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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]

Wireless sensor networks have been widely used in a variety of commercial and military applications, which often require neighbor nodes to establish connection quickly. Due to high mobility of nodes, it is a challenging issue to reduce discovery delay. Most discovery designs are based on pairwise and fixed duty cycle, in which discovery is passively achieved when two nodes wake up at the same time. In order to further improve the efficiency of neighbor discovery in mobile sensor networks, this paper proposes an improved group-based neighbor discovery algorithm (IGND) which dynamically adjusts the nodes' active time based on spatial properties. This paper describes the network model and the algorithm implementation in detail. Simulation results show that the improved algorithm has a good effect in reducing discovery latency and promoting discovery efficiency. [ABSTRACT FROM AUTHOR]

Message propagation is a primary means to locate resources in current unstructured peer-to-peer (P2P) systems and how to reduce effectively the redundant messages of propagation while keeping its high network coverage is what many researchers go in for in this field. In this paper, we propose the algorithm of LMPR, a scheme of improved MPR for message propagation in distributed unstructured P2P system. Based on analyzing the process and message redundancy of MPR, this paper proposes to add to such scheme a mechanism of specified relay-list to reduce further the number of its retransmitting of message. By attaching to each relay-peer a relay-list with peers it will relay message to, the scheme can assure that in the view of each decision-peer a broadcast message can be dispatched and only be dispatched once to every neighbor peer within 2 hops of it. Simulation results show that the scheme of LMPR has better performance than that of MPR in message redundancy, network overhead, and fluctuation brought to the system. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of group behavior recognition. Our approach is to merge all the possible features of group behavior (individuals, groups, relationships between individuals, relationships between groups, etc.) with static context information relating to particular domains. All this information is represented as a set of features by classification algorithms. This is a very high-dimensional problem, with which classification algorithms are unable cope. For this reason, this paper also presents four feature selection alternatives: two wrappers and two filters. We present and compare the results of each method in the basketball domain. [ABSTRACT FROM AUTHOR]

This paper addresses the localization of a timing signal source based on the time of arrival (TOA) measurements that are collected from nearby sensors that are position known and synchronized to each other. Generally speaking, for such TOA-based source localization, the corresponding observation equations contain nonlinear relationship between measurements and unknown parameters, which normally results in the nonexistence of any efficient unbiased estimator that attains the Cramer-Rao lower bound (CRLB). In this paper, we devise a new approach that utilizes linearization and adopts suitable coordinate system translation to eliminate nonlinearity from the converted observation equations. The performance analysis and simulation study conducted show that our proposed algorithm can achieve the CRLB when the zero-mean Gaussian and independent measurement errors are sufficiently small. [ABSTRACT FROM AUTHOR]

Node deployment is one of the fundamental tasks for underwater acoustic sensor networks (UASNs) where the deployment strategy supports many fundamental network services, such as network topology control, routing, and boundary detection. Due to the complex deployment environment in three-dimensional (3D) space and unique characteristics of underwater acoustic channel, many factors need to be considered specifically during the deployment of UASNs. Thus, deployment issues in UASNs are significantly different from those of wireless sensor networks (WSNs). Node deployment for UASNs is an attractive research topic upon which a large number of algorithms have been proposed recently. This paper seeks to provide an overview of the most recent advances of deployment algorithms in UASNs while pointing out the open issues. In this paper, the deployment algorithms are classified into three categories based on the mobility of sensor nodes, namely, (I) static deployment, (II) self-adjustment deployment, and (III) movement-assisted deployment. The differences of the representative algorithms in aspects of sensor node types, computation complexity, energy consumption, deployment objectives, and so forth, are discussed and investigated in detail. [ABSTRACT FROM AUTHOR]

Navigation with wireless sensor networks (WSNs) is the key to provide an effective path for the mobile node. Without any location information, the path planning algorithm generates a big challenge. Many algorithms provided efficient paths based on tracking sensor nodes which forms a competitive method. However, most previous works have overlooked the distance cost of the path. In this paper, the problem is how to obtain a path with minimum distance cost and effectively organize the network to ensure the availability of this path. We first present a distributed algorithm to construct a path planning infrastructure by uniting the neighbors' information of each sensor node into an improved connected dominating set. Then, a path planning algorithm is proposed which could produce a path with its length at most c times the shortest Euclidean length from initial position to destination. We prove that the distributed algorithm has low time and message complexity and c is no more than a constant. Under different deployed environments, extensive simulations evaluate the effectiveness of our work. The results show that factor c is within the upper bound proved in this paper and our distributed algorithm achieves a smaller infrastructure size. [ABSTRACT FROM AUTHOR]

Routing and spectrum allocation is an important challenge in cognitive wireless mesh networks. A distributed routing and spectrum allocation algorithm with cooperation (DRSAC-W) in cognitive wireless mesh networks is proposed in this paper. In order to show the decrease of the average end-to-end delay with cooperation in DRSAC-W, a distributed routing and spectrum allocation algorithm without cooperation (DRSAC-WO) is proposed in this paper. Minimizing the average end-to-end delay is the objective of DRSAC-W and DRSAC-WO. Simulation results show that the proposed algorithm DRSAC-W with cooperation can alleviate the high delay due to the heterogeneity of available channels of different nodes and achieve low average end-to-end delay. [ABSTRACT FROM AUTHOR]

