Pedestrian detection in agricultural environments remains an underexplored area in computer vision, particularly in the context of tea plantation management. This study proposes a vision-based personnel detection system customized for tea garden scenarios, aiming to enhance on-site monitoring and labor management. We adopted the YOLOv5 object detection framework and integrated it with the DeepSORT tracking algorithm to develop a real-time pedestrian recognition system. The model was initially trained on the MOT20 dataset and tested on video data collected from real-world tea plantations.Experimental results revealed that models trained on conventional urban pedestrian datasets struggle to maintain detection accuracy in tea garden environments due to occlusions by tea plants, varying lighting conditions, and target sizes. Recognition performance significantly decreased in steep-angle or top-down camera views, highlighting the need for optimized camera deployment and domain-adapted training. To address this, we conducted data augmentation and parameter tuning tailored to the tea plantation context, improving the model’s mean Average Precision (mAP) and tracking consistency. This work contributes a practical framework for customized-area personnel detection in agriculture and underscores the necessity of developing domain-specific datasets for better generalization. The findings provide valuable technical guidance for intelligent agricultural surveillance and pave the way for further research into vision-based monitoring in crop-specific environments.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User’s requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this article proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the MRS task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4%, and 2.2% in terms of network throughput, latency, and energy consumption, respectively.

Coverage is a fundamental issue in the Solar Insecticidal Lamps IoTs (SIL-IoTs). Compared to complete coverage, partial coverage emerges as the preferred strategy for deploying SILs within a limited budget, as this deployment solution offers the highest cost-effectiveness. In this paper, we concentrate on studying the constrained SILs deployment problem, taking into account partial coverage and energy harvesting requirements, which we refer to as the cSILDP-PCEH problem. In this context, the positions for deploying SILs are restricted to a weighted set of candidate locations on the ridges. The weight assigned to each candidate location reflects the energy harvesting potential of the SIL deployed at that position. Our objective is to deploy a group of SILs in a subset of these candidate locations, ensuring a high overall energy harvesting potential, network connectivity, and achieving partial coverage. Due to the NP-hard nature of the problem, we introduce an approximation algorithm with a provable performance ratio tailored to our problem. Finally, we conduct a theoretical analysis of our proposed algorithm and perform extensive simulations. The simulation results demonstrate that the proposed algorithm achieves a minimum improvement of 16.45% in energy harvesting potential while preserving network connectivity and maintaining a comparable coverage level.

In precision agriculture systems, camera networks serve as indispensable visual information acquisition devices. However, due to cost constraints and the complexity of field image information, coupled with environmental variability, image quality degradation often occurs, resulting in low-quality visual images. An effective solution for super-resolution (SR) is urgently needed. Traditional image super-resolution methods perform well under laboratory conditions, but their effectiveness is limited in real field environments due to the inability to accurately simulate actual degradation processes. To address this issue, a degradation framework tailored to field scenes is proposed, which involves precise estimation of image degradation features and the design of targeted degradation algorithms. Additionally, drawing inspiration from the powerful global dependency modeling capability of Transformers, an efficient hybrid architecture is designed that combines the local characteristics of convolutional neural networks with the global attention mechanism of Transformers, aiming to improve image reconstruction quality while reducing computational complexity. Experimental results demonstrate that the proposed method outperforms comparative methods, achieving better subjective and objective quality evaluations.

Owing to the swift growth in Internet of Things technology, Solar Insecticidal Lamps (SIL) are widely deployed in smart agriculture scenarios. At the same time, various safety and security issues of Solar Insecticidal Lamps - Internet of Things (SIL-IoTs) have attracted considerable attention within the academic community. This paper presents a specific False Data Injection Attack (FDIA) case in SIL-IoTs. Unlike mature urban or commercial IoTs applications, such as smart grids, there is no direct precedent for attack and detection models against FDIA in the SIL-IoTs. Moreover, as outdoor agricultural IoTs are susceptible to numerous uncontrollable environmental factors, data-driven detection methods may fail to detect FDIA in such scenarios. Therefore, we report the FDIA in SIL-IoTs and briefly analyze the detection means from two aspects: insecticidal data and sound signals by conducting experiments.

Federated Learning is a promising technique that facilitates cloud–edge–terminal collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without centralizing data, addressing privacy, and security concerns. However, when applied to AIoT, this technique faces several challenges, such as low communication efficiency among terminal devices, edges, and cloud platforms. In this article, we propose a novel approach called asynchronous federated learning with quantization (QuAsyncFL), which combines asynchronous federated learning with an unbiased nonuniform quantizer to address the issue of low communication efficiency. Moreover, we provide a detailed theoretical analysis of convergence with quantized gradients proving that the model could converge to a certain bound. Our experiments demonstrate that QuAsyncFL outperforms the original approach, achieving significant improvements in terms of communication efficiency. The research results represent a further step toward developing cloud–edge–terminal collaboration enabled AIoT.

This paper presents a low overhead multimedia data compression method for Solar Insecticidal Lamps Internet of Things (SIL-IoTs), achieving efficient audio data transmission. First, the audio and video data generated by working solar insecticidal lamps are collected to form an original dataset in the solar insecticidal lamps applications. Then, we propose SILGAN, a generative adversarial network to compress multimedia audio data in the SIL-IoTs. Specifically, a depth-wise separable convolution is adopted to reduce the computational resources for training networks and running programs. Moreover, the network parameters are optimized so that obtaining a better neural network model with stable operation on the nodes of the SIL-IoTs. Finally, experimental evaluation is conducted, showing the effectiveness of the proposed SILGAN approach.

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in China.To solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate; (2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds.

The Internet of Things (IoT) plays a critical role in enabling the seamless integration of disparate devices. Future IoT will rapidly expand its coverage to offer future worldwide omnipresent applications and services by merging communications in diverse spatial domains to build the space–air–ground–ocean-integrated network (SAGOI-Net). SAGOI-Net will include a significant number of battery-powered network nodes, such as satellites, unmanned vehicles, and underwater devices, due to the extremely vast geographic reach and dynamics in free space. Given the battery limitations, high-energy-efficiency communications and networking will be critical to the future system. Green SAGOI-Net seeks to not only bring ubiquitous connectivity to every corner of the globe but also to deliver more data with the same amount of energy.

Solar insecticidal lamps (SILs) Internet of Things is an emerging and environmentally friendly technology for preventing and controlling agricultural pests. As the disk coverage model only provides a coarse approximation of the sensing area in reality, the probabilistic coverage model (PCM) is appropriate for the deployment of SILs. However, most of the current studies on coverage problem under PCM have focused on point$\epsilon $-probability coverage, whereas a few referred to the area coverage problem since it is extremely difficult to verify the coverage of a complete continuous area under PCM, especially for irregular shaped area. In this article, we study how to deploy the minimum number of SILs with PCM to provide complete area$\epsilon $-probability coverage for actual farmland with irregular shape, where the locations used to deploy SILs are a limited set of candidates located on field ridges. We first formulate the complete area$\epsilon $-probability coverage problem into the minimum point$\epsilon $-probability coverage problem and prove that it is NP-complete. After that, we present an approximation algorithm with provable approximation rations to our problem. Finally, we analyze the performance of the proposed algorithm theoretically and perform extensive simulations to demonstrate its effectiveness.

Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-oriented binary sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoT node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoT nodes; 2) construct the fault dictionary according to the characteristics of measurements; and 3) detect faults via the fault dictionary. In addition, a binary-based sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92 B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71-B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

Solar insecticidal lamps Internet of things (SIL-IoTs) has a long-term application trend, because it makes agricultural pest control more environmentally friendly and intelligent. However, the increase of lamps deployed has resulted in the challenge about maintenance burden. In this paper, we therefore provide a constrained SIL Deployment Problem under Coverage and Maintenance Cost considerations, referred to as cSILDP-CMC, where the positions used to deploy SIL nodes are a limited set of weighted Candidate Locations (CL) located on the ridges. A novel method is proposed to quantify the maintenance cost of each CL based on their comprehensive weight of coverage and maintenance cost considerations. Then we formulate the cSILDP-CMC and propose an Iterative Deployment Method (IDM) to solve the defined optimization problem. Finally, the experimental results show that our proposal equips better performance in terms of deployment cost, total comprehensive weight and coverage uniformity compared with the other four peer algorithms.

With the proliferation of I nternet o f T hings (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated in terms of IoT services. Their collaboration is mostly achieved through the composition of functionally complementary and geographically contiguous IoT services, to achieve complex requests. Considering the capacity constraints of IoT devices, newly incoming requests may hardly be satisfied partially (or completely), since these devices are implementing subtasks of previous requests at this moment. Therefore, candidate IoT devices may have no enough remaining capacity to co-host subtasks of these new requests concurrently. To solve this problem, this article proposes a novel r esource a llocation and s ervice co-placement (RaSP) algorithm to address latency-aware online service reconfiguration problem. Specifically, IoT services are reconfigured upon IoT devices in an optimal manner, such that certain IoT services corresponding to subtasks in previous requests should be migrated online from their hosting IoT devices to neighboring ones, and constraints of these requests are still satisfiable. These released resources can be adopted to implement subtasks (or IoT services) of newly incoming requests. A prototype is implemented using anEdgeSimsimulator. The experimental results show that our RaSP algorithm performs better than the state of the art’s techniques in satisfying the latency of newly incoming and previous requests simultaneously, and reducing the energy consumption of edge networks.

Solar insecticidal lamps Internet of Things (SIL-IoT) is a new green prevention and control technology for pest management. In the implementation of SIL-IoT to large-scale regions, two practical issues remain to be solved, that is: 1) scheduling the cleaning tasks of SILs periodically and 2) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, mobile crowdsensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This article proposes an MCS-enabled framework to address the SIL maintenance problem (SILMP) and perform system analysis by considering both the partition structure of farmland and the insecticidal efficiency of SILs. In addition, considering the farmland’s practical natural geographical features, we propose dividing the regions of interest into numerous subareas, where each subarea can be considered a separate partition. Finally, we formulate the SILMP framework as two subproblems, i.e., path planning and task selection, and propose two different methods to tackle each problem based on the concept of greedy algorithm. Simulation results show that our proposed methods have improved performance in the tradeoff between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Nowadays billions of smart objects are connected to the internet and interact with the cloud. Remote monitoring, control systems, and data analysis becomes more intelligent with the huge amount of data been collecting and crowdsourcing. To reduce data transmission and improve executive efficiency, computing and storing functions are executed toward edge devices. However, the great convenience raises numerous issues including reliability and security of the sensors and the control systems because these issues have not always been considered the top priority. Accordingly, many new research opportunities and challenges for intelligent sensing and control have arisen.

The connectivity and intelligence of Internet of Things (IoT) equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. However, an important aspect that is often overlooked in security literature is IoT equipment’s physical security and safety, namely, preventing IoT equipment from vandalism and theft. Therefore, this article provides an overview of IoT equipment’s physical security and safety to draw attention to new research opportunities in this area. Afterward, we discuss, among other aspects, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Besides, we summarize the artificial intelligence solutions for the physical security and safety of IoT equipment. Finally, we conclude with four future research opportunities.

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; ahierarchical soft margin support vector machinetraining strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ($\approx 9\%$).

The diffusion of toxic gas, biochemical material, and radio-active contamination – known as continuous objects – endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm ofindustrial wireless sensor networks(IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose amotion behavior learning predictive tracking(MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizesBayesian model averaging(BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy.

Wireless Sensor Networks (WSNs) have been widely used in agricultural productions. As a critical problems in WSNs, node deployment has a direct impact on the effectiveness of routing and data fusion operations as well as on the accuracy of anticipated coverage in several agricultural scenarios, e.g., mono-crop and mixed-crop farmlands. Network simulations are necessary for testing the effectiveness of deployment algorithms, but some of them lack the accuracy of real-world deployments. This is due to the fact that analogue maps in these network simulations do not take into account the nature of the terrain, for example obstacles such as buildings and trees in the line of vision for sensors, uneven surfaces and elevations for hilly terrains, the node locations obtained by the deployment algorithms in these analogue maps cannot be mapped to the actual farmland. In this paper, we present a methodology of constructing analogue map called AnaMap for providing both simulation and visualization of the actual farmland with the characteristic of partition structure and irregular boundary to assist the investigation of node deployment algorithms in agricultural environment. One case study is described to prove the usability of AnaMap.

This article presents research challenges and a tutorial on performance evaluation of blockchain-based security and privacy systems for the Internet of Things (IoT). We start by summarizing the existing surveys that deal with blockchain security for IoT networks. Then, we review the blockchain-based security and privacy systems for seventeen types of IoT applications, e.g., Industry 4.0, software-defined networking, edge computing, Internet of Drones, Internet of Cloud, Internet of Energy, Internet of Vehicles, etc. We also review various consensus algorithms and provide a comparison with respect to the nine properties, such as latency, throughput, computation, storage, and communication costs, scalability, attack model, advantage, disadvantage, etc. Moreover, we present the security analysis techniques and provide a classification into four categories, including Burrows, Abadi, and Needham (BAN) logic, game theory, theory analysis, and AVISPA tool. In addition, we analyze the performance metrics, blockchain testbeds, and cryptography libraries used in the performance evaluation of blockchain-based security and privacy systems for the IoT networks. Based on the current survey, we discuss the major steps to follow for building and evaluating blockchain-based security and privacy systems. Finally, we discuss and highlight open challenges and future research opportunities.

Solar insecticidal lamps (SILs) play a vital role in green prevention and control of pests. By embedding SILs in wireless sensor networks (WSNs), we establish a novel agricultural Internet of Things (IoT), referred to as the SIL-IoTs. In practice, the deployment of SIL nodes is determined by the geographical characteristics of an actual farmland, the constraints on the locations of SIL nodes, and the radio-wave propagation in a complex agricultural environment. In this article, we mainly focus on the constrained SIL deployment problem (cSILDP) in a mixed-crop farmland, where the locations used to deploy SIL nodes are a limited set of candidates located on the ridges. We formulate the cSILDP in this Scenario as a connected set cover (CSC) problem and propose a hole-aware node deployment method (HANDM) based on the greedy algorithm to solve the constrained optimization problem. The HANDM is a two-phase method. In the first phase, a novel deployment strategy is utilized to guarantee only a single coverage hole in each iteration, based on which a set of suboptimal locations is found for the deployment of SIL nodes. In the second phase, according to the operations of deletion and fusion, the optimal locations are obtained to meet the requirements on complete coverage and connectivity. Experimental results show that our proposed method achieves better performance than the peer algorithms, specifically in terms of deployment cost.

