model, as it has proved its robustness in graphically expressed problems. Moreover, CAs are able to encapsulate the parallel dynamics of the model and, thus, achieve a very fast execution. [ABSTRACT FROM AUTHOR]

Since wireless sensor networks (WSNs) consist of nodes with limited battery power, collaborative data processing and balanced energy consumption should be considered as the key issue. This paper proposes an efficient cluster head selection approach for collaborative data processing in WSNs. The proposed algorithm designs an effective energy-efficient model to select the optimal cluster heads among all nodes fairly, which helps to reduce the impact of the monitoring scheme on the lifetime of network. Experimental results show that the proposed protocol is able to reduce energy consumption and obtain higher efficiency as well as effectively prolonging the lifetime of network more than a few existing cluster-based routing protocols. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle between ever-evolving malware and antivirus programs that need to process ever-increasing security related data. For malware detection, various approaches have been proposed. Among them, dynamic analysis is known to be effective in terms of providing behavioral information. As malware authors increasingly use obfuscation techniques, it becomes more important to monitor how malware behaves for its detection. In this paper, we propose a novel approach for dynamic analysis of malware. We adopt DNA sequence alignment algorithms and extract common API call sequence patterns of malicious function from malware in different categories. We find that certain malicious functions are commonly included in malware even in different categories. From checking the existence of certain functions or API call sequence patterns matched, we can even detect new unknown malware. The result of our experiment shows high enough F-measure and accuracy. API call sequence can be extracted from most of the modern devices; therefore, we believe that our method can detect the malware for all types of the ubiquitous devices. [ABSTRACT FROM AUTHOR]

Density-based clustering for big data is critical for many modern applications ranging from Internet data processing to massive-scale moving object management. This paper proposes Cludoop algorithm, an efficient distributed density-based clustering for big data using Hadoop. First, we propose a serial clustering algorithm CluC by leveraging cell partition optimization and c-cluster to fast find clusters. CluC completes classification of the points using the relationships of connected cells around points instead of expensive completed neighbor query, which significantly reduce the number of distance calculations. Second, we propose the Cludoop, which can efficiently cluster very-large-scale data in parallel using already existing data partition on Map/Reduce platform. It employs the proposed serial clustering CluC as a plugged-in clustering on parallel mapper, along with a cell description instead of completed cell in transmission to reduce both network and I/O costs. Guided by proposed cell-based principles, we also design a Merging-Refinement-Merging 3-step framework to merge c-clusters on the overlay of assigned preclustering result on reducer. Finally, our comprehensive experimental evaluation on 10 network-connected commercial PCs, using both huge-volume real and synthetic data, demonstrates (1) the effectiveness of our algorithm in finding correct clusters with arbitrary shape and (2) the fact that our proposed algorithm exhibits better scalability and efficiency than state-of-the-art method. [ABSTRACT FROM AUTHOR]

The peer-to-peer-based video resource dissemination is important for handling extreme conditions such as flash crowds which severely break the balance between supply and demand of video content and bring negative effects for quality of service (QoS). In this paper, we propose a novel interest detection-based video dissemination algorithm under flash crowd in mobile ad hoc networks (IDVD). IDVD classifies the user behavior of requesting video content in terms of the popularities and playback time of video and considers the demand of popular video as the cause of flash crowd. IDVD makes use of the prediction of necessary bandwidth and period time of intensive request to be aware of period time and scale of flash crowd. IDVD employs a resource dissemination algorithm which can fast discover interested nodes and achieve the on-demand dissemination of video resources in neighboring geographical area, in order to handle imbalance between supply and demand. IDVD uses the epidemic model to describe the state transition of nodes and define the convergence condition of resource dissemination algorithm. Extensive tests show how IDVD achieves much better performance results in comparison with other state of the art solutions. [ABSTRACT FROM AUTHOR]

This paper presents an opportunistic reception (OR) algorithm for energy-efficient transmission in cooperative wireless sensor networks (WSNs), where the characteristics of random linear network coding and the energy consumption property of WSNs are jointly considered. In OR, the sensor nodes in intermediate cluster generate the independent coding vector through simple forwarding or decoding-recoding manners opportunistically, so that the number of received packets can be reduced. To evaluate the algorithm, we derive the average number of received packets, the transmitting nodes, and decoding failure probability under specific assumption. The obtained theoretical and simulation results verify the effectiveness offered by the proposed approach. [ABSTRACT FROM AUTHOR]

Balanced energy consumption in WSNs is the key to maximize network lifetime. After analyzing laws of energy consumption of WSNs, a heterogeneous node deployment model based on area topology control is proposed in this paper. It utilizes predetermined heterogeneous node deployment to balance area energy consumption ratio and the topology control algorithm based on area energy consumption ratio to maintain dynamic equilibrium energy consumption. Simulation results show that our model takes a superior performance on maintaining network coverage, balancing energy consumption, and maximizing network lifetime. Compared with similar WSNs deployment models, our model can be more efficient to prolong network lifetime and has advantages in reducing dependence on deployment accuracy, balancing dynamic energy consumption, and improving lifetime in poor network environment. [ABSTRACT FROM AUTHOR]

With the popularity of web, the internet is becoming a major information provider and poster of an event due to its real-time, open, and dynamic features. In this paper, crowd sensing based burst computation algorithm of a web event is developed in order to let the people know a web event clearly and help the social group or government process the events effectively. Different temporal features of web events are developed to provide the basics for the proposed computation algorithm. The burst power is presented to integrate the above temporal features of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that the number of web pages and the average clustering coefficient can be used to detect events. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly. [ABSTRACT FROM AUTHOR]

A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation condition. A group of nodes satisfying the node spacing condition is selected as cooperative nodes based on the ring array to minimize communication interference among nodes. Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. Finally, the game process is continuously executed until the Nash equilibrium is reached. According to simulation results, the sidelobe level caused by the cooperative nodes is reduced and the transmission conflicts are lessened. Thus, the quality of communication links in between nodes in the network is improved. Energy efficiency is also promoted because a balancing of energy consumption is involved in the proposed potential game model, and network lifetime is effectively prolonged accordingly. [ABSTRACT FROM AUTHOR]

The sunlight intensity-based global positioning system (SGPS) is able to geolocate outdoor objects by means of the sunlight intensity detection. This paper presents the integration of SGPS into a sensor network in order to improve the overall accuracy using evolutionary algorithms. Another contribution of the paper is to theoretically solve both global and relative positioning of the sensors composing the network within the same framework without satellite-based GPS technology. Results show that this approach is promising and has potential to be improved further. [ABSTRACT FROM AUTHOR]

Precise three-dimensional measurements of surfaces are significant in many fields. Usually, three-dimensional descriptions of the object surface have to be acquired by contact measure probe or other non-contact equipment. The paper proposed a novel surface reconstruction method that uses camera relative irradiance via the image gray-scale value information under fixed ring light. After calibrations of the measurement condition, just one image of the object is necessary to reconstruct the surface. The method mainly involves two aspects: the calibration process and the surface reconstruction process. The purpose of the calibration process is to find the relation between the image gray-scale value and the relative irradiance of the charge-coupled device sensor in different expose conditions. The surface reconstruction mainly focuses on the relation between the irradiance and height information. The experiment result shows the relative error of the illumination measurement result obtained using charge-coupled device camera is less than 2.91%. Reconstruction error is mainly result from the truncation error of algorithm calculation. An example is presented to verify the performance of this technique. The reconstruction experiments demonstrated that it can successfully measure the geometrical characteristics from the specified view of the object. [ABSTRACT FROM AUTHOR]