It is necessary to investigate techniques for monitoring structures under unknown earthquakes. For this purpose, an algorithm is proposed in this paper for the identification of structures and excitation of multi-story shear buildings with limited measurements of structural responses. The equation of motion of a multi-story shear building under ground motion is established in the absolute co-ordinate system, while the multi-story building is decomposed into substructures. A novel two-step Kalman estimator approach, which is not available in the previous literature, is proposed for the identification substructures and unknown ground motion with less computational effort. Then, for the purpose of intelligent structural monitoring, a new wireless sensor network is developed in this paper. The designed wireless sensor network has a two-level cluster-tree architecture. Hardware designs of the sensor unit and the cluster head are presented; especially the cluster head contains a low power digital signal processor with strong computing capacity. Thus, the wireless sensor network has the unique feature offers distributed computing at group level. Finally, the proposed algorithm is embedded into the wireless sensor network for intelligent structural monitoring and an experimental test shows the technique is effective for intelligent monitoring of multi-story buildings under unknown earthquakes. [ABSTRACT FROM AUTHOR]

Physical layer radio jamming is a serious security threat to a wireless sensor network since the network relies on open wireless radio channels. A radio jammer is typically strategic and chooses its jamming strategy in response to the possible defense strategy taken by the sensor network. In this paper we model the interaction between the sensor network and the attacker as a noncooperative nonzero-sum static game. In such a game, the sensor network has a set of strategies of controlling its probability of wireless channel access and the attacker manipulates its jamming by controlling its jamming probability after sensing a transmission activity. We propose an algorithm for computing the optimal strategies for jamming attack and network defense. A critical issue is that there may exist a number of possible strategy profiles of Nash equilibria. To address this issue, we further propose to choose realistic Nash equilibria by applying the Pareto dominance and risk dominance. Our numerical results demonstrate that the strategies chosen by the Pareto dominance and risk dominance achieve the expected performance. Our results presented in the paper provide valuable defense guidance for wireless sensor networks against jamming attacks. [ABSTRACT FROM AUTHOR]

The paper proposes a routing scheme for IPv6-based all-IP wireless sensor networks. The paper creates the IPv6 address structure and the IPv6 address configuration algorithm for all-IP wireless sensor networks. Based on the IPv6 address structure, the paper proposes the routing algorithm in the link layer for all-IP wireless sensor networks. In the routing scheme, a sensor node stores the next channel-sampling time of its neighbor nodes. In this way, only during the next channel-sampling time, a sensor node keeps active. and during any other time it returns to sleep. Therefore, the routing performance is improved. and the power consumption is reduced. Finally, the paper discusses the reduced IPv6 protocol stack performing the routing scheme. The routing scheme's performance parameters are evaluated, including routing power consumption and delay, and the data results show the correctness and efficiency of the scheme. [ABSTRACT FROM AUTHOR]

In cellular-based machine-to-machine (M2M) networks, supporting large number of machine-type communications (MTC) users (devices) has become a critical challenge in both the uplink and downlink channels. In this paper, we focus on the downlink beam forming using zero-forcing dirty paper coding (DPC). In order to characterize the system's ability of user admission, we consider the achievable user number, which is defined as the number of users whose signal-to-interference plus-noise ratios exceed a target threshold. Due to the complexity of the optimal scheme, we propose two algorithms with random user scheduling and greedy user scheduling in maximizing the achievable user number by dynamical power assignment. Using the joint distribution of effective channel gains, we derive the achievable user number of both the scheduling schemes. An upper bound on the achievable user number of the greedy scheme is then derived which is shown to be tight when there are a large number of users. From numerical results, we show that both of the schemes enjoy a linear increase in the achievable user number as the number of transmitter antennas increases. The performance of the greedy scheduling scheme is close to the optimal scheduling scheme. [ABSTRACT FROM AUTHOR]

This paper considers the localization problem in mobile sensor networks. Such a problem is a challenging task, especially when measurements exchanged between sensors may contain outliers, that is, data not matching the observation model. This paper proposes two algorithms robust to outliers. These algorithms perform a set-membership estimation, where only the maximal number of outliers is required to be known. Using these algorithms, estimates consist of sets of boxes whose union surely contains the correct location of the sensor, provided that the considered hypotheses are satisfied. This paper proposes as well a technique for evaluating the number of outliers to be robust to. In order to corroborate the efficiency of both algorithms, a comparison of their performances is performed in simulations using Matlab. [ABSTRACT FROM AUTHOR]

Power assignment in wireless ad hoc networks is an important issue of topology control which assigns power for each wireless node so that the induced communication graph satisfies some desired properties such as the connectivity and the energy spanner. In this paper, we study the problem of power assignment in order that its induced communication graph meets the following properties: (1) it is an energy-t-spanner which is energy efficient; (2) it is k-fault resistant which can withstand up to k - 1 node failures where k⩾1; (3) the interference is minimal. We propose algorithms to address this problem. Both the theoretic analysis and the simulations in the paper prove that our algorithms can induce a k-fault resistant energy spanner and furthermore the interference is minimized. To the best of our knowledge, this is the first paper to study the power assignment problem simultaneously considering spanner properties, the fault tolerance, and the interference reduction. [ABSTRACT FROM AUTHOR]