As an important physical control technology, solar insecticidal lamp (SIL) can effectively prevent and control the occurrence of pests. The combination of SILs and wireless sensor networks (WSNs) initiates a novel agricultural Internet of Things (IoT), i.e., SIL-IoTs, to simultaneously kill pests and transmit pest information. In this article, we study the weighted SIL deployment problem (wSILDP) in SIL-IoTs, where weighted locations on ridges are prespecified and some of them are selected to deploy SILs. Different from the existing studies whose optimization objective is to minimize the deployment cost, we consider the deployment cost and the total weight of selected locations jointly. We formulate the wSILDP as the weighted set cover (WSC) problem and propose a layered deployment method based on greedy algorithm (LDMGA) to solve the defined optimization problem. The LDMGA is composed of two phases. First, SILs are deployed layer by layer from the boundary to the center until the entire farmland is completely covered. Second, on the basis of three design operations, i.e., substitution, deletion and fusion, the suboptimal locations obtained in the first phase are fine-tuned to achieve the minimum deployment cost together with the maximum total weight for meeting the coverage and connectivity requirements. Simulation results clearly demonstrate that the proposed method outperforms three peer algorithms in terms of deployment cost and total weight.

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

Depression and anxiety are a couple of pernicious mental states, which may affect lifestyle and quality and even become the primary causes of disability worldwide. Hence, daily monitoring of the mental states is significant for avoiding possible injury. Dynamics of the human blood vascular system convey significant information on recording the emotion and the mental state, which can be monitored via photoplethysmography (PPG). It is one of the best schemes with the advantages of nonintrusiveness and low cost. Conventional approaches for PPG signal analysis usually depend on handcrafted feature extraction and classification, thus resulting in a lack of feature discrimination and difficulties in generalization. In this article, we propose an attentive deep recurrent architecture called Arousal-valence Networks (ArvaNets), which benefits from graph convolutional networks and recurrent neural networks. Our approach overcomes the limitations of previous methods by automatically extracting the learnable spatial representations from a rigorous custom data set as semantic motifs to infer immediate emotions, which are mapped to a 2-D arousal-valence coordinate system. Finally, we exploit long short-term memory (LSTM) units to output the mental states by incorporating the temporal factor. During the entire inference, we propose a spatiotemporal attention mechanism based on correlation fractal dimensions (CFDs) and time-averaged wall shear stress (TAWSS) to capture and stress the key subtle motifs for performance optimization. Experimental results demonstrate the proposed architecture has enough competitiveness in the tasks of emotion and mental-state recognition for daily monitoring.

The papers in this special section focus on sustainable and intelligent precision agriculture. Human society has experienced three industrial revolutions from mechanization, and electricity to information automation. Every industrial revolution significantly alters the form of agricultural industry from labor-intensive farming, mechanized production, precision agriculture to large-scale fine grained industrial agriculture. However, the agricultural industry at current stage still faces many challenges, such as global food security, food safety, poverty reduction, and sustainable natural resource management. Now the fourth industrial revolution is ongoing, that is characterized by a fusion of emerging technologies such as Industry 4.0, Internet of Things, Cloud/Edge Computing, Big Data, Artificial Intelligence, and Blockchain.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods.

The rapid development of industrial wireless sensor networks (IWSNs) promotes the development of intelligent agricultural monitoring systems. As one of the most challenging issues, it is indispensable to improve the lifetime or sustainability of IWSNs based agricultural monitoring systems. In this paper, we employ UAV as the wireless charging component to improve the availability of IWSNs based agricultural monitoring systems. We propose a UAV-embedded IWSN architecture where IWSN is regarded as the data sensing or collecting component, and UAV serves as the data relay and charging component. We focus on the length constrained energy-optimal charging path planning issues for the UAV-aided IWSN based agricultural monitoring system, where the length of the charging path and the entire residual energy for the entire network are concurrently taken into account. We formulate the problem by utilizing linear programming (LP) technique, and propose an improved version of the max-min ant system (MMAS) algorithm, an adaptive optimization approach to plan the charging path for the UAV. Our adaptive approach can dynamically optimize the ratio of pheromone concentration and the state transfer function during the iteration. Simulation results demonstrate that our proposal is more efficient than some novel approaches in energy charging efficiency, network utilization ratio/lifetime.

As an emergent Internet-of-Underwater-Things (IoUT) system, the underwater wireless networks (UWNs), especially the autonomous underwater vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this article, we focus on improving the scalability or controllability of the AUV flock-based UWNs and utilize the paradigm of software-defined networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, and collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on the artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or “no-go” areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform an exact underwater path planning mission. The simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal-distributed path planning scheme.

In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software-defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) we propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; and 3) we utilize the time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.

The rapid development of Internet-of-Things technologies (such as edge computing) has promoted the development of numerous emerging urban applications, particularly smart transportation. As an anticipated aspect of smart transportation, bike-sharing systems have recently been deployed in many cities and are considered an efficient way to address the issue of “the last mile.” In a bike-sharing system, the supply and demand of shared bikes at each bike station frequently change over time. Consequently, one of the most challenging issues of a bike-sharing system is predicting the required number of shared bikes at each station. In this article, we take the real aspects of a bike-sharing system into account, e.g., the high complexity, nonlinearity, and uncertainty of the traffic flow, and propose a hybrid edge-computing-based machine learning model. Notably, our proposed model, which combines a self-organizing mapping network with a regression tree (RT), is applied to predict the bicycle demand of a certain station through the following steps: 1) the proposed model adopts self-organization mapping to assemble the original samples in the form of clusters and 2) each cluster is then built as an RT to forecast the required number of bikes at each station. Experiments based on real data from the Washington and London bike-sharing systems show that our proposed method achieves a higher prediction accuracy and better generalization than previous approaches.

Solar insecticidal lamp (SIL) is a green prevention and control technology for pests. With the development of wireless sensor networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things-SIL Internet of Things (SIL-IoTs). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this article, we study the SIL deployment problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary, and partition structure. According to the partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with the partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on the genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. The experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

As an essential step in quality control, fabric defect detection plays an important role in the textile manufacturing industry. The traditional manual detection method is inaccurate and incurs a high cost; as a result, it is gradually being replaced by deep learning algorithms based on cloud computing. However, a high data transmission latency between end devices and the cloud has a significant impact on textile production efficiency. In contrast, edge computing, which provides services near end devices by deploying network, computing and storage facilities at the edge of the Internet, can effectively solve the above-mentioned problem. In this article, we propose a deep-learning-based fabric defect detection method for edge computing scenarios. First, this article modifies the structure of DenseNet to better suit a resource-constrained edge computing scenario. To better assess the proposed model, an optimized cross-entropy loss function is also formulated. Afterward, six feasible expansion schemes are utilized to enhance the data set according to the characteristics of various defects in fabric samples. To balance the distribution of samples, proportions of various defect types are used to determine the number of enhancements. Finally, a fabric defect detection system is established to test the performance of the optimized model used on edge devices in a real-world textile industry scenario. Experimental results demonstrate that compared with the conventional convolutional neural network (CNN), the proposed optimized model attains an average improvement of 18% in the area under the curve (AUC) metric for 11 defects. Data transmission is reduced by approximately 50% and latency is reduced by 32% in the Cambricon 1H8 platform compared with a cloud platform.

Motivated by complementing the ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), a lot of attention from both industry and academia has been drawn to Sensor-Cloud (SC). However, SC pricing is barely investigated. Towards pricing for SC, this paper 1) introduces five SC Pricing Models (SCPMs) first. Specifically, to charge a SC user, each SCPM considers one of the following factors respectively: i) the lease period of the SC user; ii) the required working time of SC; iii) the SC resources utilized by the SC user; iv) the volume of sensory data obtained by the SC user; v) the SC path that transmits sensory data from the WSN to the SC user. Further, this paper 2) performs analysis to discuss and exhibit the characteristics of the proposed SCPMs. With that, this paper 3) presents the case studies regarding the application of SCPMs. Eventually, this paper 4) conducts a review about the user behavior study. This paper aims to serve as a very favorable guidance for future research about pricing in SC.

Edge computing brings data storage closer to the location where it is needed. Therefore, the edge devices, especially smart industrial edge devices, require higher storage systems. NAND flash memory has the advantages of small size, high speed, and strong shock resistance, which is widely used in various storage systems, providing a good choice for edge devices. NAND flash has unique physical characteristics, such as “out-of-place updates” and “prewrite erasure,” therefore, the traditional address mapping methods require improvement. This article presents a novel demand-based page-level address mapping algorithm called DPAM. The goal of DPAM is to provide efficient address translation by using a smaller address mapping table. Due to the high service cost of block-level address mapping and hybrid address mapping, a page-level address mapping scheme is proposed. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm provides improvements of 7.11% for the hit ratio and 7% for the number of block erasures compared with other approaches.

Permutation Entropy (PE) has been widely applied as a non-linear statistical indicator to estimate the change of complexity in time series. Though it is conceptually simple and computationally fast, PE encounters a few limitations. For example, the amplitude differences in time series are neglected, and the symbolic sequences generated from equal values are according to their emergence order. In this paper, an Edge Permutation Entropy (EdgePE) measure is proposed to improve the performance of PE, mainly overcoming its lack of ability to differentiate between amplitude differences in motifs that correspond to the same order pattern. The advantage of EdgePE relies on that amplitude change information can be identified and distinguished by the information underlying in the “edge” distance between data points in the reconstructed embedded vectors. To demonstrate its improvement, the proposed EdgePE is compared with other related improved PE approaches, for analyzing synthetic time series and experimental rolling bearing data sets, respectively. The results indicate that the EdgePE can effectively characterize amplitude changes in time series (e.g., for spike and stuck detection) and improve the accuracy of pattern recognition for rolling bearing fault diagnosis, compared to those of the other related PE measures.

The use of sensors has increased in the last years. They allow the real-time monitoring and reduce the processing cost because most of wireless sensor networks (WSNs) do not require the intervention of people to perform the measurement. Sensors are useful for industrial processes that can imply some danger for workers. These devices also make easy the development of some tasks as the ones carried out in Wastewater Treatment Plant (WWTP). This paper presents the design, deployment and calibration of a low cost sensor able to detect the concentration of solids in some phases of the water treatment. The sensor is based on 2 coils and a microprocessor module. Several models and combination of coils have been testes. The results show that our sensor is capable of measuring concentrations form 0% to 20% (0 mg/l to 200,000 mg/l) with an error lower than the 3.5%.

Multiscale Permutation Entropy (MPE) has been applied as a non-linear measure for estimating the complexity of time series. Nevertheless, the coarse-grained procedure in MPE only takes low-frequency information into account. To overcome this shortcoming, in this paper, a new entropy measure, named Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), is proposed to provide stable and reliable results by offering both low-frequency and high-frequency information. Firstly, the F2C signals are created based on the reconstruction of selected wavelet coefficients using wavelet packet decomposition. Then, permutation entropy is used to estimate the complexity and dynamic change of the F2C signals. Experimental analysis is carried out to investigate and compare the performance of the proposed F2CMPE with that of the MPE. Results indicate that the proposed method can give consistent and stable entropy measure for rolling bearing fault diagnosis.

The IEEE 802.15.4 standard is a widely adopted standard for low power wireless personal area networks. It defines several medium access control layer functionalities including channel access, beacon management, guaranteed time slot management, etc. These issues are relatively straight forward in star topology, but the similar tasks pose several challenges in a peer-to-peer cluster tree network. Specifically, beacon synchronization and duty cycling schemes that are influenced by superframe parameters need to operate effectively as they serve as major energy saving avenues. The former that is part of beacon management process allows a device to synchronize its transmissions with a coordinator to facilitate better channel utilization. Further, duty-cycling allows devices to enter low-power mode by scheduling their sleep period. Lack of these schemes in the standard for cluster-tree networks has motivated research in this direction. However, all the related works have aimed to address the problem of duty-cycling and synchronization independently without considering the interdependencies between them. These dependencies arise due to the common superframe parameters. In this paper, we first analyze various works carried out to address beacon synchronization and duty-cycling issue in IEEE 802.15.4 networks. Later, we establish a co-relation between these two mechanisms and show how the former effects the later and vice-versa. The analytical and simulation results allow us to understand the existing schemes better and further assist in the design of aforementioned schemes to maximize energy savings.

An intelligent manhole cover management system (IMCS) is one of the most important basic platforms in a smart city to prevent frequent manhole cover accidents. Manhole cover displacement, loss, and damage pose threats to personal safety, which is contrary to the aim of smart cities. This paper proposes an edge computing-based IMCS for smart cities. A unique radio frequency identification tag with tilt and vibration sensors is used for each manhole cover, and a Narrowband Internet of Things is adopted for communication. Meanwhile, edge computing servers interact with corresponding management personnel through mobile devices based on the collected information. A demonstration application of the proposed IMCS in the Xiasha District of Hangzhou, China, showed its high efficiency. It efficiently reduced the average repair time, which could improve the security for both people and manhole covers.

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things (IoT). In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in IoT, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the nonlinear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron-Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.

The papers in this special section examine the use of fog computing applications in industrial electronics. Due to the increased number of connected things in industrial applications, the growing volume and velocity of Internet of Things (IoTs) data exchange urge for more and more communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. Fog computing emerges as an alternative for traditional cloud computing to support geographically distributed, latency-sensitive, and QoS-aware IoT applications while reducing the burden of data centers in traditional cloud computing. In particular, fog computing with the features (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real-time applications is an attractive solution to delay- and resource-constraint large-scale industrial applications. However, with the benefits of fog computing, the research challenges arise regarding fog computing for industrial applications.