This paper discusses the distributed routing algorithm of wireless self-organized network and puts forward a new type of hybrid self-organizing clustering routing protocol by combining energy sense and maximum connectedness. This routing protocol adopts a mechanism which is mainly based on cluster routing with the theory of "on demand-based plane routing discovery mechanism" becoming secondary. The routing discovery and routing maintenance algorithm are also described in this paper. As the emulation experiment result shows, there is no significant difference between the routing protocol and the demand-based routing protocol; however, when there are quite a number of nodes in the network, the performance can be greatly enhanced compared with the demand-based routing protocol. The routing protocol discussed in this paper has better extendability, lower routing overhead, and better data transmission rate; thus it can extend network lifetime and enhance the network performance. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are tightly linked with the practical environment in which the sensors are deployed. Sensor positioning is a pivotal part of main location-dependent applications that utilize sensornets. The global topology of the network is important to both sensor network applications and the implementation of networking functionalities. This paper studies the topology discovery with an emphasis on boundary recognition in a sensor network. A large mass of sensor nodes are supposed to scatter in a geometric region, with nearby nodes communicating with each other directly. This paper is thus designed to detect the holes in the topological architecture of sensornets only by connectivity information. Existent edges determination methods hold the high costs as assumptions. Without the help of a large amount of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose a solution, named PPA based on Poincare-Perelman Theorem, to judge whether there are holes in WSNs-monitored areas. Our solution can properly detect holes on the topological surfaces and connect them into meaningful boundary cycles. The judging method has also been rigorously proved to be appropriate for continuous geometric domains as well as discrete domains. Extensive simulations have been shown that the algorithm even enables networks with low density to produce good results. [ABSTRACT FROM AUTHOR]

This paper assumes a set of identical wireless hosts, each one aware of its location. The network is described by a unit distance graph whose vertices are points on the plane two of which are connected if their distance is at most one. The goal of this paper is to design local distributed solutions that require a constant number of communication rounds, independently of the network size or diameter. This is achieved through a combination of distributed computing and computational complexity tools. Starting with a unit distance graph, the paper shows: 1. How to extract a triangulated planar spanner; 2. Several algorithms are proposed to construct spanning trees of the triangulation. Also, it is described how to construct three spanning trees of the Delaunay triangulation having pairwise empty intersection, with high probability. These algorithms are interesting in their own right, since trees are a popular structure used by many network algorithms; 3. A load balanced distributed storage strategy on top of the trees is presented, that spreads replicas of data stored in the hosts in a way that the difference between the number of replicas stored by any two hosts is small. Each of the algorithms presented is local, and hence so is the final distributed storage solution, obtained by composing all of them. This implies that the solution adapts very quickly, in constant time, to network topology changes. We present a thorough experimental evaluation of each of the algorithms supporting our claims. [ABSTRACT FROM AUTHOR]

Distributed sensing has been used for enhancing signal to noise ratios for space-time localization and tracking of remote objects using phased array antennas, sonar, and radio signals. The use of these technologies in identifying mobile targets in a field, emitting acoustic signals, using a network of low-cost narrow band acoustic micro-sensing devices randomly dispersed over the region of interest, presents unique challenges. The effects of wind, turbulence, and temperature gradients and other environmental effects can decrease the signal to noise ratio by introducing random errors that cannot he removed through calibration. This paper presents methods for dynamic distributed signal processing lo detect, identify, and track targets in noisy environments with limited resources. Specifically, it evaluates the noise tolerance of adaptive beamforming and compares it to other distributed sensing approaches. Many source localization and direction-of-arrival (DOA) estimation methods based on beamforming using acoustic .sensor array have been proposed. We use the approximate maximum likelihood parameter estimation method to perform DOA estimation of the source in the frequency domain. Generally, sensing radii are large and data from the nodes are transmitted over the network to a centralized location where beamforming is done. These methods therefore depict low tolerance to environmental noise. Knowledge based localized distributed processing methods have also been developed for distributed in-situ localization and target tracking in these environments. These methods, due to their reliance only on local sensing, are not significantly affected by spatial perturbations and are robust in tracking targets in low SNR environments. Specifically, Dynamic Space- time Clustering (DSTC)-based localization and tracking algorithm has demonstrated orders of magnitude improvement in noise tolerance with nominal impact on performance. We also propose hybrid algorithms for energy efficient robust performance in very noisy environments. This paper compares the performance of hybrid algorithms with sparse beamforming nodes supported by randomly dispersed DSTC nodes to that of beamforming and DSTC algorithms. Hybrid algorithms achieve relative high accuracy in noisy environments with low energy consumption. Sensor data from a field test in the Marine base at 29 Palms, CA. were analyzed for validating the results in this paper. The results were compared to "ground truth " data obtained from GPS receivers on the vehicles [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]

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]

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]

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]