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large.

The IEEE 802.15.4 standard does not allow to make dynamic adjustments to the inactive portion of the superframe, thus affecting the duty cycles of the coordinator and all the devices attached to it. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we present the respective limitations of both these schemes that lead to sub-optimal MAC parameter (BO and SO) settings and later propose a dynamic duty cycling mechanism based on MAC parameters (macMinBE, macMaxCSMABackoffs and macMaxFrameRetries). A Markov model is developed for IEEE 802.15.4 CSMA-CA that is used to analytically estimate the delay and energy consumption during transmission of frames using the MAC parameters.

Duty-cycling schemes allow devices to dynamically adjust their active period to conserve energy. On the other hand, synchronization schemes allow multiple coordinators to schedule their transmissions in order to prevent the overlapping of superframe schedules. In this paper, we analyze the impact of a synchronization mechanism on duty-cycling schemes in an IEEE 802.15.4 cluster tree network. We show the necessity of an operational synchronization mechanism when devices adopt an independent duty-cycling approach so that the later accounts to effective energy savings.

With the advancement in energy harvesting in terms of wireless charging techniques, it provides a novel way to solve traditional energy constraint problems in Wireless Sensor Networks (WSNs). Renewable energy such as solar, wind, and geo-thermal energy is converted to energy-storage and further use via harvest-then-transmit strategy. This article introduces a two-layer sleep scheduling system in energy-harvesting WSNs with an aim to satisfy sustainable throughput by analysis and optimization of network performance. Evaluation results provide a typical demonstration of how to obtain the appropriate value of key parameters according to specific requirement.

Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) on fault diagnosis of rotating shafts using multiscale entropy (MSE). After WPD and EMD, the most sensitive reconstruction vectors and intrinsic mode functions (IMFs) are selected using Shannon entropy. Then, these feature vectors are fed into support vector machine (SVM) for fault classification, where the entropy features represent the complexity of vibration signals with different scales. Experimental results have demonstrated that WPD combined with MSE can achieve an accuracy of 97.3% for crack fault detection in rotating shafts, whilst EMD combined with MSE has shown a higher detection rate of 98.5%.

Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery.

With the advancement in energy harvesting, wireless powered communication networks overcome the problem of replacing fixed energy sources, e.g., batteries in difficult access areas of industrial networks. However, the harvested energy is not always enough to support reliable and low end-to-end data transmission in industrial wireless sensor networks (IWSNs). This poster introduces an energy utilization-concerned sleep scheduling in wireless-powered IWSNs with an aim to balance network demand and residual energy.

Group connectivity is one of the major research challenges in large-scale industrial wireless sensor networks (IWSNs). Critical nodes (CNs) are mainly responsible to maintain group-connectivity. This article focuses on prioritize these CNs to sleep more than other nodes in order to save their energy resulting prolong global connectivity in group-based IWSNs.

The IEEE 802.15.4 standard does not support adaptive duty cycles. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we find the respective limitations of both these schemes that lead to sub-optimal duty cycle parameter settings. Afterward, a duty cycling algorithm is proposed wherein the channel state is estimated with the help of MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrameRetries) that induces dynamic adaptation of duty-cycle among the nodes. A Markov model is developed for IEEE 802.15.4 carrier sense multiple access with collision avoidance (CSMA-CA) to estimate the delay and energy consumption while transmitting frames using MAC parameters.

In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.

Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks' full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.

Underwater acoustic sensor networks (UASNs) are susceptible to a large number of security threats, e.g., jamming attacks at the physical layer, collision attacks at the data link layer, and DoS attacks at the network layer. Because of the communication, computation, and storage constraints of underwater sensor nodes, traditional security mechanisms, e.g., encryption algorithms, are not suitable for UASNs. A trust model has been recently suggested as an effective security mechanism for open environments such as terrestrial wireless sensor networks (TWSNs), and considerable research has been done on modeling and managing trust relationships among sensor nodes. However, the trust models proposed for TWSNs cannot be directly used in a UASN due to its unique characteristics such as unreliable acoustic channel, dynamic network structure, and weak link connectivity. In this paper, we propose a novel trust model based on cloud theory (TMC) for UASNs. The objective of TMC is to solve uncertainty and fuzziness of trust based on cloud theory, which ultimately improves trust evaluation accuracy. Moreover, simulation results demonstrate that our algorithm outperforms other related works in terms of detection ratio of malicious nodes, successful packet delivery ratio, and network lifetime.

Underwater environments may vary gradually even when the occurrence of events is detected. Sensory data may follow a certain trend and are predictable during certain time durations. Taking these into consideration, a simple but practical data prediction mechanism is adopted for estimating sensory data and the geographical location of sensor nodes at sink nodes, and these data are synchronized with those sensed by underwater sensor nodes only when their variation is beyond a prespecified threshold. Leveraging these predicted data, the coverage and sources of potential events are identified by the sink node, and the evolution of these events is determined accordingly. Evaluation results show the applicability and energy-efficiency of this approach, especially when the variation of network environments follows certain and simple patterns.

Recently, the low-power, low-cost and reliable ZigBee technology have received significant research attention with the increasing popularity of applications such as smart home system, patient monitor in hospitals. Coexisiting with the WiFi devices on the crowded unlicensed ISM band, such as hotspots and mobile phones, ZigBee will receive significant performance influence. The throughput and Packet Reception Rate (PRR) of ZigBee will decrease with the increasing number of WiFi devices. Because of the incompatible PHY/MAC layer, ZigBee devices will suffer near 50% packet loss when coexisting with WiFi devices. The existing mechanism such as CSMA/CA is surprisingly inadequate for solving this problem. As the WiFi traffic typically appears bursty, the channel will be free for more than 60% of the time. In this paper, we propose a new metric called channel idle indicator (CII) which can quantify the channel quality. Based on the CII and logistic regression, we build a channel idle state prediction model which can help ZigBee devices to use the white space of WiFi channel efficiently. Particularly, our approach is light-weight, which can be easily implemented on the ability-limited commercial off-the-shelf (COTS) ZigBee devices. Extensive experiments show that our scheme can achieve over 91% of the PRR, which is near 40% higher than the B-MAC protocol. When the WiFi throughput is 3Mbps, our scheme achieves near 1.5x throughput over B-MAC. Carrying on further, our scheme consumes less energy via degrading packet loss rate in the energy consumption part.

Currently, various Medium Access Control (MAC) protocols have been proposed for underwater Wireless sensor networks. Unlike terrestrial networks, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery and the energy cost of transmission is much higher than receptions. Thus, collision and retransmission should be reduced in practice in order to reduce energy cost and improve throughput. Based on these motivations, we propose a novel MAC protocol called NCDC-MAC. NCDC-MAC leverages network coding and duty cycle, the combination of which is seldom explored, to solve these challenges. Heterogeneous wireless networks and node roles are considered while designing our algorithms. Meanwhile, fairness including schedule and service time assignment is supported in our approach. Extensive simulations show that our approach can achieve significantly better performance.

In recent years, WiFi fingerprint-based localization has received much attention due to its deployment practicability. Although existing works show WiFi fingerprinting can achieve good localization accuracy, the experiments were conducted under their own testbeds within a small area and a short period. In this work, we investigate the impact of different indoor environmental factors, such as temporal and spatial similarity, on the performance of WiFi fingerprinting. We find that, WiFi fingerprinting is highly environment-sensitive. In an open space, it is quite challenging to find spatially varying but temporally stable signatures for adjacent reference locations. To address this issue, we propose a heatmap-based WiFi fingerprinting (called HMF) by utilizing layout construction as an additional input to improve WiFi fingerprint localization in open space environment. Our experimental results show, HMF can improve existing WiFi fingerprinting schemes like Radar and Horus by 28% and 80% in moderately open space, e.g., a wide corridor.

This paper proposes to detect event coverage and determines event sources. Generally, an appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph. Event sources are determined which correspond to the barycenters in this graph. Experiments show that this technique is more energy efficient, especially when the network topology is relatively steady.

In this article, we address a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining the global network connectivity with minimal awake-nodes compared to other schemes that increase the number of awake- nodes over the entire network.

In this article, we propose a new scheme DeGas to determine the boundary area of the toxic gas with planarization algorithm. This detected boundary area will ensure safe area around the toxic gas and will provide a decision reference for evacuation and rescue of the first-line workers in the large-scale petrochemical plants. Understanding the implications of these observations enable to find out an optimal trade-off between the cost of a number of deployed sensor nodes and the accuracy of the estimated toxic gas boundary area size.

Understanding the propagation dynamics of information/an epidemic on complex networks is very important for discovering and controlling a terrorist attack, and even for predicting a disease outbreak. As an effective method, with analyzing the structure of a propagation network, a large number of previous studies have analyzed the propagation dynamics. Most of these studies are based on a special network structure to make such analysis. However, a propagation network has dynamically changed structure during the propagation. How to track, recognize and model such dynamic change is a big challenge. Along with the popularity of smart devices and the rapid development of the Internet of Things (IoT), massive mobile data is automatically collected. In this article, as a typical use case, we investigate the impact of network structure on epidemic propagation dynamics by analyzing the massive mobile data collected from smart devices carried by the volunteers of Ebola outbreak areas. From this investigation, we obtain two observations. Based on these observations and the analytical ability of Apache Spark on streaming data and graphs, we propose a simple model to track and recognize the dynamic structure of a network. Moreover, we introduce and discuss open issues and future work for developing this proposed recognition model.

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

Secondary Users (SUs) have capability to sense available licensed spectrum in Cognitive Radio Networks (CRNs). Hence, SUs can opportunistically access to the licensed spectrum without disturbing Primary Users (PUs). In this paper, a novel network architecture is proposed to reduce the production cost and the energy consumption for CRNs. The proposed network architecture is based on the spectral requirement of Secondary Users (SUs). In the proposed network architecture, only parts of SUs are equipped with Cognitive Radio (CR) module. In addition, a minimum number of SUs are selected to sense available licensed spectrum, which aims at reducing the energy consumption further. The minimum number of SUs selection problem is formulated as a non-linear programming problem under the constrains of energy efficiency and the real-time available spectrum information. However, the non-linear programming problem is a NP-hard problem. Hence, a distributed heuristic algorithm is proposed to calculate the near-optimal solution. The simulation results demonstrate that the proposed heuristic algorithm in the proposed network architecture outperforms the random algorithm in the proposed network architecture and traditional Cognitive Radio Ad Hoc Networks (CRAHNs) in energy efficiency.

With the development of wireless communication, industrial wireless sensor networks (IWSNs) plays an important role in monitoring and control systems. In this paper, we extend the application of IWSNs into High Frequency Surface-Wave Radar (HFSWR) system. The traditional antenna is replaced by mobile IWSNs. In combination of the application precondition of super-directivity in HF band and circular topology of IWSNs, a super- directivity synthesis method is presented for designing super-directivity array. In this method, the dominance of external noise is ensured by constraining the Ratio of External to Internal Noise (REIN) of the array, and the desired side lobe level is achieved by implementing linear constraint. By using this method, the highest directivity will be achieved in certain conditions. Using the designed super directive circular array as sub-arrays, the compact receive antenna array is constructed, the purpose of miniaturization is achieved. Simulation verifies that the proposed method is correct and effective, the validity of the proposed method has been proved.

Internet of Things (IoT) is a fast-growing research area which spans various technological fields, including computer science, electronic engineering, mobile and wireless communications, embedded systems, etc. Many technologies serve as the building blocks of this new paradigm, such as wireless sensor networks, RFID, cloud services, machine-to-machine interfaces, and so on. IoT will allow billions of objects in the physical world as well as virtual environments to exchange data with each other in an autonomous way so as to create smart environments such as automotive, healthcare, logistics, environmental monitoring, and many others. However, IoT introduces new challenges for the security of systems and processes and the privacy of individuals. Protecting the information in IoT is a complex and difficult task. IoT requires global connectivity and accessibility, which means that anyone can access in anytime and anyway, and that the number of attack vectors available to malicious attackers might become staggering. Furthermore, the inherent complexity of the IoT, where multiple heterogeneous entities located in different contexts can exchange information with each other, further complicates the design and deployment of efficient, interoperable, and scalable security mechanisms. Ubiquitous and cloud computing also increase the urgency of the privacy leakage problem. As a result, there is an increasing demand for development of new security and privacy approaches to guarantee the security, privacy, integrity, and availability of resources in IoTs. Traditional security countermeasures cannot be directly used in IoTs because of the different standards and communication stacks involved. Moreover, the large number of interconnected devices in IoTs introduces scalability issues. Therefore, new and novel security and privacy methods, architectures, and solutions are needed to deal with security threats in IoTs. In this special issue, we are delighted to present a selection of nine papers, which, in our opinion, will contribute to the enhancement of knowledge in security and privacy research for IoTs. The collection of high-quality research papers provides a view on the latest research advances on security and privacy methods, architectures, and solutions in IoTs. The contributions of these papers are outlined in the succeeding text. In the first paper, A new authentication protocol for healthcare applications using wireless medical sensor networks with user anonymity, Xiong Li, Jianwei Niu, Saru Kumari, Junguo Liao, Wei Liang, and Muhammad Khurram Khan adopt the biometrics as the third authentication factor and propose a new authentication protocol to guarantee secure communication and protect the user privacy for healthcare application using WMSNs with user anonymity. In the proposed protocol, a wrong password detection mechanism is designed to reduce unnecessary computation and communication costs. In the second paper, Fusion: coalesced confidential storage and communication framework for the IoT, instead of developing independent security solutions, Ibrahim Ethem Bagci, Shahid Raza, Utz Roedig, and Thiemo Voigt present Fusion to address both the communication and storage security. The paper demonstrates that compared with performing traditional cryptographic operations separately, using the combined solution is much safer and more energy efficient. In the third paper, A changeable personal identification number-based keystroke dynamics authentication system on smart phones, Ting-Yi Chang, Cheng-Jung Tsai, Wang-Jui Tsai, Chun-Cheng Peng, and Han-Sing Wu propose a novel keystroke dynamics-based authentication (KDA) system to protect security of smart phones. Compared with the traditional KDA system, in the proposed new KDA system, the personal identification number codes of the subscribers can be well protected, and the users can change their personal identification number codes and passwords anytime without extra retraining. With the wide use of smart mobile devices, task collaborations among mobile devices are becoming ubiquitous and important. The security issues can be well guaranteed if the tasks can be effectively balanced. Therefore, in the fourth paper, SAFE-CROWD: secure task allocation for collaborative mobile social network, Xiaochen Fan, Panlong Yang, Qingyu Li, Dawei Liu, Chaocan Xiang, and Yonggang Zhao propose “SAFE-CROWD”, which is a secure task-allocation scheme. Using SAFE-CROWD, the tasks can be securely and collaboratively completed among mobile devices. In the fifth paper, ShoVAT: Shodan-based vulnerability assessment tool for Internet-facing services, Béla Genge and Cǎlin Enǎchescu propose a novel tool called Shodan-based vulnerability assessment tool (ShoVAT) to guarantee the automated vulnerability assessment of Internet-facing services. Based on the indexing capabilities of Shodan search engine, ShoVAT first finds services and then reconstructs key vulnerability identifiers. Finally, the vulnerabilities are obtained using National Vulnerability Database. The experiment results show that 3922 vulnerabilities are found on 1501 services in 12 different institutions. In the sixth paper, Distributed flood attack detection mechanism using artificial neural network in wireless mesh networks, Muhammad Altaf Khan, Shafiullah Khan, Bilal Shams, and Jaime Lloret propose an artificial neural network-based technique to detect distributed flooding attacks in multi-hop wireless mesh networks. The proposed scheme is named as the distributed flood attack detector. The distributed flood attack detector is designed to be implemented at mesh gateway in wireless mesh network. By using artificial neural networks, the network traffic can be divided into different categories, and thus, the flood attacks can be detected. In the seventh paper, Toward a flexible and fine-grained access control framework for infrastructure as a service clouds, Bo Li, Jianxin Li, Lu Liu, and Chao Zhou propose a flexible and fine-grained access control framework, named IaaS-oriented Hybrid Access Control (iHAC), to ensure that the resources cannot be illegally accessed or used. iHAC consists of three main parts: an IaaS-oriented Hybrid Access Control model, a VM-centric access control approach, and a VMM-enabled network access control mechanism. The simulation results show that iHAC can efficiently make correct access control decisions with acceptable performance overhead. In the eighth paper, An intrusion detection method for wireless sensor network based on mathematical morphology, Yanwen Wang, Xiaoling Wu, and Hainan Chen propose an innovative intrusion detection method called granulometric size distribution (GSD) method based on mathematical morphology to detect malicious attack in IoTs. If the number of active nodes in a wireless sensor network is fixed, the GSD curves are similar. Therefore, malicious nodes can be efficiently detected based on the abnormal GSD. In the last of the presented papers, A secure energy-efficient access control scheme for wireless sensor networks based on elliptic curve cryptography, Yuanyuan Zhang, Neeraj Kumar, Jianhua Chen, and Joel J. P. C. Rodrigues propose a secure energy-efficient access-control scheme for wireless sensor networks based on elliptic curve cryptography. The algorithm is explained in detail, and a variety of malicious attacks are simulated to evaluate the performance of the proposed algorithm. To summarize, we believe that this special issue will contribute to enhancing knowledge in security and privacy research in IoT in particular. In addition, we also hope that the presented results will stimulate further research in the important areas of information and network security. We also want to thank the editor-in-chief of the Security and Communication Networks journal, the leading researchers contributing to the special issue, and excellent reviewers for their great help and support that made this special issue possible.

The papers in this special section focus on the topic of industrial sensing intelligence. Examines the sensor and communications technologies that support these services and reports on industrial applications for their use.

In this paper, an improved localization method named three-uniform-linear-array localization is proposed for patient track systems. Three receivers adopting a smart antenna technique cooperate with each other to locate the patients using the angulation positioning method. In order to be able to track patients in environment with high patient density, a high-resolution direction-of-arrival (DOA) estimation algorithm for the coexistence of noncircular and circular signals is proposed. First, the maximal and common noncircularity rated signals are preliminarily estimated. Second, based on the noise space block matrix, the DOAs of these signals are re-estimated with high accuracy. Then, the covariance matrix of the maximal and common noncircularity rated signals is reconstructed. The contributions of these signals are eliminated after performing a subtraction operation on the covariance matrix of the received data and only those of circular signals remain. Finally, the DOAs of circular signals are obtained. Results of simulations and real tests demonstrate the effectiveness and performance of the proposed algorithm.

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geographical size. These grid cells are assigned to clusters through an algorithm inspired by the${{k}}$-dimensional tree algorithm, such that the energy consumption of each cluster is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.

Abstract Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences and then sends its one‐bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position‐based maximum likelihood estimator as well as its corresponding position‐based Cramér–Rao lower bound are derived. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator and the position‐based Cramér–Rao lower bound is less than its fixed‐quantization Cramér‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.

Incorporating ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), Sensor-Cloud (SC) is attracting growing attention from both academia and industry. However, pricing for SC is barely explored. In this paper, filling this gap, five SC pricing models (i.e., SCPM1, SCPM2, SCPM3, SCPM4 and SCPM5) are proposed first. Particularly, they charge a SC user, based on 1) the lease period of the user, 2) the required working time of SC, 3) the SC resources utilized by the user, 4) the volume of sensory data obtained by the user, 5) the SC path that transmits sensory data from the WSN to the user, respectively. Further, analysis is also presented to study and demonstrate the performance of the proposed SCPMs. We believe that the pricing designs and analysis performed in this work could be a very valuable guidance for future researches regarding pricing in SC.

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we proposes a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN.

Due to the limited energy of sensors and the difficulties in battery replacement in DTMSN (Delay Tolerant Mobile Sensor Network), unbalanced energy consumption will exhaust the batteries of active sensors soon, which can significantly reduce the network lifetime. Fortunately, this problem can be solved through multi-hop relay transmission where those energy-aware sensors with the maximum remaining energy will be selected to forward packets. However, one of the major challenges of multi-hop relay transmission in DTMSN is how to schedule these mobile sensors travelling paths in an energy-balanced way so that their overall lifetime is maximized. In this paper, an energy-balanced multi-hop relay transmission scheme based on RVNS (Reduced Variable Neighborhood Search) in DTMSN is proposed. Firstly, several parameters are designed to calculate the remaining energy of each sensor. Then RVNS is applied to obtain the global optimal solution. RVNS is implemented to select a sensor node with maximum remaining energy as the next hop relay, delivering packets to destination through multi-hop relay transmission. Simulation results demonstrate that in a delay-tolerant condition, the proposed scheme significantly balances the energy consumption and improves the packet delivery ratio.

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

In Delay Tolerant Networks (DTN), the resource utilization is decreased because of the limited resources and redundant copies. This paper proposes an improved Socially Aware Feedback Mechanism (SAFM). In this mechanism, the historical information of the encountered nodes are utilized to construct social links which indicates the level of social relationship between nodes. In the feedback process, acknowledgements are forwarded to the nodes whose Social Link (SL) is higher than a given threshold α. After getting the acknowledgements, nodes will delete the copies of messages which have been received by the destination nodes, so as to reduce the redundancy. In simulation, the threshold α is obtained to reach the best performance of SAFM. Compared with active and passive receipt approaches in an acceptable range of delay, SAFM improves the delivery probability, decreases the buffer occupancy and reduces the overhead.

Due to the limited resources of DTMSN (Delay Tolerant Mobile Sensor Networks), network congestion becomes a critical problem to resolve. Traditional congestion control methods where the number of copies is restricted to limit data packet forwarding cannot adapt to constantly changing network environment because of fixed number of copies. Fortunately, this problem can be solved through a real-time algorithm by modifying data packet forwarding conditions. However, one of the major challenges of this algorithm is detecting characteristics of the network environment accurately and efficiently. In this paper, an optimized routing algorithm, RVNS (Reduced Variable Neighborhood Search)-based Spray and Wait (SW) is proposed. In this algorithm, nodes will transmit and store the counter record of each other when they meet, based on which, RVNS is introduced to calculate a real-time threshold for the forwarding condition to control packet delivery. Simulation results show that the proposed algorithm increases delivery probability and dramatically reduces the overhead ratio. In some extreme cases, this algorithm can reach an extremely low overhead ratio (ten times lower than that of SW), meaning that the proposed algorithm suits challenged networks well.

In the probabilistic routing algorithms of DT-MSN (Delay Tolerant-Mobile Sensor Networks), packet delivery only depends on the probability of transmitting to its destination node. The prediction of probability is not reasonable because of not considering every node's activity level. In this paper, by mixing a node activity factor into the prediction of delivery probability, we propose a Node Activity-based Probabilistic Routing algorithm (NAPR). First, a physical quantity denoted by node activity is introduced to indicate the nodes' active level in the network. The proposed algorithm takes the encountering records and the nodes' active level into consideration. Second, by using parameter α to weigh the original probabilistic factor and the active level factor, a new weighted average value serves as a packet Delivery Predictability (DP). Nodes will compare the DP to decide whether the packet is delivered and the change of weighted factor α will have an impact on the relationship between nodes' active level factor and DP. Besides, NAPR adopts the TTL (Time To Live)-based discarding strategy to manage nodes' buffer space. Simulation results show that NAPR improves the DP and delivery ratio of packets and shortens the average delivery delay. Meanwhile, the number of packet copies and overhead ratio decreases accordingly.

This paper investigates the problem of link scheduling in a single-hop energy harvesting wireless body area network (EH-WBANs) where sensor devices's energy harvesting rates and data rates are spatially heterogeneous and temporally variant. To maximize the channel utilization with the lifetime operation, an adaptive TDMA-based protocol (AT-MAC) is proposed, which is suitable for communication in an EH-WBAN for remote monitoring of physiological signals. In this protocol, a duty cycle can be dynamically adjusted to maintain the harvested energy amount, which is always greater than power consumption. Also, a novel time-slot allocation algorithm is designed to automatically adjust duty cycle with various data traffic and harvesting rates. This algorithm is decomposed into two sub-processes: predistribution of time-slot and redistribution of time-slot. The former process takes the information of energy harvesting rates into account, regardless of the rates varying in a nondeterministic manner and among various sensor nodes. For the latter process, the number of distributed time slots will be further adjusted to cope with data traffic of spatially heterogeneousness. Simulation results demonstrate the proposed protocal is a promising candidate for realizing the lifetime operation in EH-WBANs.

In Energy-Harvesting Wireless Body Area Networks (EH-WBANs), how to realize a power management strategy to enable lifetime operation is one of the most concern problems. To solve this problem, a feasible solution should be used to manage the harvested energy in sensor nodes in an EH-WBAN. So far, there are extensive studies about this issue for EH-WBANs. Unfortunately, the significant character of energy harvesting rates is ignored in existing solutions, which has immediate impact on the effectiveness and efficiency of energy harvesting in EH-WBANs. To handle this critical challenge, this paper propose a harvesting-rate oriented self-adaptive algorithm for enabling lifetime operation in EH-WBANs, in which a duty cycle can be dynamically adjusted to maintain the harvested energy amount always greater than the power consumption. In this study, the widely cited TDMA-based frame structure is chosen as the EH-WBAN architecture as well. To exploit the full potentials of the TDMA-based frame structure for energy harvesting, a self-adaptive mechanism is devised to adjust the on-to-off ratio within a duty cycle automatically in the proposal, in which the information of energy harvesting rates can be taken into account, regardless of the rates how varying in a nondeterministic manner and among various sensor nodes in EH-WBANs. To evaluate this algorithm, a demo is configured, which test results demonstrate the proposal is a promising candidate for realizing the lifetime operation in EH-WBANs.

As the compensation and extension of static wireless sensor nodes, wearable helmets can build regional early warning network of personnel security. In this paper, a wearable helmet is presented towards early warning of leaking toxic gas in large-scale petrochemical plants for protecting the lives and safety of workers better.

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.

We propose a SDN-like architecture based WSN and improve the existing EC-CKN Sleep Scheduling mechanism to implement more efficient energy management. A SDN-like architecture is adopted instead of traditional WSN architecture and EC-CKN algorithm is applied as the fundamental algorithm. This paper presents the design, implementation and evaluation of the proposed SDN-ECCKN on the WSN with SDN-like architecture.

Universal-filtered multi-carrier (UFMC) technique is considered as a potential candidate for future communication systems due to its robustness against inter-carrier interference (ICI), suitability for non-contiguous fragmented available spectrum resources and low latency scenario in 5G network. In this paper, we present a novel pulse shaping approach in UFMC to reduce the spectral leakage into nearby subbands used for same or other users with low complexity and high throughput. In the new scheme, we apply Bohman filter-based pulse shaping with combination of antipodal symbol-pairs to the edge-subcarriers of the subbands, and consequently reduce the out-of-band radiation. This scheme outperforms the current state-of-the art and offers better signal-to-interference ratio (SIR) to improve the robustness against carrier frequency offset (CFO) for energy saving in loosely synchronized scenario. We further validate the proposed scheme on field programmable gate array (FPGA) hardware prototype.

Recently, wireless sensor networks (WSNs) have been widely used in many applications, such as Smart Grid. However, it is generally known that WSNs are energy limited, which makes WSNs vulnerable to malicious attacks. Among these malicious attacks, a sinkhole attack is the most destructive one, since only one sinkhole node can attract surrounding nodes with unfaithful routing information, and it executes severe malicious attacks, e.g. the selective forwarding attack. In addition, a sinkhole node can cause a large amount of energy wastes of surrounding nodes, which results in abnormal energy hole in WSNs. Thus, it is necessary to design an effective mechanism to detect the sinkhole attack. In this paper, we propose a novel Intrusion Detection Algorithm based on neighbor information against Sinkhole Attack (IDASA). Different from traditional intrusion detection algorithms, IDASA takes full advantage of neighbor information of sensor nodes to detect sinkhole nodes. In addition, we evaluate IDASA in terms of malicious node detection accuracy, packet loss rate, energy consumption and network throughput in MATLAB. Simulation results show that the performance of IDASA is better than that of other related algorithms.

The uncertainty and extreme large scale of cloud resources make task scheduling very difficult which affects the user quality of experience and probably result in a waste of cloud resources and energy consumption. Moreover, some resources stay in an unusable state for extended time. To take into account these problems a cloud resource evaluation model is proposed, termed Entropy Optimization Evaluation and ant colony clustering Model (EOEACCM). The model releases long-term unavailable resources to save energy. First, by mean of the entropy increasing minimum principle, the proposed model can maximize the system utilization and balance profits of both cloud resource providers and users. As a consequence, it can shorten task completion time. Secondly, the model narrows the task scheduling size and achieves optimal scheduling by clustering. To make the model more suitable for the dynamics of cloud resources, the model design improves pheromone update policies by fixing total path length in each function cycle when clustering by the ant colony algorithm. Evaluation of results using EOEACCM demonstrate that it may be applicable for resource management strategies for migration and release, an application which can effectively save energy. The proposed model was evaluated by simulation. Experiment results showed the positive effect of user satisfaction from entropy optimization, as well as scheduling time from clustering. Moreover, when the scale of tasks was large, this clustering algorithm performed much better than others. The clustering model also demonstrated better adaptability when some cloud resources were joined or terminated.

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

Recently, much research has proposed to integrate mobile cloud computing (MCC) with wireless sensor networks (WSNs) so that powerful cloud computing can be exploited to process the data gathered by ubiquitous WSNs and share the results with mobile users. However, all current MCC-WSN integration schemes ignore the following two observations: 1) the specific data mobile users request usually depend on the current locations of mobile users 2) most sensors are usually equipped with non-rechargeable batteries with limited energy. In this paper, motivated by these two observations, two novel collaborative location-based sleep scheduling (CLSS) schemes are proposed for WSNs integrated with MCC. Based on the locations of mobile users, CLSS dynamically determines the awake or asleep status of each sensor node to reduce energy consumption of the integrated WSN. Particularly, CLSS1 focuses on maximizing the energy consumption saving of the integrated WSN while CLSS2 considers also the scalability and robustness of the integrated WSN. Theoretical and simulation results show that for WSNs integrated with MCC, both CLSS1 and CLSS2 can prolong the WSN lifetime while still satisfying the data requests of mobile users.

Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

Underwater acoustic sensor networks (UASNs) have been widely used in many applications where a variable number of sensor nodes collaborate with each other to perform monitoring tasks. A trust model plays an important role in realizing collaborations of sensor nodes. Although many trust models have been proposed for terrestrial wireless sensor networks (TWSNs) in recent years, it is not feasible to directly use these trust models in UASNs due to unreliable underwater communication channel and mobile network environment. To achieve accurate and energy efficient trust evaluation in UASNs, an attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper. The ARTMM mainly consists of three types of trust metrics, which are link trust, data trust, and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. Simulation results demonstrate that the proposed trust model is quite suitable for mobile underwater environment. In addition, the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption.

Trust models have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs). Considerable research has been done on modeling trust. However, most current research work only takes communication behavior into account to calculate sensor nodes' trust value, which is not enough for trust evaluation due to the widespread malicious attacks. In this paper, we propose an Efficient Distributed Trust Model (EDTM) for WSNs. First, according to the number of packets received by sensor nodes, direct trust and recommendation trust are selectively calculated. Then, communication trust, energy trust and data trust are considered during the calculation of direct trust. Furthermore, trust reliability and familiarity are defined to improve the accuracy of recommendation trust. The proposed EDTM can evaluate trustworthiness of sensor nodes more precisely and prevent the security breaches more effectively. Simulation results show that EDTM outperforms other similar models, e.g., NBBTE trust model.

Cooperative spatial multiplexing (CSM) system has played an important role in wireless networks by offering a substantial improvement in multiplexing gain compared with its cooperative diversity counterpart. However, there is a limited number of research works that consider the performance of CSM systems. As such, in this paper, we have derived exact performance of CSM with amplify-and-forward and decode-and-forward relays in terms of outage capacity and ergodic capacity. We have shown that CSM systems yield a unity diversity order regardless of the number of antennas at the destination and the number of relays in the networks, which is the direct result of diversity and multiplexing gain trade-off. Our analytical expressions are corroborated by Monte-Carlo simulations

Abstract In this paper, we propose a cross‐layer optimized geographic node‐disjoint multipath routing algorithm, that is, two‐phase geographic greedy forwarding plus. To optimize the system as a whole, our algorithm is designed on the basis of multiple layers' interactions, taking into account the following. First is the physical layer, where sensor nodes are developed to scavenge the energy from environment, that is, node rechargeable operation (a kind of idle charging process to nodes). Each node can adjust its transmission power depending on its current energy level (the main object for nodes with energy harvesting is to avoid the routing hole when implementing the routing algorithm). Second is the sleep scheduling layer, where an energy‐balanced sleep scheduling scheme, that is, duty cycle (a kind of node sleep schedule that aims at putting the idle listening nodes in the network into sleep state such that the nodes will be awake only when they are needed), and energy‐consumption‐based connectedk‐neighborhood is applied to allow sensor nodes to have enough time to recharge energy, which takes nodes' current energy level as the parameter to dynamically schedule nodes to be active or asleep. Third is the routing layer, in which a forwarding node chooses the next‐hop node based on 2‐hop neighbor information rather than 1‐hop. Performance of two‐phase geographic greedy forwarding plus algorithm is evaluated under three different forwarding policies, to meet different application requirements. Our extensive simulations show that by cross‐layer optimization, more shorter paths are found, resulting in shorter average path length, yet without causing much energy consumption. On top of these, a considerable increase of the network sleep rate is achieved. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract With their proliferation and increasing capabilities, mobile devices with local wireless interfaces can be organized into delay‐tolerant networks (DTNs) that exploit communication opportunities arising out of the movement of their users. As the mobile devices are usually carried by people, these DTNs can also be viewed as social networks. Unfortunately, most existing routing algorithms for DTNs rely on relatively simple mobility models that rarely consider these social network characteristics, and therefore, the mobility models in these algorithms cannot accurately describe users’ real mobility traces. In this paper, we propose two predict and spread (PreS) message routing algorithms for DTNs. We employ an adapted Markov chain to model a node's mobility pattern and capture its social characteristics. A comparison with state‐of‐the‐art algorithms demonstrates that PreS can yield better performance in terms of delivery ratio and delivery latency, and it can provide a comparable performance with the epidemic routing algorithm with lower resource consumption. Copyright © 2013 John Wiley & Sons, Ltd.

The number of Non-communicable disease (NCD) patients in China is growing rapidly, which is far beyond the capacity of the national health and social security system. Community health stations do not have enough doctors to take care of their patients in traditional ways. In order to establish a bridge between doctors and patients, we propose eBPlatform, which is an information system based on the Internet of Things (IoT) technology for homecare of the NCD patients. The eBox is a sensor which can be deployed in the patient's home for blood pressure measurement, blood sugar measurement and ECG signals collection. Some services are running on the remote server, which can receive the samples, filter and analyze the ECG signals. The uploaded data will be pushed to a web portal, with which doctors provide treatments online. The system requirements, design and implementation of hardware and software are discussed respectively. Finally, we investigate a case study with 50 NCD patients for half a year in Beijing. The results show that eBPlatform can increase the efficiency of the doctor and make a big progress to eliminate the numerical imbalance between community medical practitioners and NCD patients.

Recently, motivated by incorporating the advantages of social networks (SNs) and mobile cloud computing (MCC), the integration of SNs and MCC receives a lot of attention from both academia and industry, since the SNs could be utilized to share the rich cloud resources and services as well as the cloud can be taken to host or enhance SNs. However, one very critical and unexplored issue when integrating SNs and MCC is evaluating the importance of users to obtain the potential influential users. In this paper, focusing on exploring the user importance evaluation issue during the integration of SNs and MCC, we propose a RFTRS scheme, which innovatively considers the Reputation, Fractal, and Topological importance in SNs as well as the Request and Storage importance in MCC to evaluate user importance. The detailed design about RFTRS is presented. A further case study is also conducted to compare the user evaluation results identified by RFTRS and other popular traditional user importance evaluation approaches in SNs. They show that new user evaluation approaches (e.g., RFTRS) when integrating SNs and MCC are needed.

The recent years realize a progressive transition where fixed computing reached maturity and the mobility age started to thrive. Nowadays, another transition from the mobility age to the “Internet of Everything” (IoE) is taking place. In the IoE vision, several types of quotidian objects will be able to communicate over the Internet. As a result, it is expected that within a decade, IoE will have an economic value of $14.4 trillion, as the number of devices connected to the Internet continues to increase exponentially. The support for security services in these emerging resource-constrained devices is considered a challenge but needs to take into account from the very early stages of the wireless network inception. This paper proposes a network-based intrusion detection system (IDS) for IPv6-enabled wireless sensor networks. The proposed IDS is used to detect security attacks based on traffic signatures and abnormal behaviors.

In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to fully utilize spectrum resources, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern how to achieve better payoffs for users, without paying much attention to the Quality of Service (QoS). Thus, we propose a new channel switching model based on game theory, using a prioritized approach to meet the diverse needs of users, such as bandwidth, delay, and jitter. Once the Nash equilibrium is achieved, our model will provide different QoSes by setting different priorities to different users. We also propose two acceleration methods to reach the Nash equilibrium more quickly. Finally, we evaluate the performance of the proposed schemes using real channel availability measurements. Experiments results show that our model can provide differentiated services and our algorithm is guaranteed to reach an approximate Nash equilibrium within polynomial time.

The routing efficiency in Delay Tolerant Networks (DTN) with social characteristics degrades owing to intermittent connection and high latency. Additionally, congestion is another issue because of the limited resources of nodes. To solve these problems, an improved Socially Aware Congestion Control algorithm (SACC) is proposed. In this algorithm, the social features and the congestion level of the node are utilized to construct a Social Congestion Metric (SCM). In the forwarding process, messages are forwarded to the nodes with higher SCM. When the congestion occurs, the node calculates the social links of itself with every message's destination node, and then drops the message with minimum social link rather than random dropping. Simulation results show that in the acceptable range of delay tolerance, the proposed algorithm improves the delivery probability, decreases the dropping probability and reduces the overhead.

Due to the explosive increment of data in big data era, it is a challenging task to analyze and extract meaningful data for users. Data needs to be timely operated because of the time sensitivity, so it faces enormous pressure in storage and computing. To deal with the problem that it is hard to achieve valuable information from out-of-order streams over big data in short time, a model-matching algorithm based on improved BP (Back Propagation) is proposed. In the algorithm, the matching model is set dynamically. Information is extracted for users according to the order of data's arriving time. Furthermore, the algorithm parameters are automatically adjusted in the process of learning and matching. Accordingly, the responding speed of learning is accelerated and the time of matching reduces. In the simulation, a group of optimum parameters of improved BP are achieved by using self-adapting adjusting mechanism. The threshold (TH), connecting weight (CW) and learning rate (LR) are equal to 1.5, 3 and 1, respectively. We implement our model-matching algorithm on 10000 sets of out-of-order streams with these parameters. Results indicate that the proposed algorithm can obviously improve the accuracy and speed of matching and achieve better stability.

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m2in a campus building and a laboratory covering 78m2. Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.

In the hybrid wireless sensor networks, mobile sinks move to pre-specified sink locations to gather sensory data provided by static sensors. In this setting, scheduling mobile sinks efficiently while prolonging the lifetime of network is a challenge. To remedy this problem, we propose a three-phase energy-balanced heuristic in this paper. We firstly divide the hybrid WSN into grid cells, cluster these grids into clusters according to data communication consumption, and then rebalance these clusters with considering movement consumption by a heuristics algorithm. Therefore, all clusters are balanced in energy consumption when considering the cost of both data gathering and sinks movement. The evaluation results indicate that our technique can compute an optimal grid division within limited time of iterations.

In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from sensors of petrochemical industries need to be timely operated because of time sensitivity, and it is hard to achieve associated information from sensors located in production site. To this end, an OLA-IBP (Online Learning Algorithm based on Improved Back Propagation) is proposed. The adaptive structure of this algorithm is settled online. Meanwhile, real-time data streams are parallelly processed according to arriving time in input layer. Simulation results show that OLA-IBP can efficiently improve learning time and accuracy rate. Finally, the adaptability of OLA-IBP is verified in leak points prediction of petrochemical equipments from processed data.

A novel near field communication system for s-martphones based on visible light (NECAS) is presented in this paper. NECAS encodes information using combinations of different colors (red, green and blue) and their intensity levels of a smartphone screen, while the receiver captures the light signal via color sensors. In our scheme, a smartphone screen is segmented into four sub-blocks, and each of them can be used to encode information independently. NECAS can be used in a range of Near Field Communication applications such as contactless payments, electronic ticket checking and device paring. We design the color encoding scheme by experimentally profiling the interference of three color channels and the refresh rate of smartphones. We implement a joint decoding algorithm to deal with the significant interference among color channels. With a feedback channel, the retransmission scheme is adopted to improve communication reliability, and the system can dynamically adjust the number of combinations of different colors and intensity levels to fit the communication environment. Our design is validated via extensive experiments using a hardware implementation on Android smartphones and an Arduino platform.

Providing reliable and efficient communication under fading channels is one of the major technical challenges in wireless sensor networks (WSNs), especially in industrial WSNs (IWSNs) with dynamic and harsh environments. In this work, we present the Reliable Reactive Routing Enhancement (R3E) to increase the resilience to link dynamics for WSNs/IWSNs. R3E is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links by utilizing the local path diversity. Specifically, we introduce a biased backoff scheme during the route-discovery phase to find a robust guide path, which can provide more cooperative forwarding opportunities. Along this guide path, data packets are greedily progressed toward the destination through nodes' cooperation without utilizing the location information. Through extensive simulations, we demonstrate that compared to other protocols, R3E remarkably improves the packet delivery ratio, while maintaining high energy efficiency and low delivery latency.

ABSTRACT Wireless sensor networks (WSNs) which is proposed in the late 1990s have received unprecedented attention, because of their exciting potential applications in military, industrial, and civilian areas (e.g., environmental and habitat monitoring). Although WSNs have become more and more prospective in human life with the development of hardware and communication technologies, there are some natural limitations of WSNs (e.g., network connectivity, network lifetime) due to the static network style in WSNs. Moreover, more and more application scenarios require the sensors in WSNs to be mobile rather than static so as to make traditional applications in WSNs become smarter and enable some new applications. All this induce the mobile wireless sensor networks (MWSNs) which can greatly promote the development and application of WSNs. However, to the best of our knowledge, there is not a comprehensive survey about the communication and data management issues in MWSNs. In this paper,focusing on researching the communication issues and data management issues in MWSNs, we discuss different research methods regarding communication and data management in MWSNs and propose some further open research areas in MWSNs.Copyright © 2011 John Wiley & Sons, Ltd.

Nowadays, networks play an indispensable role in social life, and social networks have become a new advertising medium for offline activities. Previous studies of information diffusion or behavior spread over social networks have mostly focused on diffusion models and analysis of virtual interaction between online users, and very few of them focus on the propagation of real world activities in these social networks. To address this problem, we use data obtained from the Chinese largest recommendation social network - Douban, and study how the offline activities spread from one user to another through Douban. By using cascading subgraphs and diffusion trees, we break a whole cascade into local subgraphs. After analyzing the activities of about 1.47 million users, we observe the statistical and topological characteristics of these local cascading subgraphs. Next, we find the size and degree distributions of these cascading subgraphs and several common patterns of topology of local cascades. Moreover, we also have some other interesting discoveries, like the relation between the number of initial adopters and the final cascade size, and the underlying influences driving user behaviors. Finally, we propose a diffusion model that can generate information cascades that follow the patterns we have observed, and validate it by empirical analysis.

This paper presents the design of a duty-cycling MAC, called PRI-MAC (Pipelined-forwarding, Routing-integrated, and effectively-Identifying MAC), for large-scale wireless sensor networks. PRI-MAC divides the whole network into grades around the sink node. The higher grade a node is in, logically the further away it is from the sink which is in the lowest grade. Staggered sleep-wakeup schedules are established between any two adjacent grades such that data can be forwarded in a pipelined fashion, largely reducing the packet delivery latency to meet the real-time transmission requirement. Meanwhile, the routing function is seamlessly integrated into PRI-MAC, which reduces the protocol overhead and increases the network scalability. Furthermore, each node utilizes a randomly-generated integer as its identifier only when it is involved in a data transmission, instead of allocating a unique address for each sensor node. The performance of PRI-MAC is evaluated in comparison with PW-MAC by OPNET, in terms of the packet delivery latency, energy efficiency, and throughput.

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery ratio. In this paper, we propose a novel P2P Query algorithm based on Betweenness Centrality Forwarding (PQBCF), for opportunistic networks. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social networks, to quantify the active degree of nodes in the networks. In PQBCF, nodes with higher BC are preferable to serve as relays, leading to higher inquiry success ratio and lower inquiry delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success ratio and query delay, and approaches the performance of Flooding with much less resource consumption.

Partitioning and offloading some parts of mobile applications onto remote servers is a promising approach to extend the battery life of mobile devices. However, since available network bandwidths vary in a wireless environment, static partitionings proposed by previous works with a fixed bandwidth assumption are unsuitable for mobile platforms, while dynamic partitionings result in high overhead due to continuously partitioning. Targeting this problem, we propose a novel partitioning scheme taking the bandwidth as a variable to improve static partitioning and avoid high costs of dynamical partitioning. Based on the application Object Relation Graph, we propose a partitioning optimization model and two bandwidth-adaptive partitioning algorithms: Branch-and-Bound based Application Partitioning (BBAP) and Min-Cut based Greedy Application Partitioning (MCGAP). BBAP is suitable for obtaining the optimal partitionings for small applications, while MCGAP is applicable to large-scale applications by quickly obtaining suboptimal solutions. Experimental results demonstrate that both algorithms can adapt to bandwidth fluctuations well, and significantly reduce the execution time and energy consumption by optimally distributing components between mobile devices and servers.

This paper focuses on studying the sensor nodes deployment towards accurate data fusion for ship movement controlling. Furthermore, this study provides a node deployment layout with better measurement accuracy, which is surprisedly different from the layout that we originally predicted.

In Mobile Wireless Sensor Networks (MWSNs), location information of nodes is the basis of many applications. Compared with that in a static network, the localization issue in a mobile network is more difficult due to uncertainty of node positions, thus much more challenges are brought to MWSNs. In this paper, we propose three localization models to evaluate performances of DV-hop localization algorithm in MWSNs. We compare the performance of three localization models, DV-hop+RWP, DV-hop+RD and DV-hop+RPGM, with that of DV-hop. Simulation results show that, DV-hop+RD has higher localization accuracy and lower energy consumption than others. However, when the number of nodes increases, more unknown nodes in DV-hop+RWP can be localized than that in DV-hop+RD. The localization success and localization accuracy of DV-hop+RPGM, which is similar to that of DV-hop, is lower than DV-hop+RWP.

WMN (Wireless Mesh Network) is a useful wireless multi-hop network with tremendous research value. A good routing mechanism for WMN can use the whole bandwidth of the network and can assure the quality of service of traffic. Through simulations we verify that the routing metric ETX (Expected Transmission Count) cannot assure good quality of wireless links. To improve the routing performance, a QoS-assured opportunistic routing mechanism is proposed in this paper. This mechanism can choose the highest throughput links to improve the performance of routing over WMN and then reduce the energy consumption of mesh routers. The analyses show that the opportunistic routing mechanism is better than the mechanism with the routing metric of ETX.

In the Internet of Things, smart things communicate with each other, and sensed data are aggregated and queried for satisfying certain requests of end-users. When a region of interest requires to be monitored continuously, the strategy that each query is to be executed independently through gathering sensed data of target sub-regions may not be energy efficient, since the values of sensors may have no significant difference in proximate sensing time-slot in some applications. To mitigate the energy consumption in this context, in this paper we propose an energy-efficiency hierarchical clustering index tree for organizing these grid cells. Then, we develop a time-correlated region query technique for answering continuous queries. Theoretical analysis show that our technique is energy efficient compared with traditional techniques.

In this paper, to satisfy the need of fault monitoring, dynamic real time vibration monitoring and vibration signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online and fast fault diagnosis, an intelligent fault diagnosis system is developed using artificial immune algorithm and dimensionless indicators, innovated with a focus on reliability, remote monitoring and practicality, and be applied to the Third Catalytic Flue Gas Turbine in a petrochemical enterprise and have got good effects.

Finding basic laws that govern human crowd behavior is a subject that deserves to study. Crowd behavior is a natural instinct for human, which directly impacts how we form opinions and make decisions. Moreover, it is common that people change their behavior in a group. In pervasive computing research, substantial work has been directed towards discovering human movement patterns based on wireless networks. This research has, however, mainly been focused on movements of individuals. Mobile phones offer on-body tracking and they are already deployed on a large scale, allowing the characterization of user behavior through the information related to individual movements. In this paper, we observe and analyze the impact of friendship and location attributes on crowd behavior, using location-based wireless mobility information. These preliminary studies will be a good cornerstone for a crowd behavior prediction.

In this poster, we propose a novel energy efficient asynchronous MAC protocol for WSNs-WX-MAC. WX-MAC supports nodes to exchange their sampling schedules by query and report mechanisms. With the aid of neighbors' information and strobed preamble approach, WX-MAC shortens senders' preamble before data transmission and reduces nodes' wakeup duration. WX-MAC not only reduces the energy consumption on the senders and targets, but also decreases the possibility of overhearing on the surrounding neighbors, leading to a further energy conservation. Preliminary simulations have shown that WX-MAC saves more energy than B-MAC, Wise MAC, and X-MAC.

No abstract available.

With the development of Internet of Things (IoT), bridging wireless sensor networks (WSNs) and other networks has become important. We divide bridging solutions into two categories: hardware solutions and middleware solutions. Hardware solutions have both low power short distance wireless interfaces and other types of transmission interfaces, e.g. GPRS, 3G/4G, via hardware implementations. This kind of solutions is more stable and more applicable for deployed sensor networks. In middleware solutions, the whole system processes appropriate protocol conversion. This kind of solutions is more independent of hardware, making it easier to be reused in different networks. This paper briefly presents key points of each solution and evaluates advantages and disadvantages of them in terms of different criteria. Eventually, we derive the most appropriate situation for each solution from our comparisons and discussions.

Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of ``radio map'' is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.

The design and analysis of routing algorithm is an important issue in Wireless Sensor Networks (WSNs). Most traditional geographical routing algorithms cannot achieve good performance in duty-cycled networks. In this paper, we propose a k-connected overlapping clustering approach with energy awareness, namely k-OCHE, for routing in WSNs. The basic idea of this approach is to select a cluster head by energy availability (EA) status. The k-OCHE scheme adopts a sleep scheduling strategy of CKN, where neighbors will remain awake to keep it k-connected, so that it can balance energy distributions well. Compared with traditional routing algorithms, the proposed k-OCHE approach obtains a balanced load distribution and consequently a longer network lifetime.

Indoor localization is essential to enable location-based services in wireless pervasive computing environment. In recent years, WiFi fingerprint-based localization has received considerable attention due to its deployment practicability. In order to achieve on-the-fly localization, WiFi receivers (e.g., mobile phones or laptops) being located need to scan WiFi signals continuously. Since they are normally battery driven, energy efficiency is a very important consideration in WiFi fingerprinting localization systems. Motivated by the fact that IEEE 802.11 (WiFi) and 802.15.4 (ZigBee) channels overlap in the 2.4GHz ISM band, in this work, we develop a WiFi fingerprint-based localization system using ZigBee radio, called ZiLoc. We first present a novel RSS-location fingerprint model to identify the features of surrounding APs. We then propose a simple yet effective method to compute the similarity of two RSS fingerprints. Experimental results demonstrate that ZiLoc can achieve an average of 85% room-level localization accuracy and reduce more than 60% energy consumption compared with the method using WiFi interfaces to collect RSS fingerprints.

Mobile devices with local wireless interfaces can be organized into opportunistic networks which exploit communication opportunities arising from the movement of their users. With the proliferation and increasing capabilities of these devices, it is significant to investigate message dissemination over opportunistic networks to maximize the potential of those networks. In this paper, we analyze the performance of copy-limited flooding over opportunistic networks, where each node can only send no more than k copies of the same message. For this purpose, we propose a network model called Markov and Random graph Hierarchic Model (MRHM), where a node transfers among different Main-areas (places frequently visited by nodes) according to the Markov rule, and two different nodes in the same Main-area can establish a connection with a certain probability. We theoretically analyze the performance of k-copy limited flooding over MRHM in terms of delivery rate and delay. Our extensive experiments over MRHM and real traces reveal that when k equals 3, the performance of k-copy limited flooding is very close to that of Epidemic Routing.

Accurately locating unknown nodes is a critical issue in the study of wireless sensor networks (WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checking their coordinates, identities and time of sending information. Then, by using a modified mesh generation scheme, malicious nodes are isolated and the WSN is divided into areas with different trust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.

SUMMARY Wireless converge‐cast networks (WCNs), such as data collection‐based wireless sensor networks, exhibit certain phenomena called funneling effect, where the region close to the sink node is heavily overloaded. In this paper, we identify that the funneling effect occurs not only close to the sink but also within the network region where nodes have collision and induce heavy traffic to relay; we name it hot‐spot funneling effect. This paper aims to improve the throughput and fairness of WCNs by mitigating the micro funneling effect. We propose a new mechanism, the backoff differentiation for contention resolution (BDCR), which is targeted to a system‐wide high throughput on the basis of the contention resolution mechanism. To achieve high spatial reuses, BDCR divides the network into several regions and does backoff differentiation within each region. Within each backoff differentiation region, the backoff window range is adjusted according to the traffic rate, and at the same time, the backoff values are set with the awareness of the traffic intensity level. All regions share the same algorithm, which uses Kelly's rate control theory and method to allow each sensor to locally adjust its backoff value. One of the key advantages of BDCR is that it is extremely easy to implement. With extensive simulations and testbed experiments, BDCR is proved to achieve much higher throughput over the traditional carrier sense multiple access and some recent media access control protocols in literature, particularly when the network suffers intensive congestions. Copyright © 2012 John Wiley & Sons, Ltd.

Owing to wireless communication's broadcast nature, wireless sensor networks (WSNs) are vulnerable to denial‐of‐service (DoS) attacks. It is of great importance to design an efficient intrusion detection scheme (IDS) for WSNs. In this study, the authors propose a novel IDS based on energy prediction (IDSEP) in cluster‐based WSNs. The main idea of IDSEP is to detect malicious nodes based on energy consumption of sensor nodes. Sensor nodes with abnormal energy consumption are identified as malicious ones. Furthermore, IDSEP is designed to differentiate categories of ongoing DoS attacks based on energy consumption thresholds. The simulation results show that IDSEP detects and recognises malicious nodes effectively.

Due to the limited resources and dynamically varying nature of wireless links, guaranteeing high quality demands for a large number of heterogeneous users is very challenging in video streaming over wireless networks. In this paper, we introduce a layered multiple description coding with an embedded forward error correction scheme (LMDC-FEC). The combination of layered MDC and FEC aims at coping with not only the diverse bandwidth and unreliability of wireless links but also the heterogeneous user devices. We further propose a joint replication density and rate allocation (RD-RA) optimization problem in the context of video on-demand systems (VoDs) over wireless mesh networks (WMNs), and employ a genetic algorithms based approach to solve the optimization problem. Our objective is to elaborately distribute proper replication density for each video segment and allocate optimal bit rate to each layer of each segment in order to gain high user-perceived quality (UPQ) with small consumption of storage resource. By this method, both optimal replication densities and rate allocations are found in accordance with the access rate of segments, the diverse loss characteristics of descriptions in each segment, and the rate-distortion relationship of segments, so as to thoroughly optimize UPQ and storage resource consumption. Simulation results demonstrate that our proposed method significantly enhances the streaming performance of VoDs over WMNs.

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using "tags” or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. In this paper, we introduce an object-centered social network based on tagging practices across different sources, and then we show how this network can be built and emerged over time.

ABSTRACT In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Health monitoring of patients is a common task in healthcare houses from nursing homes to hospitals. Medical staff moves close to patients and collects their monitoring body parameters. To help the overall state control of monitored patients, it could be performed in autonomous, real-time, and remotely way. The application of healthcare wireless sensor networks to these scenarios could perform this job. Through a network it is possible to reach each one of the patients' nodes anytime anywhere as long as a network terminal is accessible. Considering this scenario, the paper proposes a solution to deal with mobile nodes in healthcare wireless sensor networks. The proposed solution is based on a handover procedure that guarantees continuous accessibility to the mobile nodes while moving through different access points' coverage range within the same network (intra-mobility). The most recent mobility approaches although supporting handover solutions, they are not compatible with continuous access to the nodes of a WSN. This proposal uses a distributed method to evaluate and initiate the handover process. Therefore, the procedure could be performed either by the nodes or by the access points. To validate this solution it is presented a laboratory prototype and two evaluation tools.

Searching for available parking spots in congested areas has arouse widespread and close concerns from academia and industry in recent decades. Plenty of related work has been done to design multiple parking systems, but few of them has taken fairness into account. In this paper, we propose a smart parking scheme towards fairness. The scheme is characterized by allowing parking lots to select prospective vehicles, in order to avoid the phenomenon called “multiple cars chasing single spot”. Moreover, the proposed scheme manages reservation period of each parking spot, therefore, time wasted on cruising for vacant parking space greatly reduces, and the smart parking system is guaranteed to be more equitable. Performance analysis via extensive simulations demonstrates its efficiency and practicality.

In this paper, we aim at maximizing quality playback of received video streaming perceived by users in video on-demand systems (VoDs) over wireless mesh networks (WMNs). To do so, we exploit the benefits of both multi-source multi-path (MSMP) delivery scheme and broadcast nature of wireless media in WMNs. In this way, a new user can efficiently receive the descriptions of a video streaming from different peers (i.e., the available users who have cached the video) via the best parallel selected paths between each peer-user pair. Based on MSMP delivery scheme, we propose an optimization problem and solve it for optimal encoding rate (i.e., optimal bit allocation) of the video streaming encoded by multiple description coding (MDC) in order to provide the user with maximum reconstructed quality playback. In this technique, the optimal bit allocation is found in connection with not only the skewed lossy characteristic of multi-path but also the skewed intra popularity of the video streaming modeled by Zipf-like distribution. Furthermore, we apply MDC embedded forward error correction (MDC-FEC) for more robust in video transmission. Simulation results demonstrate that the proposed method efficiently improves the quality playback of MSMP P2P video streaming in VoDs over WMNs.

Sleep scheduling algorithms for duty-cycled sensor networks have received great attention, since sensors should dynamically be awake and asleep to save energy consumption. Among all current sleep scheduling algorithms, only the recently proposed energy consumed uniformly-connected k-neighborhood (EC-CKN) algorithm focuses on not only the coverage of the resultant network, but also the network lifetime after sleep scheduling. However, all sleep scheduling processes ignore the fact that potential insider attacks actually can seriously affect or destroy proper sleep scheduling operations and properties. In this paper, we discuss these vulnerabilities and propose corresponding countermeasures for EC-CKN, i.e., secure neighborhood authentication.

Geographic routing is assumed to be the most potential routing scheme in wireless sensor networks (WSNs) due to its scalability and efficiency. Recently more and more research work about geographic routing pay attention to its application scenarios in duty-cycled WSNs because of the natural advantage of saving energy consumption with duty-cycling. However, it may cause significant latency issue when applying geographic routing in duty-cycled WSNs and almost all current researches try to handle the latency problem from the point of changing the geographic forwarding mechanism, apart from the connected-k neighborhood (CKN) algorithm which focuses on sleep scheduling. In this paper, we discuss and analyze the first transmission path's performance of the two-phase geographic forwarding (TPGF) in a CKN based WSN and further propose a geographic routing oriented sleep scheduling (GSS) algorithm to shorten the first transmission path of TPGF in duty-cycled WSNs. Further theoretical and simulation results show that GSS can achieve a good tradeoff between the length of the first transmission path explored by TPGF and the total energy consumption to transmit data with the explored first transmission path, compared with the CKN sleep scheduling algorithm.

One fundamental requirement of the trust-aware recommender system (TARS) is to efficiently find as many recommenders as possible for the active users. Existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism of the scale-free network. This is because it chooses the local highest-degree node at each step of the trust propagation. Since the power of the trust network's degree distribution is not big enough, the selected nodes cannot cover superior number of users. In this paper, we propose an efficient searching mechanism, named S_Searching, for TARS based on the scale-freeness of trust networks: choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.

Localization is a vital foundation in Wireless Sensor Networks (WSNs). However, most previous localization methods assume an idealistic radio propagation model that is far from reality. This will lead to inaccurate localization, since unknown nodes cannot receive enough location messages under the radio irregularity model. In our previous work “LMAT”, we solved the path planning problem of the mobile anchor node without taking into account radio irregularity. This paper further studies how localization performance is affected by radio irregularity. In order to improve localization accuracy, the anchor node's radio range is adjusted to guarantee that all unknown nodes can receive sufficient localization information. Furthermore, it points out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability in presence of 2-hop localization. Finally, simulation results show that, compared with 1-hop localization algorithm, 2-hop localization along with a good trajectory reduces average localization error.

When a wireless mesh network accommodates interactive applications with quality of service requirements, schedule-based protocols are more suitable than contention-based protocols. In this paper, the problem of determining an appropriate schedule assignment for multiple group transmissions within a spatial time division multiple access link scheduling network is referred to as an integrated multiple-group communication and link scheduling problem. A polynomial-time scheduling algorithm, designated as a source-parallel-aware assignment (SPAA), is proposed to increase the spatial utilisation within each time slot in order to enhance the network throughput. Furthermore, an advanced version of SPAA, designated as joint source-parallel-aware assignment with network coding (JSANC), is proposed to reduce the effects of bottleneck paths on the schedule frame length by flexibly applying conventional or opportunistic network coding approaches. Simulation results show that the proposed algorithms achieve a better network throughput than existing flow-based or particular order-based scheduling schemes.

Currently, in Wireless Sensor Networks (WSNs), the main idea in most localization algorithms has been that a mobile anchor node, e.g., GPS-equipped (Global Positioning System) nodes, broadcasts its coordinates to locate unknown nodes. In this case, a basic problem is the path planning of the mobile anchor node which should move along the trajectory to minimize the localization error and locate the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT). LMAT algorithm uses a mobile anchor node to move according to the equilateral triangle trajectory in deployment area. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms, e.g., SPIRAL, SCAN, DOUBLE SCAN and HILBERT algorithms.

Location information of nodes is the basis for many applications in wireless sensor networks (WSNs). However, most previous localization methods make the unrealistic assumptions: (i) all nodes in WSN are always awake and (ii) the radio range of nodes is an ideal circle. This overlooks the common scenario that sensor nodes are duty-cycled in order to save energy and the radio range of nodes is irregular. In this paper we revisit the Distance-Vector-based (DV-based) positioning algorithms, particularly, Hop-Count-Ratio based Localization (HCRL) algorithm and investigate the following problems: (i) how is the relationship between the number of sleeping neighbor sensor nodes and the localization accuracy and (ii) how is the relationship between the degree of irregularity (DOI, which is a parameter of radio range irregularity) and the localization accuracy. We conduct a large number of experiments in WSNs' simulator NetTopo, and find that the parameters: the number of waking nodes, DOI, anchor node density and localization error, are interactional, i.e., for a given deployed static WSN, there is an optimal number of waking nodes and an optimal anchor node density, which can minimize network energy consumption without losing much of the localization accuracy. Furthermore, waking up more sensor nodes cannot always help to increase the localization accuracy, which actually is different from our intuitive thinking: more waking nodes can help to increase the localization accuracy of DV-based localization algorithms at all time.

Although the conventional duty cycle MAC protocols such as RMAC for wireless sensor networks (WSNs) perform well in aspects of saving energy and reducing end-to-end delivery latency, they are designed independently and require an extra routing protocol in the network layer to provide path information for the MAC layer. We propose a novel cross-layer duty cycle MAC protocol with data forwarding towards pipeline feature (P-MAC) for WSNs. P-MAC divides all sensor nodes in the network into different grades around the sink. Each node identifies its grade according to its logic hop distance to the sink and simultaneously establishes a sleep/wakeup schedule based on its grade. Those nodes in the same grade keep the same schedule, while which is staggered with the schedule of the nodes in the adjacent grade. Then a variation of RTS/CTS handshake mechanism is used to forward data continuously in a pipeline fashion from the higher grade nodes to the lower grade nodes and finally to the sink. No extra routing overhead is needed, and the superiority of duty-cycling is kept. The performance of PMAC and RMAC are evaluated and compared in terms of packet delivery latency and energy efficiency by OPNET simulation.

In this paper, we propose a cross-layer optimization approach for replication of video streaming over multihop wireless network by jointly taking into account application, network, MAC, and physical layers. Specifically, we design a middleware replication strategy layer with a stack profile, in which parameters across the hops and all considered layers are exchanged for optimal video replication. The simulation results demonstrate that our method improves the replication strategy performance in terms of user-perceived video quality. In turn, it satisfies the heterogeneous receive bandwidth constraint of users for efficiently using the network bandwidth compared to other optimal replication strategies without cross-layer approach.

In this paper we propose a technique called replaceability assessment where, according to the adaptation mechanisms of a certain adapter, it provides a set of condition pairs that determine when one protocol can be replaced by another, and computes a replacement degree that specifies how replaceable two protocols are. The set of condition pairs and the replacement degree are complementary criteria to be used by the requestor for identifying the most suitable provider service from a set of functionally equivalent candidates.

To solve the secure localization problems, a number of secure localization schemes have been developed at present. However, most of these techniques cannot survive collusion attacks where a majority of malicious nodes launch colluding attacks. In this paper, we introduce a new collusion attack model called Collaborative Collusion Attack Model (CCAM) and propose a novel scheme called Two-Step Format Detection (TSFD) that is well suited to WSN which is a resource constrained environment. The TSFD has reasonable and acceptable communication cost and algorithm complexity. Through simulations, we compare the performance of TSFD with other secure localization schemes and show that TSFD has more efficient and resilient performance.

Network lifetime is one of the most critical issues for wireless sensor networks (WSNs) since most sensors are equipped with non-rechargeable batteries with limited energy. To prolong the lifetime of a WSN, one common approach is to dynamically schedule sensors' active/sleep cycles (i.e., duty cycles) with sleep scheduling algorithm. In this paper, we propose a new sleep scheduling algorithm, named EC-CKN (Energy Consumed uniformly-Connected K-Neighborhood) algorithm, to prolong the network lifetime. The algorithm EC-CKN, which takes the nodes' residual energy information as the parameter to decide whether a node to be active or sleep, not only can achieve the k-connected neighborhoods problem, but also can assure the k awake neighbor nodes have more residual energy than other neighbor nodes at the current epoch. Based on the algorithm EC-CKN, we can obtain the state transition probability at the n'th epoch, and upper bound and lower bound of the network lifetime by Markov chain and Markov decision chain.

Top-k query is a very useful and important query in wireless sensor networks (WSNs), aiming to find the k nodes with highest readings among the sensor nodes. In WSNs, there are generally two kinds of networks: always-on WSNs (AO-WSNs) in which sensors always keep awake and duty-cycled WSNs (DC-WSNs) where sensors dynamically sleep and wake. To the best of our knowledge, there are a lot of work about top-k query in AO-WSNs but little research has been done regarding top-k query in DC-WSNs. However, DC-WSN is a very practical network model in which energy consumption can be greatly saved. In this paper, we analyze the research issues when implementing top-k query in DC-WSNs and propose the DCDC-WSNs (DC-WSNs with data replication (DR) and connected k-neighborhood (CKN)) to implement top-k query. Further theoretical analysis and simulation results show that implementing top-k query in DCDC-WSNs can achieve the best tradeoff with respect to query data accessibility and query cost (total energy consumption, query response time), compared with AO-WSNs and DCC-WSNs (DC-WSNs with only CKN).

In this poster, we present the architecture, design, and the preliminary simulation results of a system for Metropolitan Heating, via intelligent temperature monitoring. The system consists of several Crossbow TelosB-compatible motes, a Nokia uSDCard, and a smart phone. The motes are used to collect indoor temperature data, which is further relayed to the smart phone's uSD interface. The uSDCard connects the phone with IEEE 802.15.4/ZigBee compatible devices. We use the smart phone as the sink terminal because it has a rich set of user interfaces and can access to different kinds of networks, such as GPRS and 3G. In real scenes, our system can reduce the reading fluctuation, and ultimately save the energy consumed for heating company, achieving a better living condition in the houses.

In Wireless Sensor Networks (WSNs) a multitude of location-dependent applications have been proposed recently, which is very intriguing for researchers to discover and design more accurate and cost-effective localization algorithms. In an isotropic networks, the Euclidean distance between a pair of nodes may not correlate closely with the hop count between them because the corresponding shortest path may have to curve around intermediate holes, resulting in poor distance estimation. And without the help of a large number of uniformly deployed seed nodes, those schemes fail in an isotropic WSNs. To address this issue and improve the accuracy of localization, we propose the Removing Heavily Curved Path (RHCP) scheme in this paper. RHCP takes advantage of selecting the paths which are not heavily affected by the holes to recalculate the location of each unknown node. Through simulation, the results reveal that RHCP performs better than original DV-Hop in an isotropic networks with different shape of holes. In addition, through iterations of RHCP, the results get improved for different anchor node densities.

User profiles backup and migration among social networking providers have become more urgent after the conflict between Tencent, the largest Instant Messaging(IM) service provider in China, and Qihu360, the largest antivirus company in China. So far, exchanging contact list among emails has been wildly used. Mainstream email service providers commonly make use of Comma Separated Value (CSV) files to export/import contact lists. Meanwhile, XML format is popular to transfer information among different platforms due to its rich hierarchical data structures. By using CSV files and XML files, we propose a new method to do relationship migration among social networking providers, which consists of three stages: 1) information retrieval from original social networking provider, 2) information storage and re-processing, and 3) relationship migration and recovery in the target provider. In order to evaluate our solution, we have designed and implemented a real experiment to test the migration from RenRen (the largest social network in China) to my Elgg (our test bed server).

Range-Free algorithms, appealing to people for their cost-efficiency, suffer from the precision problem. Some methods try to combine received signal strength indication (RSSI) with range-free localization algorithms to improve the accuracy, but RSSI is sensitive to the radio irregularity. Based on the well known RIM model, we present a new method of RSSI calibration, namely MRIRC, to mitigate the impact of radio irregularity. MRIRC divides nodes within a continuous angle into groups with the same level of RSSI deviation. By doing this, given an irregular deviation input, MRIRC can get a maximum angle (worst case), which guarantees that the nodes in the same group are in the same level of radio irregularity, thereby improving the accuracy of the distance estimations. We conduct simulations for large-scale sensor networks, and the results show that MRIRC achieves superior performance over the other two typical Range-Free algorithms.

In this paper, we focus on achieving better energy conservation for geographic routing algorithms in duty-cycled WSNs when there is a mobile sink. We simplify the problem as a topology coverage one, and propose a multi-metric geographic algorithm (MMGR) which uses multi-metric candidates (MMCs) for geographic routing. The analysis and extensive simulation results show that MMGR can achieve better energy conservations than McTPGF, while retaining good performance of end-to-end delay and hop counts.

Focusing on achieving better geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) when there is a mobile sink, this paper proposes a geographic distance based connected-k neighborhood (GCKN) algorithm. The algorithm analysis and simulation results show that GCKN can obtain shorter length of the transmission paths explored by TPGF in duty-cycled mobile sink WSNs, compared with the original connected-k neighborhood (CKN).

Abstract A heterogeneous distributed sensor network (HDSN) is a type of distributed sensor network where sensors with different deployment groups and different functional types participate at the same time. In other words, the sensors are divided into different deployment groups according to different types of data transmissions, but they cooperate with each other within and out of their respective groups. However, in traditional heterogeneous sensor networks, the classification is based on transmission range, energy level, computation ability, and sensing range. Taking this model into account, we propose a secure group association authentication mechanism using one‐way accumulator which ensures that: before collaborating for a particular task, any pair of nodes in the same deployment group can verify the legitimacy of group association of each other. Secure addition and deletion of sensors are also supported in this approach. In addition, a policy‐based sensor addition procedure is also suggested. For secure handling of disconnected nodes of a group, we use an efficient pairwise key derivation scheme to resist any adversary's attempt. Along with proposing our mechanism, we also discuss the characteristics of HDSN, its scopes, applicability, future, and challenges. The efficiency of our security management approach is also demonstrated with performance evaluation and analysis. Copyright © 2010 John Wiley & Sons, Ltd.

Key management is considered to be the prerequisite for secure communication in WSNs. However, many of those key management schemes normally either require the pre-deployment knowledge to generate keys, which is unrealistic, especially in large scale sensor networks, or consume a large amount of energy. In this paper, we present a new key management scheme named Energy-efficient Location-dependent Key Management scheme (ELKM). ELKM generates keys for each node based on their relative locations. Based on loose time synchronization, ELKM reduces energy consumption significantly on the total size of transmitted message comparing with previous works. The performance analysis shows that ELKM guarantees high security level and good network connectivity. Simulation results demonstrate that ELKM consumes less energy compared with LDK.

Visualization tools make it easier for users to observe the status of the wireless sensor networks (WSNs). So far, developers of WSNs have created various visualization tools under certain project/research backgrounds. These tools, however, are limited in certain application scenarios, since the underlying packet formats are hard coded into the programs. For this purpose, we present NetViewer, a universal visualization tool for all the applications, and we provide many useful data services to fulfill different needs of various fields, such as Replay lets researchers and developers debug their WSNs easily, Server gives user a method to disseminate data through Internet. To achieve better compatibility, NetViewer allows the user to set the application-related packet format through an XML file. Based on the packet format defined, users can also extract the desired fields from the packets. Furthermore, NetViewer provides a rich set of interfaces as well as a well defined API for future improvements.

Two Phase geographic Greedy Forwarding (TPGF) is a pure on-demand geographic greedy forwarding protocol for wireless multimedia sensor networks (WMSNs). Unlike position-based routing protocols, TPGF has explicit route discovery, i.e., a node greedily forwards a routing packet to the neighbor that is the closest one to the destination to build a route. Thus, TPGF is vulnerable to some greedy forwarding attacks, e.g., spoofing or modifying control packets. In this paper, we identify such vulnerabilities and propose corresponding countermeasures for TPGF, e.g., secure neighbor discovery, route discovery.

How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error.

Wireless sensor networks (WSNs) have been widely used in many applications. The highly correct location information of sensor nodes is crucial for these applications. Nowadays there are mainly two types of localization algorithms: Range-based localization algorithms and Range-free localization algorithms. The drawback of former type is strict requirements on the hardware configuration. The latter type is cost-effective alternative approach. However, this approach can achieve high accuracy only in some ideal scenarios, and some methods even require a lot of complex calculations. In this paper, we put forward a novel PAV-based Calibration localization method (PAVC) locating the unknown nodes. The Pool Adjacent Violators (PAV) algorithm can be used to calibrate the average distance per hop of hop-based positioning algorithm. PAVC improves the localization accuracy compared with previous hop-based algorithms, which is demonstrated by the simulation and testbed results.

This paper aims at mitigating the so-called Funneling Effect for S-MAC, particularly by improving the throughput and fairness of S-MAC. Wireless sensor networks (WSNs) exhibit some phenomenon named Funneling Effect resulting from the accumulation of disproportionate large number of packets in the regions close to the sink. The collision and congestion due to the Funneling Effect strongly weaken the vitality and robustness of WSNs. As for S-MAC which achieves great energy efficiency, the mitigation of funneling effect seems more significant and urgent. In this paper, targeted to alleviate the funneling effect for S-MAC, we propose a SMAC-based proportional fairness backoff scheme (SPFB). Based on the schedule and contention scheme in S-MAC, SPFB employs Kelly's shadow price theory to achieve the proportional fairness as well as optimizes the back off mechanism to improve the throughput. The contention window range is dynamically adjusted according to the load of individual node. With extensive simulations, we can show that SPFB can achieve much higher throughput than traditional S-MAC, especially when the network is heavy loaded. SPFB can also gain good energy efficiency.

This paper focuses on studying the impacts of a duty-cycle based CKN sleep scheduling algorithm for our previous designed TPGF geographical multipath routing algorithm in wireless sensor networks (WSNs). It reveals the fact that waking up more sensor nodes cannot always help to improve the exploration results of TPGF in a duty-cycle based WSN. Furthermore, this study provides the meaningful direction for improving the application-requirement based QoS of stream data transmission in duty-cycle based wireless multimedia sensor networks.

Considering sensor nodes deployed densely and uniformly a mobile sink moving through the sensing field queries a specific area of interest for monitoring information. The Query packet, injected by the mobile sink, is routed to the specific area and the corresponding Response packet is expected to return via multi-hop communication. In this paper, we analyze such a network model to address the problem of efficient data collection for mobile wireless monitoring applications. We first propose a meeting position-aware routing (MPAR) protocol for routing the Response packet efficiently and then propose an efficient query-based data collection scheme (QBDCS) for mobile wireless monitoring applications based on the MPAR. In order to minimize the energy consumption and packet delivery latency, the QBDCS chooses the optimal query time of injecting the Query packet and tailors the routing mechanism for sensor nodes forwarding packets. Simulation study has verified the analysis and demonstrated that the QBDCS can significantly reduce the energy consumption and end to end delivery latency.

Trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing works of TARS suffers from the problem that they need extra user efforts to label the trust statements. The authors propose a novel model named iTARS to improve the existing TARS by using the implicit trust networks: instead of using the effort-consuming explicit trust, the easy available user similarity information is used to generate the implicit trusts for TARS. Further analysis shows that the implicit trust network has the small-world topology, which is independent of its dynamics. The rating prediction mechanism of iTARS is based on the small worldness of the implicit trust network: the authors set the maximum trust propagation distance of iTARS approximately equals the average path length of the trust network's corresponding random network. Experimental results show that with the same computational complexity, iTARS is able to improve the existing TARS works with higher rating prediction accuracy and slightly worse rating prediction coverage.

Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning, but also to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

This paper proposes a formal technique to verify whether or not an expected interaction is adaptable. We first present our observation that a mediated service interaction is synchronizable. This fact is a prerequisite of our approach. Hereafter, we formally model a protocol scenario (i.e., a part of

This paper introduces an intelligent remote monitor system based on ZigBee sensor networks for IDC (Internet Data Center) room. The system consists of two parts: three types of ZigBee modules and the server module. The three types of ZigBee modules contain first-cluster node, second-cluster node and sensor end-node. The system can control remote IDC room based on the connection of the ZigBee protocols and traditional Internet networks. The data collected by the sensor end-nodes in the IDC room are transmitted through ZigBee networks to the server that can communicate with the remote monitor PC with Internet connection. The functions the system implements include data collection, analysis, management, storage, automatic alarm, display and control. By monitoring the IDC room unmanned, the proposed system reduces the energy consumption and the number of management staff, at the same time, provides reliable and robust monitoring, then control the equipments in real-time. Consequently, it ensures the safety and stable operation for the IDC room.

Instant wireless sensor network (IWSN) is a type of WSN deployed for a class of special applications which have the common requirement on instantly responding for collecting and transmitting sensory data, e.g., volcanic eruption monitoring or nuclear leakage detection. In this paper, having a cluster-based WSN, we present a compensation-based reliable data delivery protocol (CRDD) to collect and transmit sensory data timely, reliably, and energy-efficiently. The CRDD consists of three important parts: (1) reliability calculating mechanism, (2) information classifying mechanism, and (3) intelligent balancing mechanism. By using these three mechanisms, the CRDD can reduce redundant messages for enhancing the transmission performance and compensate the deficient messages for reliability. The simulation results show that CRDD can outperform both LEACH and ECDG and significantly improve sensory data collection speed and system dependability.

This paper aims at improving the throughput of the wireless sensor networks (WSNs), particularly to overcome the so-called funneling effect for WSNs with converge-cast patterns. Due to the disproportionate larger number of packets accumulated in the sensors that are closer to the sink, there is a need to decrease the collisions and increase the throughput around the sink area as well as the nodes that experience a heavy pass-through traffic. In this paper, we proposed a new scheme, namely PFB (Proportional Fairness Backoff), which provides additional scheduling opportunities to nodes closer to the sink. The new scheme employs Kelly's shadow price theory to achieve the proportional fairness, which takes advantage of the tree topology that is the de facto standard in today's WSNs. In PFB, the size of backoff window is dynamically adjusted with respect to the height of nodes belong in the tree. With close-form analysis and extensive simulations, we show that PFB can achieve up to 100% throughput increase over the widely used CSMA when the network is highly loaded.

Abstract Packet filtering used in query process is an important approach to reduce energy consumption in sensor networks.Interestis used as a constraint to filter uninterested data when users query information. Within interested data some are more important because they have more valuable information than that of others. We hope to process these more important data first. In this paper, we firstly definerewardto denote the importance level of data, and then we present a two tiers buffer model as the platform for our ETRI packet filtering algorithm. We especially present two packet filtering algorithms for real time and non‐real time transmissions. Based on simulation result, we find out that both packet filtering algorithms can substantially improve the quality of information and reduce energy consumption. Copyright © 2007 John Wiley & Sons, Ltd.

Web service interoperations have triggered a growing interest in analyzing compatibility and similarity for public processes. However, current approaches are limited since they mainly focus on control-flow but largely ignore data-flow. This ignorance causes that they improperly regard public processes, which can interact with or be replaced by each other with the help of process mediators, as incompatible or different. To address these problems, we propose a novel approach to check compatibility and similarity of public processes considering both control-flow and data-flow. We firstly generate scenarios and views to describe a public process. Then the degree of compatibility and similarity of public processes are computed based on pairwise compatibility or similarity of their views. Our approach will benefit service modelers and users not only for a better understanding of public processes, but also for checking compatibility and similarity of public processes, which will further identify and solve mismatches, and thus facilitate Web service interoperations.

Web service interactions lie in the core of SOA. Due to the autonomy, heterogeneity and continuous evolution of Web services, mediators are usually needed to support service interactions to overcome possible mismatches that may exist among business processes. In this paper, we introduce a space-based architecture for process mediator which considers both control-flow and data-flow, present possible mismatch patterns, and suggest how they can be automatically mediated. Our work can be used to perform runtime mediation and thus to facilitate service interactions.

Within the vision of ubiquitous multimedia, using multimedia sensor nodes, such as audio, image, and video sensor nodes, in wireless sensor networks can dramatically enhance the capability of sensor networks for event description. Different kinds of holes can easily appear in sensor networks. For example, tens of sensor nodes can consist of a hole in a small area because of overload. Efficiently and reliably transmit these multimedia streaming data back to the base station while bypassing these static or dynamic holes is one of the key challenge issues. In this paper, we propose a new Two Phase geographical Greedy Forwarding (TPGF) routing algorithm for exploring one or multiple optimized hole-bypassing transmission paths. We demonstrate the execution of TPGF routing algorithm by using our implemented simulator NetTopo. Comparison and simulation results show that our algorithm can effectively solve the identified problems and is better than the famous GPSR routing algorithm in both functionality and performance aspects.